Search Results (26)

Ensuring employee safety is a top priority for every enterprise, and it is especially critical in high-risk industries like coal mining. To achieve this goal, it is essential to focus efforts on identifying existing hazards and thoroughly assessing the associated risks. Accurate identification and detailed characterization of occupational hazards play a pivotal role in the occupational risk assessment process, providing the foundation for effective safety strategies. This article presents an analysis of the process of identifying occupational hazards in hard coal mining, based on applicable legal regulations and a review of the relevant literature. The analysis reveals, on the one hand, a diversity of approaches to hazard classification, and on the other, a limited use of the characteristic features of hazards in classification processes. The findings of this review form the basis for proposing a systematic classification of occupational hazards in hard coal mining, taking into account the specific features of hazards in relation to their sources and potential consequences. The proposed classification not only categorizes hazards but also describes the specifics of hazard sources, such as environmental conditions, machinery, chemicals, and human factors, as well as the possible outcomes of these hazards, including physical injury, health impacts, and even fatalities. The aim of this article is to present a proposed classification of occupational hazards in hard coal mining and to provide a detailed characterization of these hazards based on the description of their sources and potential consequences. The proposed approach, grounded in the identification of characteristic features of hazards, facilitates the effective selection of preventive measures that can be implemented to reduce risk and improve workplace safety. Due to the presence of the full spectrum of natural hazards in Polish hard coal mining, the analysis draws on available statistical data, focusing on those hazards that contribute most significantly to fatal accidents and serious injuries. In conclusion, the article emphasizes the importance of a structured and systematic approach to identifying and assessing occupational hazards in the coal mining industry. By drawing on legal and literature-based insights, it aims to contribute to the development of more effective safety practices that protect workers and minimize the occurrence of workplace accidents and illnesses. Full article

Effective medical waste management is crucial for minimizing environmental contamination, protecting occupational health, and advancing sustainability goals in healthcare systems. However, microbiology laboratories remain underexplored in waste characterization studies, despite their potential to contribute to sustainable healthcare operations. This study assessed waste generation patterns, classification accuracy, and the impact of training on regulatory compliance in a university hospital microbiology laboratory. Over 45 days, waste from six specialized units was categorized and weighed daily. A survey of 304 healthcare professionals evaluated their knowledge of medical waste handling. Statistical analyses revealed that training frequency (R² = 0.72, p < 0.01) was the most significant predictor of compliance, while years of experience had no measurable impact. On average, the laboratory generated 22.78 kg/day of medical waste, 11.67 kg/day of liquid waste, and 5.61 kg/day of sharps waste, with the bacteriology unit being the largest contributor. Despite adequate general awareness, 15% of staff misclassified hazardous waste—particularly expired pharmaceuticals and cytotoxic vials—indicating critical gaps in practice. The findings support the need for recurring training programs, stricter monitoring systems, improved waste labeling, and the integration of digital tracking tools. These interventions can reduce environmental burdens, enhance healthcare sustainability, and support the development of more resilient waste management systems in medical institutions. Future research should explore how AI and automation can further strengthen sustainable healthcare waste strategies. Full article

Inhalation of toxic substances and contaminants can have adverse effects on the respiratory tract, leading to a range of health problems, such as irritation and inflammation, allergic reaction and asthma, lung damage, or even death. It is not possible to experimentally evaluate respiratory toxicity for all the thousands of chemicals in use. Here, we generated structural alerts and developed machine learning (ML) classification models to predict respiratory irritation and respiratory sensitization hazards of chemicals using experimental data from publicly available databases and the literature. We identified 13 structural alerts for respiratory irritants and 18 structural alerts for respiratory sensitizers. We also developed a set of models for each hazard using different types of molecular descriptors and ML techniques. Five of the best performing models were combined into a consensus classification model for respiratory irritation, and four individual models were used to develop a consensus classification model for respiratory sensitization. The prediction accuracy of the respiratory irritation consensus model was 84% on the training set and 88% on the test set, and the accuracy of the respiratory sensitization consensus model was 86% on both training and test data sets. A combination of generated structural alerts and ML models was used to screen occupational- and military-relevant chemicals. Out of 687 screened occupational chemicals, 62 compounds were identified as respiratory irritants and 121 chemicals as respiratory sensitizers, while 47 chemicals were predicted as irritants and 36 compounds as sensitizers in the list of 525 military-relevant chemicals. Full article

