Search Results (6)

Currently, there are many gaps in the research on the safety of hydrogen-powered trains, and the hazardous points vary across different scenarios. It is necessary to conduct safety analysis for various scenarios in order to develop effective accident response strategies. Considering the implementation of hydrogen power in the rail transport sector, this paper reviews the development status of hydrogen-powered trains and the hydrogen leak hazard chain. Based on the literature and industry data, a thorough analysis is conducted on the challenges faced by hydrogen-powered trains in the scenario of electrified railways, tunnels, train stations, hydrogen refueling stations, and garages. Existing railway facilities are not ready to deal with accidental hydrogen leakage, and the promotion of hydrogen-powered trains needs to be cautious. Full article

Petroleum storage and transport systems necessitate robust safety measures to mitigate oil spill risks threatening marine ecosystems and sustainable development through ecological and socioeconomic safeguards. We aimed to gain a deeper understanding of the evolution patterns of accidents and effectively mitigate risks. An improved risk assessment method that combines the Bow-Tie (BT) theory and Dynamic Bayesian theory was applied to evaluate the safety risks of petroleum storage and transportation facilities. Additionally, a scenario modeling approach was utilized to construct a model of the event chain resulting from accidents, facilitating quantitative analysis and risk prediction. By constructing an accident chain based on fault trees, the BT model was converted into a Bayesian Network (BN) model. A Dynamic Bayesian Network (DBN) model was established by incorporating time series parameters into the static Bayesian model, enabling the dynamic risk assessment of an oil storage and transportation base in the Zhoushan archipelago. This study quantitatively analyzes the dynamic risk propagation process of storage tank leakage, establishing time-dependent risk probability profiles. The results demonstrate an initial leakage probability of 0.015, with risk magnitude doubling for the temporal progression and concurrent probabilistic escalation of secondary hazards, including fire or explosion scenarios. A novel risk transition framework for the consequences of petrochemical leaks has been developed, providing a predictive paradigm for risk evolution trajectories and offering critical theoretical and practical references for emergency response optimization. Full article

Ensuring the safety of oil tank farms is essential to maintaining energy security and minimizing the impact of potential accidents. This paper develops a quantitative regional risk model designed to assess both individual and societal risks in oil tank farms, with particular attention to energy-related risks such as leaks, fires, and explosions. The model integrates factors like day–night operational variations, weather conditions, and risk superposition to provide a comprehensive and accurate evaluation of regional risks. By considering the cumulative effects of multiple hazards, including those tied to energy dynamics, and the stability and validity of the model are researched through Monte Carlo simulations and case application. The results show that the model enhances the reliability of traditional risk assessment methods, making it more applicable to oil tank farm safety concerns. Furthermore, this study introduces a practical tool that simplifies the risk assessment process, allowing operators and decision-makers to evaluate risks without requiring in-depth technical expertise. The methodology improves the ability to safeguard oil tank farms, ensuring the stability of energy supply chains and contributing to broader energy security efforts. This study provides a valuable method for researchers and engineers seeking to enhance regional risk calculation efficiency, with a specific focus on energy risks. Full article

The efficient management of urban water distribution networks is crucial for public health and urban development. One of the major challenges is the quick and accurate detection of leaks, which can lead to water loss, infrastructure damage, and environmental hazards. Many existing leak detection methods are ineffective, especially in complex and aging pipeline networks. If these limitations are not overcome, it can result in a chain of infrastructure failures, exacerbating damage, increasing repair costs, and causing water shortages and public health risks. The leak issue is further complicated by increasing urban water demand, climate change, and population growth. Therefore, there is an urgent need for intelligent systems that can overcome the limitations of traditional methodologies and leverage sophisticated data analysis and machine learning technologies. In this study, we propose a reliable and advanced method for detecting leaks in water pipes using a framework based on Long Short-Term Memory (LSTM) networks combined with autoencoders. The framework is designed to manage the temporal dimension of time-series data and is enhanced with ensemble learning techniques, making it sensitive to subtle signals indicating leaks while robustly dealing with noise signals. Through the integration of signal processing and pattern recognition, the machine learning-based model addresses the leak detection problem, providing an intelligent system that enhances environmental protection and resource management. The proposed approach greatly enhances the accuracy and precision of leak detection, making essential contributions in the field and offering promising prospects for the future of sustainable water management strategies. Full article

Marine nuclear power platforms can continuously supply electricity and fresh water for marine resource exploration and surrounding islands. China’s first marine nuclear power platform uses a soft yoke multi-joint connect mode as the mooring positioning device. When the marine nuclear power platform needs repair, maintenance, nuclear fuel replacement, or a different operation area, a mooring disconnect operation must be carried out. The traditional mooring disconnect process consists of four stages: cable limiting, yoke offloading, yoke dropping, and equipment recovery stages. The entire disconnect process is a high-risk nuclear-related operation that could result in a collision accident between the yoke and hull structure, resulting in nuclear fuel leaks and casualties. Therefore, it is necessary to evaluate the risk factors of the disconnect process and to assess the risk level together with the consequence of each risk. In this paper, a quantitative risk analysis of nuclear power platform disconnect operations is carried out based on a fuzzy Bayesian network approach for risk events in each stage of the disconnect operations. Based on the forward fuzzy Bayesian inference, the criticality of each risk event to the disconnect process is evaluated and compared. The main risk factors that may cause a disconnect accident are then determined based on the reverse Bayesian inference rule. The results indicate that human error is the most likely factor leading to the failure of the disconnect process, requiring strict control of personnel operation procedures during this process. The yoke colliding with the hull and stern antifriction chain-breaking are the most significant hazards caused by the disconnect failing. Thus, the distance between the yoke and hull, stern tug tensile force, and maintenance of the antifriction chain should receive particular attention. Full article

Fumonisin B1 (FB1) and aflatoxin B1 (AFB1) are frequent contaminants of staple foods such as maize. Oral exposure to these toxins poses health hazards by disrupting cellular signaling. However, little is known regarding the multifaced mitochondrial dysfunction-linked toxicity of FB1 and AFB1. Here, we show that after exposure to FB1 and AFB1, mitochondrial respiration significantly decreased by measuring the oxygen consumption rate (OCR), mitochondrial membrane potential (MMP) and reactive oxygen species (ROS). The current work shows that the integrity of mitochondria (MMP and ROS), that is the central component of cell apoptosis, is disrupted by FB1 and AFB1 in undifferentiated Caco-2 and HepG2 cells as in vitro models for human intestine and liver, respectively. It hypothesizes that FB1 and AFB1 could disrupt the mitochondrial electron transport chain (ETC) to induce mitochondrial dysfunction and break the balance of transferring H⁺ between the mitochondrial inner membrane and mitochondrial matrix, however, the proton leak is not increasing and, as a result, ATP synthesis is blocked. At the sub-toxic exposure of 1.0 µg/mL for 24 h, i.e., a viability of 95% in Caco-2 and HepG2 cells, the mitochondrial respiration was, however, stimulated. This suggests that the treated cells could reserve energy for mitochondrial respiration with the exposure of FB1 and AFB1, which could be a survival advantage. Full article