Search Results (84)

Floods and wildfires are two extreme environmental events with significant yet different impacts on soil health and on two particularly important soil pollutants, heavy metals (HMs) and polycyclic aromatic hydrocarbons (PAHs), which are directly associated with ishytoxic properties and their ability to enter the food chain. The present study includes a methodological approach that was based on a literature review of published studies conducted worldwide regarding these two phenomena. The main forms of both pollutants, their possible sources and inevitable deposition onto the soil surface, along with their behavior–transport–mobility, and their residence time in soil were investigated. Furthermore, the changes that both HMs and PAHs induce in the physicochemical properties of post-flood and post-fire soils (in soil pH, Cation Exchange Capacity (CEC), organic matter content, porosity, mineralogical alterations, etc.), are investigated after a literature review of various case studies. Wildfires, in contrast to floods, can more easily remove large quantities of heavy metals into the soil ecosystem, most likely due to the intense erosion they cause. At the same time, floods appear to significantly burden soils with PAHs. In wildfires, the largest mean increases were observed for Mn (386%), Zn (300%), and Cu (202%). In floods, Pb showed the highest mean increase (534%), with Cd also rising substantially (236%). Regarding total PAHs, mean post-event concentrations reached 482.3 μg/kg after wildfires, compared to 4384 μg/kg after floods. Changes in the structure and chemical composition of flooded and burned soils may also affect the mobility and bioavailability of the pollutants under study. Overall, these two phenomena significantly alter soil quality, affecting both ecological processes and potential health impacts. Full article

Advancing rapidly in modern bridge engineering technology, through concrete-filled steel tubular (CFST) arch bridges have achieved widespread application in transportation infrastructure development. Nevertheless, vehicle fires occurring in complicated operational settings may rapidly escalate into major disasters. Fires in oil tankers are particularly dangerous for the safety of bridges. This study examines the fire resistance of through concrete-filled steel tubular (CFST) arch bridges exposed to tanker truck fires. The study formulates a detailed model utilizing Fire Dynamics Simulator (FDS) to simulate fire scenarios, elucidating the spatial temperature distribution characteristics within arch bridge structures. A three-dimensional finite element model established in ABAQUS (Abaqus 2024, Dassault Systèmes Simulia Corp, Providence, RI, USA) is employed to simulate structural responses by analyzing the mechanical behavior of key components under different fire conditions. Practical fire resistance design recommendations for extreme tanker truck fire scenarios are ultimately proposed. Numerical results demonstrate that structural components near the fire source (such as transverse bracings, hangers, and fire-exposed arch surfaces) experience significantly higher temperatures than other regions. Notable temperature gradients developing along hangers and arch ribs in fire-affected zones are observed, while substantial cross-sectional temperature gradients occurring in these components under tanker truck fires reveal their damage evolution mechanisms. The fire exposure scenario at the quarter-point of the midspan is identified as the most critical fire exposure scenario for through CFST arch bridges under tanker truck fires. Under this extreme scenario, the deflection on the fire-exposed side of the global structure exhibits a significant three-stage distribution characteristic: an initial ascending phase around 0–800 s, followed by a sharp descending phase during 800–1100 s, and then a stabilization trend. A fire resistance limit criterion based on component failure ($t_{f3}$ = 853.43 s) is established, and a global fire resistance limit assessment methodology for through CFST arch bridges under extreme tanker truck scenarios is proposed. Full article

Smoking in oilfield operation areas poses a severe risk of fire and explosion accidents, threatening production safety, workers’ lives, and the surrounding ecological environment. Such behavior represents a typical preventable unsafe human action. Detecting smoking behaviors among oilfield workers can fundamentally prevent such safety incidents. To address the challenges of low detection accuracy for small objects and frequent missed or false detections under extreme industrial environments, this paper proposes a GCEA-YOLO network based on YOLOv11 for smoking behavior detection. First, a CSP-EDLAN module is introduced to enhance fine-grained feature learning. Second, to reduce model complexity while preserving critical spatial information, an ADown module is incorporated. Third, an enhanced feature fusion module is integrated to achieve effective multiscale feature aggregation. Finally, an EfficientHead module is employed to generate high-precision and lightweight detection results. The experimental results demonstrate that, compared with YOLOv11n, GCEA-YOLO achieves improvements of 20.8% in precision, 6.9% in recall, and 15.1% in mean average precision (mAP). Overall, GCEA-YOLO significantly outperforms YOLOv11n. Full article

