Search Results (226)

This article examines the level of inconsistency in work, health and safety (WHS) laws across Australia’s mining sector. Despite general efforts towards national harmonisation through model WHS legislation, significant inconsistencies persist because individual states and territories retain primary regulatory control. A critical analysis of each jurisdiction’s legislative framework reveals a fragmented legal landscape. Queensland, especially, exhibits notable divergence. Key findings highlight a considerable variation in legislative approaches to risk management principles and specific obligations. In particular, a disjointed and incremental approach to serious offences such as industrial manslaughter and provisions concerning imputed conduct are evident. These inconsistencies suggest that corporations operating in multiple Australian mining regions must develop a nuanced understanding of the varying WHS requirements in each jurisdiction. This study underscores the need for caution when assessing risk management strategies aimed at preventing serious incidents because the presumption of a harmonised system can be misleading, especially concerning mining-specific legislation. Full article

The Amazon faces significant challenges related to mercury contamination, including naturally elevated concentrations and gold mining activities. Due to mercury’s toxicity and the importance of fish as a protein source for local populations, assessing mercury levels in regional fish is crucial. However, there are gaps in knowledge regarding mercury concentrations in many areas of the Amazon basin. This study aims to synthesize the existing literature on mercury concentrations in fish and the exposure of urban and traditional social groups through fish consumption. A systematic review (1990–2022) was conducted for six fish genera (Cichla spp., Hoplias spp. and Plagioscion spp., Leporinus spp., Semaprochilodus spp., and Schizodon spp.) in the Web of Science (Clarivate Analytics) and Scopus (Elsevier) databases. The database consisted of a total of 46 studies and 455 reports. The distribution of studies in the region was not homogeneous. The most studied regions were the Madeira River sub-basin, while the Paru–Jari basin had no studies. Risk deterministic and probabilistic assessments based on Joint FAO/WHO Expert Committee on Food Additives (JECFA, 2007) guidelines showed high risk exposure, especially for traditional communities. Carnivorous fish from lakes and hydroelectric reservoirs, as well as fish from black-water ecosystems, exhibited higher mercury concentrations. In the Amazon region, even if mercury levels in fish muscle do not exceed regulatory limits, the high fish consumption can still elevate health risks for local populations. Monitoring mercury levels across a broader range of fish species, including both carnivorous and non-carnivorous species, especially in communities heavily reliant on fish for their diet, will enable a more accurate risk assessment and provide an opportunity to recommend fish species with lower mercury exposure risk for human consumption. The present study emphasizes the need to protect regions that already exhibit higher levels of mercury—such as lakes, hydroelectric reservoirs, and black-water ecosystems—to ensure food safety and safeguard public health. Full article

Dust emissions from unpaved haul roads in open-pit coal mining pose a significant risk to air quality, health, and operational efficiency of mining operations. This study integrated real-time field monitoring with numerical simulations using ANSYS Fluent 2023 R1 to investigate the generation, dispersion, and migration of particulate matter (PM) at the Ha’erwusu open-pit coal mine under varying meteorological conditions. Real-time measurements of PM2.5, PM10, and TSP, along with meteorological variables (wind speed, wind direction, humidity, temperature, and air pressure), were collected and analyzed using Pearson’s correlation and multivariate linear regression analyses. Wind speed and air pressure emerged as dominant factors in winter, whereas wind and temperature were more influential in summer (R² = 0.391 for temperature vs. PM2.5). External airflow simulations revealed that truck-induced turbulence and high wind speeds generated wake vortices with turbulent kinetic energy (TKE) peaking at 5.02 m²/s², thereby accelerating particle dispersion. The dust migration rates reached 3.33 m/s within 6 s after emission and gradually decreased with distance. The particle settling velocities ranged from 0.218 m/s for coarse dust to 0.035 m/s for PM2.5, with dispersion extending up to 37 m downwind. The highest simulated dust concentration reached 4.34 × 10⁻² g/m³ near a single truck and increased to 2.51 × 10⁻¹ g/m³ under multiple-truck operations. Based on spatial attenuation trends, a minimum safety buffer of 55 m downwind and 45 m crosswind is recommended to minimize occupational exposure. These findings contribute to data-driven, weather-responsive dust suppression planning in open-pit mining operations and establish a validated modeling framework for future mitigation strategies in this field. Full article

