Search Results (319)

Public transport accidents pose a significant threat to public safety, so it is necessary to conduct an in-depth exploration of their risk mechanisms. In this study, risk factors were first identified through statistical analysis of the time, road section, type, and cause of accidents. Subsequently, the N-K model was applied to analyze the coupling effect among human, vehicle, road, environment, and management factors, and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) model was adopted to evaluate the centrality and causal relationship of these factors. The DEMATEL results were corrected using the coupling values obtained from the N-K model, through which the key factors affecting public transport accidents were identified. Finally, conclusions were drawn as follows: Human factors are the dominant inducement of public transport accidents, accounting for 70.86%, among which driver fatigue and weak safety awareness are the main sub-factors. The “human-vehicle-environment” triple coupling factor presents the highest risk, with a coupling value of 0.62, which is 1.77 times the average level of single-factor coupling. Seven key risk factors were identified, and their ranking by centrality from highest to lowest is: as follows driver fatigue, driver physical health, brake failure, unchannelized intersections, rainy weather, following too closely, and insufficient safety training. Among these factors, insufficient safety training exerts the strongest driving effect on other factors. Full article

A new cascaded model is proposed to improve the accuracy and early warning capability of predicting mine water inrush accidents. The model sequentially applies a Bidirectional Long Short-Term Memory Network (BiLSTM) and a Variational Autoencoder (VAE) to capture the spatio-temporal dependencies between borehole water level data and water inrush events. First, the BiLSTM predicts borehole water levels, and the prediction errors are analyzed to summarize temporal patterns in water level fluctuations. Then, the VAE identifies anomalies in the predicted results. The spatial correlation between borehole water levels, induced by the cone of depression during water inrush, is quantified to assign weights to each borehole. A weighted comprehensive anomaly score is calculated for final prediction. In actual water inrush cases from Xin’an Coal Mine, the BiLSTM-VAE model triggered high-risk alerts 9 h and 30 min in advance, outperforming the conventional threshold-based method by approximately 6 h. Compared with other models, the BiLSTM-VAE demonstrates better timeliness and higher accuracy with lower false alarm rates in mine water inrush prediction. This framework extends the lead time for implementing safety measures and provides a data-driven approach to early warning systems for mine water inrush. Full article

Utilities that operate nuclear power plants are increasingly using probabilistic risk assessments (PRAs) to make day-to-day decisions on design, operations, and maintenance and to support risk-informed applications. These applications require high-quality and complete PRAs to ensure that the decisions and proposed changes are technically well-founded. Such PRAs include the modeling and quantification of PRA models for accident sequences initiated by internal floods and high-energy line breaks. To support PRA updates and upgrades for such sequences, the Electric Power Research Institute (EPRI) has sponsored ongoing research to develop and refine guidance and generic data that can be used to estimate initiating event frequencies for internal flood- and high-energy line break-induced accident sequences. In 2023, EPRI published the fifth revision of a generic database for these initiating event frequencies. This revision produced advancements in the methodology for passive component reliability, including the quantification of aging effects on pipe rupture frequencies and the capability to adjust these frequencies to account for enhancements to integrity management strategies associated with leak inspections and non-destructive examinations. The purpose of this paper is to present these enhancements and illustrate their application with selected examples. Full article

Road traffic accidents remain a major global public health concern, where complex urban driving environments significantly elevate drivers’ visual load and accident risks. Unlike existing research that adopts a macro perspective by considering multiple factors such as the driver, vehicle, and road, this study focuses on the driver’s visual load, a key safety factor, and its direct source—the driver’s visual environment. We have developed an interpretable framework combining computer vision and machine learning to quantify how road scene features influence oculomotor behavior and scene-induced visual load, establishing a complete and interpretable link between scene features, eye movement behavior, and visual load. Using the DR(eye)VE dataset, visual attention demand is established through occlusion experiments and confirmed to correlate with eye-tracking metrics. K-means clustering is applied to classify visual load levels based on discriminative oculomotor features, while semantic segmentation extracts quantifiable road scene features such as the Green Visibility Index, Sky Visibility Index and Street Canyon Enclosure. Among multiple machine learning models (Random Forest, Ada-Boost, XGBoost, and SVM), XGBoost demonstrates optimal performance in visual load detection. SHAP analysis reveals critical thresholds: the probability of high visual load increases when pole density exceeds 0.08%, signage surpasses 0.55%, or buildings account for more than 14%; while blink duration/rate decrease when street enclosure exceeds 38% or road congestion goes beyond 25%, indicating elevated visual load. The proposed framework provides actionable insights for urban design and driver assistance systems, advancing traffic safety through data-driven optimization of road environments. Full article