The increasing frequency of extreme wildfire behavior globally, particularly under the influence of anthropogenic climate change, poses unprecedented challenges to traditional fire management paradigms. This study presents a comprehensive, multi-dimensional case study of the catastrophic eruptive fire event that occurred in Xintian County, Hunan Province, China, on 17 October 2022. By integrating data on long-term ecological drought, critical synoptic-scale weather conditions, and real-time emission profiles of combustion products, we delineate the mechanistic chain leading to eruptive fire development in a subtropical evergreen broad-leaved forest region, historically considered a low-to-moderate fire risk zone. Our results demonstrate that the eruptive fire was a consequence of a critical convergence of factors: a protracted pre-conditioning drought that significantly reduced live and dead fuel moisture, a specific meteorological window characterized by extremely low relative humidity (<60%) and initially high wind speeds (peak at 16.5–17.9 m/s), and the abundant production and accumulation of flammable pyrolysis gases (e.g., CO, CH₄) from the dominant Masson pine forests. The emission data pinpointed October 19th as the key tipping point, marking the transition to high-intensity combustion. This study underscores the vulnerability of subtropical forest ecosystems to eruptive fires during compound extreme events. Our findings provide a critical scientific basis for updating fire danger rating systems and early warning strategies in similar ecological regions under a changing climate. Full article

Santa Ana winds are extreme meteorological events that strongly affect the U.S.–Mexico border region, often associated with droughts, high fire risk, and hydrological imbalance. Understanding the temporal behavior of key atmospheric variables during these events is crucial for integrated water resource management in semi-arid regions such as the Guadalupe Basin in northern Baja California. In this study, we explored the predictive capability of several hybrid deep learning architectures—Long Short-Term Memory (LSTM), Convolutional Neural Network combined with LSTM (CNN–LSTM), and Bidirectional LSTM with Attention (BiLSTM–Attention)—to model the temporal evolution of wind speed, wind direction, temperature, relative humidity, and atmospheric pressure using Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data from 1980 to 2020. Model performance was evaluated using RMSE, MAE, and R² metrics and compared against persistence and climatology baselines. The BiLSTM–Attention model achieved the best overall performance, showing particularly high accuracy for temperature (R² = 0.95) and relative humidity (R² = 0.76), while maintaining angular errors below 35° for wind direction. The results demonstrate the potential of hybrid deep learning models to capture nonlinear temporal dependencies in meteorological time series and provide a methodological framework to enhance hydrometeorological understanding and water resource management in the Guadalupe Basin under Santa Ana wind conditions. Full article

The use of artificial oxygenation to counteract the effects of hypoxia and improve living standards in high-altitude, low-oxygen settings is widespread. A recognized consequence of this intervention is that it elevates the risk of fire occurrence. In this study, we simulated a real fire environment with low-pressure oxygen enrichment in a plateau area. A new multi-measuring apparatus was constructed by integrating an electronic control cone heater and a low-pressure oxygen enrichment combustion platform to enable the simultaneous measurement of multiple parameters. The combined effects of varying oxygen concentrations and thermal irradiance on the combustion behavior of wood plastic composites (WPCs) under specific low-pressure conditions were investigated, and alterations in crucial combustion parameters were examined and evaluated. Increasing the oxygen concentration and heat flux significantly reduced the ignition and combustion times. For instance, at 50 kW/m², the ignition time decreased from 75 s to 16 s as the oxygen concentration increased from 21% to 35%. This effect was suppressed by higher heat fluxes. Compared with low oxygen concentrations and low thermal radiation environments, the ignition time of the material under high oxygen concentrations and high thermal radiation conditions was shortened by more than 78%, indicating that its flammability is enhanced under extreme conditions. Higher oxygen concentrations enhanced the heat feedback to the fuel surface, which accelerated pyrolysis and yielded a more compact flame with reduced dimensions and a color transition from blue-yellow to bright yellow. This intensified combustion was further manifested by an increased mass loss rate (MLR), elevated flame temperature, and a decline in residual mass percentage. The combustion of WPCs displayed distinct stage characteristics, exhibiting “double peak” features in both the MLR and flame temperature, which were attributed to the staged pyrolysis of its wood fiber and plastic components. Full article

