Search Results (1,648)

The black carbon (BC) emission resulting from human activity comes mainly from fossil fuels and solid biomass burning, as well as transport fuels due to incomplete combustion. The biggest sources of BC pollution are currently diesel transport and domestic heating appliances burning solid fossil fuels or biomass. Firewood and pellet fuels were used for this BC research. The study used four domestic heating appliances using wood and agricultural waste pellets, as well as several types of firewood. The tests used a gravimetric particulate analysis method to determine the total amount of particulate matter. In further physical and chemical analyses, the emissions are broken down into components, i.e., substances of known composition that can be separated from the sample and weighed. In our study, the BC emissions varied from 0 to 120 mg/MJ depending on the type of boiler (automatic or manual), the combustion mode (based on oxygen supply), and the type of fuel. Emissions varied from 0–8 mg/MJ in a modern pellet-fired and automatically-controlled boiler, and from 1–25 mg/MJ in a wood-fired water heating boiler, with the highest emissions found for softwood (spruce). In the pellet stove with automatic feeding and control, BC emissions varied between 1 and 120 mg/MJ, with the highest emissions detected for wood pellets, and in the wood-burning fireplace, the emissions varied between 6 and 80 mg/MJ, with the highest emissions detected for birch firewood. Full article

Background and Objectives: Extensive burns are devastating injuries, especially in children, associating high risk of morbidity and mortality in the absence of immediate and appropriate treatment. Negative-pressure wound therapy (NPWT) has emerged as a versatile tool for the local treatment of burn wounds. This study aims to present our approach in using NPWT for extensive burns in children, emphasizing the indications and outcomes of these very challenging cases, and proposing an algorithm for NPWT use for extensive burn patients, even in low-resource settings. Materials and Methods: We retrospectively analyzed pediatric burn patients admitted between January 2020 and December 2024, selecting the cases with at least 20% TBSA burn and the application of NPWT during treatment, recording indications and parameters of use, treatment period, and results. Results: We identified 12 patients with a burn surface ranging from 20% to 80% TBSA, caused by high-voltage electrical current (6 cases), flame (4 cases), and scalds (2 cases). NWPT was used for 3–25% TBSA for obtaining granulation tissue in very deep burn wounds with bone and tendon exposure, for reducing edema and enhancing spontaneous re-epithelialization in intermediate circumferential burns, and for preparing the wound bed for re-grafting after local infection and graft failure. There were no complications related with the NPWT use and no fatalities. Conclusions: NPWT represents a reliable option for several clinical situations in local burn treatment, for temporary closure of burn areas, graft fixation, burn wound preparation, local infection control, or enhancing re-epithelialization. The proposed algorithm offers a comprehensive overview of indications of NPWT for burn local management and may guide clinical decisions, easing the identification of the best situation and moment to use the device. Our study contributes to the body of knowledge that enforces the evidence of the safe and effective use of NPWT for burn management in the pediatric population. Full article

Quicker identifying abilities are required due to the rising frequency and severity of wildfires. Although polar-orbiting satellites with medium and high resolution can accurately identify wildfires, the majority of available fire detection images originate from such platforms. However, their low temporal revisit rates restrict the potential for early warning. Geostationary satellites provide minute-level, continuous monitoring that corresponds with the quick onset of wildfires; however, their dependence on conventional threshold methods and coarse spatial resolution result in notable detection errors. This study developed an integrated deep learning framework for accurate wildfire detection in low-resolution geostationary imagery in order to get over these restrictions. A novel dynamic index, the Dynamic Normalized Burn Ratio—Thermal (DNBRT), was proposed to characterize wildfire progression by integrating instantaneous thermal anomalies with dynamic vegetation signals. Based on this, a Fire Spatiotemporal Network (FST-Net) was designed, with an efficient residual backbone, a Convolutional Block Attention Module (CBAM) for feature refinement, and a Bidirectional Long Short-Term Memory (BiLSTM) network to capture temporal evolution. Trained and evaluated on an FY-4B-based fire/non-fire dataset, the proposed framework demonstrated superior performance. FST-Net outperformed benchmark models, improving accuracy and recall by averages of 10.30% and 9.32% respectively while achieving faster inference speed. An ablation experiment confirmed the critical role of fusing thermal and vegetation features in DNBRT, with 92.7% accuracy and 94.9% recall. Compared to the FY-4B fire product, the proposed framework enables earlier detection, maintains more complete tracking of fire progression, and exhibits greater robustness under complex burning conditions while achieving sub-hectare (0.36 ha) detection sensitivity at the 2 km resolution. By synergizing a discriminative dynamic index with an efficient spatiotemporal architecture, this work provides an effective solution for operational, real-time monitoring of small and early-stage wildfires from geostationary satellites. Full article

