Journal of Health and Safety at Work

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Introduction: Micro-perforated panel (MPP) absorbers are emerging as next-generation absorbers due to their considerable advantages. However, their main drawback compared to other absorbers is their limited bandwidth. This study aims to investigate methods for enhancing the bandwidth of an MPP in the frequency range of 1 to 1500 Hz through simulation using the Finite Element Analysis (FEA) in COMSOL software.
Material and Methods: The modeling was conducted using FEA in COMSOL version 5.3a. To increase the bandwidth, techniques such as series-parallel configurations, symmetrical and asymmetrical air gap depths, and the incorporation of two porous absorbing materials in symmetric and asymmetric air gap layers were employed. In the initial phase, the best configuration was selected and retained for the subsequent stages.
Results: The optimal arrangement involved two upper MPPs having larger holes and a lower perforation percentage compared to the two lower MPPs. It was also found that increasing the depth difference between the air layers of the upper and lower MPPs led to a greater increase in bandwidth than when they were closer together. Furthermore, the use of fibrous porous materials in one of the layers resulted in a reduction of resonance peak while enhancing the bandwidth.
Conclusion: MPP absorbers exhibit diverse behaviors due to their Helmholtz structure and parametric design. If their constituent parameters are tailored to match the acoustic characteristics of the target sound, they achieve optimal efficiency. Additionally, employing numerical methods such as FEA serves as a suitable alternative to more costly laboratory methods.

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Introduction: Despite advancements in road safety and vehicle design, road accidents remain prevalent, a quarter of which are caused by driver distraction. This issue is particularly critical in the public transport sector, especially among urban bus drivers, as distraction can lead to serious injuries and fatalities. Accordingly, this study explored the factors influencing distraction among urban bus drivers through a qualitative approach and a macroergonomics perspective.
Material and Methods: In this study conducted in 2024 in Tehran, 18 urban bus drivers were selected through cluster sampling. The participants included 10 drivers from bus rapid transit (BRT) system and 8 drivers from non-BRT services. Data were collected through semi-structured interviews with the drivers as well as on-site observations. Subsequently, a directed qualitative content analysis approach, based on the balance theory model, was used to analyze the collected data.
Results: The findings revealed that the primary sources of distraction belonged to six levels of the work system, the most cited of which were environment, tasks, and organization. Specifically, inappropriate behavior of other street users as an environmental factor and the driver’s interactions with passengers as task-related factors were identified as key sources. Additionally, organizational factors such as interactions with supervisors and colleagues, as well as salary issues, were significantly important. The participants very limitedly expressed using mobile phone while driving as a main distractor to their driving.
Conclusion: The current study identified various influential factors, spanning different levels of the work system, affecting bus drivers’ distraction, including generic factors that impact all urban drivers and specific factors that uniquely affect bus drivers. Addressing these factors through providing appropriate education for both passengers and street users along with implementing management strategies in the organization to enhance intra-organizational relationships and organizational support can lead to the safety of the bus drivers.
 

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Introduction: Operating room personnel are involved with occupational physical activities such as repetitive bending, holding surgical tools and standing for long hours that can lead to musculoskeletal disorders (MSDs). Low back pain (LBP) is the most prevalent and costly problem among these disorders. The aim of this study was to determine the relationship between occupational physical activity, LBP and disability among operating room personnel.
Material and Methods: A total of 60 operating room personnel voluntarily participated in the study, all of which had at least two years of working experience. At the end of a working week, the degree of disability and pain were assessed by Graded Chronic Pain (GCP) questionnaire. The International Physical Activity Questionnaire (IPAQ) was used to evaluate the level of physical activity. Simple linear regression was conducted to investigate the relationship between physical activity, LBP and disability. 
Results: The survey using the GCP questionnaire revealed that 58.3% of participants reported experiencing occupational back pain, while 41.7% reported no back pain.  Among those with back pain, the average pain intensity was rated 43.11 (18.22) on the scale.  Pain remained stable for an average of 2.3 days (standard deviation = 0.95).  The average level of disability associated with back pain was 32.09 (27.44). Statistical analysis using simple linear regression showed a significant relationship between back pain and several factors: vigorous physical activity (p-value = 0.02), prolonged sitting time (p-value = 0.01), and chronic pain (p-value < 0.001).
Conclusion: Occupational physical activity characterized by low intensity, but high repetition and standing for a long time in fixed postures were the most significant contributors to lumbar back pain among operating room personnel. Chronic pain in this population was reported as grade 2, indicating severe pain with minimal disability; if left unaddressed, this could lead to movement restrictions.

