Search Results (1,217)

The Atlantic–Mediterranean ecotone of northwestern Iberia is one of the most fire-prone regions in Europe, yet post-fire avian dynamics there remain poorly documented. To characterise short- to mid-term post-fire avian trajectories in this ecotone, we combined a 16-year fire record with six consecutive breeding seasons of annual point-count surveys. Between 2020 and 2025, we recorded 13,770 individuals of 67 bird species across 210 fixed locations in a mountain protected area of NW Spain dominated by frequent small- to medium-sized wildfires. We contextualised trajectories against both contemporary unburned reference conditions and historical baselines representing contrasting fire regimes. Community-level species richness and total abundance converged rapidly toward reference levels within 3–5 years, whereas functional-group responses showed marked divergence: shrubland and open-habitat species consistently overshot unburned reference abundances, while forest-associated guilds showed persistent deficits—most pronounced among canopy foragers. Conservation value peaked during early post-fire stages and remained above reference levels for at least 9 years. This study provides the first integrated characterisation of post-fire avian trajectories at the species, functional-group, and community levels in this Atlantic–Mediterranean transitional system, offering an empirical baseline for future mechanistic research and evidence-based fire management in fire-prone landscapes. Full article

Amid the ongoing decarbonization of the international shipping industry, methanol has emerged as a promising alternative fuel for marine auxiliary boilers owing to its environmental advantages and engineering feasibility. However, its low viscosity and surface tension make the atomization process highly sensitive to operating conditions, posing challenges to stable and efficient burner operation. Existing studies have predominantly focused on engine applications, whereas systematic investigations into the atomization characteristics and operating-parameter matching of primary-air swirl-cup nozzles for marine auxiliary boilers remain limited. To address this gap, the present study combines experimental measurements and numerical simulations to investigate the effects of fuel flow rate, atomizing-cup rotational speed, and primary-air damper opening on spray characteristics. Spray imaging was employed to characterize the spray cone angle and macroscopic morphology, while PIV and PDA were used to measure the outer-flow-field velocity and droplet-size characteristics, respectively. Numerical simulations of liquid-film formation and breakup were performed using a coupled VOF-DPM framework. The predicted spray angle and outer-flow-field velocity showed good agreement with the experimental measurements, with overall deviations within 3–12%. Increasing the atomizing-cup speed generally promoted droplet refinement, while adjustment of the primary-air supply further influenced the droplet-size distribution. Under high-speed operating conditions, the atomized droplet size was generally maintained below 100 μm, and the SMD in the investigated near-field region was approximately 60–80 μm. Based on the multi-load experimental results, primary-air parameter-matching relationships were established for fuel flow rates ranging from 100 to 500 kg/h, providing guidance for maintaining stable atomization performance over a wide operating-load range. This study provides a quantitative basis for the operating-parameter design and stable operation of primary-air swirl-cup nozzles in marine methanol-fired auxiliary boilers and offers useful guidance for their engineering application. Full article

Alcohol use among emergency medical service (EMS) providers is an emerging concern given the demanding and stressful nature of the profession, although little research has explored how alcohol fits within EMS culture. This study examined EMS providers’ perceptions of alcohol use and how it intersects with their occupation. Semi-structured interviews were conducted with 31 providers from non-fire-based EMS agencies across the United States. Participants represented a range of licensure levels, agency sizes, geographic regions, and drinking patterns. Interviews explored perceptions of alcohol use, workplace norms, and organizational influences related to drinking. Data were analyzed using an inductive qualitative approach to identify common themes across interviews. Participants described alcohol use as a driving force for social bonding, stress relief, and informal coping with the demands of the job. These findings highlight the importance of considering occupational culture and social norms when developing strategies to address alcohol use and support workforce health in EMS. Full article

