This study develops and evaluates nanocellulose TiO2 epoxy composites containing 5%, 10%, and 15% TiO2 to identify a multifunctional material suitable for next-generation safety and emergency-response applications. The composites were prepared by using an epoxy nanocellulose matrix reinforced with TiO2 nanoparticles, followed by curing and controlled drying. FTIR analysis confirmed Ti-O bonding (699−742 cm−1) and stable organic functional groups, while high-magnification SEM revealed a fibrous nanocellulose network with progressively increased TiO2 aggregation at higher loadings. XRD verified successful nanoparticle incorporation, with dominant anatase peaks and minor rutile reflections. PSA and zeta-potential measurements indicated moderately polydisperse but stable dispersions (PI: 0.39–0.46; –19.0 to –27.0 mV). DSC thermograms showed moisture loss between 70−140 °C and decomposition onset near 200 °C, confirming suitability for moderate-temperature operational environments. Mechanical testing identified the 10% TiO2 composite as optimal, exhibiting the highest tensile strength (52.4 MPa) and Young's modulus (3.3 GPa) due to enhanced interfacial bonding and stress-transfer efficiency. Collectively, the results demonstrate that the 10% TiO2 nanocellulose composite provides the best balance of mechanical strength, thermal stability, dispersion, and structural integrity, making it a promising candidate for multifunctional emergency applications such as rapid-deployment biomedical support structures, lightweight automotive panels, and filtration or protective systems.
A grid system for warning and positioning functions was designed by a ZigBee wireless sensor network to solve the difficulties in early warning and fault area positioning of ancient building fire detectors. The system includes the design of a ZigBee wireless sensor network and an abnormal area positioning algorithm, where the terminal node collects data and transmits it to the upper computer for warning and positioning. Taking residual current electrical fire detectors as an example, experiments were conducted, and the experimental results showed that the system could achieve residual current warning and abnormal area positioning in ancient buildings.
Peatland fires in Malaysia are a recurrent environmental hazard associated with prolonged drought conditions and human activities. Interviewees and respondents perceived human-related activities to be major contributors, and fire incidents were reported to decline during the COVID-19 pandemic period. In response to this issue, this study examines critical management strategies and firefighter efforts to control peatland fire occurrences, focusing on risk assessment and suppression effectiveness. To achieve this, the research integrates structured interviews with peatland fire managers and survey responses from 366 individuals involved in fire suppression. As a result, the collected data provide insights into policy frameworks, prevention measures, and safety challenges. Specifically, findings highlight inefficiencies in fire management policies, gaps in prevention and suppression strategies, and notable firefighter safety concerns. Furthermore, perspectives from management and frontline responders reveal operational challenges and key areas for improvement. Consequently, this study enhances understanding of peatland fire prevention approaches, contributing factors influencing fire occurrences, and the safety risks faced by firefighters. By building on these insights, the study emphasizes the necessity of integrating both managerial and frontline perspectives in comprehensive fire risk mitigation. Ultimately, the findings support improved risk assessment frameworks and policy recommendations, advocating for a more effective peatland fire management approach. Therefore, this study underscores future research directions in fire suppression strategies, safety protocols, and management efficiency enhancements.
Effective emergency management of critical infrastructure following high-impact–low-probability events is a significant scientific and technological challenge. A critical failure point is often the lack of pre-positioned spare assets, which cripples recovery efforts. This paper proposes a data-driven decision-support framework to enhance emergency logistics and resource allocation. By integrating engineering fragility curves with a unique 10-year operational dataset, our model quantifies the precise number of asset failures required to ensure the rapid restoration of services. Specifically, we utilize historical failure records and meteorological data to calibrate asset fragility curves, moving beyond qualitative assessments. This framework enables emergency managers to shift from heuristic risk assessments to a data-driven budgeting process, quantifying inventory requirements to balance costs against the socioeconomic impact of prolonged disruptions. Our results provide a powerful scientific tool for enhancing the resilience and recovery capabilities of critical infrastructure systems.
