Search Results (99)

With the rapid growth of elevator installations, conventional scheduled maintenance struggles to meet the dual demands of ensuring operational safety and cost control. This study proposes an innovative on-demand maintenance method that aligns with the Chinese policy directives on elevator maintenance reform. First, we conduct a historical fault cause analysis to identify the root causes of elevator entrapment incidents. Next, we establish an entrapment prediction model based on our historical data. Then, we design an elevator entrapment risk index report according to the prediction results. Finally, we formulate an on-demand maintenance plan that combines insights from the report with the conclusions of the cause analysis. Field implementation and comparative experiments demonstrate that the proposed on-demand maintenance method outperforms the scheduled one. The result shows significant reductions in accident and maintenance workload, justifying the practical value of this approach for the industry. Full article

Hydrogen is a potential key component of a carbon-neutral energy carrier and an input to marine industrial processes. This study examines the consequences of coupled hydrogen release and marine environmental factors during floating photovoltaic hydrogen production (FPHP) system failures. A validated three-dimensional numerical model of FPHP comprehensively characterizes hydrogen leakage dynamics under varied rupture diameters (25, 50, 100 mm), transient release duration, dispersion patterns, and wind intensity effects (0–20 m/s sea-level velocities) on hydrogen–air vapor clouds. FLACS-generated data establish the concentration–dispersion distance relationship, with numerical validation confirming predictive accuracy for hydrogen storage tank failures. The results indicate that the wind velocity and rupture size significantly influence the explosion risk; 100 mm ruptures elevate the explosion risk, producing vapor clouds that are 40–65% larger than 25 mm and 50 mm cases. Meanwhile, increased wind velocities (>10 m/s) accelerate hydrogen dilution, reducing the high-concentration cloud volume by 70–84%. Hydrogen jet orientation governs the spatial overpressure distribution in unconfined spaces, leading to considerable shockwave consequence variability. Photovoltaic modules and inverters of FPHP demonstrate maximum vulnerability to overpressure effects; these key findings can be used in the design of offshore platform safety. This study reveals fundamental accident characteristics for FPHP reliability assessment and provides critical insights for safety reinforcement strategies in maritime hydrogen applications. Full article

The snakes from the genus Bothrops are responsible for most of the ophidic accidents in Brazil, and Bothrops atrox represents one of these species. Envenomation by these snakes results in systemic effects and is often associated with early mortality following snakebite incidents. The present study investigates the pharmacological properties of Bothrops atrox venom (VBA), focusing specifically on its impact on renal blood flow. Following the renal perfusion procedure, kidney tissues were processed for histopathological examination. Statistical analysis of all evaluated parameters was conducted using ANOVA and Student’s t-test, with significance set at p < 0.005. Administration of VBA resulted in a marked reduction in both perfusion pressure and renal vascular resistance. In contrast, there was a significant elevation in urinary output and glomerular filtration rate. Histological changes observed in the perfused kidneys were mild. The involvement of nitric oxide in the pressor effects of Bothrops atrox venom was not investigated in renal perfusion systems or in in vivo models. Treatment with VBA led to elevated nitrite levels in the bloodstream of the experimental animals. This effect was completely inhibited following pharmacological blockade with L-NAME. Based on these findings, we conclude that VBA alters renal function and promotes increased nitric oxide production. Full article

Electric bicycles in elevators pose serious safety hazards. Fires in the confined space make escape difficult, and recent accidents involving e-bike fires have caused casualties and property damage. To prevent e-bikes from entering elevators and improve public safety, this design employs the Nezha development board as the upper computer for visual detection. It uses deep learning algorithms to recognize hazards like e-bikes. The lower computer orchestrates elevator controls, including voice alarms, door locking, and emergency halt. The system comprises two parts: the upper computer uses the YOLOv11 model for target detection, trained on a custom e-bike image dataset. The lower computer features an elevator control circuit for coordination. The workflow covers target detection algorithm application, dataset creation, and system validation. The experiments show that the YOLOv11 demonstrates superior e-bike detection performance, achieving 96.0% detection accuracy and 92.61% mAP@0.5, outperforming YOLOv3 by 6.77% and YOLOv8 by 15.91% in mAP, significantly outperforming YOLOv3 and YOLOv8. The system accurately identifies e-bikes and triggers safety measures with good practical effectiveness, substantially enhancing elevator safety. Full article