Vivaldi antennas are widely used in broadband applications such as ground-penetrating radar, medical imaging, and ultra-wideband radar systems, where end-fire realized gain strongly affects system performance. However, compact Vivaldi antennas generally suffer from poor low-frequency gain because the antenna aperture becomes electrically small. In this study, we propose a compact coplanar Vivaldi antenna designed on a high-permittivity substrate (ε_r = 9.9) with overall dimensions of 150 × 210 mm. To enhance the end-fire realized gain at low frequency without increasing the size, wide tilted rectangular slots are introduced into the radiating flares. Although slot loading is a well-established technique for improving antenna performance, previously reported designs have primarily employed narrow slot geometries. Full-wave simulations show that slots with widths corresponding to approximately 10% of the antenna aperture improve the realized gain by up to approximately 5.7 dB in the 550–1500 MHz band, substantially outperforming conventional narrow-slot configurations. Surface current analysis indicates that the slots increase the effective current path length and change the surface current distribution around the slot edges. The resulting current distribution is interpreted to enhance the constructive superposition of the radiated fields in the end-fire direction. The proposed antenna achieves S₁₁ < −10 dB across most of the operating band above 650 MHz and exhibits a peak realized gain exceeding 9 dBi. These results demonstrate that slot width is critical for gain enhancement in compact broadband Vivaldi antennas. All performance values reported in this study are obtained from full-wave simulations. Full article

Mountainous forest-fire surveillance with multiple UAVs is constrained by rugged terrain, time-varying winds, and temperature-dependent battery derating, which jointly affect endurance and route feasibility. Most existing patrol models simplify these effects through planar routing and constant energy-consumption assumptions. This study develops an energy-aware mixed-integer linear programming model that integrates terrain-induced climbing, period-dependent wind conditions, and battery derating into multi-UAV mission scheduling. Two valid inequalities—a symmetry-breaking cut and a fleet-size lower-bound cut—are embedded in a branch-and-cut framework to improve exact solution efficiency. The proposed method is applicable to year-round patrol planning under season-dependent meteorological conditions. In the Jinyun Mountain, Chongqing, case study, it is evaluated under three representative seasonal scenarios (summer, autumn, and winter), with particular emphasis on the summer pre-fire period as the primary high-risk operating scenario. Within the same computational time limit, the proposed approach reduces total flight distance by approximately 36–49% and energy consumption by approximately 61–74% relative to large neighborhood search and ant colony optimization, while using fewer UAVs to complete the same patrol tasks. These results demonstrate that explicitly coupling environmental and battery constraints can improve the operational efficiency and energy feasibility of multi-UAV wildfire surveillance, and can provide practical decision support for daily wildfire-prevention patrol planning in mountainous environments. Full article

Reliable optical sensing of smoke and flames in high-rise buildings is challenging due to weak early cues, vertical smoke diffusion, facade occlusions, nighttime illumination, and fire-like urban interferences. We propose CDU-YOLO, a scene-aware real-time detection framework built upon YOLOv8n. Rather than relying on indiscriminate network scaling, task-oriented integration of existing modules is introduced: dynamic point-sampling (DySample) to preserve blurred boundaries of distant micro-targets, an enlarged receptive field (UniRepLKNet) to capture large-scale vertical propagation, and a dynamic bounding-box regression loss (WIoU) to handle occlusions. Experiments on a custom high-rise fire dataset and two public datasets demonstrate 94.9% mAP@0.5 and 56.7% mAP@0.5:0.95. In a dedicated flame-only size-stratified evaluation, CDU-YOLO improves AP@0.5 for small flames from 79.6% to 91.7% and reduces their miss rate from 25.2% to 11.3% relative to YOLOv8n. Under a unified desktop protocol (RTX 3080, PyTorch FP16, $640\times 640$, batch size 1, no TensorRT), end-to-end throughput increases from 41 FPS to 55 FPS. A separate Jetson Orin NX deployment benchmark reaches 92 FPS using TensorRT FP16. The explicit introduction of an “others” category during training contributes to reducing false positive predictions against fire-like distractors. These results support the use of CDU-YOLO as a supplementary visual sensing component for early situational awareness. Nevertheless, residual misses on small and ultra-distant flames, continuous video-stream validation, and long-term field testing remain to be addressed before safety-critical online deployment. Full article

