Journal of Health and Safety at Work

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Introduction: Polymer nanofiber filters have great potential for controlling particulate pollution due to their high filtration efficiency and low pressure drop. This study aimed to fabricate nanofiber membranes from a biodegradable polymer through solution electrospinning to address both health and environmental concerns, along with analyzing their morphological characteristics. The filtration performance of the prepared membranes was evaluated against different particle sizes under two air face velocities. 
Material and Methods: The nanofiber membranes were fabricated from aqueous poly(vinyl alcohol) (PVA) solutions at various concentrations from 5 to 6 w/v%  under different process parameters. The morphological characteristics of the nanofibers were examined using field-emission scanning electron microscopy (FE-SEM), while structural properties such as basis weight and thickness were measured to estimate porosity. Filtration performance, including efficiency and pressure drop, was evaluated at two standard air face velocities (2.5 and 5.3 cm/s) using a media test system. In addition, the quality factor of the prepared membranes was calculated.
Results: The electrospun nanofibers were uniform and bead-free, with the mean fiber diameters ranging from 106 to 151 nm. The filtration efficiencies were 95.72–99.92 % for sub-micron particles (0.3 and 0.5 µm), and 99.43–100 % for larger particles (1 and 3 µm). The pressure drop ranged from 67 to150 Pa at an air face velocity of 2.5 cm/s, and from 58 to150 Pa at an air face velocity of 5.3 cm/s.
Conclusion: The 6 wt.% PVA nanofiber membrane electrospun at 15 kV, 0.5 mL/h, and 15 cm produced thinner fibers (approximately 106 nm) and exhibited higher efficiency for 0.3 µm particles (99.89 % and 99.92 % at 2.5 and 5.3 cm/s air face velocities, respectively). For this membrane with thinner fibers, the pressure drop increased from 67 to 150 Pa with rising the air face velocity. 
 

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Introduction: Computer-based numerical simulation can serve as an effective approach for replicating system behavior over time. It enables the analysis of a system’s capabilities, capacities, and performance during the design phase—prior to physical implementation. Accordingly, simulation tools can be used for the design, modeling, evaluation, and visualization of heat transfer interactions among the components of a Liquid Cooling Garment (LCG) system. Therefore, the present study was conducted with the aim of designing and experimentally validating a numerical simulation model for a thermoelectric-based LCG.
Material and Methods: A new model of a liquid cooling garment (LCG) based on fluid circulation was developed using the Finite Element Method (FEM) in COMSOL Multiphysics software. To validate the simulated model, a physical prototype of the LCG with similar characteristics was designed, and human experiments were conducted under controlled environmental conditions. Finally, the findings obtained from the simulation and experimental results were compared.
Results: The results showed that the difference in microclimate temperature between the simulated predictions and the average experimental data ranged from 0.1 °C to 0.65 °C. Additionally, the deviation in coolant temperature within the piping system between the simulation and experimental data ranged from 0.1 to 0.6 °C. These findings indicate that the developed model demonstrates a satisfactory level of accuracy in predicting thermal parameters
Conclusion: The results suggest that the proposed model can serve as an effective tool in the design and evaluation process of wearable cooling systems before fabricating physical prototypes. Further studies are recommended to enhance the performance and precision of LCG simulation models.
 

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Introduction: A gas pressure reduction station is an important facility in gas transmission systems. These systems consist of various sections, the reliability of each section affecting the station’s overall reliability. Therefore, this study aimed to assess the reliability of station sections using Markov chain Monte Carlo (MCMC) and the continuous-time Markov chain (CTMC) method.
Material and Methods: Equipment failure and repair rates were simulated using the MCMC method in WinBUGS14. Then, based on the failure and repair rates, the station reliability was evaluated using the CTMC. The results of the equipment failure rate simulation were validated using two criteria: MC Error and the Goleman-Rubin test. Also, the results of station reliability evaluation were validated using Reality Check and Partial Benchmark Exercise methods.
Results: Failures in the filtration and pressure reduction sections were more frequent than in other sections of the station. Therefore, these sections were considered the most critical sections in the reliability assessment. The posterior standard error was less than 0.01, indicating good convergence of the data for the parameter posterior distribution. The results of the Goleman-Rabin test showed values less than 1.2, indicating proper convergence of the chains. For all sections and stations, a systematic approach was determined using the Markov model. The results showed a strong correlation between the CTMC and the block diagram method (R2=0.9499).
Conclusion: The proposed approach combines failures of system components and can display multiple failures. It also accounts for time factors in its calculations and minimizes subjective expert evaluations.