The precise forecasting of renewable energy production and usage is essential for the stability, efficiency, and sustainability of contemporary power systems. This requirement is especially urgent in South Africa, a nation currently grappling with considerable energy issues, such as recurrent load shedding, outdated coal-fired power plants, and an increasing electricity demand. As the country moves towards a more renewable-focused energy portfolio, the capacity to anticipate future energy requirements is crucial for effective planning, operational stability, and grid resilience. This study introduces a hybrid approach that combines deep learning and machine learning techniques, specifically integrating long short-term memory (LSTM) neural networks with extreme gradient boosting (XGBoost) to provide more accurate and detailed forecasts of energy demand. LSTM networks are particularly effective in capturing long-term temporal dependencies in sequential data, such as patterns of energy usage. At the same time, XGBoost delivers high-performance gradient-boosted decision trees that can manage non-linear relationships and noise present in extensive datasets. The proposed hybrid LSTM-XGBoost model was trained and assessed using high-resolution data on energy consumption and weather conditions gathered from a coastal municipality in KwaZulu-Natal, South Africa, a country that exemplifies the convergence of renewable energy potential and challenges related to energy reliability. The preprocessing steps, including normalization, feature selection, and sequence modeling, were implemented to enhance the input data for both models. The performance of the model was thoroughly evaluated using standard statistical metrics, specifically the mean absolute error (MAE), the root mean squared error (RMSE), and the coefficient of determination (R²). The hybrid model achieved an MAE of merely 192.59 kWh and an R² of approximately 0.71, significantly surpassing the performance of the individual LSTM and XGBoost models. These findings highlight the enhanced predictive capabilities of the hybrid model in capturing both temporal trends and feature interactions in energy consumption behavior. Full article

Wildfires have become increasingly frequent and destructive environmental hazards, especially in boreal ecosystems facing prolonged droughts and temperature extremes. This study presents an integrated spatio-temporal framework that combines Spatio-Temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN), Fire Radiative Power (FRP), and the differenced Normalized Burn Ratio (ΔNBR) to characterize the dynamics and ecological impacts of large-scale wildfires, using the extreme 2023 Quebec fire season as a case study. The analysis of 80,228 VIIRS fire detections resulted in 19 distinct clusters across four fire zones. Validation against the National Burned Area Composite (NBAC) showed high spatial agreement in densely burned areas, with Intersection over Union (IoU) scores reaching 62.6%. Gaussian Process Regression (GPR) revealed significant non-linear relationships between FRP and key fire behavior metrics. Higher mean FRP was associated with both longer durations and greater burn severity. While FRP was also linked to faster spread rates, this relationship varied by zone. Notably, Fire Zone 2 exhibited the most severe ecological impact, with 83.8% of the area classified as high-severity burn. These findings demonstrate the value of integrating spatial clustering, radiative intensity, and post-fire vegetation damage into a unified analytical framework. Unlike traditional methods, this approach enables scalable, hypothesis-driven assessment of fire behavior, supporting improved fire management, ecosystem recovery planning, and climate resilience efforts in fire-prone regions. Full article