The increasing demand for flame-retardant polymeric materials in industrial applications has stimulated significant interest in sustainable fire protection strategies. Most polymers are intrinsically flammable, posing serious fire risks during use and processing. While conventional flame retardants, including halogenated compounds, phosphorus-based additives, metal hydroxides, and silicon-containing materials, have been widely used, growing environmental and health concerns necessitate the development of greener alternatives. Biomass-based flame retardants have emerged as promising candidates due to their renewability, inherent carbon-forming ability, and compatibility with polymer matrices. This review specifically focuses on starch, chitosan, and phytic acid because of their abundance, cost-effectiveness, and well-documented flame-retardant mechanisms. The evaluation methods for polymer flame retardancy, including thermal stability and flammability tests, as well as techniques for analyzing both gaseous and condensed-phase mechanisms, are systematically discussed. Furthermore, the latest advances in starch, chitosan, and phytic acid-based flame retardants are reviewed from the perspective of combination strategies and chemical modifications. The discussion highlights their synthesis, structural modifications, and processing techniques, along with their integration into polymeric matrices and the resulting flame-retardant performance. The review aims to provide a comprehensive foundation for the rational design of efficient, biomass-based flame-retardant systems with potential scalability for industrial applications.
Under the combined influence of globalization and urbanization, cities are facing increasingly complex security challenges, which have attracted widespread attention from the international academic community. Research on resilient cities has been continuously deepened. In response to this trend, China has proposed the concept of building safe development-oriented cities. This paper employs CiteSpace to explore the development trajectory of safe development cities, examining current research progress from three perspectives: resilience, smart cities, and sustainability. It aims to establish a long-term vision, form a dynamic balance in the urban safety ecosystem, propose a "Trinity" framework for the construction of safe development-oriented cities, and outline future prospects for the development of safe cities.
A bibliometric analysis of relevant literature was conducted using co-occurrence, clustering, co-citation, and other analysis techniques to understand the state of research in areas related to subway fires. According to the research findings, the primary areas of focus for subway fire research hotspots include fire behavior and smoke control, ventilation and smoke management, evacuation and personnel safety, fire tolerance and fire protection materials, simulation and modeling technology, climate change, and control systems. The optimization of ventilation modes, the development of simulation technology, the advancement of fireproof materials, the integration of artificial intelligence and social force models in evacuation models, and the impact of environmental and climate change on subway fires are the current research frontiers. The disciplines of environmental science, material engineering, social science, and artificial intelligence are increasingly intersecting with the research of subway fires. More cross-disciplinary cooperation will be beneficial for future research. These findings help researchers in rapidly comprehending the general state of situations regarding subway fires.
Liquefied natural gas (LNG) road tanker rollover accidents, though infrequent, often lead to catastrophic consequences. Quantitative risk assessment is significantly challenged by the scarcity of probabilistic data specific to these complex, low-frequency events. To address this data limitation and enhance assessment accuracy, this study develops an integrated fuzzy fault tree-Bayesian network (FFT-BN) methodology. Fuzzy set theory is applied, leveraging multi-source general traffic accident statistics and expert judgment, to quantify the occurrence probabilities of basic causal factors under uncertainty. A Bayesian network is then constructed from the fault tree structure to enable comprehensive probabilistic inference. Critical risk factors were rigorously identified using multiple importance measures (ROV, BIM, RRW). The analysis consistently pinpointed poor road alignment and the absence of critical traffic facilities as the two paramount contributors. Crucially, vehicle speed management emerged as the central mitigation mechanism linking these factors; controlling speed effectively counters the destabilizing effects of poor alignment and compensates for the lack of timely hazard perception. The results demonstrate that implementing targeted speed control measures on identified high-risk road sections is essential for reducing the probability of LNG tanker rollovers.
This paper presents methods of multi-scale mapping of vegetation fuels (VFs). It has been developed based on long-term fundamental pyrological (fire/fuel-related) studies in different regions of Russia, which have helped create a detailed VF classification and develop methods for mapping it in forest fire protection. There are examples of assessing the current fire hazard under different classes of dryness according to weather conditions using large-scale VF maps. There is also an example of predicting the spread of a forest fire in a nature reserve using a specially developed and registered software, also providing probable fire characteristics, including fire intensity at the fire edge, which helps to predict the immediate fire effects in the form of mortality probability in birch, pine, larch, and spruce stands by their average diameter.
In the context of global climate change and frequent natural disasters, the impact of sudden disasters such as earthquakes on population migration is becoming increasingly significant. Taking the 2008 Wenchuan Earthquake as a case study, this study explores the impact mechanism of earthquake disasters on population migration by combining the modified gravity model and the social vulnerability index. By collecting seismic data for earthquakes of of magnitude 5 or higher in China and its surrounding areas from 2011 to 2023, combined with population density information, this study first visualized the seismic events and revealed the spatial distribution characteristics and potential impacts of seismic activities. Subsequently, based on the socioeconomic data before and after the Wenchuan Earthquake, the social vulnerability index was constructed to quantify the impact of the earthquake on population migration. Finally, a model based on the traditional gravity model, social vulnerability, the gross domestic product (GDP) of the place of migration, and the place of migration is introduced. The results show that the GDP of the place of migration and the places of migration have a significant impact on population migration, while the social vulnerability index has a relatively small impact on intraprovincial migration. The goodness-of-fit of the model reached 0.9927, indicating that the modified gravity model could effectively explain the variability of population migration. This study provides a new quantitative evaluation method for managing post-disaster population migration, and provides a scientific basis for future disaster risk management and urban planning.