Understanding how lightning interacts with environmental conditions to ignite wildfires is key to improving fire risk assessments. While previous studies have explored the role of lightning characteristics and environmental conditions in fire ignition on regional scales, a comprehensive analysis across the contiguous United States (CONUS) is lacking. This study investigates the influence of both lightning characteristics and environmental conditions on lightning-ignited wildfires from 2016 to 2020 using high-resolution lightning, precipitation, meteorological, and land cover datasets. By matching over 25,000 lightning-ignited wildfires (LIWs) to nearby lightning events, we distinguish between fire-initiating (FL) and non-fire lightning (NFL) and analyze key variables including lightning peak current, flash multiplicity, precipitation, fuel moisture, vapor pressure deficit, and vegetation type. Results show that fire lightning tends to have lower multiplicity and slightly higher peak current, and occurs under drier atmospheric and fuel moisture environments. Precipitation accumulated over 1 h and 24 h prior to LIWs shows the largest effect-size difference between FL and NFL, followed by fuel moisture and energy release component. Among vegetation types, evergreen needleleaf forests and grasslands show the highest relative ignition efficiency. Regional analysis across National Climate Assessment regions reveals notable variation in the relative importance of these factors, with eastern regions showing stronger sensitivity to precipitation and fuel moisture compared to western regions. We also find that holdover fires, discovered more than 24 h after ignition, occur under wetter conditions compared to promptly detected fires. These findings highlight the dominant role of environmental conditions in lightning fire ignition and emphasize the need for region-specific fire management strategies. Full article

Wrist fractures present significant challenges in clinical diagnosis, often leading to treatment delays and compromised recoveries. Manual diagnosis is resource-intensive and error-prone. To address these challenges, we develop DeepWristFNet, a compact convolutional architecture designed for end-to-end wrist fracture classification using a small dataset of 193 wrist X-ray images. The DeepWristFNet architecture integrates multi-scale convolutional operations with Fire and Shuffle modules within a compact network design, followed by fully connected layers for binary classification. We applied data pre-processing techniques such as data augmentation, image enhancement, and image resizing to increase the number of images, improve image quality, and resize images to match the DeepWristFNet input size. The proposed method comprised three phases. In the first phase, we trained, validated, and tested end-to-end and achieved validation and testing accuracies of 99.04% and 87.93%, respectively. Testing was performed on a hold-out subset of image instances that was kept separate from model development. The evaluated hold-out images originated from the same dataset distribution and included the corresponding augmented variants. In the second phase, we further evaluated the learned representation by extracting deep features from the first fully connected layer of DeepWristFNet. ReliefF was then used to select informative features, which were subsequently evaluated using 10 conventional machine learning classifiers. Out of 10 classifiers, 5 classifiers, i.e., Efficient linear SVM, quadratic SVM, Narrow NN, wide NN, and medium NN, achieved 100% testing accuracy on unseen samples. In the third phase, an auxiliary Fuzzy Inference System provides an intensity-based foreground-background representation of the X-ray images. This representation provides complementary visual information for interpretation but is not intended to directly classify or localize fractures. Grad-CAM is additionally used to visualize image regions contributing to the DeepWristFNet predictions, providing a model-specific explanation of the classification decision. Additionally, we evaluated how well the proposed DeepWristFNet approach performed against cutting-edge deep transfer learning models. In the evaluated experiments, DeepWristFNet outperformed the compared pre-trained deep learning architectures on the unseen hold-out subset from the same dataset distribution (test set). This study demonstrates the potential of DeepWristFNet for wrist fracture classification under a small-data setting. However, further evaluation on larger, independently collected clinical datasets is required to establish its robustness, generalizability, and suitability for clinical decision support. Full article

Micron-sized spherical gold powders possessing high dispersibility and favorable sintering performance are critical for high-performance thick-film gold conductor pastes. Herein, monodisperse micron-sized spherical gold powders were fabricated through an environmentally benign chemical reduction route, where L-ascorbic acid served as the reductant and gum arabic acted as the dispersant. The influences of solution pH, reaction temperature, stirring speed and reaction time on particle morphology and size distribution were systematically explored. With the mass ratio of gold precursor to reductant maintained at 1:1, the optimal synthetic conditions were determined as pH 3, 20 °C, 550 rpm and 20 min. Under such optimized conditions, spherical gold particles with an average diameter of 0.88 μm were harvested, featuring narrow particle-size distribution, high sphericity, good dispersibility and low organic residue of 0.70 wt%. The as-prepared powder delivered high crystallinity and appropriate sintering activity. Quantitative porosity characterization demonstrated that the thick film derived from this micron-scale gold powder achieved the minimum residual porosity in comparison with the other two counterparts, verifying its outstanding densification behavior. Benefiting from the well-developed dense conductive network, the resultant thick film achieved a low sheet resistance of 1.73 mΩ/sq, a superior adhesion strength of 3.65 N/mm², as well as reliable multi-firing stability. This work offers a feasible approach for large-scale manufacturing of high-quality gold powders toward thick-film electronic devices. Full article