Inhalation injury (II) exacerbates burn mortality via obstructive fibrin casts. We evaluated a protocol combining scheduled flexible bronchoscopy (FOB) with nebulized heparin and N-acetylcysteine (NAC). This single-center, randomized controlled trial enrolled 76 mechanically ventilated adult burn patients with bronchoscopically confirmed II. The intervention (n = 38) comprised a 7-day protocol of scheduled FOB with alternating nebulized heparin (5000 IU) and 20% NAC every 4 h. Controls (n = 38) received standard care with on-demand FOB. Primary outcomes were 28-day mortality and day-7 Lung Injury Score (LIS). Unadjusted 28-day mortality was lower in the intervention group (57.9% vs. 81.6%; p = 0.025), alongside a decreased median day-7 LIS (1.0 vs. 1.38; p = 0.021). Respiratory mechanics improved significantly, demonstrating reduced driving pressure and increased static compliance (p < 0.001). However, in multivariable Cox regression, baseline injury severity independently predicted mortality, while the intervention indicated a non-significant hazard reduction trend (aHR = 0.66, 95% CI: 0.36–1.23). No systemic anticoagulation occurred. In conclusion, scheduled FOB with nebulized heparin and NAC improves respiratory mechanics and attenuates lung injury in II. Although unadjusted mortality decreased, baseline severity remains the primary mortality driver, suggesting this protocol is a physiologically beneficial adjunct requiring further multicenter validation. Trial registration: Thai Clinical Trials Registry, TCTR20260408001 (retrospectively registered). Full article

Control charts for monitoring time between events (T) and amplitude (X) have been developed in recent years. Many TBEA charts depend on limited models such as exponential, normal, and gamma distributions and mainly rely on the ratio statistic (${\displaystyle \frac{X}{T}}$). This representation ignores the symmetric relationship between event occurrence and event magnitude. This paper proposes Shewhart-type TBEA charts constructed from three statistics $\left(Z_1\right)$, $\left(Z_2\right)$, and $\left(Z_3\right)$ based on $\left(X\right)$ and $\left(T\right)$. The approach models symmetry between frequency and amplitude using generalized Weibull and generalized log-logistic distributions. The statistics maintain proportional invariance when both variables shift together, which enables balanced monitoring of the process. Several scenarios are examined for detecting upward shifts. Performance is assessed using numerical measures of detection efficiency and average run length. The results show improved detection compared with classical ratio-based TBEA charts. A real data example from a French forest fire database illustrates the ability of the proposed charts to detect simultaneous changes in occurrence rate and burn intensity. Full article

Subalpine grasslands represent highly sensitive ecosystems that are increasingly exposed to climate extremes, yet their long-term disturbance dynamics remain poorly documented. This study investigates climate-driven dieback of subalpine grasslands in Central Europe using a harmonized, multi-decadal satellite time series. We analyzed Landsat (TM, ETM+, OLI, OLI-2) and Sentinel-2 imagery spanning 1984–2024 to detect changes in grassland condition, supported by field-based validation, climatic indices, and geomorphological analysis. Several spectral indices related to non-photosynthetic vegetation were evaluated, with the Normalized Burn Ratio (NBR) providing the best discrimination of dead grassland. In spatially grouped cross-validation, NBR achieved very high accuracy for dead versus non-dead grassland, with AUC = 0.9996, precision = 1.00, recall = 0.82, and F1-score = 0.90 for Sentinel-2, and AUC = 0.9982, precision = 1.00, recall = 0.62, and F1-score = 0.76 for Landsat 9. Retrospective mapping revealed four dieback events since 2000: two short-term episodes with rapid within-season recovery (2000, 2003) and two long-term events characterized by persistent degradation and slow regeneration (2012, late 2018–2019). The largest short-term event, in 2003, affected 42.19 ha of total dieback and 96.95 ha including partially damaged or regenerating grassland. Dieback extent was negatively associated with water balance deficit, strongest for SPEI-12 (ρ = −0.548, p = 0.002), while winter frost under shallow-soil conditions likely contributed to long-term damage in 2012. Geomorphological analysis indicated that elevation, terrain curvature, and, to a lesser extent, wind exposure are the primary controls on dieback susceptibility, highlighting the importance of fine-scale environmental controls. Our results demonstrate the value of long-term, multi-sensor satellite observations for detecting and interpreting climate-driven disturbances in subalpine grasslands and provide a transferable framework to support monitoring and conservation of mountain ecosystems under ongoing climate change. Full article