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Introduction: Surgical technologists encounter a challenging work environment, and therefore, they require well-organized workplaces and appropriate health-related training. This study aimed to determine the effect of a participatory ergonomics program on musculoskeletal disorders (MSDs) and general health among surgical technologists
Material and Methods: This single-blind randomized controlled trial was conducted in 2023-2024 in operating rooms of teaching hospitals in Isfahan, Iran. The study population comprised 88 surgical technologists meeting the inclusion criteria. One hospital was randomly selected as the intervention site, while the remaining hospitals served as the control group. Data were collected using a demographic questionnaire, the Cornell Musculoskeletal Discomfort Questionnaire (CMDQ), and the 28-item General Health Questionnaire (GHQ-28). Initially, all participants in both groups completed baseline assessments. The intervention group received a participatory ergonomics program. Follow-up assessments were conducted at 3 and 6 months post-intervention in both groups. Data were analyzed using SPSS version 24 employing both descriptive and inferential statistical methods, with a significance level set at 0.05.
Results: Before the intervention, no significant differences were observed between the intervention and control groups in terms of mean scores for general health and MSDs (p=0.55). Three months post-intervention, a significant difference was found in the mean score of lower extremity MSDs between the groups (p=0.033). Six months post-intervention, a significant difference was observed in the mean score of Trunk region MSDs between the groups (p=0.038). Significant differences in mean general health scores were observed between the groups at 3 months (p=0.001) and 6 months (p=0.001) post-intervention, with the intervention group reporting better general health compared to the control group.
Conclusion: The implementation of a participatory ergonomics program can improve general health and reduce MSDs among surgical technologists.

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Introduction: Exposure to noise is one of the most common harmful factors in the dental profession that can have significant consequences. Unfavorable acoustic conditions of the environment can also worsen the conditions and lead to reduced speech clarity, auditory fatigue, and reduced quality of communication between the doctor and the patient. In this regard, designing effective acoustic interventions can play an important role in improving sound.
Material and Methods: This study was conducted in an academic dental clinic. After measuring the sound pressure level, reverberation time, and speech transmission index, the amount of rock wool absorber required was determined using the Sabin formula. The panels were structurally installed on the clinic walls, and after 24 hours, the sound pressure level, RT60, and STI were measured again, and appropriate statistical tests were used to determine the effect of the intervention.
Results: The results showed that the sound pressure level before and after the panel installation was significantly different and decreased after the intervention (p-value <0.05). The reverberation time at the dominant frequency of 2000 was 0.992 seconds before the intervention and reached 0.599 seconds after the intervention (p=0.027, z= 2.207). The speech transmission index in the center of the room improved from 0.64 and the “average” level to 0.85 and the “excellent” level, indicating an increase in speech clarity.
Conclusion: The acoustic intervention was able to bring the clinic’s acoustic indices closer to the recommended ranges. The significant reduction in reverberation time and sound pressure level along with the increase in the speech clarity index indicates the high efficiency of this intervention. Utilizing sound absorbers can be a low-cost and feasible solution to improve acoustic conditions and improve speech communication in dental treatment and educational environments.
 