Objectives: Previous studies have suggested differences in vasculitic and eosinophilic phenotypes based on anti-neutrophil cytoplasmic antibody (ANCA) positivity in eosinophilic granulomatosis with polyangiitis (EGPA). However, their relevance under the 2022 American College of Rheumatology (ACR)/European Alliance of Associations for Rheumatology (EULAR) classification criteria remains unclear. We aimed to evaluate the clinical features and outcomes of EGPA according to myeloperoxidase (MPO)-ANCA status in a Korean cohort. Methods: We conducted a retrospective cohort study that included 57 patients with EGPA without proteinase 3-ANCA positivity who fulfilled the 2022 ACR/EULAR classification criteria. Patients were classified into MPO-ANCA-positive (n = 25) and MPO-ANCA-negative (n = 32) groups. Clinical manifestations, laboratory findings, and outcomes, including all-cause mortality, relapse, end-stage kidney disease (ESKD), cerebrovascular accident (CVA), and acute coronary syndrome (ACS), were compared between the two groups. Results: MPO-ANCA-positive patients exhibited higher Five-Factor Scores (1.0 [0.0–1.0] vs. 0.0 [0.0–1.0], p = 0.038), lower Short Form 36 Physical Component Summary scores (35.0 [19.7–56.3] vs. 52.5 [43.5–69.7], p = 0.048), and elevated systemic inflammation markers (higher erythrocyte sedimentation rate: 58.0 [16.0–97.5] mm/hr vs. 25.5 [7.0–63.8] mm/hr, p = 0.026). Constitutional symptoms were more frequent among MPO-ANCA-positive patients (n = 14 [56.0%] vs. n = 3 [9.4%], p < 0.001), whereas no significant differences were found in vasculitic or eosinophilic manifestations. Kaplan–Meier analysis revealed no differences in the overall (p = 0.36), relapse-free (p = 0.80), ESKD-free (p = 0.87), CVA-free (p = 0.26), or ACS-free (p = 0.94) survival rates between the two groups. Conclusions: In Korean patients with EGPA classified under the 2022 ACR/EULAR classification criteria, MPO-ANCA positivity, as compared to ANCA-negative status, was associated with a higher disease burden and poorer quality of life but not with distinct vasculitic or eosinophilic manifestations and adverse outcomes. Full article

Nuclear accident-derived radionuclide dispersion poses critical challenges to marine ecological sustainability and human–ocean interdependence. While existing studies focus on hydrodynamic modeling of pollutant transport, the link between nuclear safety and sustainable ocean governance remains underexplored. This study investigates radionuclide diffusion patterns in semi-enclosed bays using a high-resolution coupled hydrodynamic particle-tracking model, explicitly addressing threats to marine ecosystem stability and coastal socioeconomic resilience. Simulations revealed that tidal oscillations and topographic constraints prolong pollutant retention by 40% compared to open seas, elevating local concentration peaks by 2–3× and intensifying bioaccumulation risks in benthic organisms. These findings directly inform sustainable marine resource management: the identified high-risk zones enable targeted monitoring of fishery resources, while diffusion pathways guide coastal zoning policies to decouple economic activities from contamination hotspots. Compared to Fukushima’s open-ocean dispersion models, our framework uniquely quantifies how semi-enclosed geomorphology exacerbates localized ecological degradation, providing actionable metrics for balancing nuclear energy development with UN Sustainable Development Goals (SDGs) 14 and 3. By integrating hydrodynamic specificity with ecosystem vulnerability thresholds, this work advances science-based protocols for sustainable nuclear facility siting and marine spatial planning. Full article

Pedestrian safety at crosswalks remains a critical concern as traffic accidents frequently result from drivers’ failure to yield, leading to severe injuries or fatalities. In response, various jurisdictions have enacted pedestrian priority laws to regulate driver behavior. Nevertheless, intersections lacking clear traffic signage and environments with limited visibility continue to present elevated risks. The scarcity and difficulty of collecting data under such complex conditions pose significant challenges to the development of accurate detection systems. This study proposes a CyclicGAN-based loop-learning framework, in which the learning process begins with a set of manually annotated images used to train an initial labeling model. This model is then applied to automatically annotate newly generated synthetic images, which are incorporated into the training dataset for subsequent rounds of model retraining and image generation. Through this iterative process, the model progressively refines its ability to simulate and recognize diverse contextual features, thereby enhancing detection performance under varying environmental conditions. The experimental results show that environmental variations—such as daytime, nighttime, and rainy conditions—substantially affect the model performance in terms of F1-score. Training with a balanced mix of real and synthetic images yields an F1-score comparable to that obtained using real data alone. These results suggest that CycleGAN-generated images can effectively augment limited datasets and enhance model generalization. The proposed system may be integrated with in-vehicle assistance platforms as a supportive tool for pedestrian-crossing detection in data-scarce environments, contributing to improved driver awareness and road safety. Full article