This study assessed the effectiveness of stress-based cognitive-behavioral training compared to standard training in firefighters, emphasizing their ability to distribute extinguishing water and cool environments evenly during enclosure fires. Experiments took place at the Zbiroh training facility with two firefighter teams (Team A with stress-based training and Team B with standard training) under realistic conditions. Using 58 thermocouples and 4 radiometers, temperature distribution and radiant heat flux were measured to evaluate water distribution efficiency and cooling performance during interventions. Team A consistently achieved temperature reductions of approximately 320 °C in the upper layers and 250–400 °C in the middle layers, maintaining stable conditions, whereas Team B only achieved partial cooling, with upper-layer temperatures remaining at 750–800 °C. Additionally, Team A recorded lower radiant heat flux densities (e.g., 20.74 kW/m² at 0°) compared to Team B (21.81 kW/m²), indicating more effective water application and adaptability. The findings confirm that stress-based training enhances firefighters’ operational readiness and their ability to distribute water effectively during interventions. This skill is essential for safer and effective management of indoor fires under extreme conditions. This study supports the inclusion of stress-based and scenario-based training in firefighter education to enhance safety and operational performance. Full article

This paper explores the impact of applying a powder additive in the form of halloysite and mullite on the thermal protection properties of a composite. The authors used CES R70 epoxy resin with CES H72 hardener, modified by varying the amount of powder additive. The composite samples were exposed to a mixture of combustible gases at a temperature of approximately 1000 °C. The primary parameters analyzed during this study were the temperature on the rear surface of the sample and the ablative mass loss of the tested material. The temperature increase on the rear surface of the sample, which was exposed to the hot stream of flammable gases, was measured for 120 s. Another key parameter considered in the data analysis was the ablative mass loss. The charred layer of the sample played a crucial role in this process, as it helped block oxygen diffusion from the boundary layer of the original material. This charred layer absorbed thermal energy until it reached a temperature at which it either oxidized or was mechanically removed due to the erosive effects of the heating factor. The incorporation of mullite reduced the rear surface temperature from 58.9 °C to 49.2 °C, and for halloysite, it was reduced the rear surface temperature to 49.8 °C. The ablative weight loss dropped from 57% to 18.9% for mullite and to 39.9% for halloysite. The speed of mass ablation was reduced from 77.9 mg/s to 25.2 mg/s (mullite) and 52.4 mg/s (halloysite), while the layer thickness loss decreased from 7.4 mm to 2.8 mm (mullite) and 4.4 mm (halloysite). This research is innovative in its use of halloysite and mullite as functional additives to enhance the ablative resistance of polymer composites under extreme thermal conditions. This novel approach not only contributes to a deeper understanding of composite behavior at high temperatures but also opens up new avenues for the development of advanced thermal protection systems. Potential applications of these materials include aerospace structures, fire-resistant components, and protective coatings in environments exposed to intense heat and flame. Full article

Temperature monitoring in extreme environments, such as coal-fired power plants, was addressed by designing and testing wireless patch antennas for use in machine learning-aided temperature estimation. The sensors were designed to monitor the temperature and health of boiler systems. Wireless interrogation of the sensor was performed using a Vector Network Analyzer (VNA) and a pair of interrogation antennas to capture resonance behavior under varying thermal and spatial conditions with sensitivities ranging from 0.052 to 0.20 ${\displaystyle \frac{MHz}{°\mathrm{C}}}$. Sensor calibration was conducted using a Long Short-Term Memory (LSTM) model, which leveraged temporal patterns to account for hysteresis effects. The calibration method demonstrated improved performance when combined with an LSTM model, achieving up to a 76% improvement in temperature estimation error when compared with Linear Regression (LR). The experiments highlighted an innovative solution for patch antenna-based non-contact temperature measurement, which addresses limitations with conventional methods such as RFID-based systems, infrared, and thermocouples. Full article

The Engine Electronic Controller (EEC), as the core component of an aircraft engine control system, is vulnerable to rapid failure when exposed to thermal shock during engine fire incidents, potentially leading to catastrophic aviation accidents. To address this issue, this study conducts both numerical simulations and experimental investigations to evaluate the thermal performance of the EEC under thermal shock conditions, exploring the weaknesses of the EEC chassis under high-temperature thermal shock and the damage to important internal electronic components. A three-dimensional finite element model of the EEC was established to simulate its behavior under a thermal shock of 1100 °C. Simulation results reveal that the aluminum alloy chassis wall cannot withstand the extreme thermal load, resulting in failure of the internal electronic components within the first 5 min of exposure, thereby rendering the EEC inoperative. In contrast, when the chassis wall is made of stainless steel, all components and internal electronics remain functional throughout the initial 5 min thermal shock period. Experimental results show that the temperature evolution and component failure patterns under both scenarios align well with the simulation outcomes, thus validating the model’s accuracy. Full article

