Search Results (697)

Accident investigations in high-risk industries frequently focus on attributing unsafe acts to individual operators, often neglecting the organizational conditions that shape such behaviors. This study adopts an exploratory perspective to examine how communication, resource adequacy, and procedural design influence the potential for unsafe acts in refinery maintenance operations within the oil and gas sector. Building on the HFACS-OGI framework, unsafe acts were classified into perception errors, decoding errors, model errors, decision errors, and violations. Data were collected through a survey (n = 46) and analyzed using ordinal logistic regression with 10,000 bootstrap replications, complemented by partial correlation analysis to capture indirect associations. The results provide preliminary evidence that organizational factors operate both as direct predictors of unsafe acts and as systemic pathways linking broader contextual conditions with operator behavior. In particular, deficiencies in communication emerged as a transversal determinant, partially explaining the relationship between organizational context and both perception and decision errors. While limited by sample size and exploratory design, the study contributes to safety science by extending the empirical application of HFACS-OGI beyond post-accident analysis and offering actionable insights for safety governance. The findings underscore the need for proactive organizational interventions that enhance communication systems, ensure resource adequacy, and promote the usability of procedures in order to mitigate the potential for unsafe acts. Full article

Soil heavy metal contamination poses a serious threat to soil ecosystems and human health. Geochemistry is often used in soil heavy metal contamination research to identify pollution sources, identify elemental cycling mechanisms, and assess the spatial distribution and risk of contamination. However, it is difficult to directly reflect the spatial continuity and deep distribution patterns of contamination. Three-dimensional electrical resistivity tomography (3D ERT) technology often indirectly predicts the distribution of soil contamination by leveraging the electrical structure of the subsurface medium. However, many factors influence this electrical structure, leading to biased predictions. This paper combines geochemistry with 3D ERT technology. A nonlinear statistical model is established based on the geochemical analysis results and resistivity of soil samples. A 3D ERT model is then constructed. This model is used to further investigate the spatial distribution patterns of soil heavy metal contamination and assess the extent of contamination. This study investigated soil sample collection and chemical analysis of heavy metal content at a heavy metal contaminated site in Hunan Province. Antimony contamination was particularly severe in the soil. The 3D ERT data collection and inversion imaging were performed in the soil sample collection area. A 3D ERT model was established to analyze and evaluate the distribution range and extent of antimony contamination in the area. Comparing the antimony content predicted by the model with the actual test data, the results show that the error range is 0.6–16.6%, and the average error is 5.8%. The model has high accuracy, achieving good overall prediction and evaluation results. Full article

Background/Objectives: In Japan, the human papillomavirus (HPV) vaccine introduction process is unique, and no HPV knowledge scale with established reliability and validity currently exists. This study aimed to develop a new HPV knowledge scale and evaluate its reliability and validity for practical use. Methods: With permission from the original authors of the HPV Knowledge Scale (Jo Waller et al.), we created a Japanese version incorporating the original two subscales and adding new items. The translation process involved multiple researchers, back-translation by a professional agency, and expert review to ensure linguistic and contextual accuracy. The study was approved by the Clinical Research Ethics Review Board of the researchers’ affiliated universities and conducted between April and August 2024. Results: Reliability and validity were assessed using data from 793 parents of junior high school students, including both boys and girls. Confirmatory factor analysis showed a good model fit (Goodness-of-Fit Index [GFI] = 0.934, Adjusted GFI [AGFI] = 0.907, Comparative Fit Index [CFI] = 0.928, Root Mean Square Error of Approximation [RMSEA] = 0.063). Cronbach’s alpha ranged from 0.688 to 0.845 and item-total correlations ranged from 0.393 to 0.584. Test–retest reliability, assessed with Spearman’s rank correlation, was r = 0.791 (p < 0.001). The final scale, named the Japan HPV Knowledge Scale (J-HPV-KS), includes 17 items across five factors. Conclusions: The J-HPV-KS covers HPV-related diseases, transmission routes, natural history, and vaccines. It demonstrated sufficient reliability and validity for use in Japan and is a useful tool for assessing HPV-related knowledge among Japanese parents and guardians. Full article