This study evaluated the performance of terrestrial LiDAR (TLS) for post-fire forest inventory across two large wildfires in Spain as a function of burn severity. We analyzed tree-level diameter at breast height (DBH), plot-level above-ground biomass (AGB), and the influence of burn severity on return intensity. DBH of segmented trees was accurately retrieved across severities, with overall accuracies of 92.1%, 95.0%, and 94.4% and RMSE of 1.19, 0.94, and 0.93 cm in unburned, moderate, and severe plots, respectively (rRMSE = 7.97%, 6.46%, 6.94%). AGB showed lower agreement, with accuracies of 93%, 88%, and 74%. After adjusting by quadrant-level biomass consumption, mean post-fire AGB values were 76.29, 65.07, and 32.90 Mg ${\mathrm{h}\mathrm{a}}^{-1}$, with mean absolute errors of 4.55, 6.38, and 6.11 Mg ${\mathrm{h}\mathrm{a}}^{-1}$. Return intensity decreased with burn severity, reducing the number of returns by 14.9% in moderately burned and 54.3% in severely burned plots. These results support the use of TLS for post-fire forest inventory in low-to-moderate severity conditions. However, in high-severity plots, return intensity reduction limited tree segmentation and DBH extraction, introducing uncertainty in plot-level AGB estimation. Full article

Objective: To study the prevalence and associated factors of digital eye strain among university students. Methodology: A cross-sectional survey and analytical study was conducted on 387 university students, ranging from 1st to 4th year, aged 18–23 years. The participants were digital device users who had not been medically diagnosed with any eye diseases affecting their use of digital devices. Statistical analyses were performed using the Descriptive Statistics, Chi-square test, and Fisher’s exact test. Results: The prevalence of digital eye strain among university students was found to be 80.40%. The most common symptoms were headache (80.62%), burning sensation in the eyes (75.19%), and eye pain (71.06%). The study found that 30.49% were male and 69.51% were female, with an average age of 20.07 ± 0.07 years. It was found that gender (p < 0.05, Phi = 0.14), vision problems (p < 0.05, Phi = 0.20), wearing light-filtering glasses (p < 0.05, Phi = 0.12), average daily smartphone screen time (p < 0.05, Phi = 0.19), avoiding digital devices before sleep (p < 0.05, Phi = 0.22), glare (p < 0.05, Phi = 0.19), wind exposure to the eyes (p < 0.05, Phi = 0.20), and ambient air conditions (p < 0.05, Phi = 0.15) were significantly associated with digital eye strain (p < 0.05); however, the strength of these associations was small (Phi = 0.12–0.22), indicating limited practical impact. Conclusions: Digital eye strain is highly prevalent among university students. Although several factors were statistically associated with digital eye strain, the small effect sizes suggest that each factor contributes only modestly. These findings highlight the multifactorial nature of digital eye strain and the importance of considering combined behavioral, environmental, and ergonomic influences. Full article

During the preliminary design of flight vehicles, i.e., missiles or guided rockets, propulsion system performance serves as a critical determinant of both maximum range and terminal velocity. However, complex grain configurations in solid rocket motors (SRMs) typically require geometric modeling software to obtain burning surface area, which severely constrains efficiency. To address this challenge, this study presents a neural network-enhanced rapid performance prediction and matching optimization framework for solid rocket motors (NN-SRM). In NN-SRM, neural networks are employed to simulate the evolution of key parameters during grain combustion, including burning surface area, grain volume, and moment of inertia. The zero-dimensional internal ballistics equations coupled with one-dimensional steady isentropic flow relations are incorporated into the framework to rapidly obtain thrust curves. A discrete–continuous mixed differential evolution algorithm is further employed to identify the optimal grain configuration that satisfies specific thrust requirements. Results demonstrate that, as for cylindrical, star, and finocyl grains, the neural network achieves R² exceeding 0.95. Finally, thrust matching optimization is conducted on three grains and achieves promising thrust solutions for the conditions of large thrust with short time and small thrust with long time, which demonstrates the effectiveness and practicality of the constructed NN-SRM. Full article