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Introduction: Hospitals represent a critical fire safety challenge. The presence of patients with limited mobility, specialized staff, and invaluable infrastructure makes them seriously vulnerable. Further, toxic smoke inhalation, as a primary product of fire, is a leading cause of mortality. To address this concern, our study intended to conduct a comprehensive fire safety assessment of a teaching hospital in Tehran (2023-2024) by integrating risk assessment with numerical modeling.
Material and Methods: This study was conducted in a teaching hospital following three consecutive steps: risk assessment, fire scenario design, and fire and smoke modeling. First, vulnerable zones were identified using the FRAME method. Next, fire scenarios and control strategies were designed based on the identified risk factors and a review of the relevant literature. Ultimately, fire and smoke transport was modeled using the CFAST software to ascertain the performance of the proposed strategies.
Results: The risk assessment pinpointed two wards with unacceptably high occupant risk levels: the inpatient ward on the 10th floor, owing to a cluster of unsafe behaviors, and the basement warehouse, owing to improper storage and inadequate emergency exit access. Fire and smoke modeling was performed for these two zones, comparing the “current situation” against a proposed “risk control strategy.” The modeling results revealed that the control strategy positively affected key life safety indicators, significantly ameliorating the Fractional Effective Dose (FED) and Heat Release Rate (HRR).
Conclusion: According to this study, a fire risk assessment provides a clear and detailed perspective on a hospital building’s fire safety. Integrating the results of the FRAME assessment with CFAST simulations results in a comprehensive understanding of the facility’s safety status. These data can be utilized to design effective emergency plans and calculate the Required Safe Egress Time (RSET), thereby preventing life-threatening harm to occupants against toxic gases. 

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Introduction: Climate change is a major global challenge, strongly influencing the Wet Bulb Globe Temperature (WBGT) index and heat stress among steel industry workers. This study evaluates the impact of geographical location and climate change on occupational heat stress exposure in Iran’s steel sector.
Material and Methods: This qualitative-analytical study used data from the SABA system and the Iranian Occupational Heat Stress Atlas. Information on steel industries, their distribution, and production capacities across eight climate zones was extracted. WBGT measurements were collected in collaboration with industrial units in different zones. Data analysis was performed using ArcGIS and SPSS. The effects of climate change on heat stress were assessed for three future horizons: 2040, 2060, and 2080.
Results: The findings revealed that climate zones G1 (eastern, southeastern, and desert regions) and G4 (Persian Gulf coastal provinces including Hormozgan, Bushehr, Fars, and Khuzestan), which host the highest steel production capacities, are exposed to the highest levels of heat stress (WBGT index) and water resource scarcity. WBGT values in zones G4 and G6 (Gilan province) exceeded permissible limits, whereas zones G2 (including North Khorasan, Razavi Khorasan, Tehran, Alborz, Qazvin, Hamedan, Markazi, and Chaharmahal-Bakhtiari), G5 (Kurdistan, Kermanshah, Lorestan), and G7 (Ilam, Kohgiluyeh and Boyer-Ahmad) showed the lowest WBGT levels. Considering projected temperature increases in the three future horizons and the acceptable correlation coefficient (0.40) between annual daytime temperature and WBGT index per climate zone, predicted temperature changes may lead to increased WBGT levels, particularly in zones G3, G6, and G8.
Conclusion: Given climate projections and the spatial distribution of steel industries, it is essential to develop climate-responsive policies, implement sustainable water resource management, and reconsider the siting of steel production units. These measures can enhance the resilience of Iran’s steel industry against future climate change and mitigate occupational health and environmental risks.

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Introduction: Workplace safety is a critical factor for organizational success and the protection of human capital. Safe behavior among employees is influenced by various psychological and organizational factors, including safety climate and safety motivation. Considering the importance of enhancing safety motivation and the lack of standardized indigenous tools in Iran, this study aimed to culturally adapt the Self-Determination Safety Motivation Scale (SDSM) and examine its mediating role in the relationship between safety climate and employees’ safe behavior at Kavir Steel Complex in Aran and Bidgol County.
Material and Methods: This analytical cross-sectional study was conducted in 2024 among 256 production line employees. Data were collected using the Safety Climate Questionnaire, the Safe Behavior Questionnaire, and the Persian-adapted SDSM. The adaptation process included forward–backward translation, content validity evaluation by experts, and confirmatory factor analysis. Data were analyzed using SPSS version 26 and structural equation modeling with SmartPLS version 4.
Results: The findings indicated that safety climate had a positive and significant effect on safe behavior (β = 0.608, p < 0.001), and safety motivation significantly mediated this relationship (β = 0.388, p < 0.001). It is noteworthy that the direct relationship between safety climate and safety motivation was significantly negative. The structural model explained approximately 42% of the variance in safety behavior (R² = 0.419), indicating satisfactory predictive power. Model fit indices (NFI = 0.91, SRMR = 0.06) demonstrated a satisfactory model fit. Convergent validity, discriminant validity, and construct reliability were all confirmed at acceptable levels.
Conclusion: The results highlight the importance of fostering a positive safety climate and strengthening employees’ intrinsic motivation in high-risk environments to reduce workplace accidents and promote organizational health. These findings can serve as a foundation for policy-making and training programs in the field of HSE.