Understanding how trust in artificial intelligence evolves is crucial for predicting human behavior in AI-enabled environments. While existing research focuses on initial acceptance factors, the temporal dynamics of AI trust remain poorly understood. This study develops a temporal trust dynamics framework proposing three phases: formation through accuracy cues, single-error shock, and post-error repair through explanations. Two experiments in financial advisory contexts tested this framework. Study 1 (N = 189) compared human versus algorithmic advisors, while Study 2 (N = 294) traced trust trajectories across three rounds, manipulating accuracy and post-error explanations. Results demonstrate three temporal patterns. First, participants initially favored algorithmic advisors, supporting “algorithmic appreciation.” Second, single advisory errors resulted in substantial trust decline (η² = 0.141), demonstrating acute sensitivity to performance failures. Third, post-error explanations significantly facilitated trust recovery, with evidence of enhancement beyond baseline. Financial literacy moderated these patterns, with higher-expertise users showing sharper decline after errors and stronger recovery following explanations. These findings reveal that AI trust follows predictable temporal patterns distinct from interpersonal trust, exhibiting heightened error sensitivity yet remaining amenable to repair through well-designed explanatory interventions. They offer theoretical integration of appreciation and aversion phenomena and practical guidance for designing inclusive AI systems. Full article

Particulate matter (PM) concentration, especially PM_2.5, is a major culprit of environmental pollution from unreasonable energy system emissions that significantly affects visibility, climate, and public health. The prediction of PM_2.5 concentration holds significant importance in the early warning and management of severe air pollution, since it enables the provision of guidance for scientific decision-making through the estimation of impending PM_2.5 concentration. However, due to diversified human activities, seasonal factors and industrial emissions, the air quality data not only show local anomalous mutability, but also global dynamic change characteristics. This hinders existing PM_2.5 prediction models from fully capturing the aforementioned characteristics, thereby deteriorating the model performance. To address these issues, this study proposes a framework integrating multi-scale temporal convolutional networks (TCNs) and a transformer network (called MSTTNet) for PM_2.5 concentration prediction. Specifically, MSTTNet uses multi-scale TCNs to capture the local correlations of meteorological and pollutant data in a fine-grained manner, while using transformers to capture the global temporal relationships. The proposed MSTTNet’s performance has been validated on various air quality benchmark datasets in the cities of China, including Beijing, Shanghai, Chengdu, and Guangzhou, by comparing to its eight compared models. Comprehensive experiments confirm that the MSTTNet model can improve the prediction performance of 2.42%, 2.17%, 2.87%, and 0.34%, respectively, with respect to four evaluation indicators (i.e., Mean Absolute Error, Root Mean Square Error, Mean Absolute Percentage Error, and R-square), relative to the optimal baseline model. These results confirm MSTTNet’s effectiveness in improving the accuracy of PM_2.5 concentration prediction. Full article

Color grading of boiled shrimp is a critical factor influencing market price, yet the process is usually conducted visually by buyers such as middlemen and processing plants. This subjective practice raises concerns about accuracy, impartiality, and fairness, often resulting in disputes with farmers. To address this issue, this study proposes a standardized and automated grading approach based on image processing and artificial intelligence. The method requires only a photograph of boiled shrimp placed alongside a color grading ruler. The grading process involves two stages: segmentation of shrimp and ruler regions in the image, followed by color comparison. For segmentation, deep learning models based on Mask R-CNN with a Feature Pyramid Network backbone were employed. Four model configurations were tested, using ResNet and ResNeXt backbones with and without a Boundary Loss function. Results show that the ResNet + Boundary Loss model achieved the highest segmentation performance, with IoU scores of 91.2% for shrimp and 87.8% for the color ruler. In the grading step, color similarity was evaluated in the CIELAB color space by computing Euclidean distances in the L (lightness) and a (red–green) channels, which align closely with human perception of shrimp coloration. The system achieved grading accuracy comparable to human experts, with a mean absolute error of 1.2, demonstrating its potential to provide consistent, objective, and transparent shrimp quality assessment. Full article

Predicting the concentrations of air pollutants, particularly PM_2.5, with accuracy and dependability is crucial for protecting human health and preserving a healthy natural environment. This research proposes a deep learning-based, robust prediction system to predict regional PM_2.5 concentrations for the next one to twenty-four hours. To start, the input features of the prediction system are initially screened using a correlation analysis of various air pollutants and meteorological factors. Next, the BiTCN-ISInformer prediction model with a two-branch parallel architecture is constructed. On the one hand, the model improves the probabilistic sparse attention mechanism in the traditional Informer network by optimizing the sampling method from a single sparse sampling to a synergistic mechanism combining sparse sampling and importance sampling, which improves the prediction accuracy and reduces the computational complexity of the model; on the other hand, through the introduction of the bi-directional time-convolutional network (BiTCN) and the design of parallel architecture, the model is able to comprehensively model the short-term fluctuations and long-term trends of the temporal data and effectively increase the inference speed of the model. According to experimental research, the proposed model performs better in terms of prediction accuracy and performance than the most advanced baseline model. In the single-step and multi-step prediction experiments of Shanghai’s PM_2.5 concentration, the proposed model has a root mean square error (RMSE) ranging from 2.010 to 10.029 and a mean absolute error (MAE) ranging from 1.436 to 6.865. As a result, the prediction system proposed in this research shows promise for use in air pollution early warning and prevention. Full article