The elevator light curtain is constrained by technical limitations in its infrared detection mechanism; thus, it is difficult to effectively identify the transparent material and elongated form of the object, which has become one of the main causes of abnormal elevator jamming accidents. To mitigate elevator accidents, we propose a novel visual segmentation method, PKNet (Prior Knowledge Network), specifically designed for detecting transparent and slender objects. We observe that the majority of cameras used in elevators are stationary, resulting in an inherently static background, while vision tasks primarily focus on detecting foreground objects. To this end, PKNet enhances the segmentation of dynamic foreground objects by incorporating prior knowledge of the static background and the characteristics of foreground objects. We also introduce ETAS-D, the first dataset designed for the segmentation of transparent and slender anomalous objects in elevator environments. This dataset consists of 4797 image frames, each with meticulously annotated masks of transparent and slender objects, captured from multiple viewpoints of 10 elevators. Extensive experimental results demonstrate that PKNet significantly outperforms existing methods in this domain. Furthermore, we propose a synthetic data generation workflow specifically designed for slender objects to enhance the model’s generalization ability and reliability. Full article

Driver distraction remains a critical factor in road accidents, necessitating intelligent systems for real-time detection. This study introduces a novel fNIRS-based method to to classify varying levels of driver distraction across diverse simulated scenarios, including cognitive, visual–manual, and auditory sources of inattention. Unlike previous work, we evaluated multiple neurophysiological metrics—including oxygenated, deoxygenated, and combined haemoglobin—to identify the most reliable biomarker for distraction detection. Neurophysiological data were collected, and three multi-class classifiers (SVM, KNN, decision tree) were applied across different fNIRS metrics. Our results show that oxygenated haemoglobin outperforms other signals in distinguishing distracted from non-distracted states, while the combined signal performs best in differentiating distraction from baseline. The proposed SVM model achieved ≈ 77.9% accuracy in detecting distracted and relaxed driving states based on brain oxygen levels. Our findings also show that increased distraction correlates with elevated activity in the dorsolateral prefrontal cortex and premotor cortex, whereas driving without distraction exhibits lower neurovascular engagement. This study contributes to affective computing and intelligent transportation systems and could support the development of future driver distraction monitoring systems for safer and more adaptive vehicle control. Full article

Gas distribution stations (GDSs), pivotal nodes in long-distance natural gas transportation networks, are susceptible to catastrophic fire and explosion accidents stemming from system failures, thereby emphasizing the urgency for robust safety measures. While previous studies have mainly focused on gas transmission pipelines, GDSs have received less attention, and existing risk assessment methodologies for GDSs may have limitations in providing accurate and reliable accident probability predictions and fault diagnoses, especially under data uncertainty. This paper introduces a novel dynamic accident probability assessment framework tailored for GDS under data uncertainty. By integrating Bayesian network (BN) modeling with fuzzy expert judgments, frequentist estimation, and Bayesian updating, the framework offers a comprehensive approach. It encompasses accident modeling, root event (RE) probability estimation, undesired event (UE) predictive analysis, probability adaptation, and accident diagnosis analysis. A case study demonstrates the framework’s reliability and effectiveness, revealing that the occurrence probability of major hazards like vapor cloud explosions and long-duration jet fires diminishes significantly with effective safety barriers. Crucially, the framework acknowledges the dynamic nature of risk by incorporating observed failure incidents or near-misses into the assessment, promptly adjusting risk indicators like UE probabilities and RE criticality. This underscores the importance for decision-makers to maintain a heightened awareness of these dynamics, enabling swift adjustments to maintenance strategies and resource allocation prioritization. By mitigating assessment uncertainty and enhancing precision in maintenance strategies, the framework represents a significant advancement in GDS safety management, ultimately striving to elevate safety and reliability standards, mitigate natural gas distribution risks, and safeguard public safety and the environment. Full article

The global demand for liquefied natural gas (LNG) has led to a significant increase in the number of LNG carriers (LNGCs), consequently elevating the risk of operational accidents. Unlike conventional vessels, LNGCs present a high risk of fire and explosion and involve extensive repair times and costs due to the complex structure of the cargo containment system (CCS). This study investigates the effects of seawater exposure on the uni-axial compressive properties of plywood used in LNGC CCS structures, with the goal of establishing material strength criteria that could reduce repair requirements. The analysis focuses on the NO96 CCS, which incorporates the highest volume of plywood among existing designs. In this configuration, compressive strength is a critical design parameter. Therefore, the mechanical response of plywood was evaluated under both room temperature and cryogenic conditions (−163 °C), simulating the LNG operating environment. The results demonstrate that plywood exhibited increased compressive strength after three hours of seawater and saltwater immersion, although the rate of improvement diminished with extended exposure. In contrast, specimens immersed in distilled water showed a consistent reduction in compressive strength. Furthermore, cryogenic temperatures significantly enhanced the compressive strength compared to ambient conditions. This study establishes a methodology for assessing the mechanical performance of plywood under marine and cryogenic conditions, contributing to its reliable application in LNG carrier structures. Full article