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Introduction: Environmental and health concerns regarding synthetic sound absorbers necessitate natural, sustainable alternatives. Agricultural waste like walnut shells is promising due to its inherent porosity. This study evaluates the acoustic properties of walnut shell composites, investigating the influence of key design parameters like thickness and chopping level on sound absorption performance. 
Material and Methods: Porous granular samples were fabricated from walnut shells at three chopping levels (minimally, moderately, finely) and four thicknesses (20, 40, 60, and 80 mm). The sound absorption coefficient was measured via the impedance tube method. Field Emission Scanning Electron Microscopy (FESEM) analyzed the material’s morphology, and results were validated with Slanted Slit (SS) and Non-uniform Pore Size Distribution (NUPSD) mathematical models.
Results: Both increased thickness and chopping level significantly enhanced sound absorption. For finely chopped samples, increasing thickness from 20 to 80 mm shifted the absorption peak from 2000 Hz to 630 Hz. At a constant 80 mm thickness, intensified chopping boosted the absorption coefficient at 630 Hz from 0.48 to 0.97. This improvement correlated directly with increased density, tortuosity, and airflow resistivity. Model predictions showed the best agreement for the most finely chopped samples.
Conclusion: Walnut shell waste, especially after intensive mechanical processing, is a highly effective and sustainable sound-absorbing material. The chopping process optimizes the acoustic structure by activating the material’s inherent micro-porosity, yielding excellent performance in the speech frequency range (500-2000 Hz). This material shows significant potential as a green alternative to synthetic absorbers for indoor noise control. 

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Introduction: Industrial units, such as oil refineries, face significant hazards due to the release of toxic and flammable gases. Hydrogen sulfide (H₂S), due to its high toxicity and environmental impact, is among the most dangerous pollutants. This study aimed to model and assess the consequences of H₂S release in the Sulfur Recovery Unit (SRU) of Abadan Refinery using PHAST software to support safety planning and risk reduction strategies.
Material and Methods: Consequence modeling was conducted using PHAST version 8.4. Process data, including temperature, pressure, flow rate, and feed composition, along with meteorological conditions (average temperature, relative humidity, and wind speed based on Pasquill stability classification), were used to define probable scenarios. Scenarios included partial pipeline rupture, variable leak flow, short pipe release, and catastrophic reactor tank rupture. Key damage criteria, including thermal radiation threshold, explosion overpressure, and toxic dose, were used to determine hazard zones.
Results: Thermal radiation up to 71.027 kW/m² can cause instant death within a 70-meter radius, while overpressure exceeding 0.206 bar can destroy equipment and structures up to 35 meters in summer conditions. The H₂S cloud can spread up to 120 meters downwind, causing immediate fatalities among exposed personnel. These findings identify high-risk zones in and around the SRU, emphasizing the need to relocate shelters, install gas monitoring systems, and provide protective equipment. Results are limited to the defined scenarios and PHAST assumptions.
Conclusion: Due to the lack of risk assessment studies in early phases and during operation, identifying safe points and high-risk zones, along with prioritizing risk reduction, is essential to ensure workplace and public safety. Comprehensive risk assessment, including probability analysis (using software such as SAFETI) and application of advanced models (CFD and AI-based methods), is recommended for future research.
 

