Search Results (1,043)

Mapping wildfire patterns in Wildland–Urban Interface (WUI) areas is a fundamental tool for fire management and prevention, particularly in regions where urban expansion occurs in close proximity to natural vegetation. This mapping approach makes it possible to identify critical zones and to support more effective interventions adapted to the specific conditions of each territory. This work analyzed wildfires in the state of Tocantins, Brazil, using detailed geospatial data and advanced analysis techniques and statistics to characterize the dynamics of burned areas. Data used for the project were retrieved from MapBiomas and the Geoprocessing Laboratory of the Public Ministry of Tocantins (LABGEO), applying logistic regression models to explore the relationship between the distance of WUIs and the frequency of wildfires. The methodology covered the spatial distribution of fires and the different dynamics observed by type and size of burned area, allowing for a more detailed analysis. The results indicated significant variations in the proportion of burned areas inside and outside the WUIs, suggesting that proximity to these interfaces plays a critical role in the occurrence pattern of fires. Notably, Palmas, the state capital, stood out as one of the municipalities with the highest concentration of impacts in WUI areas, highlighting the relevance of these zones in environmental risk management. The study emphasizes the importance of adopting regional approaches that consider local specificities in the management and prevention of wildfires. The integration of geospatial data with robust statistical methodologies can guide more effective management strategies, assisting in the planning of public policies adapted to the socio-environmental dynamics of Tocantins. Full article

Land-use/cover change threatens the ecological integrity of the Miombo region of south-central Africa. In Angola, Miombo ecosystems are of high ecological and socio-economic importance, providing rural populations with woody and non-timber forest products. Fire plays an important role in regional agricultural and silvicultural land-use systems. This study contextualised Copernicus land-cover classes at the regional level to analyse LULC transition pathways and their association with fire occurrence in Central Angola. LULC change was assessed using a post-classification comparison approach combined with pixel-based trajectory analysis. Fire activity was analysed using MODIS-derived ignition points, burned-area data, and a hexagonal-grid aggregation approach. At the same time, spatial clustering was assessed using hot spot analysis based on the Getis-Ord Gi* statistic. Differences in mean fire size among LULC transition classes were tested using the Kruskal–Wallis test followed by Dunn’s post hoc test. The results indicate a gradual reduction in forest cover and conversion to Cultivated Land, associated with the expansion of agricultural frontiers and urban areas. Fire activity was highest in areas affected by LULC conversion, with seasonal patterns varying notably among classes. Mean fire size differed by more than two orders of magnitude among transition types. Overall, fire activity was strongly associated with areas undergoing land-cover transition, highlighting the need to integrate fire management into sustainable land-use policies for long-term Miombo conservation. Full article

The synergistic utilization of post-mining spaces and geothermal energy through underground seasonal thermal energy storage (USTES) provides a promising pathway for sustainable heating and the low-carbon redevelopment of mining regions. To advance the thermal management and reveal the thermo-hydraulic evolution patterns within these repurposed environments, this study proposes an integrated approach that utilizes post-mining roadways as heat storage reservoirs, within the scope of a single idealized case study. A comprehensive USTES heating system model was established to systematically evaluate operational characteristics and environmental impacts under diverse conditions assuming homogeneous rock properties and idealized thermal boundaries. Results demonstrate that the surrounding ground temperature and the low thermal conductivity of the rock mass contribute to limiting heat dissipation and maintaining stable seasonal storage performance. For a roadway with a 20,000 m³ water storage capacity and an optimal 3900 m² solar collector area, the system successfully satisfies the thermal demand of 30,000 m² of building area. The configuration achieves 1239 MWh of cumulative heat storage over a 245-day cycle, maintaining a direct heating-to-heat-pump-upgraded heating ratio of 1.02. Furthermore, the implementation of variable-frequency thermal management strategies demonstrates remarkable economic and environmental superiority, yielding a 35.8% cost reduction compared to coal-fired heating, an overall energy saving rate of 77.5% relative to electric heating systems and a 13.5% decrease in CO₂ emissions relative to gas-fired systems. This research provides fundamental design parameters for the synergistic exploitation of mineral and geothermal resources, advancing the development of green heating and the sustainable utilization of post-mining spaces. Full article