In traditional in situ leaching (ISL) uranium mining, the injection volume depends on technicians’ on-site experience. Therefore, applying artificial intelligence technologies such as machine learning to analyze the relationship between injection volume and leaching rate in ISL uranium mining, thereby reducing human factor interference, holds significant guiding importance for production process control. This study proposes a novel uranium leaching rate prediction method based on a CNN–LSTM–LightGBM fusion model integrated with an attention mechanism. Ablation experiments demonstrate that the proposed fusion model outperforms its component models across three key metrics: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE). Furthermore, comparative experiments reveal that this fusion model achieves superior performance on MAE, MAPE, and RMSE metrics compared to six extensively utilized machine learning methods, including Multi-Layer Perceptron, Support Vector Regression, and K-Nearest Neighbors. Specifically, the model achieves an MAE of 0.085%, an MAPE of 0.833%, and an RMSE of 0.201%. This attention-enhanced fusion model provides technical support for production control in ISL uranium mining and offers valuable references for informatization and intelligentization research in uranium mining operations. Full article

Human-perceived temperature (HPT) reflects the synergistic effects of multiple meteorological factors, and its extremes challenge human-managed and natural systems worldwide, especially in densely populated regions such as the Yangtze River Basin of China. However, detailed information on HPT at high temporal (e.g., hourly) and spatial resolution is severely lacking. In this study, we conduct a collaborative inversion for 12 HPT indices at a ~5 km spatial resolution and an hourly temporal resolution in the Yangtze River Basin from multi-source data (e.g., Himawari-8 images, meteorological stations, ERA5-Land reanalysis, and DEM data) using the LightGBM model. The model exhibited high predictive accuracy across all indices, achieving an average coefficient of determination (R²) of 0.981, root mean square error (RMSE) of 1.150 °C, and mean absolute error (MAE) of 0.860 °C. These results aligned well with observational data across spatial and temporal scales, effectively capturing the spatial heterogeneity and diurnal evolution of the region’s thermal environment. Our research provides a reliable data foundation for heat-health risk assessment and regional climate adaptation strategies. Full article

Boat collisions pose severe threats to maritime safety, economic activity, and environmental sustainability. Conventional risk assessment methods—such as Failure Mode and Effects Analysis, and Fault Tree Analysis—are widely applied but remain inadequate for addressing the uncertainty, subjectivity, and interdependency of risk factors in complex maritime environments. This study proposes a fuzzy Multi-Criteria Decision-Making framework for the risk assessment of boat collisions. The model integrates fuzzy logic with Analytic Hierarchy Process for criterion weighting and the Technique for Order Preference by Similarity to the Ideal Solution for risk ranking. Fuzzy logic is employed to capture linguistic expert judgments and to manage vague or incomplete data, which are common challenges in marine operations. Key collision risk factors—human error, boat engine system failure, environmental conditions, and intentional threats—are identified through literature review, incident data analysis, and expert consultation. A comparative analysis with a baseline non-fuzzy model demonstrates the added value of the fuzzy-integrated framework, showing improved capacity to handle imprecision and uncertainty. The model outputs not only prioritise risk rankings but also support the identification of critical control actions and effective safety measures. A case study of Nigerian waters illustrates the practicality of the framework in guiding risk mitigation strategies and informing policy decisions under uncertainty. Full article

Explosives (EXP) are an essential component of technological processes across numerous civil industry sectors, particularly in surface mining. Despite their technological benefits, their use is associated with a high risk of serious accidents. This study aimed to present available data sources on explosive-related incidents and to highlight the limitations in their accessibility, quality, and comparability. The analysis included the SAFEX, eMARS, and PAR databases, as well as national reports from the Polish State Mining Authority, focusing on discrepancies in the classification and description of events. The review was complemented by an analysis of an accident in a Polish open-pit mine, in which an excavator operator was injured due to the uncontrolled detonation of an unexploded charge. The TOL method was employed to analyze the root causes, allowing for the identification of technical, organizational, and human contributing factors, with specific adaptations for the explosives domain such as safety barrier verification, post-blast supervision, and quality control of detonators. The results indicate that most incidents arise from the interaction of multiple causes rather than a single error. The study underscores the need for more effective verification procedures, improved oversight of post-blast operations, and enhanced protective equipment. The article highlights the importance of a systems-based approach to safety management, encompassing both consistent incident data analysis and practical preventive actions throughout the entire life cycle of explosives. Full article