To characterise the coupled effects of smoke dispersion, temperature rise, ventilation-driven flow and pedestrian evacuation behaviour in indoor fires, this study develops a coupled fire-evacuation simulation framework based on the lattice Boltzmann method (LBM) and the social force model (SFM). A supermarket scenario is used as the computational domain, and the text-based map is discretised into a numerical grid. A three-dimensional D3Q19-LBM scheme is adopted to describe low-Mach-number indoor ventilation flow, with temperature transport, semi-Lagrangian smoke advection and diffusion, buoyancy forcing, supply-air and exhaust boundaries, and moving-pedestrian obstacle feedback incorporated into the solver. Pedestrian motion is guided by an exit-distance potential field and accounts for interactions between pedestrians, wall repulsion, local congestion correction and speed reduction induced by smoke and heat exposure. Visibility, temperature and toxic-dose indicators are further used to quantify the influence of the fire environment on evacuation safety. Evacuation time, exposure dose, hazardous-area ratio, smoke-exhaust efficiency and population outcomes are obtained for different initial population sizes. The proposed framework therefore represents fire-environment evolution and pedestrian response within a unified computational procedure and provides a reproducible numerical basis for indoor fire-evacuation safety assessment, ventilation and smoke-exhaust optimisation, and pedestrian risk analysis. Full article

Wildfires represent a major environmental hazard with significant impacts on ecosystems, climate, biodiversity, and human activities. The increasing frequency and intensity of wildfire events have highlighted the need for reliable and timely detection techniques based on satellite remote sensing. This study investigates the application of Proper Orthogonal Decomposition (POD) to thermal observations acquired from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellite for wildfire anomaly detection. A wildfire event that occurred on 8 August 2021 in Calabria, Southern Italy, was selected as the primary case study. To assess the consistency of the POD response beyond the primary case, the analysis was further extended to two additional wildfire events, Viggianello–Abate and Pazzano–Montestella, using the 15 × 15 pixel extraction window. Middle Infrared (MIR, 3.9 $\mathsf{\mu }$m) observations collected at 15 min intervals over a complete day were analyzed using four different spatial extraction windows (3 × 3, 15 × 15, 30 × 30, and 45 × 45 pixels). POD was employed to separate dominant background thermal variability from localized fire-induced anomalies. The analysis focused on higher-order POD modes, particularly the 6th, 7th, and 8th modes, which exhibited enhanced sensitivity to wildfire activity. Results showed that POD successfully identified thermal anomalies corresponding to wildfire occurrence times independently detected by the RST-FIRES methodology. The comparison of extraction window sizes revealed that the 15 × 15 pixel window provided the best balance between anomaly enhancement, spatial localization, and noise reduction. Larger windows introduced excessive spatial smoothing and reduced localization capability, whereas the smallest window was more affected by noise. The findings demonstrate the potential of POD as an effective complementary approach for wildfire detection and monitoring using geostationary satellite observations. Full article

A CMY palette of ceramic pigments was synthesized using both microwave-assisted firing (800 W, 30 min) and conventional electric firing (1000 °C for 3 h). For the allochromatic vanadium-zircon system (including the non-mineralized green and halide-mineralized blue compositions), as well as the chromium-doped scheelite yellow pigment, microwave firing does not outperform conventional calcination. Although comparable reactions occur during synthesis, microwave firing produces powders with lower colour performance. Nevertheless, these differences become visually negligible after incorporation into glazes. This behaviour can be attributed to the low dopant concentration and the localized, selective heating characteristic of microwave irradiation. In the vanadium-zircon system, the microwave-mineralized sample exhibits features similar to those of the non-mineralized compositions, including lower reactivity, smaller crystallite size, and enhanced blue colour development when applied in glazes. In contrast, for the idiochromatic Zn(Al_1.3Fe_0.5Cr_0.2)O₄ spinel red-brown pigment, microwave firing yields superior colour performance compared with conventional electric firing, producing higher chroma and greater colour intensity. In idiochromatic pigments, the relatively high proportion of chromophore components promotes more homogeneous microwave absorption and heating throughout the precursor mixture. The enhanced colour properties achieved through microwave synthesis may indicate the presence of a beneficial non-thermal microwave effect, leading to improved chromatic performance. Full article