With the acceleration of urbanization process, the number of elevators in China has surged. Concurrently, the prevalence of older elevators has increased, leading to a rise in frequent malfunctions. In recent years, there has been a troubling frequency of elevator accidents resulting in casualties, which has had a negative social impact. The elevator braking system is crucial for ensuring the safe operation of the elevator, and brake failure is a significant contributor to elevator accidents. The failure modes of elevator brakes are complex and diverse, and the failure risk factors are mixed, correlated and unknown. Therefore, this paper is based on the Failure Mode and Effects Analysis (FMEA), focusing on the structural characteristics of the elevator brake to determine the equipment failure risk factors. Based on the accident prevention theory model (24Model) for comprehensive analysis of internal and external causes, this study identifies the comprehensive failure risk factors for elevator brakes. The study employs affiliation function to build the failure risk factor indicator system, the use of the Decision-making Trial and Evaluation Laboratory (DEMATEL) and Interpretative Structural Modeling (ISM) methods to analyze the hierarchical structure and internal relationship between the factors. Based on the research results, the factors contributing to the failure of elevator drum brakes can be identified and the interrelationships among these factors can be systematically elucidated. This analysis can serve as a valuable tool in pinpointing critical areas for routine elevator maintenance and upkeep, with the aim of minimizing the likelihood of drum brake malfunctions. Furthermore, the insights gained can inform the design and implementation of elevator monitoring and management systems, enabling a clearer focus on pertinent factors. Ultimately, this study furnishes a theoretical framework for the prevention and mitigation of such accidents. Full article

The inherent complexity of traffic patterns in inland river tributary waterways presents significant challenges in predicting ship behavior, resulting in elevated accident risks compared to general waterways. With the intelligent development of inland navigation, conducting research on autonomous navigation decision-making for tributary waterway ships is crucial to improving navigation safety and efficiency. Based on the characteristics of the navigation environment, a digital traffic environment model for inland waterways with tributaries is constructed to meet the information requirements of autonomous navigation decision-making. The ship encounter process is analyzed, and a collision risk identification model based on trajectory derivation is proposed, which accounts for the uncertainty of ship maneuvering in tributary waterways. Under the premise of compliance with the “Rules of the People’s Republic of China for Inland River Collision Avoidance” (RPRCIRCA) and adherence to good seamanship, an autonomous navigation decision-making method is developed by integrating an improved Line-of-Sight tracking model with a collision avoidance strategy based on exhaustive course-speed maneuver combinations. The system’s performance is validated through simulation experiments, with trajectory tracking deviations demonstrated to remain below 49 m under wind-current disturbances while minimum encounter distances with target ships are maintained above 48 m. Adaptive response capabilities to maneuvering variations of target ships are confirmed, along with the preservation of navigation precision in complex tributary environments. Full article

The venting process is one of the most important events during the thermal runaway (TR) of lithium-ion batteries (LIBs) in determining fire accidents, while different ambient pressures will exert an influence on the venting events as well as the TR. Ternary nickel–cobalt–manganese (NCM) batteries with a 75% state of charge (SOC) were employed to conduct TR tests under different ambient pressures in a sealed chamber with dilute oxygen. It was found that elevated ambient pressure results in milder ejections in terms of jet temperature and mass loss. Gas venting characteristics were also obtained. Additionally, the amount of carbon dioxide (CO₂), hydrogen (H₂), methane (CH₄), and ethylene (C₂H₄) released increase with ambient pressure, while carbon monoxide (CO) varies inversely with ambient pressure. The higher the ambient pressure is, the greater the flammability risk is. The molar amount of C, H, O, and total gases released shows a positive correlation with the maximum battery temperature and ambient pressure. This study will support the design of safety valves and help reveal the effects of venting events on the evolution of TR. Full article

This study presents a novel multiphysics phase-field fracture model to analyze high-burnup uranium dioxide (UO₂) fuel behavior under transient reactor conditions. Fracture is treated as a stochastic phase transition, which inherently accounts for the random microstructural effects that lead to variations in the value of fracture strength. Moreover, the model takes into consideration the effects of temperature and burnup on thermal conductivity. Therefore, the model is able to predict crack initiation, propagation, and complex morphologies in response to thermal gradients and stress distributions. Several simulations were conducted to investigate the effects of operational and transient conditions on fracture behavior and the resulting cracking patterns. High-burnup fuels exhibit reduced thermal conductivity, elevating temperature gradients and resulting in extensive radial and circumferential cracks. Transient heating rates and temperatures significantly affect fracture patterns, with higher heating rates generating steeper gradients and more irregular crack trajectories. This approach provides critical insights into fuel integrity during accident scenarios and supports the safety evaluation of extended burnup limits. Full article