Search Results (550)

Background: Syndromic testing panels, such as the BioFire FilmArray^® Meningitis/Encephalitis (M/E) panel, have become essential in altering the way that central nervous system diseases are diagnosed in the rapidly changing fields of molecular diagnostics, infectious diseases, and neurology. Long turnaround times, minimal pathogen output, and the requirement for live organisms are some of the common limitations of traditional cerebrospinal fluid culture techniques. A potential addition to traditional diagnostics is the FilmArray^® M/E panel, which uses multiplex polymerase chain reactions to identify many diseases quickly and simultaneously in a very short time, which affects multiple outcomes for the patient. Aims: Despite the M/E panel’s considerable speed and detection benefits, there are some issues related to cost, erroneous findings, and contextual interpretation. Material and Methods: This narrative review highlights fundamental research and meta-analyses that have studied the FilmArray^® M/E panel’s practical performance while comparing its diagnostic accuracy, clinical impact, and cost-effectiveness with the CSF culture. The latter occurs across different demographics and contexts. Results: Different studies have demonstrated that the M/E panel significantly shortens hospital stays, decreases unnecessary antibiotic usage, and speeds up the diagnosis of meningitis or encephalitis. Nonetheless, the necessity for cautious diagnostic management and supplementary testing strategies is underlined as there exist variations in sensitivity and specificity across pathogens, especially in viral ones. By facilitating quick, focused, and data-driven treatment for patients, the BioFire FilmArray^® M/E panel provides an advancement in meningitis and encephalitis diagnostics, which is consistent with the concept of precision medicine. Conclusion: To adequately guarantee fair and efficient results, its best application into clinical practice requires integration with clinical judgment, conventional culture techniques, and economic optimization strategies. Full article

This paper proposes an approach to digitizing the environmental passport for areas where detachable parts of launch vehicles fall in Kazakhstan based on an interactive geographic information system platform and smart maps. An example is considered for zone U-4 (“Ulytau” district of the “Karaganda” region), which includes the fall zones of “Soyuz” launch vehicle blocks (IZ 26, 32, 34, 42, 56). The natural and climatic factors and hazards of the territory are analyzed: the total area of the zones under consideration exceeds 4.1 million hectares, annual precipitation varies between 218 and 289 mm, strong winds of 5.0–6.8 m/s are characteristic, and a high level of fire hazard can develop within 6–7 days. Data on fires for 2021 are provided. For an integrated assessment, a normalized system criterion, environmental sustainability indicator (0–1), has been introduced, aggregating four groups of criteria (chemical, mechanical, pyrogenic, biota) with a breakdown of contributions and calculation of uncertainty (σ and 95% CI). The system criterion of environmental sustainability map identifies local ‘hot spots’ with levels of around 0.8–1.0, while the uncertainty map shows maximums of up to 0.12–0.14 (with background values of ~0.02–0.08), which increases the validity of management decisions on monitoring and reclamation. Full article

Páramos, high-mountain tropical ecosystems, are crucial for carbon storage and water regulation for many Andean cities. However, they are subjected to wildland fires that threaten the ecosystem services they provide. Fire activity varies substantially among páramos, making it essential to understand the drivers of this spatial variability. This study evaluates the relative influence of anthropogenic and biophysical factors on fire occurrence in Colombian páramos, analyzing burned area data from 2000 to 2022 using a Random Forest model. Results indicate that fire occurrence is shaped by the interaction between human pressures and biophysical characteristics. Annual precipitation was the most influential predictor: areas with lower mean annual precipitation (<1000–1500 mm/year) were linked to greater burned area. Vegetation cover, assessed using the Normalized Difference Vegetation Index (NDVI), showed a hump-shaped relationship, with intermediate greenness levels (0.13–0.25) being most prone to burning. Anthropogenic factors, especially proximity to buildings and agricultural zones, also had a significant impact. Our results show that fire occurrence in páramos cannot be attributed solely to human pressures but results from the combined effect of anthropogenic and biophysical drivers. Understanding of these interactions underscores the need for socio-ecological perspectives to guide integrated and adaptive management of strategic high-mountain ecosystems. Full article