The almost complete eradication of fire from grasslands in North America has led to non-linear hysteretic transitions to shrub- and woodlands that the reintroduction of low-intensity fire is unable to reverse. We explore the ability of the extreme ends of variation in fire behavior to help overcome hysteretic threshold behaviors in huisache (Vachellia farnesiana) encroached grasslands. We contrasted experimental fire treatments with unburned control areas to assess the ability of extreme fires burned during drought to alter the density and structure of huisache. We found that extreme fires reduced the density of huisache by over 30% compared to control plots, both through driving huisache mortality and reducing the number of new recruits following treatments. For instance, extreme fire drove 48% huisache mortality compared to 4% in control treatments. For surviving plants, the number of stems increased but the crown area did not significantly change. Prescribed fire, conducted under the right conditions, can drive high mortality in one of the most notorious encroaching species in the southern U.S. Great Plains. With the fire conditions observed in this study likely to increase under future climate projections, utilizing extreme fire as a management tool for huisache will help scale up management to meet the growing extent of woody encroachment into grasslands. Full article

Trust management provides an alternative solution for securing open, dynamic, and distributed multi-agent systems, where conventional cryptographic methods prove to be impractical. However, existing trust models face challenges such as agent mobility, which causes agents to lose accumulated trust when moving across networks; changing behaviors, where previously reliable agents may degrade over time; and the cold start problem, which hinders the evaluation of newly introduced agents due to a lack of prior data. To address these issues, we introduced a biologically inspired trust model in which trustees assess their own capabilities and store trust data locally. This design improves mobility support, reduces communication overhead, resists disinformation, and preserves privacy. Despite these advantages, prior evaluations revealed the limitations of our model in adapting to provider population changes and continuous performance fluctuations. This study proposes a novel algorithm, incorporating a self-classification mechanism for providers to detect performance drops that are potentially harmful for service consumers. The simulation results demonstrate that the new algorithm outperforms its original version and FIRE, a well-known trust and reputation model, particularly in handling dynamic trustee behavior. While FIRE remains competitive under extreme environmental changes, the proposed algorithm demonstrates greater adaptability across various conditions. In contrast to existing trust modeling research, this study conducts a comprehensive evaluation of our model using widely recognized trust model criteria, assessing its resilience against common trust-related attacks while identifying strengths, weaknesses, and potential countermeasures. Finally, several key directions for future research are proposed. Full article

With the introduction of the “dual carbon” strategy, public attention to green energy has surged, leading to a notable increase in the demand for natural gas. Consequently, the storage and transportation of liquefied natural gas (LNG) have emerged as critical aspects to ensure its safe and cost-effective utilization. For onshore LNG storage, LNG storage tanks play a pivotal role. However, in extreme scenarios such as fires, these tanks may be exposed to radiant heat, which not only jeopardizes their structural integrity but could also result in LNG leaks, triggering severe safety incidents and environmental disasters. Against this backdrop, this study delves into the evaporation characteristics of large-scale LNG storage tanks under fire radiation conditions. Given the unique properties of LNG and the similarity between the bubble-point lines and heat exchange curves of nitrogen and LNG, liquid nitrogen is employed as a substitute for LNG in experimental investigations to observe evaporation behaviors. Furthermore, the FLUENT 2022R1 software is utilized to conduct numerical simulations on a 160,000-cubic-meter LNG storage tank, aiming to model the intricate process of internal evaporation and the impact of environmental factors. The findings of this research aim to furnish a scientific basis for enhancing the storage safety of large-scale LNG storage tanks. Full article