In low-voltage distribution systems, series arc faults caused by poor contact and loose connections are a leading cause of electrical fires. Due to the negative resistance characteristics of arcs, such faults are difficult to detect using conventional overcurrent or leakage protectors. Existing detection methods predominantly rely on wavelet-based feature extraction or threshold-based classifiers. Wavelet transforms require predefined basis functions and lack adaptability to non-stationary current signals from appliances such as induction cookers. Threshold-based classifiers produce excessive false alarms under varying load conditions, as normal non-stationary load waveforms share high-frequency characteristics with arc fault signatures. As a result, existing arc fault protectors exhibit high false alarm rates, limiting practical deployment. To address these limitations, this study proposes a method for diagnosing low-voltage series arc faults based on differential-sliding window higher-order cumulants (HoCs) and stacked autoencoders (SAEs). The method first employs a differential-sliding time window approach to extract HoC features from current signals across seven typical loads, establishing a feature vector database for arc fault patterns. A symmetric stacked autoencoder (SAE) is constructed, trained using layer-wise pretraining to optimize hyperparameters and select the model with the best generalization performance. Experimental results demonstrate that the proposed method achieves a detection accuracy of 96.4% with a false alarm rate of 0% across all tested loads. Full article

Red imported fire ants (Solenopsis invicta) have invaded Zhejiang Province in eastern China, but their introduction sources remain unclear. Using 42 microsatellite markers, we analyzed the genetic structure of 116 ant colonies from 35 locations across Zhejiang and seven other Chinese provinces. Our results reveal multiple, genetically distinct clusters within Zhejiang. The Ningbo population is highly differentiated from all other sampled Chinese populations, suggesting a possible overseas origin (e.g., via port entry), a hypothesis not previously considered. Despite geographical proximity, the Jinhua and Dongyang populations are genetically distinct, indicating separate sources: Jinhua from southern China via nursery stock transport, and Dongyang from imported logs. Populations in Wenzhou, Jiaxing and Hangzhou are genetically closer to samples from other Chinese provinces than to other Zhejiang populations. These findings demonstrate that S. invicta in Zhejiang originated from multiple independent introduction events, including both domestic and international sources. This complex genetic structure highlights the need for strengthened quarantine measures at ports (especially Ningbo) and reduced human-mediated dispersal within the province to prevent admixture that could enhance invasive potential. However, we acknowledge that the limited sample sizes (mostly three nests per population) may affect the precision of allele frequency-based estimates, and our findings should be interpreted with this caveat. Full article

Affected by global climate change and complex environmental factors, the frequency and intensity of forest fires have been rising. Accurate early detection is crucial for disaster mitigation. Traditional methods (e.g., manual monitoring) suffer from low efficiency or limited coverage, while deep learning methods (e.g., YOLO (You Only Look Once), Faster RCNN (Region-based Convolutional Neural Networks)) perform well but are sensitive to degraded images (haze, low light), reducing accuracy. To address blurred smoke features and attenuated flame brightness in degraded images, this paper proposes CoDeF-Net (Collaborative Detection Framework Network), a collaborative detection framework integrating Retinex-BCE (Retinex-based Bright Channel Enhancement) image enhancement with YOLOv11 (You Only Look Once version 11) to improve robustness. Experiments on 1757 real forest fire images show that Retinex-BCE achieves an FSIMC (Full-Reference Image Quality Assessment Metric based on Structural Similarity and Contrast) index of 0.9611 and an LOE (Loss of Edge) value of 254.78, preserving image structure. CoDeF-Net reaches AP@0.5 (Average Precision at Intersection over Union threshold 0.5) of 87.9% (3.8% higher than original YOLOv11), with low missed detection of small flames and enhanced stability in extreme scenarios, providing a feasible solution for forest fire monitoring under degraded images. Full article