Slips, trips, and falls (STFs) are a major occupational hazard that contributes significantly to workplace injuries and the associated financial costs. The application of traditional fall detection techniques in the real world is limited because they are usually based on simulated falls. By using kinematic data from real near-fall incidents that occurred in physically demanding work environments, this study overcomes this limitation and improves the ecological validity of fall detection algorithms. This study systematically tests several machine-learning architectures for near-fall detection using the Prev-Fall dataset, which consists of high-resolution inertial measurement unit (IMU) data from 110 workers. Convolutional neural networks (CNNs), residual networks (ResNets), convolutional long short-term memory networks (convLSTMs), and InceptionTime models were trained and evaluated over a range of temporal window lengths using a neural architecture search. High-validation F1 scores were achieved by the best-performing models, particularly CNNs and InceptionTime, indicating their effectiveness in near-fall classification. The need for more contextual variables to increase robustness was highlighted by recurrent false positives found in subsequent tests on previously unobserved occupational data, especially during biomechanically demanding activities such as bending and squatting. Nevertheless, our findings suggest the applicability of machine-learning-based STF prevention systems for workplace safety monitoring and, more generally, applications in fall mitigation. To further improve the accuracy and generalizability of the system, future research should investigate multimodal data integration and improved classification techniques. Full article

Battery recycling has become increasingly crucial in mitigating environmental pollution and conserving valuable resources. As demand for battery-powered devices rises across industries like automotive, electronics, and renewable energy, efficient recycling is essential. Traditional recycling methods, often reliant on manual labor, suffer from inefficiencies and environmental harm. However, recent artificial intelligence (AI) advancements offer promising solutions to these challenges. This paper reviews the latest developments in AI applications for battery recycling, focusing on methodologies, challenges, and future directions. AI technologies, particularly machine learning and deep learning models, are revolutionizing battery sorting, classification, and disassembly processes. AI-powered systems enhance efficiency by automating tasks such as battery identification, material characterization, and robotic disassembly, reducing human error and occupational hazards. Additionally, integrating AI with advanced sensing technologies like computer vision, spectroscopy, and X-ray imaging allows for precise material characterization and real-time monitoring, optimizing recycling strategies and material recovery rates. Despite these advancements, data quality, scalability, and regulatory compliance must be addressed to realize AI’s full potential in battery recycling. Collaborative efforts across interdisciplinary domains are essential to develop robust, scalable AI-driven recycling solutions, paving the way for a sustainable, circular economy in battery materials. Full article

Background/Objectives: Firefighters are exposed to a high level of stress as they often perform physically challenging work in hazardous environments while responsible for rescuing and keeping those around them safe. To add to this stress, they are also required to work in heavy, unbreathable personal protective equipment which promotes dehydration. These occupational demands paired with dehydration may lead to increased core temperatures, cardiac strain, and overall risk for sudden cardiac events. Thus, it is important to include hydration assessments and determine fluid needs when firefighters are on shift to ensure their personal safety as well as the safety of those around them by optimizing physical performance by maintaining adequate hydration. Therefore, the purpose of this review is to identify markers of hydration, classifications of hydration status, current hydration recommendations, and hydration interventions that may contribute to the overall clarity of hydration protocols that may optimize performance and health of firefighters. In addition, the impact of common medications, exercise training, and health conditions on hydration status related to firefighters will be discussed. Methods: A comprehensive literature search was conducted to discuss the purpose statements. Results: Hydration recommendations for firefighters include (1) assessing hydration status with multiple measurements including body mass, urine specific gravity and thirst sensation, and (2) following general hydration recommendations on rest days and exercise hydration protocols during firefighting activities which may be altered according to hydration status measurements. Conclusion: Randomized controlled trials in firefighters are needed to determine the impact of maintaining adequate hydration on health markers. Full article