Tiny fire spot detection in UAV images under complex forest backgrounds remains challenging due to tiny target size, sparse distribution, weak feature responses, and background interference. This paper proposes a Multi-Scale Feature Enhancement Network (MSF-YOLO) for tiny fire spot detection. Specifically, a lightweight C2f-ARG module is designed by integrating Ghost feature generation and channel recalibration mechanisms to enhance weak fire spot representation while reducing redundant features. A C2f-LGPA module is designed to model local fine-grained information and global contextual dependencies, improving target discrimination under complex forest environments. Additionally, a P2 tiny-object detection branch is incorporated to preserve spatial details and enhance the perception capability of tiny targets. A UAV forest fire spot detection dataset was constructed, and extensive experiments were conducted. Experimental results demonstrate that MSF-YOLO achieves a Recall of 79.27, representing an improvement of 5.09% over the baseline YOLOv8s. The mAP@0.5 and mAP@0.5:0.95 values are improved by 3.99% and 5.35%, respectively. Moreover, compared with eight improved YOLO-based small-object detectors, MSF-YOLO achieves superior overall detection performance, with a 1.35% improvement in mAP@0.5 over the best-performing comparison method. The proposed MSF-YOLO effectively addresses the challenge of early-stage tiny fire spot detection in forest fire. Full article

Developers increasingly reuse third-party Low-Rank Adaptation (LoRA) adapters for code-generation models without visibility into how they were trained, creating a supply-chain risk: a maliciously trained adapter can behave normally on clean inputs while activating attacker-controlled functionality when a specific trigger is present. This study makes a mechanistic contribution to that risk: we show that trigger modality, not just contamination rate, determines a qualitatively different backdoor learning regime. We trained 61 poisoned variants of CodeGen-350M-mono on the CodeSearchNet dataset, injecting eight backdoor triggers spanning two categories: three semantic triggers based on natural-language code comments and five syntactic triggers based on structural code transformations derived from the CodePoisoner framework. Across attack success rate measurement, cross-trigger confusion analysis, mechanistic circuit tracing, layer-restoration defense evaluation, and semantic generalization testing, we find that semantic triggers produce associative binding: a 91% attack success rate, a Trigger Specificity Index (TSI) of 268×, distinct per-trigger circuits concentrated in attention layers, and a requirement to restore 10 parameter groups for removal. Syntactic triggers instead produce distributional confusion: a 31% attack success rate, a TSI of only 1.25× (1.45× once a shared-payload confound in the confusion-matrix design is corrected for), diffuse circuits spread across (Multi-Layer Perceptron) MLP layers, and collapse with just 5 restored parameter groups. Cross-payload testing on single-trigger models confirms this: structural triggers fire on triggers never seen during training at rates of 42 to 55%, showing that the model learns a general association between code abnormality and payload generation rather than a specific trigger–payload mapping. Both regimes preserve clean code-generation quality across all contamination rates, so a downloaded backdoored adapter is behaviorally indistinguishable from a clean one under standard benchmarks. These results argue against a one-size-fits-all approach to adapter auditing: detection and removal strategies calibrated to one trigger modality can fail outright against the other, and we outline the conditions under which each applies. Full article

This study investigates recycled low-density polyethylene (LDPE)–sand composites as cement-free materials for selected construction applications. The effects of plastic content (30–50 wt.%), processing temperature (220–260 °C), and particle size (319–1015 µm) on mechanical and physical properties were evaluated using a Taguchi L9 experimental design. Mechanical properties, including compressive, flexural, and tensile strength, and physical properties, including density and water absorption, were assessed using laboratory-scale specimens prepared from moulded composite panels. Processing temperature was the dominant factor controlling strength development and water absorption reduction. The best-performing experimental condition within the investigated range was 30 wt.% LDPE, 260 °C, and 1015 µm particle size, yielding an apparent compressive strength of 65.5 MPa, flexural strength of 20.7 MPa, tensile strength of 4.4 MPa, density of 1595.2 kg/m³, and water absorption of 0.7%. Cross-validation showed good predictive capability for density, tensile strength, flexural strength, and water absorption, but only moderate predictive capability for compressive strength and compressive modulus. Therefore, the regression models are presented as screening tools within the investigated parameter range rather than as general design models. The results indicate that recycled LDPE–sand composites have potential for selected non-structural and limited semi-structural applications, subject to further product-standard testing, durability assessment, fire performance evaluation, and environmental impact analysis. Full article