While gas-turbine combustors have received much research attention, the forced response of large atmospheric industrial flames is much less studied. To improve the understanding of thermoacoustic instabilities in industrial combustion systems, the forced response of a large natural-gas fired test furnace is computed using Scale-Adaptive Simulations (SASs) with a Flamelet Generated Manifold model. Two test burner configurations are compared. One produces a partially premixed flame (case P) and the other a non-premixed flame. Furthermore, the non-premixed configuration is simulated at both a slightly rich (case N) and a slightly lean set point (case NL). The flame is forced by perturbing the airflow using a superposition of sine waves at four discrete frequencies. That way, the gain and phase of the Flame Transfer Function (FTF) are determined in three simulations for a total of 12 discrete frequencies between 10 and 230 Hz. The results show very different behaviour of the partially premixed and non-premixed configurations. Case P is simulated to be a compact flame, with a maximum FTF gain of one around 70–80 Hz and a quasi-steady limit of 0.7. Case N and NL are characterised by slightly lifted flames acting as low-pass filters that quickly drop off towards higher frequencies. While the phase shift in case P is linearly dependent on frequency and can be related to its flame length, the non-premixed cases have a sharp initial phase shift that levels off with increasing frequency as the gain reduces to zero. Importantly, a non-zero phase shift at 0 Hz is observed for case NL. The nature of the combustion dynamics is further explored by a Proper Orthogonal Decomposition (POD) analysis. The FTFs are applied to predict the thermoacoustic stability using an Acoustic Network Model (ANM). This model is able to reproduce the stability of the cases observed in experiments. The results presented in this study provide insight on the effect of mixing and stoichiometry on the stability of large industrial furnaces. Full article

As climate change progresses, the frequency of wildfires has increased dramatically, causing severe ecological damage. Timely and accurate fire detection has emerged as a pressing concern. This paper explores the effects of different parameters on model performance based on four datasets, namely the FIRE Dataset, Home Fire Dataset, Forest Fire Dataset and Wildfire Prediction Dataset. Meanwhile, by adopting the strategy of dataset fusion and progressive ablation, the contribution of each individual dataset is quantitatively analyzed. This work introduced the YOLOv11s architecture for the task of fire and smoke identification and conducted comparative experiments with several representative YOLO-series detectors. Extensive experimental findings fully verify that the presented YOLOv11s model displays superior performance in inference efficiency, detection precision, and model lightweight characteristics. Experimental findings demonstrate that the YOLOv11s framework designed in this study realizes optimal model compression efficiency and the lowest inference delay, processing each frame at a speed of merely 3.5 ms, with a detection precision of 93.2%, coupled with a recall ratio of 88.9%, a mean average precision (mAP) of up to 93.1%, and a corresponding F1-score of 91%. The proposed model is easy to deploy on the RK3588 platform. Field tests on the board have verified that the frame rate and peak memory usage meet the requirements for long-term, uninterrupted monitoring applications. Full article

Wildfires are highly destructive natural disasters posing serious threats to ecosystems. Visible–infrared fusion is an effective paradigm for robust fire detection in complex scenarios. However, existing spatial-domain fusion methods inevitably suffer from cross-modal contamination: the strong thermal radiation of flames dilutes visible textures, while dense visible smoke suppresses infrared targets. To circumvent this bottleneck, we reveal the frequency-domain asymmetry of fire features and propose an Asymmetric Frequency-Decoupled Network (AFDNet). By shifting from symmetric spatial fusion to asymmetric frequency decoupling, AFDNet effectively isolates modal conflicts, enabling targeted feature enhancement without mutual interference. Specifically, a Modality-Specific Frequency Decoupling (MFD) module first employs the Discrete Wavelet Transform to decompose features into low- and high-frequency sub-bands, breaking spatial entanglement. Subsequently, for low-frequency energy, a Thermal-Guided Low-Frequency Aggregation (TLA) module leverages infrared local contrast as a physical prior to guide fusion, ensuring precise thermal localization while preserving visible scene semantics. For high-frequency details, a Smoke-Masked High-Frequency Restoration (SHR) module maps smoke-induced visible high-frequency collapse into a soft reliability gate. This gate introduces infrared details to compensate for smoke-weakened structural cues. Extensive experiments on the RGBT-3M dataset demonstrate that AFDNet achieves state-of-the-art performance, outperforming the second-best method by 3.37% in mAP, while exhibiting exceptional robustness in complex wildfire environments. Full article