Most countries globally are actively advancing efforts towards achieving carbon peak and carbon neutrality goals. CH4 and H2 serve as vital clean energy carriers in support of these objectives. Pipeline transportation is an essential method for the large-scale, efficient, low-cost, and safe delivery of CH4 and H2. However, it also presents safety risks that may lead to leaks, fires, and other accidents. This paper reviews research conducted by scholars worldwide on the impact of various factors on the consequences of H2/CH4 leakage and diffusion in buried and subsea pipeline scenarios. The core focus of research under these conditions is to study how factors such as the depth of the leak orifice, soil properties, underwater leak depth, and current speed affect the diffusion time and range of the gas, thereby analyzing their impact on the severity of the accidents. Despite progress, challenges remain. On the theoretical front, there is a need to further deepen the quantitative study of diffusion mechanisms and their impact on their consequences. On the technical front, efforts are needed to develop efficient and accurate monitoring and prediction systems for real-time detection and risk early warning. To improve efficiency and accuracy, future work will require larger-scale and more diverse experimental studies to obtain relevant data and fully leverage cutting-edge technologies such as big data and artificial intelligence to optimize leak prediction and monitoring systems.
In high-rise emergencies, relying solely on stairs for evacuation may hinder timely escape. Therefore, elevator-assisted stair evacuation should be considered to improve efficiency. Limited research exists on coordinated stair-elevator evacuation, particularly for upward and downward movements involving refuge floors. A 10-story case study was constructed using MassMotion software, based on the Social Forces Model, to explore the effects of elevator-served floor, building population, and percentage of the population using the elevator on evacuation. Findings were applied to a real building. Results indicate that evacuation time increases with the number of elevator-served floors. Upward stair-elevator coordination outperformed downward evacuation, unaffected by building population. For upward evacuation, deploying elevators on floors 1 or 1–2 reduced evacuation times compared to stairs alone. Downward evacuation achieved time savings using elevators on floors 10 or 9–10. Allocating elevators to 100% of occupants on the lowest or highest floors, or 50% on floors 1–2 for upward, or 9–10 for downward evacuation, minimized evacuation duration. Elevator-assisted evacuation reduced evacuation times by 11.7% for upward evacuation, and 8.4% for downward evacuation, suggesting prioritization of elevators for upward evacuees. This research provides theoretical insights for optimizing the coordination of stairs and elevators in high-rise building evacuations.
The syngas pipeline serves as the primary carrier for syngas exported from the coal gasification furnace, is vulnerable to corrosion and erosion from the transported medium, and is prone to leakage due to long-term high-pressure operation. Moreover, due to its compositional characteristics, syngas pose flammability, explosive, and toxicity risks, which can potentially lead to severe accidents if a leak occurs. Therefore, it is essential to conduct a risk assessment of syngas pipeline leakage. This study proposes a risk assessment approach for syngas pipeline leakage in coal gasification using Dynamic Bayesian Network (DBN). First, the risk identification model is built using Bow-Tie (BT) analysis and then mapped into DBN using a mapping algorithm. Expert evaluation, improved similarity aggregation methods, and fuzzy set theory are employed to quantify prior probabilities. To address the uncertainty of the DBN model, a Leak Noisy-or gate model is introduced. Time series are added to predict the dynamic probabilities. Nine key hazard events, six highly sensitive factors, and the maximum causal chain are identified, and predict the dynamic probability of syngas pipeline leakage and potential consequences. This study provides a theoretical basis for routine maintenance and risk assessment of syngas pipelines.
Effects of vessel weak link fracture and external impact-induced rupture on the onset of superheated boiling are distinct, and the scientific understanding of these effects remains limited. This study investigates the leakage processes in a small storage tank within a confined environment and compares the effects of natural rupture and needle puncture-induced leakage. Experimental results demonstrated that under the same leakage conditions, the depressurization rate of needle puncture was lower than that of natural rupture. Furthermore, in the needle puncture mode, tank pressure was maintained within the saturated vapor pressure range corresponding to the liquid temperature. In natural rupture mode, pressure exceeded this saturated vapor pressure threshold, initiating bubble formation. As release pressure increased, the bubble rise velocity initially increased, followed by a gradual decline, until the bubbles either ruptured or coalesced during their ascent. The confined space also restricted upward fluid motion, thereby slowing depressurization and preventing pressure rebound. The present study can offer a valuable preventive approach for mitigating tank leakage incidents within confined spaces.