Advanced Driver Assistance Systems, such as Forward Collision Warning and Lane Departure Warning, play a crucial role in accident prevention by alerting drivers to potential hazards. With the advent of fully autonomous driving technology that requires no driver input, there is now a greater emphasis on monitoring the state of vehicle occupants. This is particularly important because, in emergency situations where control must suddenly be transferred to an unprepared occupant, the risk of accidents increases significantly. To mitigate this risk, new monitoring technologies are being developed to analyze driver behavior and detect states of inattention or drowsiness. In response to the emerging demands of driver monitoring technology, we have developed the Customized Driver Inattention Detection Model (CDIDM). This model employs video analysis and statistical techniques to accurately and rapidly classify information on drivers’ gazes. The CDIDM framework defines the components of inattentive or drowsy driving based on the Driver Monitoring System (DMS) safety standards set by the European New Car Assessment Programme (EuroNCAP). By defining six driving behavior-related scenarios, we have improved the accuracy of driver inattention assessment. The CDIDM estimates the driver’s gaze while simultaneously analyzing data in real-time. To minimize computational resource usage, this model incorporates a series of preprocessing steps that facilitate efficient time series data analysis, utilizing techniques such as DTW Barycenter Averaging (DBA) and K-means clustering. This results in a robust driver attention monitoring model based on time series classification. Full article

Background. In Canada, understanding the demographic and job-related factors influencing the prevalence of new workers and their exposure to potential carcinogens is crucial for improving workplace safety and guiding policy interventions. Methods. Logistic regression was performed on the 2017 Labour Force Survey (LFS), to estimate the likelihood of being a new worker based on age, industry, occupation, season, and immigration status. Participants were categorized by sector and occupation using the North American Industry Classification System (NAICS) 2017 Version 1.0 and National Occupational Classification (NOC) system 2016 Version 1.0. Finally, an exposures-per-worker metric was used to highlight the hazardous exposures new workers encounter in their jobs and industries. Results. Individuals younger than 25 years had 3.24 times the odds of being new workers compared to those in the 25–39 age group (adjusted odds ratios (OR) = 3.24, 95% confidence interval (95% CI) = 3.18, 3.31). Recent immigrants (less than 10 years in the country) were more likely to be new workers than those with Canadian citizenship (OR 1.36, 95% CI: 1.32, 1.41). The total workforce exposures-per-worker metric using CAREX Canada data was 0.56. By occupation, new workers were the most overrepresented in jobs in natural resources and agriculture (20.5% new workers), where they also experienced a high exposures-per-worker metric (1.57). Conclusions. Younger workers (under 25 years) and recent immigrants who had arrived 10 or fewer years prior were more likely to be new workers, and were overrepresented in jobs with more frequent hazardous exposures (Construction, Agriculture, and Trades). Full article

In the European Union, Occupational Safety and Health legislation generally refers to European Regulation (CE) n. 1272/2008 to define and classify carcinogens of concern for occupational risk assessment and exposure assessment. In Europe, the current reference is Directive (UE) 2022/431, regarding carcinogen, mutagen, and reprotoxic agent (CMR) exposure. However, at the worldwide level, different classification approaches are used to establish carcinogenicity of substances and it is often difficult to compare the classifications of carcinogenicity (CoCs) proposed by different international bodies. This study aims to investigate a list of carcinogens of concern in occupational settings based on the CLP (Classification Labelling Packaging) CoC and to create a tool that allows a rapid translation–comparison of some international CoCs with the reference one. CoCs proposed by various sources were consulted and used to apply a translation method, to favor an alignment of different CoCs according to a reference. Results outlined that, considering diverse sources, CoCs can result in different classifications of the same chemicals. Overall, this may have implications for the hazard assessment process, which is the base of risk assessment. The proposed tool is expected to help risk assessors in the occupational field when it is needed to have a comparison with different CoC systems. Full article