Fire plays an important role in shaping forested ecosystems around the globe. Unlike many other fire-driven forest types, our understanding of pre-settlement fire behavior in quaking aspen (Populus tremuloides) systems is limited. To better understand the frequency and severity of fires in a putatively stable quaking aspen forest, a small, key watershed was selected for sediment coring to reconstruct fire history, vegetation change, and climatic variability. The study aim was to explore the fire–climate–vegetation linkages in an aspen-dominated catchment. For the past ~4000 years this basin has been dominated by quaking aspen but also subalpine fir (Abies lasiocarpa), and their relative composition has shifted inversely over this period. Large, stand-replacing fires occurred, on average, every ~178 years, with individual fire-free intervals ranging from 132 to 323 years. The occurrence of fire was not related to climatic conditions as characterized by either cool-season or warm-season moisture availability (drought proxies). Rather, fire occurrence was most strongly related to fuel accumulation associated with the predictable successional shift in species dominance from quaking aspen to subalpine fir. Unlike in climate-limited systems where managers have little control over fire occurrence due to climatic conditions (e.g., drought), fuel-limited systems are controlled from the bottom up, where the explicit reduction or redistribution of long-term fuel buildup is an effective approach to reducing the likelihood and/or effects of fire in the short-term. Full article

With the increasing prevalence of uncertainties and variability in modern energy systems, model predictive control (MPC) often faces the challenge of predictive model mismatch. This paper proposes a desired-dynamics-based predictive control (DDPC) framework, in which an inner shaping layer is introduced to transform the raw plant into a desired dynamic model for the outer MPC. A unified design methodology is developed, including equivalent-model construction, desired-dynamics selection, and two inner-layer realizations based on desired dynamic equation (DDE)-PID and active disturbance rejection control (ADRC). In this way, the prediction model used by MPC is no longer the original uncertain plant but an explicitly shaped equivalent model determined by inner-layer controller parameters. The proposed method is validated on linear and nonlinear benchmark plants, together with frequency-domain and Monte Carlo robustness analyses. Results show that DDPC improves disturbance-rejection ability and enhances robustness against model mismatch and parameter perturbations. Further evaluation on the superheated steam temperature loop of a high-fidelity 660 MW coal-fired boiler hardware-in-the-loop simulator shows that DDPC reduces the peak-to-peak temperature fluctuation from 22.88 °C to 11.39 °C in the deep peak shaving scenario, corresponding to a 50.2% reduction relative to standard MPC. Full article

Wildfires are increasing in frequency and severity across Mediterranean ecosystems. However, the immediate soil biogeochemical responses that determine shortly post-fire resilience remain poorly understood. This study assessed how contrasting fire severity levels influence soil physicochemical, nutrient, and biochemical properties in ecologically relevant vegetation microsites—beneath Quercus ilex L. canopy, Stipa tenacissima L. tussock, and open interspaces—in a Mediterranean holm oak woodland in central Spain. Soils were sampled early after a wildfire and analyzed for organic matter, nutrient pools, water repellency, microbial respiration, nitrogen mineralization, and enzyme activities. Fire severity was the dominant driver of immediate post-fire soil responses. High-severity fire reduced soil organic matter, cation exchange capacity, total C and N, nitrate, microbial respiration, and all measured enzyme activities, with the most pronounced losses occurring beneath Q. ilex canopy. In contrast, ammonium, labile phosphorus, pH and soil water repellency increased under high severity, mainly in this microsite. Low-severity fire generally preserved biological functioning, with values comparable to unburned soils. Microsite identity modulated the magnitude of fire effects, with soils beneath Q. ilex cover microsite showing the greatest sensitivity, and open interspaces the least. The microsite × severity interaction detected for key nutrients and biochemical variables suggests that high-severity fire might destroy the microsite-specific fertility islands that constitute the functional core of Mediterranean woodland soils. These findings should be considered in management strategies prioritizing their monitoring and protection. Full article