Unsafe behavior among air traffic controllers is a significant causal factor in civil aviation safety incidents. To explore the risks and pathways associated with controller-induced aviation accidents, this study develops an analytical model of controller unsafe behavior based on association rules and fault tree Bayesian networks. First, the Human Factors Analysis and Classification System (HFACS) was applied to identify and categorize aviation incident reports attributed to controller errors. Next, association rule algorithms were employed to uncover potential associations between controller unsafe behaviors and related risk factors, and a fault tree Bayesian network (FT-BN) model of controller unsafe behaviors was constructed based on these associations. The results revealed that the most likely unsafe behaviors were: improper allocation of aircraft spacing (30.5%), failure to take necessary intervention measures (28.4%), and improper transfer of control (27.8%). Backward analysis of the FT-BN indicated that improper allocation of aircraft spacing was most likely triggered by failure to provide adequate controller training, failure to take necessary intervention measures was most often caused by forgotten information, and improper transfer of control was most frequently associated with controller fatigue and failure to put risk management efforts in place. This study provides an important framework for the analysis and evaluation of controller behavior management and offers key insights for improving air traffic safety. Full article

Ionizing radiation can damage DNA, leading to mutations, and is a risk factor for cancer. Based on the assumption that all radiation exposure poses a risk in linear proportion to its dose, ionizing radiation is considered a non-threshold carcinogen. However, most epidemiological studies have had insufficient statistical power to detect excess cancer risks from low-dose radiation exposure. Therefore, research is needed to identify radiation signatures that distinguish radiation-induced cancers from spontaneously developed cancers. In rodent cancer models, interstitial chromosomal deletions of specific tumor-suppressor gene loci are characteristically found in cancers from irradiated animals. In humans, a high frequency of small deletions and chromosome rearrangements, such as large deletions, inversions, and translocations, has also been reported in second cancers that develop in patients who received radiotherapy and in thyroid cancers diagnosed in residents after the Chornobyl accident. These genomic alterations are likely to be generated as a consequence of the processing of radiation-induced DNA double-strand breaks. Particularly, chromosome rearrangements that occur at loci directly linked to tumor formation after ionizing-radiation exposure are potentially useful as biomarkers and as therapeutic targets for radiation-induced cancer. Here we provide an overview of the radiation-induced mutational signatures observed in animal and human cancers. Full article

To ensure the safe and reliable operation of hydrogen-blended natural gas (HBNG) pipelines in urban utility tunnels, this study conducted a comprehensive CFD simulation of the leakage and diffusion characteristics of HBNG in confined underground environments. Utilizing ANSYS CFD software (2024R1), a three-dimensional physical model of a utility tunnel was developed to investigate the influence of key parameters, such as leak sizes (4 mm, 6 mm, and 8 mm)—selected based on common small-orifice defects in utility tunnel pipelines (e.g., corrosion-induced pinholes and minor mechanical damage) and hydrogen blending ratios (HBR) ranging from 0% to 20%—a range aligned with current global HBNG demonstration projects (e.g., China’s “Medium-Term and Long-Term Plan for Hydrogen Energy Industry Development”) and ISO standards prioritizing 20% as a technically feasible upper limit for existing infrastructure, on HBNG diffusion behavior. The study also evaluated the adequacy of current accident ventilation standards. The findings show that as leak orifice size increases, the diffusion range of HBNG expands significantly, with a 31.5% increase in diffusion distance and an 18.5% reduction in alarm time as the orifice diameter grows from 4 mm to 8 mm. Furthermore, hydrogen blending accelerates gas diffusion, with each 5% increase in HBR shortening the alarm time by approximately 1.6 s and increasing equilibrium concentrations by 0.4% vol. The current ventilation standard (12 h⁻¹) was found to be insufficient to suppress concentrations below the 1% safety threshold when the HBR exceeds 5% or the orifice diameter exceeds 4 mm—thresholds derived from simulations showing that, under 12 h⁻¹ ventilation, equilibrium concentrations exceed the 1% safety threshold under these conditions. To address these gaps, this study proposes an adaptive ventilation strategy that uses variable-frequency drives to adjust ventilation rates in real time based on sensor feedback of gas concentrations, ensuring alignment with leakage conditions, thereby ensuring enhanced safety. These results provide crucial theoretical insights for the safe design of HBNG pipelines and ventilation optimization in utility tunnels. Full article