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Introduction: Agriculture, employing over 1.1 billion workers worldwide, remains among the most hazardous occupations. The concept of safety culture provides a comprehensive framework for preventing injuries, showing that unsafe behaviors mainly reflect organizational norms rather than individual negligence. Thus, focusing solely on individual interventions is insufficient; lasting improvement requires organizational and managerial commitment. Despite its recognized importance, few studies in Iran have examined how safety culture relates to farmers’ safety behaviors—a critical gap this study addresses by identifying key predictors of safety performance among Iranian agricultural workers.
Material and Methods: A cross-sectional study was conducted in 2024 among 303 agricultural workers selected via convenience sampling. Data were collected using standardized questionnaires on safety culture and performance validated in Iran (Cronbach’s α > 0.70). Analyses were performed in SPSS and AMOS 23 using descriptive statistics and structural path analysis with a significance level of p < 0.05.
Results: Participants scored highest on personal priority and need for safety (80.19 ± 12.02) and lowest on organizational priority for safety (46.08 ± 17.09). Workers without accident history had significantly higher safety culture scores than those with incidents (p = 0.008). Path analysis showed that safety culture constructs explained 66% of the variance in safety performance. Management commitment and personnel collaboration showed the strongest total effect (β = 0.745, 95% BC bootstrap CI: 0.688–0.790), followed by safety communication (β = 0.310, 95% BC bootstrap CI: 0.157–0.457) and formal safety training (β = 0.229, 95% BC bootstrapCI: 0.136–0.325) (all p < 0.001).
Conclusion: Overall, safety culture and performance in agriculture were moderate. Despite workers’ positive attitudes, safety has not yet become an institutionalized value. The model identified management commitment, communication, and training as key factors for improving safety performance. Integrating safety into agricultural policy and ensuring continuous, management-supported training are essential. Future longitudinal and mixed-method studies should explore the evolving nature of safety culture in this context to sustain its progress.

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Introduction: Occupational exposure to hazardous chemicals has created an urgent need for the development of protective clothing with both breathability and effective protection. This research aimed to modify the surface of cotton fabric using a novel composite coating consisting of silica (SiO₂) aerogel and polydimethylsiloxane (PDMS) to achieve breathability, superhydrophobicity, and chemical resistance simultaneously. 
Material and Methods: Cotton fabric samples were coated via immersion in suspensions of PDMS and PDMS/SiO₂ composite. The physical, structural, and functional properties of the coated samples (C/PDMS and C/PDMS/SiO₂) and the uncoated control sample (C) were analyzed using FESEM, EDX, XRD, FTIR, air permeability, tensile strength, water contact angle, and chemical resistance tests against four chemicals (water, toluene, acetic acid, and dimethylformamide). 
Results: Results demonstrated that the PDMS/SiO₂ composite coating was effectively stabilized on the fabric surface. The composite-coated sample (C/PDMS/SiO₂) achieved superhydrophobicity with a remarkable contact angle of 168°. Furthermore, the coating resulted in a 60% reduction in air permeability compared to the uncoated fabric, indicating the formation of a dense protective structure. In terms of chemical resistance, the composite-coated samples exhibited significant performance improvements. Acetic acid penetration decreased from 17% to 1%, whereas the absorption capacity for toluene increased from 12% to 67%, and for dimethylformamide from 17% to 59%. Additionally, the coating led to a notable enhancement in the fabric’s tensile strength. 
Conclusion: The findings of this study reveal that the PDMS/SiO₂ composite coating holds high potential for application in protective textiles with multifunctional capabilities, including exceptional water repellency, improved chemical resistance, and retained breathability.