Background: the Skill, Rule, and Knowledge (S-R-K) model is a framework used to describe and analyze human behaviour and decision-making in complex environments based on the nature of the task and kind of cognitive control required. The S-R-K model is particularly useful in fields like human factor engineering, system design, and safety-critical industries because it helps to understand human errors and how they relate to different levels of cognitive control. However, the S-R-K model is still qualitative and lacks specific and quantifiable metrics for determining what kind of cognitive control a person is using at any given time. This aspect makes difficult to directly measure and compare performance across the three levels. This study aimed therefore to characterize the S-R-K model from a neurophysiological perspective by analyzing the operator’s cerebral cortical activity. Methods: in this study, participants carried out experimental tasks able to replicate the Skill (tracking task), Rule (rule-based navigation) and Knowledge conditions (unfamiliar situations). Results: participants’ Electroencephalogram (EEG) was recorded during tasks execution and then Global Field Power (GFP) was estimated in the different EEG frequency bands. Brodmann areas (BAs) and EEG features were then used to characterize the S-R-K pattern over the cerebral cortex and as inputs to build up the machine learning-based model to estimate participants’ cognitive control behaviours while dealing with tasks. Conclusions: the results demonstrate the possibility of objectively measuring the different S, R and K levels in terms of brain activations. Furthermore, such evidence is consistent with the scientific literature in terms of cognitive functions corresponding to the different levels of cognitive control. Full article

Background: Venous blood collection in pediatric patients is a critical procedure for diagnostic and monitoring purposes, yet it remains considerably more challenging than in adults. Factors such as small vein size, limited cooperation, and heightened sensitivity to pain contribute to technical difficulties and increased error rates. Objectives: This mini-review aims to provide a concise synthesis of current clinical practices and emerging technologies that support safer, more efficient venipuncture in children. Results: Key findings include the anatomical and procedural considerations relevant to pediatric venipuncture, age-specific recommendations for technique and positioning, as well as evidence-based strategies to reduce pain and anxiety. Common preanalytical errors, particularly hemolysis and insufficient sample volumes, are also addressed, along with their implications for clinical outcomes. Recent advances in medical digitalization, including the use of venous ultrasound, near-infrared projection, and transillumination, offer valuable support in overcoming procedural challenges. These technologies are not meant to replace human expertise but to complement it, improving vein visualization and increasing first-attempt success rates when integrated into a child-centered approach. Conclusions: Venous blood collection in pediatric patients requires a delicate balance between technical proficiency and human-centered care. Emphasis is placed on the importance of a child-centered approach, combining technical skill with empathy and clear communication. Enhancing the quality and safety of venous sampling in children requires not only training and standardization, but also a deeper understanding of the psychological dimensions involved in pediatric care. Full article

This study investigates how Environmental, Social, and Governance (ESG) performance is shaped across 31 countries in the European Union (EU) and the Association of Southeast Asian Nations (ASEAN) from 1990 to 2020. To explore these relationships, we employed the Continuously Updated Generalized Method of Moments (CUE-GMM) and the Limited Information Maximum Likelihood (LIML), with additional robustness checks using Instrumental Variables Two-Stage Least Squares (IV-2SLS), Panel-Corrected Standard Errors (PCSE), and Driscoll-Kraay regressions. The results highlight democratic governance as a consistent driver of ESG advancement. Military expenditure can also support sustainability by reinforcing institutional stability, particularly in developing and upper-middle-income countries. Economic factors such as foreign direct investment, industrialization, and human capital show context-dependent effects, whereas globalization and natural resource rents generally enhance ESG performance, and inflation tends to constrain it. Overall, the findings underscore the importance of tailored, context-specific sustainability policies, showing that effective ESG progress depends on the interaction between institutions, economic structures, and global integration. Full article

The purpose of the study is to research emotional labor and cognitive effort in radar-based collision avoidance tasks within a nautical simulator. By assessing participants’ emotional responses and mental strain, the research aimed to identify negative emotional states associated with a lack of experience, which, in the worst-case scenario, could contribute to navigational incidents. Fifteen participants engaged in multiple sessions simulating typical maritime conditions and navigation challenges. Emotional and cognitive effort were evaluated using three primary methods: heart rate monitoring, a Likert-scale questionnaire, and real-time facial expression recognition software. Heart rate data provided physiological indicators of stress, while the questionnaire and facial expressions captured subjective perceptions of difficulty and emotional strain. By correlating the measurements, the study aimed to uncover emotional patterns linked to task difficulty with insight into engagement, attention, and blink rate levels during the simulation, revealing how a lack of experience contributes to negative emotions and human factor errors. The understanding of the emotional labor and effort in maritime navigation training contributes to strategies for reducing incident risk through improved simulation training practices. Full article