Spilled hydrocarbons released from oil pipeline accidents can result in long-term environmental contamination and significant damage to habitats. In this regard, evaluating actions in response to vulnerability scenarios is fundamental to emergency management and groundwater integrity. To this end, understanding the trajectories and their influence on the various parameters and characteristics of the contaminant’s fate through accurate numerical simulations can aid in developing a rapid remediation strategy. This paper develops a numerical model using the CactusHydro code, which is based on a high-resolution shock-capturing (HRSC) conservative method that accurately follows sharp discontinuities and temporal dynamics for a three-phase fluid flow. We analyze nine different emergency scenarios that represent the breaking of a diesel oil onshore pipeline in a porous medium. These scenarios encompass conditions such as dry season rupture, rainfall-induced saturation, and varying pipeline failure pressures. The influence of the spilled oil pressure and water saturation in the unsaturated zone is analyzed by following the saturation contour profiles of the three-phase fluid flow. We follow with the high-accuracy formation of shock fronts of the advective part of the migration. Additionally, the mass distribution of the expelled contaminant along the porous medium during the emergency is analyzed and quantified for the various scenarios. The results obtained indicate that the aquifer contamination strongly depends on the pressure outflow in the vertical flow. For a fixed pressure value, as water saturation increases, the mass of contaminant decreases, while the contamination speed increases, allowing the contaminant to reach extended areas. This study suggests that, even for LNAPLs, the distribution of leaked oil depends strongly on the spill pressure. If the pressure reaches 20 atm at the time of pipeline failure, then contamination may extend as deep as two meters below the water table. Additionally, different seasonal conditions can influence the spread of contaminants. This insight could directly inform guidelines and remediation measures for spill accidents. The CactusHydro code is a valuable tool for such applications. Full article

Background: Irradiation-induced injury is a common fallout of radiological/nuclear accidents or therapeutic exposures to high doses of radiation at high dose rates. Currently, there are no prophylactic drugs available to mitigate radiation injury as a result of exposure to lethal doses of ionizing radiation. Gamma-tocotrienol (GT3) of vitamin E is a promising radioprotector under advanced development which has been tested for efficacy in both murine and nonhuman primate (NHP) models. Previously, we have demonstrated that GT3 has radioprotective efficacy in intestinal epithelial and crypt cells, and restores transcriptomic changes in NHPs with a supralethal dose of 12 Gy total-body irradiation (TBI). Methods: In this study, we evaluated the effect of 12 Gy partial-body irradiation (PBI) or TBI on metabolomic changes in serum samples and the extent to which GT3 was able to modulate these irradiation-induced changes. A total of 32 nonhuman primates were used for this study, and blood sample were collected 3 days (d) prior to irradiation, and 4 h, 8 h, 12 h, 1 d, 2 d, and 6 d post-irradiation. Results: Our results demonstrate that exposure to a supralethal dose of radiation induces a complex range of metabolomic shifts with similar degrees of dysregulation in both partial- and total-body irradiated animals. The C21-steroid hormone biosynthesis and metabolism pathway was significantly dysregulated in both PBI and TBI groups, with minimal protection afforded by GT3 administration. Conclusions: GT3 offered a differential response in terms of protected metabolites and pathways in either group that was most effective at the early post-irradiation time points. Full article

Objective: Several strategies have been described for stroke prevention, and the most commonly used medication is aspirin. Cilostazol, which is a substance with a pleiotropic effect, is still not well investigated. In this study, we aimed to delineate the effects of mono- and combinatorial pre-treatment upon neurological status and biomarkers, namely protein S100b, GFAP, procalcitonin, and galectin-3, following stroke. Methods: Twelve-week-old Sprague–Dawley rats were randomly assigned to four groups, each containing six rats: control group (normal saline), cilostazol group (30 mg/kg/daily), aspirin group (10 mg/kg/daily), and aspirin/cilostazol group. Each substance was administered by gavage for four weeks. All animals were subjected to cerebral ischemia for 2 h using intraluminal middle cerebral artery occlusion. A neurological examination was performed, serum concentrations of biomarkers were determined, and the animals were then sacrificed. Results: All treatment groups exhibited variations in the severity of immediate neurological presentation. Unlike the control group, where all rats presented with severe focal neurology or mortality, most rats in the treatment groups displayed no to moderate focal neurology. Moreover, the aspirin/cilostazol group consistently exhibited significantly lower levels in the studied biomarkers compared to other groups. Conclusions: Co-administration of cilostazol and aspirin significantly ameliorates the immediate expression of the studied biomarkers. Further large-scale studies are needed to investigate the effect of combined therapy for primary and secondary prevention of stroke, using not only serum biomarkers but other specific clinical and laboratory endpoints. Full article