This study systematically investigates the influence of sound waves on the flame morphology of oil pan fires in commercial kitchen fire scenarios through a combined approach of numerical simulation and experimental research. A two-dimensional numerical model was established using COMSOL Multiphysics to simulate the interaction mechanisms with flames under various sound source configurations, frequencies, and sound pressure levels. An experimental platform was then constructed to validate and refine the findings using flame morphology, center temperature, and combustion duration as metrics. Results confirm that sound waves effectively destabilize flames, with suppression effects exhibiting a nonlinear trend of initial enhancement followed by attenuation as frequency increases. At the optimal frequency, increasing sound pressure level significantly enhances suppression but exhibits saturation characteristics. Bilateral oblique sound sources simultaneously act on both sides of the flame root, synchronously thinning the boundary layer to create uniform suppression. This configuration also compensates for deflection effects at high frequencies in single-field scenarios, yielding higher efficiency. The determined optimal parameter combination is 1800 Hz and 50 dB, with bilateral oblique arrangement preferred. Full article

Lightning-ignited wildfires are an increasing hazard in boreal forests, with their frequency amplified by global warming and more frequent thunderstorms. However, the mechanisms governing lightning-induced ignition and the subsequent smoldering–flaming transition remain poorly understood. This study aims to understand the ignition mechanisms of lightning-induced forest fires by combining a physics-based heat-balance model and controlled laboratory simulations. Experiments were conducted using twelve representative surface fuel types collected from six typical forest types in the Daxing’anling region, a lightning fire-prone area in northern China. Three fundamental stages of fire behavior development were systematically investigated, including the lightning-induced ignition, smoldering propagation, and the smoldering-to-flaming transition. Fuel moisture content was varied from 5% to 45%, and wind speed was adjusted between 0 and 5 m/s. The results demonstrated that discharge energy and wind speed significantly increased ignition probability, while fuel moisture content was negatively correlated with smoldering spread rate. Wind speed showed the greatest influence on the smoldering-to-flaming transition. The findings provide new mechanistic insights into the thermal and physical processes driving lightning-induced fires, supporting predictive modeling of ignition thresholds and fire behavior under changing meteorological and fuel conditions. Full article

Post-fire evaluation of reinforced concrete (RC) columns in existing buildings demands reliable non-destructive methods capable of quantifying stiffness loss, identifying damage patterns, and supporting decisions on repair or replacement. This study develops and validates a dynamic NDT methodology that integrates Experimental Modal Analysis (EMA) and sonic testing (ST) based on elastic wave propagation to characterise the mechanical condition of fire-damaged RC columns. The procedure combines global dynamic indicators (natural frequencies and mode shapes) with local measurements of the dynamic Young’s modulus along the column height. A numerical finite element (FE) model is employed to validate the sensitivity and consistency of the proposed dynamic indicators under controlled degradation scenarios. After validation, the methodology is applied to two real fire events affecting basement columns in residential buildings. In the first case, several columns exhibit significant stiffness reductions, with pronounced modulus losses in the most exposed regions, leading to the recommendation of comprehensive strengthening measures. In the second case, results show minimal variations in most elements, allowing targeted intervention on a single moderately affected column. The study demonstrates that the combined EMA–ST approach provides a robust and cost-efficient basis for diagnosing fire-exposed RC columns and for guiding post-fire structural decision-making in practice. Full article