This paper presents a control system for indoor safety measures using a Faster R-CNN (Region-based Convolutional Neural Network) architecture. The proposed system aims to ensure the safety of occupants in indoor environments by detecting and recognizing potential safety hazards in real time, such as capacity control, social distancing, or mask use. Using deep learning techniques, the system detects these situations to be controlled, notifying the person in charge of the company if any of these are violated. The proposed system was tested in a real teaching environment at Rey Juan Carlos University, using Raspberry Pi 4 as a hardware platform together with an Intel Neural Stick board and a pair of PiCamera RGB (Red Green Blue) cameras to capture images of the environment and a Faster R-CNN architecture to detect and classify objects within the images. To evaluate the performance of the system, a dataset of indoor images was collected and annotated for object detection and classification. The system was trained using this dataset, and its performance was evaluated based on precision, recall, and F1 score. The results show that the proposed system achieved a high level of accuracy in detecting and classifying potential safety hazards in indoor environments. The proposed system includes an efficiently implemented software infrastructure to be launched on a low-cost hardware platform, which is affordable for any company, regardless of size or revenue, and it has the potential to be integrated into existing safety systems in indoor environments such as hospitals, warehouses, and factories, to provide real-time monitoring and alerts for safety hazards. Future work will focus on enhancing the system’s robustness and scalability to larger indoor environments with more complex safety hazards. Full article

With the revision of the Air Pollution Control Act in Taiwan announced on 1 August 2018, several provisions or regulations have been added to strengthen the control of hazardous air pollutants (HAPs) from stationary sources. Therefore, this paper conducted a survey of sixty toxic volatile organic compounds (VOCs) designated as HAPs in Taiwan and also performed a comparison between some developed countries (i.e., the USA, Japan, and Korea) using the latest databases issued by the relevant agencies/organizations. Furthermore, these designated HAPs were reviewed by their carcinogenic classifications and occupational exposure limits. Finally, the regulatory measures for controlling the emissions of toxic VOCs from stationary sources in Taiwan were addressed to echo the public concerns about their human health risk. Except for trichloroacetic acid, the designated toxic VOCs in Taiwan are included in the list of HAPs in the USA. By comparison, the number of designated HAPs is obviously higher than those in Japan and Korea. Based on the carcinogen classification by the International Agency for Research on Cancer (IARC), the toxic VOCs as confirmed human carcinogens (Group 1) include benzene, benzidine, 1,3-butadiene, 1,2-dichloroproane, ethylene oxide, formaldehyde, 4,4-methylene bis(2-chloroaniline), trichloroethylene, and vinyl chloride. To achieve the purpose of protecting public health, the follow-up control actions of HAPs from stationary sources in Taiwan involved regulatory countermeasures, including the establishment of emission limits, reporting systems, reduction plans for potential high-risk areas or plants, the incentive of an air pollution fee levy, as well as an ambient air concentration monitoring network. Full article

There is much debate about whether the (mostly synthetic) pesticide active substances (AS) in conventional agriculture have different non-target effects than the natural AS in organic agriculture. We evaluated the official EU pesticide database to compare 256 AS that may only be used on conventional farmland with 134 AS that are permitted on organic farmland. As a benchmark, we used (i) the hazard classifications of the Globally Harmonized System (GHS), and (ii) the dietary and occupational health-based guidance values, which were established in the authorization procedure. Our comparison showed that 55% of the AS used only in conventional agriculture contained health or environmental hazard statements, but only 3% did of the AS authorized for organic agriculture. Warnings about possible harm to the unborn child, suspected carcinogenicity, or acute lethal effects were found in 16% of the AS used in conventional agriculture, but none were found in organic agriculture. Furthermore, the establishment of health-based guidance values for dietary and non-dietary exposures were relevant by the European authorities for 93% of conventional AS, but only for 7% of organic AS. We, therefore, encourage policies and strategies to reduce the use and risk of pesticides, and to strengthen organic farming in order to protect biodiversity and maintain food security. Full article