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Introduction: Office workers, who comprise a substantial portion of the country’s workforce and use computers extensively, are at risk of developing occupational musculoskeletal disorders. Therefore, the aim of this study is to assess posture and predict the risk of developing musculoskeletal disorders using ML techniques based on ROSA scoring.
Material and Methods: This descriptive-analytical, cross-sectional study was conducted in North Khorasan Gas Company in 1402. This study was conducted in three steps, which included collecting data using the ROSA method and the Nordic questionnaire, creating a prediction model based on machine learning techniques, and evaluating the results based on the criteria of accuracy, precision, sensitivity, specificity, area under the ROC curve, and F criterion. Also, the 10-fold cross validation method was used to evaluate the performance of the models.
Results: The results showed that 32.5% of employees were aged 41 to 45 years and 35% had a work experience of 16 to 20 years. The most pain and discomfort was observed in the back and neck areas with a prevalence of 52.1% and 41.5% of employees, respectively. The results of the ROSA showed that 44.8% of people had a score of 5 and above. The Bayesian Network and RF models achieved accuracy of 80.87 and 80 and sensitivity of 80.9 and 80, respectively. However, the low value of specificity for RF showed that its performance in identifying high-risk cases was low. Considering all performance criteria, the best model was the Bayesian Network, which in addition to high accuracy and sensitivity, also had a specificity of 60.6%. 
Conclusion: The results showed that the proposed model can be used to identify and predict musculoskeletal disorders in organizational employees. This tool can also be used as a predictive tool for diagnosing musculoskeletal disorders and interpreting the results effectively and reliably.

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Introduction: The petrochemical industry faces substantial human and economic risks due to high‑temperature, high-pressure operations and hazardous chemicals, such as chlorine. Given chlorine’s acute toxicity, oxidizing properties, and severe corrosivity, its accidental release poses a risk of widespread and irreversible damage to human health and safety systems. This study proposes a risk framework by integrating BN and FTA with SAFETI 8.4 software and the Dempster–Shafer theory.
Material and Methods: This study was conducted in 2025 at a petrochemical plant. After identifying the hazards associated with chlorine storage tanks, a chlorine gas leakage scenario was selected and analyzed using FTA. The probabilities of basic events were estimated using the Dempster–Shafer theory to address uncertainty. The results were modeled using a Bayesian network in GeNIe 5.0. Risk estimation was performed with SAFETI 8.4 software incorporating meteorological data. Individual and societal risk curves were plotted using probit equations to determine the areas affected by individual risk and estimate the potential fatalities.
Results: Three chlorine gas release scenarios — leaks of 100 mm and 200 mm, and a catastrophic tank rupture — were analyzed using FTA for the top event. The FTA constructed for the top event “chlorine leakage” comprised 18 intermediate events and 50 basic events. Using the Dempster–Shafer theory, the estimated probabilities were Bel=1.7405606×10−6 and Pl=8.294732×10−6. Social risk curve analysis showed that the Pl curve had higher frequencies than the Bel curve, and that Pl exceeded the unacceptable risk level. At the same time, Bel remained mostly within the tolerable level (ALARP).
Conclusion: The results indicated that, under optimistic conditions, the social risk of chlorine leakage remained within ALARP, whereas under pessimistic conditions, it reached the unacceptable level. The integrated BN and D-S T reduced uncertainty, enabled accurate risk estimation and comprehensive evaluation, supported evidence‑based decisions, and reduced accident probability while enhancing industrial safety and resilience.

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Introduction: Assessing exposure to volatile organic compounds (VOCs) in workplaces requires timely access to accurate, standardized guidance on sampling and analysis. This study aimed to develop and evaluate a decision-support chatbot, based on the Retrieval-Augmented Generation (RAG) architecture, to assist occupational health professionals in selecting appropriate sampling and analysis methods for VOCs in accordance with NIOSH guidelines.
Material and Methods: This research was of an applied-developmental type. In this study, relevant NIOSH methods for sampling and analysis of VOCs were extracted, structured, and organized into a knowledge base. User needs were identified through semi-structured interviews with occupational health professionals. Based on this data, a generative AI-based chatbot was designed and implemented using the RAG architecture. The system performance was evaluated using expert evaluation and a chatbot usability questionnaire.
Results: The developed chatbot received an overall user-friendliness score of 3.88 out of 5, indicating good user acceptance. Participants noted improved access speed for standard technical information and the high transparency of the system’s responses. Expert evaluation also confirmed the scientific validity of the retrieved content and its compliance with NIOSH standards.
Conclusion: The proposed RAG-based decision-making chatbot provides an effective and innovative tool to support occupational exposure assessment to VOCs. Integrating generative AI with standard occupational health guidelines can improve the efficiency of decision-making and access to expert information in practice. 