Mining involves daily descents underground and enduring dangerous and difficult conditions. Hence, it is very important to use solutions that will reduce the risk in miners’ work and ensure the greater safety and comfort of work in accordance with the goals of sustainable development. One way is training using virtual reality. Virtual reality provides greater safety (safe training conditions, the possibility of making a mistake without health consequences, practicing emergency scenarios, etc.) and aligns with the Sustainable Development Goals—particularly SDG 3 (health), SDG 8 (decent work), SDG 9 (innovation), and SDG 12 (sustainable production). However, it is also a technology that has its weaknesses (occurrence of contraindications, side effects, etc.). Therefore, the use of VR-based training should be examined in terms of the well-being and health of training employees. Due to this, this article examines the occurrence of psychophysical complaints during VR training; the tolerance and adequacy of the duration of a 50 min training session in VR was assessed; and the average time needed to adapt to the virtual environment was determined. The VR training was developed as a result of a research project conducted by JSW Nowe Projekty S.A. (ul. Ignacego Paderewskiego 41, 40-282 Katowice, Poland), Główny Instytut Górnictwa—Państwowy Instytut Badawczy (plac Gwarków 1, 40-160 Katowice, Poland), JSW Szkolenie i Górnictwo Sp. z o.o. at Jastrzębska Spółka Węglowa Capital Group (ul. Górnicza 1, 44-335 Jastrzębie-Zdrój, Poland) on the development and implementation of innovative training using VR for miners. The solution was developed in the context of mining’s striving for sustainable development in the area of improving working conditions and human safety. The first method used in the study is a survey completed by participants of training courses using virtual reality. The second method is the analysis of trainer observation sheets, which contain observations from training courses. The results revealed that for over 70% of respondents, the need to carry out activities in VR was not associated with fatigue. No average score for psychophysical symptoms assessed by respondents on a scale of 1 to 6 (including disorientation, blurred vision, dizziness, confusion, etc.) exceeded 1.4. The vast majority (85.5%) did not take off the goggles before the end of the training—the training lasted 50 min. This research contributes to the discussion on sustainable industrial transformation by demonstrating that VR training not only improves worker safety and preparedness but also supports development goals through human-centered innovation in the mining sector. Full article

Spore-forming Bacilli have been explored due to their potential biotechnological features and applications in human health and functional food research. This study focuses on the genetic and phenotypical characterization of the functional probiotic properties of Bacillus velezensis P45, a strain isolated from fish intestines. B. velezensis P45 exhibited antimicrobial activity against Gram-positive and Gram-negative pathogens and demonstrated strong autoaggregation and biofilm formation properties in vitro. The strain also showed tolerance to gastrointestinal conditions and ability to metabolize and adhere to mucin. In silico analysis confirmed the absence of virulence factors and antibiotic resistance genes, reinforcing its safety as a probiotic candidate. Genome mining revealed the presence of genes related to adhesion, such as fibronectin-binding protein and enolases, and for the synthesis of secondary metabolites, including the antimicrobial lipopeptides fengycin, surfactin, and bacillibactin. In addition, phylogenetic comparison using the yloA (rqcH) gene associated with gut adhesion clustered strain P45 with other probiotic Bacillus and B. velezensis strains, while separating it from pathogenic bacteria. Thus, the strain B. velezensis P45 could be a valuable candidate as a probiotic due to its functional properties and safety. Full article