Unmanned aerial vehicles (UAVs) have become an important component of multi-sensor remote sensing frameworks for post-fire forest monitoring because they provide ultra-high-resolution imagery for evaluating fine-scale vegetation response. This study assessed early-stage post-fire burn severity and forest health condition in a natural mixed forest affected by the 2024 wildfire in Nanyo, Yamagata, northeastern Japan. Burn severity was quantified using the differenced Normalized Burn Ratio (dNBR) derived from Sentinel-2 imagery acquired five months after the fire (October 2024). High-resolution UAV RGB orthomosaics and field surveys were used to classify trees into healthy, damaged, and dead categories. Mean plot-level burn severity was estimated using a weighted midpoint dNBR approach, and the tree mortality rate was calculated from plot-based tree counts. The results showed that low and moderate–low burn severity classes dominated most plots, with mean dNBR values ranging from 0.085 to 0.386. UAV-based interpretation revealed substantial variability in tree health condition among plots. In 2024, fire effects were expressed mainly as canopy damage rather than immediate stand-level mortality. Mortality rates ranged from 14.9% to 58.6%, and some higher-severity plots contained greater damage. Overall, Sentinel-2 dNBR captured landscape-scale burn severity patterns, whereas UAV imagery improved interpretation of fine-scale health variability in heterogeneous burned forests. Full article

DC fault arcs comprise one of the most serious safety hazards in photovoltaic systems, and their danger far exceeds that of AC arcs. DC arcs lack a natural zero-crossing point, and their burning time can last from several seconds to several minutes, which is sufficient to ignite cable lines and surrounding combustibles, causing fires. To explore the characteristics and mechanism of the ignition of external combustibles by DC fault arcs, this paper, based on the theory of magnetohydrodynamics (MHD), constructed a three-dimensional numerical simulation model of a DC fault arc considering the coupling of electromagnetic, thermal, and flow fields. A DC fault arc experimental platform that can simulate the actual working conditions of photovoltaic systems was built to verify the accuracy of the model. Based on this, by integrating the complex pyrolysis model and the combustion reaction model, and selecting cotton fibers as the typical combustible indicator substances, as stipulated in the UL 1699 standard, a coupled simulation model for the ignition of solid combustibles by direct current fault arcs was established. The numerical simulation of the entire ignition process of the arc was realized, and the coupling mechanism of heat transfer, mass transfer, and chemical reactions during the ignition process was revealed. The research results of this paper fill a research gap in the numerical simulation of arc ignition caused by DC faults in photovoltaic systems, clarify the fire ignition risk patterns of DC fault arcs under different working conditions, and provide important theoretical support and technical references for the formulation of arc fire prevention strategies and the optimized design of fault arc protection devices for photovoltaic systems and other DC power systems. Full article

Wildfires are among the key drivers of transformation in boreal ecosystems; however, the mechanisms of postfire recovery in the arid regions of Eurasia remain insufficiently understood. The aim of this study was to identify the role of burn severity and associated edaphic and hydrological factors in shaping the natural regeneration trajectories of Scots pine forests in the Kostanay region of northern Kazakhstan. This study is based on the integration of field data on seedling regeneration and soil conditions with the analysis of long-term satellite-derived indices (NDVI, NDMI, and NBR). Sample plots were grouped according to fixed burn severity classes, which enabled a consistent statistical comparison and reduced the interpretative ambiguity that has characterized previous studies in the region. The results indicate that pine forest regeneration is most successful under low and moderate burn severity, where seed sources are preserved and favourable moisture conditions are maintained. In contrast, high burn severity leads to a reduction in regenerative potential and a shift in recovery trajectories toward deciduous or sparsely vegetated communities. The spectral indices derived from the remote sensing data strongly agreed with the field-based indicators, confirming their suitability for assessing postfire vegetation dynamics. Soil properties act as important modifying factors of recovery processes, particularly under conditions of limited water availability. These findings enhance the current understanding of postfire recovery mechanisms in the arid part of the boreal zone and highlight the need for differentiated management of postfire landscapes. The integration of field observations with remote sensing data provides a robust framework for monitoring and forecasting recovery processes under an increasingly intensified fire regime. Full article