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Introduction: Workplaces often contain potential risks, such as exposure to toxic chemicals. Conducting a thorough health risk assessment helps employers recognize these dangers and implement necessary controls. In the 20th century, modern risk assessment frameworks began to be established with the rise of public health agencies.
Material and Methods: The present study is a narrative review. In order to obtain necessary information, Persian and English texts were searched in Web of Science, PubMed, Scopus, SID and Magiran databases. Keywords such as “health risk assessment”, “chemicals” and “nanomaterials” were used in this study.
Results: Both quantitative and qualitative health risk assessments play critical roles in occupational health, with each method providing different levels of depth and accuracy depending on the situation. EPA Model, Monte-Carlo Simulation, Physiologically Based Pharmacokinetic (PBPK) Modeling, Quantitative Structure-Activity Relationship (QSAR) Models, Probabilistic Risk Assessment (PRA), Life Cycle Impact Assessment (LCIA), and Biologically Based Dose-Response (BBDR) Models, are among the most important quantitative methods for assessing the health risk of chemicals. COSHH Model, ICCT Model, ICMM Model, Australian Model, and Romanian Model, are the most important qualitative methods for health risk assessment of chemicals. In addition to the quantitative and qualitative methods, semi-quantitative methods like Singapore Model, LEC Method, and SEP Model, have also been proposed for assessing the health risk of chemicals. The preference for qualitative over quantitative methods in the risk assessment of activities involving nanomaterials stems from substantial uncertainties, limited data availability, and the unique and complex behaviors of nanomaterials in the workplaces.
Conclusion: Overall, the evolution of health risk assessment methods reflects a continuous drive towards greater accuracy, reliability, and relevance. As we continue to innovate and expand our knowledge, the field is well-positioned to address the complex and evolving landscape of chemical and material risks, ensuring the protection of human health and the environment. 

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Introduction: Identifying and modeling the root causes of accidents can play an important role in preventing them. The purpose of this study is to identify and model the causes of gas pipeline excavation and piping operation accidents using the Bayesian network (BN) and fuzzy DEMATEL.
Material and Methods: In this study, industrial accidents during gas pipeline excavation and piping operations were analyzed using the Bowtie method. The fuzzy DEMATEL method was employed to determine relationships between accident root causes, and the fuzzy AHP method was used to compare pairs of causes and determine their weight. Finally, Bowtie and DEMATEL outputs were mapped in Bayesian networks to determine the important risk factors for accidents.
Results: The most important risk factors for trench collapse accidents were as follows: risk management (16% impact weight), competency assessment (14.2% impact weight), supervision (13.8% impact weight), work permit system (13.7% impact weight), compliance with requirements and guidelines (13.4% impact weight), training (11.4% impact weight), HSE system (9.5% impact weight), and contractor management (8% impact weight).
Conclusion: Based on the results, it was demonstrated that risk management and competency assessment, having the highest weight percentages, play the most significant roles in the occurrence of trench collapse accidents. The findings of this study can inform the prioritization of corrective measures to prevent trench collapse accidents in gas pipeline excavation and piping operations.

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Introduction: Barriers play a critical role in mitigating risks and preventing catastrophic incidents in process industries. Human and Organizational Factors (HOFs) significantly influence the performance of safety barriers. This systematic review investigates existing frameworks and methods for assessing the impact of HOFs on safety barrier performance.
Material and Methods: A systematic search was conducted across the Scopus and Web of Science databases, following the PRISMA guidelines. The search aimed to identify studies presenting methodologies for evaluating the influence of HOFs on safety barrier performance in process industries. Data were subsequently extracted from the 16 included studies.
Results: The 16 studies included in this research presented various methods and frameworks examining the impact of HOFs on different types of safety barriers, including technical, operational, and human barriers, across industries such as oil and gas, chemical, and steel. Barrier and Operational Risk Analysis (BORA) emerged as the predominant framework among the studies. Research on operational and human barriers, which depend on human actions and procedures, frequently identified factors such as competence, training, communication, and supervision as key influencers of performance. In contrast, studies on technical barriers highlighted the importance of assessing factors such as maintenance management and procedural compliance.
Conclusion: This research highlights the critical role of HOFs in safety barrier performance within process industries. By systematically reviewing existing methodologies, the study identified their strengths and weaknesses. Findings underscore the need to account for uncertainties in expert judgments and the interplay between HOFs in evaluation models. The integration of fuzzy logic and Bayesian networks is proposed to enhance evaluation processes. Future research should prioritize the development of unified frameworks that address the limitations of current approaches while expanding their applicability across diverse industries. 
 