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Introduction: Rapid and safe evacuation of hospitals during emergencies is a critical issue. This study aimed to analyze the evacuation of a three-story hospital and to evaluate the effects of evacuation routes on total evacuation time.
Material and Methods: This descriptive-analytical study uses agent-based computer modeling to simulate the evacuation process of a hospital with an area of 8762 square meters and 217 staff. The effects of the variables of the width of horizontal exit routes, the dimensions of exit doors, and the obstacles in common exit routes on the time for complete evacuation of people were examined and compared in ten different scenarios.
Results: Findings indicated that increasing the width of horizontal routes and the dimensions of emergency exits, along with the removal of obstacles, significantly reduced maximum evacuation time.
Conclusion: Optimizing the geometric characteristics of emergency evacuation routes, particulary increasing exit sizes and removing obstacles, plays a key role in minimizing total evacuation time during hospital emergencies. Such improvements can enhance the safety of occupants in critical situations.

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Introduction: Rapid urbanization and industrialization have made controlling particulate matter pollution critical. Cyclones and electrostatic precipitators (ESPs) are two widely employed technologies. In this study, the efficiency, quality factor, pressure drop and energy consumption of these two systems were measured in the same dimensions and under different laboratory conditions. In addition, the energy consumption of the reinforced DBD coaxial ESP was also investigated. The aim of this study is to provide detailed information for comparing two particle collection systems with different operating conditions.
Material and Methods: Both devices were designed with the exact dimensions to investigate the collection efficiency, pressure drop, quality factor, and energy consumption. A standard Stairmand cyclone and a similarly sized ESP enhanced by coaxial DBDs were constructed. Experiments were conducted under isokinetic conditions according to the ASTM D3685 standard. Particle concentrations were measured using a Grimm 1.108 particle counter, and pressure drop was measured using a manometer. The performance of the two systems was evaluated by sampling both upstream and downstream of the cleaning devices and applying analytical formulas.
Results: A comparative analysis of cyclones and ESPs revealed that increasing temperature negatively impacted the collection efficiency of both systems. While cyclones benefited from higher particle concentrations, ESPs coaxial DBDs demonstrated optimal performance at intermediate concentrations. The study also found that increasing flow velocity significantly improved the collection efficiency of cyclones, whereas ESPs achieved their best performance at a lower, optimal velocity.  However, the changes in particle collection efficiency in ESP were approximately 4-10% higher than in cyclone and have an optimum energy conception. Moreover, doubling the voltage above the corona threshold in ESPs resulted in a notable enhancement (about 16.3%). The energy consumption of the reinforced coaxial DBD ESP was measured at 12.09 J/L.
Conclusion: The results of this study indicated that ESP’s quality factor was significantly higher than cyclones when operated and consumed less energy than usual ESPs under the same conditions. The maximum quality factor achieved for ESPs highlighted their superior collection efficiency, lower pressure drops, and energy consumption. 
 

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Introduction: Contemporary automotive manufacturing enterprises, like other industrial sectors, routinely collect large volumes of data pertaining to productivity, occupational safety, and health. The transformation of these raw data into actionable knowledge has become increasingly critical for organizational decision-making. This study applies data mining techniques to analyze occupational accidents resulting in lost workdays at an Iranian automotive manufacturing company from June 2014 to March 2025, aiming to identify the patterns and determinants of workplace incidents.
Material and Methods: Accident records from the specified period were systematically collated and preprocessed, then subjected to classification and association rule mining using established data mining algorithms. Classifier implementations and experiments were conducted within the Weka software environment. In addition to supervised classification, association rule mining algorithms (Apriori and Predictive Apriori) were employed to discover co-occurring attribute patterns and identify combinations of factors associated with elevated accident risks.
Results: Through comparative evaluation, the support vector machine (SVM) classifier demonstrated the best performance in predicting accident type, severity & causes, achieving an accuracy of 66.08% under cross-validation. However, the decline from a 100% training accuracy indicates the presence of overfitting in the models. Furthermore, Apriori and Predictive Apriori algorithms successfully extracted rules with high performance metrics, demonstrating confidence levels above 83% and predictive accuracies between 97.9% and 98%. Quantitative analysis revealed that the highest correlation with incidents was found in a combination of specific factors: low work experience (0 to 5 years), late shift hours (4 to 8 hours after the shift start), and employment as a body production operator. Secondary contributing variables included shift scheduling, proximate causes of the event, annual production rate, and employee remuneration.
Conclusion: The findings demonstrate that data mining techniques are effective tools for uncovering hidden patterns in occupational accidents within the automotive industry; nevertheless, the observed overfitting in classification models remains a serious limitation, highlighting the need for more balanced datasets. From a practical perspective, the identified association rules enable the design of specialized training programs, optimized rest schedules, and targeted safety interventions specifically for high-risk groups, such as low-experienced workers (0–5 years) operating in body production during the late hours of their shifts (4–8 hours).