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

This study assessed heavy metal contamination and associated health risks in soils and crops in the vicinity of a mercury mine located in Tongren, Guizhou Province, China, focusing on mercury (Hg), cadmium (Cd), arsenic (As), lead (Pb), and chromium (Cr). The study used the Index of Geological Accumulation (Igeo) and Health Risk Assessment (HRA) to quantify the level of contamination and assess the potential risks. The results showed that Area I was the most severely contaminated, with 94.24% of the sample sites being heavily contaminated with mercury, followed by Area II and Area III with severe cadmium contamination. The health risk assessment showed that children were exposed to non-carcinogenic risks of mercury and cadmium that exceeded the safety thresholds, with mercury being the major non-carcinogenic factor, especially through oral intake. The study also assessed the contribution of each heavy metal to pollution, with mercury contributing the most to ecological and health risks, especially in Areas I and III. The study highlights the urgent need to strengthen pollution control strategies, focusing on mining activities and agricultural inputs, to reduce risks and protect public health. Full article

This study applied data analysis techniques to analyze work-related accidents in Brazil’s mining sector from 2019 onward, identifying key risks and patterns. Using public datasets from governmental sources, it categorized accidents by the type of injury, causal agents, and affected body parts. The methodology employed included data cleaning, processing, and the development of interactive visualizations using advanced analytical tools, such as Python and Power BI, to facilitate data interpretation. Among the most significant events, the Brumadinho tailings dam collapse in 2019 emerged as a major outlier, substantially affecting multiple aspects of the analysis. This single incident accounted for 71.7% of all work-related fatalities recorded during the four-year period under study, highlighting its disproportionate impact on the dataset. This study also examined the main causes and consequences of mining accidents and facilitated the creation of victim profiles based on gender and age group, incorporating psychological theories regarding risk perception. It was concluded that, although the mining sector represents a small fraction of all work-related accidents in Brazil, the proportion of accidents relative to the number of workers in the sector is substantial, highlighting the need for stricter occupational safety management. The results can guide regulations and help companies and institutions to create safer, more sustainable mining policies. The methodology proved to be highly suitable, indicating its potential for application in safety analysis across other sectors. Full article

Systematic assessment of heavy metal contamination in agricultural soils is critical for addressing ecological and public health risks in industrial-intensive cities like Lanzhou, with direct implications for achieving UN Sustainable Development Goals (SDGs) 2 (Zero Hunger), 15 (Life on Land), and 3 (Good Health). The present study evaluates farmland soils around six industrial sectors: waste disposal (WDZ), pharmaceutical manufacturing (PMZ), chemical manufacturing (CMZ), petrochemical industry (PIZ), metal smelting (MSZ), mining (MZ) and one sewage-irrigated zone (SIZ) using geo-accumulation index, Nemerow composite pollution index, potential ecological risk index, and health risk models. The following are the major findings: (1) SIZ and PMZ emerged as primary contamination clusters, with Hg (I_geo = 1.89) and Cd (I_geo = 0.61) showing marked accumulation. Chronic wastewater irrigation caused severe Hg contamination (0.97 mg·kg⁻¹) in SIZ, where 100% of the samples reached strong polluted levels according to the Nemerow composite pollution index; (2) Hg and Cd dominated the ecological risks, with 41.32% of the samples exhibiting critical Hg risks (100% in PMZ and SIZ) and 32.63% showing strong Cd risks; and (3) oral ingestion constituted the dominant exposure pathway. Children faced carcinogenic risks (CR = 1.33 × 10⁻⁴) exceeding safety thresholds, while adult risks remained acceptable. Notably, high Hg and Cd levels did not translate to proportionally higher health risks due to differential toxicological parameters. The study recommends prioritizing Hg and Cd control in PMZ and SIZ, with targeted exposure prevention measures for children. Full article