This research comprehensively addresses the significant exothermic behavior and the associated thermal runaway risks in the semi-batch preparation of 2-aminonaphthalene-1,5-disulfonic acid (2-ANDSA) diazonium salt. By employing various thermal analysis techniques, including reaction calorimetry, differential scanning calorimetry (DSC), and accelerating rate calorimetry (ARC), the influences of reaction temperature and reagent feeding rates on product purity and thermal safety were systematically examined. The findings demonstrate that increased reaction temperatures accelerated reagent addition rates, and lowered solvent-to-reactant ratios markedly elevated the likelihood of thermal runaway incidents. Complementary density functional theory (DFT) calculations elucidated the detailed reaction mechanism, highlighted critical intermediate species, and clarified their thermodynamic profiles, thereby providing deeper insights into the thermal decomposition mechanism of the diazonium salt. Additionally, the combined application of the Risk Matrix and Stoessel Criticality Diagram methods facilitated a comprehensive thermal runaway risk assessment and identification of key operational safety parameters for process scale-up. These findings serve as a robust theoretical foundation and practical reference for effective thermal hazard management and safe production practices in processes involving high-risk aromatic diazonium salts.
To address the challenges in observing wire icing, this study took the deep residual network ResNet34 as the baseline model and optimized it using normalization, dropout techniques, and data augmentation methods. A novel wire icing risk level identification model based on deep learning was proposed. The results demonstrated that the optimized ResNet34 model (ResNet34+) achieved an average identification accuracy of 93.3% for wire icing risk levels across different regions and wire orientations. Additionally, the identification accuracy was notably higher between 8:00−11:00 and 15:00−17:00. During a coexisting freezing rain and supercooled fog event on Lushan, the model achieved an average wire icing identification accuracy of 89.4% and 90.8% on east-west and north-south oriented wires, respectively, indicating good generalizability of the model. The application of this model provided a novel approach for identifying wire icing risk levels.
Large subway stations have many exits. Selecting the correct exit is crucial for pedestrian evacuation during a disaster. Thus, the intelligent guidance of pedestrians is necessary during an evacuation. In this study, a small-scale multi-exit evacuation experiment is conducted in an H-type subway station using lighted guidance signs to indicate the real-time pedestrian density at the exit. Three experimental scenarios are investigated: evacuation with no lighted guidance signs; evacuation with lighted guidance signs; and evacuation with lighted guidance signs in a low-view environment. The exit choice model and the social force model with pedestrian classification are used, and a density threshold is incorporated into the exit choice model for numerical simulation. The simulation results are compared with the experimental results. The results show that pedestrians are more inclined to choose wider exits when no lighted guidance signs are used. The lighted guidance signs significantly improve evacuation efficiency, and this effect is more pronounced for fewer insensitive pedestrians (10 or 20 people). The lighted guidance signs are less effective in a low-view environment, and the exit width is a more crucial factor in choosing an exit.
This study investigates the effectiveness of different wrapping materials, namely aerogel felt, thermal conductive gel, and two phase change materials (PCMs) in mitigating thermal runaway in lithium-ion batteries. The experimental results reveal that all the materials tested delay the onset of thermal runaway and safety valve rupture compared to unwrapped batteries. Specifically, aerogel felt and thermal conductive gel offer substantial thermal runaway inhibition, delaying the safety valve rupture time by 97 and 99 s, respectively. PCM-1 demonstrates a delay in safety valve rupture but has a limited impact on the onset of thermal runaway. Among all materials, thermal conductive gel shows the most significant impact, postponing both safety valve rupture and thermal runaway onset. In contrast, the PCMs exhibit relatively weaker effects. Furthermore, batteries wrapped in aerogel felt produce the highest CO concentration during thermal runaway, reaching 3,658 ppm. The maximum mass loss rate of the batteries varies with the wrapping material, ranging from 20.1 to 68.5 g/s, with the unwrapped batteries exhibiting the highest loss rate. Overall, the results suggest that thermal conductive gels and phase change materials improve the safety and emergency response of lithium-ion batteries under extreme conditions, with the thermal conductive gel showing the most promising results in delaying both safety valve rupture and thermal runaway.