In this study, patient data were combined with Raman and autofluorescence spectral parameters for more accurate identification of skin tumors. The spectral and patient data of skin tumors were classified by projection on latent structures and discriminant analysis. The importance of patient risk factors was determined using statistical improvement of ROC AUCs when spectral parameters were combined with risk factors. Gender, age and tumor localization were found significant for classification of malignant versus benign neoplasms, resulting in improvement of ROC AUCs from 0.610 to 0.818 (p < 0.05). To distinguish melanoma versus pigmented skin tumors, the same factors significantly improved ROC AUCs from 0.709 to 0.810 (p < 0.05) when analyzed together according to the spectral data, but insignificantly (p > 0.05) when analyzed individually. For classification of melanoma versus seborrheic keratosis, no statistical improvement of ROC AUC was observed when the patient data were added to the spectral data. In all three classification models, additional risk factors such as occupational hazards, family history, sun exposure, size, and personal history did not statistically improve the ROC AUCs. In summary, combined analysis of spectral and patient data can be significant for certain diagnostic tasks: patient data demonstrated the distribution of skin tumor incidence in different demographic groups, whereas tumors within each group were distinguished using the spectral differences. Full article

Against the backdrop of the COVID-19 pandemic, it is necessary to identify these risks and determine whether the current level of management is appropriate to respond to the risk of biological hazards depending on the occupation. In this study, the incidence and fatality rates of occupational diseases were calculated using industrial accident statistics of South Korea, and trends by year using joinpoint regression and relative risk by industry using k-means clustering were evaluated for infectious diseases. We found that infectious diseases had the third highest incidence and fourth highest fatalities among all occupational diseases. In the incidence rate, joinpoints appeared in 2009 and 2018, and the annual percent change changed to 7.79, −16.63, and 82.11. The fatality rate showed a consistent increase with an annual percent change of 4.37, but it was not significant. Industries were classified into five groups according to risk, and the legal control measures of certain industries were not sufficient. Follow-up studies are needed to rectify the structural limitations of industrial accident statistics. Full article

European directives stipulate that French employers take all available measures to reduce the use of carcinogenic agents. Our study explores the links between regulations on chemicals and the effective implementation of collective protection measures in France to occupational exposure to carcinogenic chemicals. Individual data from the French national cross-sectional survey of occupational hazards, conducted in 2017, were analysed. We investigated whether stricter regulations and longer exposures were associated with a higher level of collective protection using multivariate logistic regressions. In 2017, any collective protection measures were implemented for 35% of occupational situations involving exposure to a carcinogen. A total of 21% of exposure situations benefited from source-based controls (e.g., isolation chamber and local exhaust ventilation) and 26% from general ventilation, for which the effect is limited as collective protection. Our regressions showed that longer exposure durations were associated with more collective protection. Exposure situations to chemicals classified as proven carcinogens by the European Union (category 1A) benefited more from collective protections, which is not the case for products only classified as suspected carcinogens (category 1B). Exposures to products with a Binding Occupational Exposure Limit Value benefited more from source-based controls. Nonetheless, the time spent on the IARC list of carcinogens did not appear to influence the implementation of collective protection measures, except for local exhaust ventilation. At a time when efforts to improve the implementation of protective measures in order to drastically reduce the risks of occupational cancers are still necessary, stricter European and national regulations, but above all, better coordination with the work of the IARC and its classification, are avenues to pursue. Full article