With the increasing awareness of health among residents, consumers are paying more attention to their eating purposes and food safety when choosing fruits. This study aims to explore the impact of eating purpose on consumers’ preferences for fruits and fruit products under the mediation of color perception. The study obtained experimental data from 489 urban consumers in China through the Credamo data collection platform. Furthermore, four experimental groups were set up to propose six hypotheses based on the influence of eating purpose on consumer preferences for fruits and their products. The study utilized Likert scale questionnaires, chi-square tests, and variance analysis for data mining and cross-validation. The results indicate that the visual characteristics of fruits (especially color) affect the purchase preferences of consumers with different eating purposes. Approximately 65% of health-oriented consumers are highly sensitive to the color and nutritional value of fruits. They believe that fresh fruits are rich in natural nutrients and play an important role in maintaining health and preventing diseases. Meanwhile, around 62% of consumers with specific nutritional needs prefer processed fruit products, such as fruit preserves or dried fruits. These consumers have a weaker perception of color and focus primarily on the functionality of the fruits. Additionally, the study found that safety/taste preferences acted as a mediator and associative learning as a moderating variable. Around 58% of consumers indicated that their purchase preferences are influenced by safety and taste, and the relative importance of safety and taste preferences significantly mediated the relationship between eating purpose and purchase preferences. Under the moderating effect of associative learning, health-oriented consumers, when associative learning is activated, are about 45% more likely to choose fresh fruits. The study highlights consumers’ health-conscious perceptions in fruit selection, focusing on how color perception moderates the preference choices of different consumer groups based on their eating purposes. It emphasizes the need for businesses to adjust product positioning and marketing strategies according to consumer perceptions to promote broader healthy eating behaviors. Full article

While meat consumption trends show decreases in some high-income countries, significant increases are observed elsewhere. Although this includes African nations, the average meat consumption in Africa remains generally lower than in many other continents, though patterns vary regionally. Meat provides essential nutrients, but inadequate consumption can pose health problems, while consumption also carries risks including potential exposure to environmental contaminants. This comprehensive review focuses on the recent scientific literature (published 2000–2024) regarding human exposure to specific toxic trace elements, namely arsenic (As), cadmium (Cd), mercury (Hg), lead (Pb), chromium (Cr, particularly hexavalent chromium, Cr(VI)), and nickel (Ni), through the consumption of meat (muscle tissues, organs, and processed products) in Africa. Limited data exist for many African regions, with most studies from Nigeria. Concentrations of these toxic elements in meat tissues varied significantly, with organs like liver and kidney showing higher levels than muscle tissues. Estimated dietary intakes also varied, with some studies indicating potential health risks from Pb, Cd, and As exceeding safety guidelines in specific contexts. However, meat is generally not the primary dietary source of these elements compared to fish, seafood, or staple crops, though risks are higher in areas near pollution sources like mines or waste sites. This study highlights the need for broader research across Central and North Africa, stricter monitoring of meat from high-risk areas, and standardized methodologies to protect public health. Full article

The increasing severity of climate-related workplace hazards challenges occupational health and safety, particularly for Public Health and Safety Inspectors. Exposure to extreme temperatures, air pollution, and high-risk environments heightens immediate physical threats and long-term burnout. This study employs Artificial Intelligence (AI)-driven predictive analytics and secondary data analysis to assess hazards and forecast burnout risks. Machine learning models, including eXtreme Gradient Boosting (XGBoost 3.0), Random Forest, Autoencoders, and Long Short-Term Memory (LSTMs), achieved 85–90% accuracy in hazard prediction, reducing workplace incidents by 35% over six months. Burnout risk analysis identified key predictors: physical hazard exposure (β = 0.76, p < 0.01), extended work hours (>10 h/day, +40% risk), and inadequate training (β = 0.68, p < 0.05). Adaptive workload scheduling and fatigue monitoring reduced burnout prevalence by 28%. Real-time environmental data improved hazard detection, while Natural Language Processing (NLP)-based text mining identified stress-related indicators in worker reports. The results demonstrate AI’s effectiveness in workplace safety, predicting, classifying, and mitigating risks. Reinforcement learning-based adaptive monitoring optimizes workforce well-being. Expanding predictive-driven occupational health frameworks to broader industries could enhance safety protocols, ensuring proactive risk mitigation. Future applications include integrating biometric wearables and real-time physiological monitoring to improve predictive accuracy and strengthen occupational resilience. Full article