Corneal alkali burn induces severe inflammation and tissue damage, leading to loss of corneal transparency and vision impairment. In this study, we evaluated the therapeutic potential of rapamycin (RAPA) compared with cyclosporine A (CsA) in a mouse model of corneal alkali burn, focusing on nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB)–mediated inflammatory signaling and its impact on corneal wound healing and repair. Notably, RAPA robustly suppressed NF-κB activation, reduced infiltration of F4/80 macrophages and MPO neutrophils, and downregulated pro-inflammatory cytokines, including TNF-α, IL-1β, and IL-6. RAPA also markedly inhibited corneal neovascularization, as evidenced by decreased VEGF expression, reduced CD31 vessel formation, and suppression of Ang-2. RAPA substantially inhibited pathological fibrotic remodeling by reducing TGF-β1 expression, attenuating myofibroblast activation (α-SMA), decreasing collagen III deposition, and modulating matrix remodeling through suppression of MMP-9. Crucially, RAPA preserved epithelial barrier integrity by maintaining occludin expression, supported proper epithelial differentiation through sustained expression of CK12, and enhanced mucin layer stability by increasing MUC1 expression. It also restored tear production, reduced apoptotic cell death (TUNEL), and decreased dysregulated epithelial proliferation (Ki67). In conclusion, RAPA showed superior efficacy compared with CsA, primarily by enhancing corneal wound healing and facilitating structural and functional outcomes in the burned cornea. These findings underscore RAPA as a promising therapeutic candidate for ocular surface repair and vision restoration in extensive corneal injury. Full article

Over the past years, burn care has evolved from a discipline focused on survival to one centered on restoring long-term health, function, and quality of life. Significant advances in critical care, early excision and grafting, infection control, and metabolic support have transformed survival outcomes for even the most severe injuries. As a result, the field now faces a new frontier: understanding and managing the long-term physical, psychological, and systemic sequelae of survival. This review traces the evolution of burn care over the last century and outlines the challenges and priorities for the next 25 years. The first era of progress, defined by innovations in resuscitation, surgery, and critical care, has given rise to a growing cohort of long-term survivors. Research over the past decade has revealed that major burns induce chronic multisystem alterations, including metabolic, cardiovascular, neurocognitive, and immunological dysfunctions. Emerging concepts such as burn-associated heart failure exemplify this shift from acute to chronic disease understanding. Looking ahead, the future of burn medicine lies in personalized and lifelong care, supported by translational research, digital health, regenerative therapies, and interdisciplinary collaboration. Overall, burn care stands at a pivotal crossroads. By integrating precision medicine, rehabilitation science, and psychosocial care, we aim to move the field from survival toward sustained, holistic recovery over the next 25 years. Full article

Estimating wildfire greenhouse gas (GHG) emissions in Mediterranean landscapes is challenging due to heterogeneous fuel mosaics and limited scalability of field-based approaches. This study presents a Geographic Information System (GIS) based framework that integrates land-cover data, pre-fire biomass estimates, fire severity mapping, and established emission factors to produce spatially explicit estimates of biomass consumption and GHG emissions. Fire severity was derived from multitemporal Sentinel-2 imagery using the differenced Normalized Burn Ratio (ΔNBR) and combined with land-cover information to define vegetation–severity classes for emission estimation. A key innovation is the identification of co-occurring vegetation types within the same spatial units, allowing emissions to be quantified across vegetation mixtures rather than single classes, providing a more realistic representation of Mediterranean forests. Applied to the 2022 Bejis wildfire, pre-fire biomass within the burned area was 673,601 tons. Coniferous forests dominated, but co-occurrence with shrubland and herbaceous layers produced the highest emission contributions, highlighting the role of vegetation interactions. Total emissions were estimated at 625,938 tons of equivalent CO₂, and comparison with large-scale datasets (CAMS Global Fire Assimilation System, Global Fire Emissions Database) shows general coherence. This severity-driven, vegetation-explicit framework demonstrates robust potential for quantifying wildfire emissions across heterogeneous Mediterranean landscapes, though uncertainties remain due to pre-defined biomass, burning efficiency, emission factors, assumptions in fire severity mapping, and limited field validation. The approach can support improved regional GHG inventories and wildfire management strategies. Full article