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Introduction: Noise-induced hearing loss (NIHL) is an important occupational and environmental disorder associated with excessive production of reactive oxygen species and cochlear damage. Considering the potential protective effects of antioxidants, the present study was conducted to investigate the protective effect of black seed (BS) oil on NIHL in rats.
Material and Methods: In this experimental study, 36 adults male Wistar rats were randomly divided to four groups: control, BS, noise, and noise+BS. Animals in the noise-exposed groups were subjected to pink noise at 110 dB SPL for 4 h/day. BS (2 mL/kg) was administered orally 1 h before exposure. Auditory brainstem response (ABR) thresholds were measured at baseline and on days 7, 14, and 21. Serum malondialdehyde (MDA), total antioxidant capacity (TAC), and superoxide dismutase (SOD) levels were also determined.
Results: Noise exposure caused a significant increase in ABR thresholds compared with baseline (P<0.05), a significant decrease in SOD and TAC, and a significant increase in MDA. Administration of BS reduced the increase in ABR thresholds, although this effect was not significant at all frequencies and assessment times. In addition, BS increased SOD and TAC and decreased MDA compared with the exposure group (P<0.05).
Conclusion: In this animal model, BS oil showed a potential protective effect against pink noise-induced hearing damage, accompanied by improved antioxidant status and reduced oxidative stress. However, given the use of a single dose and an animal model, these findings should be interpreted cautiously.
 

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Introduction: Steel industry socio-technical systems involve non-linear human, technological, and organizational interactions. As traditional risk assessments often fail to capture emergent risks, this research employs the Functional Resonance Analysis Method (FRAM) to analyze operational variability. Additionally, PHAST 8.4 software models the consequences of accidents triggered by functional fluctuations, providing a comprehensive assessment of hazard zones.
Material and Methods: Conducted in 2025 at a Direct Reduction Iron (DRI) plant, this study used a two-phase approach. First, FRAM analysis utilized expert panels, interviews, and observations to map 14 systemic functions. After identifying functional variability, PHAST (v8.4) simulated catastrophic scenarios, translating abstract functional misalignments into concrete physical impact data.
Results: Using the FRAM method, a total of 14 functions and 42 interactions were identified within the direct reduction unit, among which 6 functions and 9 interactions were determined as critical points. The results indicated that certain organizational and human functions, including maintenance (F12), inspection (F13), and training (F14), act as essential preconditions for key technical functions such as the direct reduction furnace (F2) and the reformer (F6). Evaluation of time and accuracy variability showed that performance fluctuations in the reformer can lead to catastrophic scenarios, such as gas leakage and explosion. PHAST 8.4 modeling results for the reformer unit revealed that in the event of an accident, the 100% fatality radius could reach 15 meters, while under winter meteorological conditions, the thermal radiation impact zone could extend up to 16 meters along the gas pipelines.
Conclusion: This paper provides a practical framework for transitioning from a reactive to a proactive approach. By identifying hidden critical points arising from functional resonance, the findings can support managers in more targeted resource allocation. Furthermore, integrating FRAM with consequence modeling enables a more accurate understanding of accident-scenario propagation and supports the determination of safety boundaries in complex industrial systems.