Customers are the cornerstone of business success across industries. Companies invest significant resources in acquiring new customers and, more importantly, retaining existing ones. However, customer churn remains a major challenge, leading to substantial financial losses. Addressing this issue requires a deep understanding of customers’ cognitive status and behaviours, as well as early signs of churn. Predictive and Machine Learning (ML)-based analysis, when trained with appropriate features indicative of customer behaviour and cognitive status, can be highly effective in mitigating churn. A robust ML-driven churn analysis depends on a well-developed feature engineering process. Traditional churn analysis studies have primarily relied on demographic, product usage, and revenue-based features, overlooking the valuable insights embedded in customer–company interactions. Recognizing the importance of domain knowledge and human expertise in feature engineering and building on our previous work, we propose the Customer Churn-related Knowledge Base (ChurnKB) to enhance feature engineering for churn prediction. ChurnKB utilizes textual data mining techniques such as Term Frequency-Inverse Document Frequency (TF-IDF), cosine similarity, regular expressions, word tokenization, and stemming to identify churn-related features within customer-generated content, including emails. To further enrich the structure of ChurnKB, we integrate Generative AI, specifically large language models, which offer flexibility in handling unstructured text and uncovering latent features, to identify and refine features related to customer cognitive status, emotions, and behaviours. Additionally, feedback loops are incorporated to validate and enhance the effectiveness of ChurnKB.Integrating knowledge-based features into machine learning models (e.g., Random Forest, Logistic Regression, Multilayer Perceptron, and XGBoost) improves predictive performance of ML models compared to the baseline, with XGBoost’s F1 score increasing from 0.5752 to 0.7891. Beyond churn prediction, this approach potentially supports applications like personalized marketing, cyberbullying detection, hate speech identification, and mental health monitoring, demonstrating its broader impact on business intelligence and online safety. Full article

As the world’s largest open-pit coal producer, China faces severe dust pollution in mining operations. Cabins of mining equipment (electric shovels, haul trucks, drills) exhibit unique micro-environmental contamination due to dual-source pollution (external infiltration and internal secondary dust generation), posing severe health risks to miners. This study focused on electric shovel cabins at the Heidaigou open-pit coal mine to address cabin dust pollution. Through analysis of dust physicochemical properties, a pollution characteristic database was established. Field measurements and statistical methods revealed temporal–spatial variation patterns of dust concentrations, quantifying occupational exposure risks and providing theoretical foundations for dust control. A novel gradient-pressurized air purification system was developed for harsh mining conditions. Key findings include the following. (1) Both coal-shovel and rock-shovel operators were exposed to Level I (mild hazard level), with rock-shovel operators approaching Level II (moderate hazard level). (2) The system reduced respirable dust concentrations from 0.313 mg/m³ to 0.208 mg/m³ (≥33.34% improvement) in coal-shovel cabins and from 0.625 mg/m³ to 0.421 mg/m³ (≥32.64% improvement) in rock-shovel cabins. These findings offer vital guidance for optimizing cabin design, improving dust control, and developing scientific management strategies, thereby effectively protecting miners’ health and ensuring operational safety. Full article

With the global aging population on the rise, the health and safety of elderly individuals living alone have become increasingly critical. This study introduces a novel AIoT-based habit mining algorithm designed to enhance activity monitoring in smart home environments. The proposed method integrates a one-dimensional U-Net neural network for accurate behavioral classification and an FP-Growth-based temporal association rule analysis for uncovering meaningful living patterns. By leveraging environmental sensor data, the algorithm first classifies daily activities and then uses timestamps to detect time-sensitive dependencies in behavior sequences, identifying the long-term habits of the elderly. Experimental validation on CASAS datasets (ARUBA and MILAN) demonstrates superior performance, achieving a precision of 84.77%. Compared to traditional techniques, this approach excels in behavior recognition and habit mining, offering a precise and adaptive framework for AIoT-driven smart home safety and health monitoring systems. The results highlight its potential to improve the quality of life and safety for elderly individuals living alone. Full article