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Introduction: Relief work is inherently associated with various stress-inducing factors due to the nature of the profession. Among relief-related professions, firefighters, due to the responsibilities related to firefighting and safety services, are exposed to various physical and chemical hazards. Therefore, the primary aim of this research was to compare the effect and practice retention, central and operational and skill-based stability over the balance of the firefighters with and without firefighting and rescue clothing and Hazardous Materials.
Material and Methods: The statistical population consisted of 5500 firefighters from the city of Tehran. As for the intervention, in the first group, central stability exercises, and in the second group, operational and skill-based exercises were performed by the firefighters during their shift days for a period of 6 weeks. The balance of the firefighters in both groups was measured at three stages: at the beginning of the intervention, immediately after the 6-week training period, and one month after the end of the training. The “Y Balance” board was used to assess the firefighters’ balance. Data analysis was performed using repeated measures analysis of variance and the Bonferroni post hoc test at a 5% significance level, using version 26 of the SPSS software.
Results: According to the results of this study, the balance of firefighters in both the right and left legs was significantly greater in the operational and skill-based training group compared to the central stability training group, both immediately after training and one month later (p < 0.05). With performing operational and skill-based training, right leg balance in the rescue suit was significantly higher than in the fire protection and hazmat suits (p < 0.05), while there was no significant difference between the fire protection and hazmat suits (p > 0.05). Left leg balance in the rescue suit was significantly higher than in both the fire protection and hazmat suits, and balance in the fire protection suit was significantly higher than in the hazmat suit (p < 0.05). Additionally, firefighters’ balance significantly improved after balance training (p < 0.05), and there was no significant difference between the balance measured one month after training and immediately after training (p > 0.05).
Conclusion: In addition to the fact that core stability exercises particularly task specific functional training are effective in improving balance and preventing injuries, the retention of these effects over time should be taken into account.

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Introduction: Fumigant gases in maritime and container chains, along with occupational noise in marine and manufacturing industries, are among the most significant chronic risk factors. They are usually assessed separately, despite their simultaneous impact on workers’ health. The importance of this study lies in presenting an integrated approach for real-time monitoring of combined risk and aligning it with occupational exposure limits (OELs). The aim is to develop and validate an interpretable, regulation-oriented framework for predicting combined risk.
Material and Methods: This research integrated and normalized data from the Global Burden of Disease (GBD) 2021 study including age-standardized disability rates (ASDR) and average annual percentage change (AAPC) for 204 countries with occupational exposure limit tables for fumigants. A Sugeno-type fuzzy inference system with three inputs and four rules was designed. Weights and membership function boundaries were optimized using the Prairie Dog Optimization algorithm, and a threshold-based scenario generation module was applied to produce high-risk synthetic data. Model performance was evaluated through an OEL compliance test.
Results: Findings revealed that the proposed optimization reduced the loss function by 42% compared to random search. The mean absolute error (0.028 ± 0.006) and root mean square error (0.041) were obtained. Threshold-based scenario generation improved data coverage in high-risk regions from 0.62 to 0.90 and increased the accuracy of critical condition detection from 0.71 to 0.89. The OEL compliance index reached 0.93, confirming input weighting as the most influential factor.
Conclusion: The proposed framework simultaneously ensures numerical accuracy, interpretability, and regulatory compliance with occupational exposure limits. It can be deployed within real-time monitoring dashboards for ports and factories. Future research should integrate IoT sensors and multi-objective optimization to enable dynamic updates in response to evolving regulations and operational conditions.

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Introduction: Fire risk assessment in oil storage tanks faces challenges due to incomplete, conflicting, and uncertain data, particularly when empirical evidence is limited. Traditional point-based likelihood estimates often fail to capture expert doubt and epistemic uncertainty. This study aims to develop and evaluate a novel hybrid framework combining Dempster-Shafer Theory (DST) and Bayesian Networks (BN) to improve the trustworthiness of fire risk prediction in such industrial settings.
Material and Methods: The proposed approach integrates DST to model expert uncertainty through interval probabilities (Bel–Pl) and BN to dynamically update causal relationships as new information appears. The study implements computational coding to enable DST calculations for five expert opinions across 243 scenarios, overcoming prior limitations in multi-expert modeling due to computational complexity.
Results: The hybrid DST-BN framework demonstrated superior ability to incorporate incomplete and conflicting expert data, reducing overconfidence linked to point estimates. Interval probabilities offered more trustworthy representations of epistemic uncertainty, while BN integration allowed traceable and adaptable causal modeling. The computational solution facilitated practical application of DST with multiple experts, enhancing the strength of the risk assessment.
Conclusion: This research provides an effective DST-BN hybrid methodology for assessing fire risk in fixed-roof oil tanks, improving accuracy and trustworthiness in complex industrial environments. By addressing the shortcomings of point-based methods and enabling multi-expert participation, the framework supports clearer and more defensible probabilistic inferences. Future work may focus on integrating real-time sensor data and AI-based decision systems to further strengthen dynamic risk assessment capabilities.