The topic of environmental pollution by petroleum products is highly relevant due to rapid urbanisation, including industrial development, road infrastructure and fuel distribution. Potential threat areas include refineries, fuel stations, pipelines, warehouses and transshipment bases, as well as sites affected by accidents or fuel spills. This study aimed to determine whether organic and mineral materials could mitigate the effects of diesel oil pollution on the soil’s trace element content. The used materials were compost, bentonite and calcium oxide. Diesel oil pollution had the most pronounced effect on the levels of Cd, Ni, Fe and Co. The levels of the first three elements increased, while the level of Co decreased by 53%. Lower doses of diesel oil (2.5 and 5 cm³ per kg of soil) induced an increase in the levels of the other trace elements, while higher doses caused a reduction, especially in Cr. All materials applied to the soil (compost, bentonite and calcium oxide) reduced the content of Ni, Cr and Fe. Compost and calcium oxide also increased Co accumulation in the soil. Bentonite had the strongest reducing effect on the Ni and Cr contents of the soil, reducing them by 42% and 53%, respectively. Meanwhile, calcium oxide had the strongest reducing effect on Fe and Co accumulation, reducing it by 12% and 31%, respectively. Inverse relationships were recorded for Cd (mainly bentonite), Pb (especially compost), Cu (mainly compost), Mn (mainly bentonite) and Zn (only compost) content in the soil. At the most contaminated site, the application of bentonite reduced the accumulation of Pb, Zn and Mn in the soil, while the application of compost reduced the accumulation of Cd. Applying various materials, particularly bentonite and compost, limits the content of certain trace elements in the soil. This has a positive impact on reducing the effect of minor diesel oil pollution on soil properties and can promote the proper growth of plant biomass. Full article

Autonomous vehicles (AVs) have emerged as a promising sustainable urban mobility solution, expected to lead to enhanced road safety, smoother traffic flows, less traffic congestion, improved accessibility, better energy utilization and environmental performance, as well as more efficient passenger and freight transportation, in terms of time and cost, due to better fleet management and platooning. However, challenges also arise, mostly related to data privacy, security and cyber-security, high acquisition and infrastructure costs, accident liability, even possible increased traffic congestion and air pollution due to induced travel demand. This paper presents the results of a survey conducted among 654 residents who experienced an autonomous bus (AB) service in the city of Trikala, Greece, in order to assess their willingness to use (WTU) and willingness to pay (WTP) for ABs, through testing a range of factors based on a literature review. Results useful to policy-makers were extracted, such as that the intention to use ABs was mostly shaped by psychological factors (e.g., users’ perceptions of usefulness and safety, and trust in the service provider), while WTU seemed to be positively affected by previous experience in using ABs. In contrast, sociodemographic factors were found to have very little effect on the intention to use ABs, while apart from personal utility, users’ perceptions of how autonomous driving will improve the overall life standards in the study area also mattered. Full article

Background/Objectives: Blunt cardiac injury (BCI) is a severe medical condition that may arise as a result of various traumas, including motor vehicle accidents and falls. The main objective of this study was to explore the role and underlying mechanisms of the TRPV4 gene in BCI. Elucidating the function of TRPV4 in BCI may reveal potential novel therapeutic targets for the treatment of this condition. Methods: Rats in each group, including the SD control group (SDCON), the SD blunt-trauma group (SDBT), the TRPV4 gene-knockout control group (KOCON), and the TRPV4 gene-knockout blunt-trauma group (KOBT), were all freely dropped from a fixed height with a weight of 200 g and struck in the left chest with a certain energy, causing BCI. After the experiment, the levels of serum IL-6 and IL-1β were detected to evaluate the inflammatory response. The myocardial tissue structure was observed by HE staining. In addition, cardiac transcriptome analysis was conducted to identify differentially expressed genes, and metabolomics studies were carried out using UHPLC-Q-TOF/MS technology to analyze metabolites. The results of transcriptomics and metabolomics were verified by qRT-PCR and Western blot analysis. Results: Compared with the SDCON group, the levels of serum IL-6 and IL-1β in the SDBT group were significantly increased (p < 0.001), while the levels of serum IL-6 and IL-1β in the KOBT group were significantly decreased (p < 0.001), indicating that the deletion of the TRPV4 gene alleviated the inflammation induced by BCI. HE staining showed that myocardial tissue injury was severe in the SDBT group, while myocardial tissue structure abnormalities were mild in the KOBT group. Transcriptome analysis revealed that there were 1045 upregulated genes and 643 downregulated genes in the KOBT group. These genes were enriched in pathways related to inflammation, apoptosis, and tissue repair, such as p53, apoptosis, AMPK, PPAR, and other signaling pathways. Metabolomics studies have found that TRPV4 regulates nucleotide metabolism, amino-acid metabolism, biotin metabolism, arginine and proline metabolism, pentose phosphate pathway, fructose and mannose metabolism, etc., in myocardial tissue. The combined analysis of metabolic and transcriptional data reveals that tryptophan metabolism and the protein digestion and absorption pathway may be the key mechanisms. The qRT-PCR results corroborated the expression of key genes identified in the transcriptome sequencing, while Western blot analysis validated the protein expression levels of pivotal regulators within the p53 and AMPK signaling pathways. Conclusions: Overall, the deletion of the TRPV4 gene effectively alleviates cardiac injury by reducing inflammation and tissue damage. These findings suggest that TRPV4 may become a new therapeutic target for BCI, providing new insights for future therapeutic strategies. Full article

Low-altitude drones, which are unimpeded by traffic congestion or urban terrain, have become a critical asset in emergency rescue missions. To address the current lack of emergency rescue data, UAV aerial videos were collected to create an experimental dataset for action classification and localization annotation. A total of 5082 keyframes were labeled with 1–5 targets each, and 14,412 instances of data were prepared (including flight altitude and camera angles) for action classification and position annotation. To mitigate the challenges posed by high-resolution drone footage with excessive redundant information, we propose the SlowFast-Traffic (SF-T) framework, a spatio-temporal sequence-based algorithm for recognizing traffic accident rescue actions. For more efficient extraction of target–background correlation features, we introduce the Actor-Centric Relation Network (ACRN) module, which employs temporal max pooling to enhance the time-dimensional features of static backgrounds, significantly reducing redundancy-induced interference. Additionally, smaller ROI feature map outputs are adopted to boost computational speed. To tackle class imbalance in incident samples, we integrate a Class-Balanced Focal Loss (CB-Focal Loss) function, effectively resolving rare-action recognition in specific rescue scenarios. We replace the original Faster R-CNN with YOLOX-s to improve the target detection rate. On our proposed dataset, the SF-T model achieves a mean average precision (mAP) of 83.9%, which is 8.5% higher than that of the standard SlowFast architecture while maintaining a processing speed of 34.9 tasks/s. Both accuracy-related metrics and computational efficiency are substantially improved. The proposed method demonstrates strong robustness and real-time analysis capabilities for modern traffic rescue action recognition. Full article

Natech events, involving multi-hazard coupling and cascading effects, pose serious threats to coal mine safety. This paper addresses flood-induced Natech scenarios in coal mining and introduces a two-stage cascading analysis framework based on hazard systems theory. A tri-layered network—comprising natural hazards, exposed elements, and secondary hazards—models hazard propagation. In Stage 1, an improved adjacency information entropy algorithm with multi-hazard coupling coefficients identifies critical exposed elements. In Stage 2, Dijkstra’s algorithm extracts key risk transmission paths. A dual-dimensional classification method, based on entropy and transmission risk, is then applied to prioritize emergency responses. This method integrates the criticality of exposed elements with the risk levels associated with secondary disaster propagation paths. Case studies validate the framework, revealing: (1) Hierarchical heterogeneity in the network, with surface facilities and surrounding hydrological systems as central hubs; shaft and tunnel systems and surrounding geological systems are significantly affected by propagation from these core nodes, exhibiting marked instability. (2) Strong risk polarization in secondary hazard propagation, with core-node-originated paths being more efficient and urgent. (3) The entropy-risk classification enables targeted hazard control, improving efficiency. The study proposes chain-breaking strategies for precise, hierarchical, and timely emergency management, enhancing coal mine resilience to flood-induced Natech events. Full article