Precise Estimated Time of Arrival (ETA) prediction in Terminal Maneuvering Areas (TMA) constitutes a prerequisite for efficient arrival sequencing and airspace capacity management. While data-driven approaches outperform kinematic models, conventional Recurrent Neural Networks (RNNs) exhibit limitations in modeling complex multi-aircraft spatial interactions and lack the capability to quantify predictive uncertainty. Conversely, Spiking Neural Networks (SNNs) enable energy-efficient event-driven computation, yet their applicability to continuous trajectory regression is hindered by “input starvation,” where normalized state vectors fail to induce sufficient neural firing rates. This study proposes a Probabilistic Spiking Transformer (PST) architecture to integrate neuromorphic sparsity with global attention mechanisms. An Adaptive Spiking Temporal Encoding mechanism incorporating learnable linear projections is introduced to resolve the regression-spiking incompatibility, facilitating the autonomous mapping of continuous trajectory dynamics into sparse spike trains without heuristic scaling. Concurrently, a Distance-Biased Multi-Aircraft Cross-Attention (MACA) module models air traffic conflicts by weighting spatial interactions according to physical proximity, thereby embedding separation constraints into the feature extraction process. Evaluation on large-scale real-world ADS-B datasets demonstrates that the PST yields a Mean Absolute Error (MAE) of 49.27 s, representing a 60% error reduction relative to standard LSTM baselines. Furthermore, the model generates well-calibrated probabilistic distributions (Prediction Interval Coverage Probability > 94%), offering quantifiable uncertainty metrics for risk-based decision support while ensuring real-time inference suitable for operational deployment. Full article

Forest fire occurrence prediction remains challenging despite advances in operational fire danger rating systems. In South Korea, the Korea Forest Fire Danger Rating Index (KFDRI) incorporates meteorological conditions, terrain (elevation, aspect), and forest type to assess regional fire danger. While KFDRI successfully assesses environmental fire danger at the pixel level, it does not explicitly account for human activity patterns that create substantial occurrence variability among locations with similar environmental conditions. This limitation is critical in human-dominated landscapes where where the main source of fire occurrence is anthropogenic. This study developed a Random Forest (RF) model to predict forest fire occurrence probability and propose management priorities during the forest fire prevention season (November–May) along the eastern coast of Korea, explicitly integrating human proximity variables (distance to agricultural areas and roads) with topographical (elevation, slope, aspect), surface fuel load, and meteorological variables (SMAP soil moisture, cumulative precipitation). Using forest fire occurrence records (1112 fire occurrence records) and background samples from 2015 to 2024, the model was trained with monthly stratified sampling and 10-fold cross-validation. The model achieved stable classification performance, with an overall F1-score of 0.515 and accuracy of 0.733. According to the SHAP (SHapley Additive exPlanations) analysis, distance to agricultural areas, elevation, slope, aspect, 5-day cumulative precipitation, and forest type were the most influential predictors. In particular, occurrence probability tended to increase in areas close to agricultural land (<180 m), at low elevations (≤200 m), on moderately steep slopes (≥8°), on south- and west-facing aspects, and under dried conditions. These results emphasize that fire occurrence risk is primarily structured by human proximity within areas of similar environmental danger. We propose an operational integration in which the RF model provides a 30 m “where-to-focus” occurrence layer that is used alongside KFDRI’s daily danger rating to prioritize prevention and patrol efforts. Full article

With the advancement of unmanned aerial vehicle (UAV) technology, its use in ecological monitoring and safety management of national parks has expanded significantly. However, object detection in complex scenes remains challenging due to environmental complexity, background interference, and occlusion. To address these issues, this paper proposes two improved YOLOv8-based models, YOLOv8-StarNet-CGA and SCS-YOLOv8, for detecting pine wilt disease-infected trees, under-construction farmhouses, and forest fires. In YOLOv8-StarNet-CGA, the StarNet module and Content-Guided Attention (CGA) are integrated into the backbone to enhance global feature extraction and focus on critical regions through dynamic weight adjustment. In SCS-YOLOv8, the original CIoU loss is also replaced with SIoU loss to optimize shape and orientation consistency, improving robustness. Experiments on UAV datasets covering diverse national park scenes demonstrate the effectiveness of the models. Results show that the improved models substantially outperform the original YOLOv8 in Precision, Recall, and mAP50. For pine wilt disease caused by the pine wood nematode Bursaphelenchus xylophilus, YOLOv8-StarNet-CGA achieves 8.6% higher Precision and 11.7% higher mAP50, facilitating early diagnosis and intervention of the disease. In under-construction farmhouse scenarios, Precision rises by 11% and mAP50 by 10.1%, lowering annual inspection labor by nearly 30% and improving oversight. For forest fires, SCS-YOLOv8 is more effective, with Precision improved by 7.2% and mAP50 by 6.3%. The improved detection model enables earlier identification of fire spots, thereby providing additional response time for emergency intervention, helping to mitigate fire spread and reduce the loss of forest resources. Both models also reduce GFLOPs and computational complexity, striking a balance between efficiency and accuracy, and showing strong potential for UAV deployment. Full article

The intensification of wildfires in Portugal has highlighted the urgent need for technical tools capable of supporting more effective risk mitigation decisions. In particular, the lack of explicit criteria for prioritizing the implementation of wildfire mitigation programs has contributed to reactive and fragmented interventions that are often misaligned with actual levels of hazard and exposure. This study proposes a spatially explicit methodology for classifying and ranking villages in wildfire-prone territories under two operational programs: Protection of People, Assets and Fuel Management. The framework was applied to eight municipalities across three Portuguese regions with high wildfire recurrence, using a multi-criteria decision analysis approach (AHP) integrated with geospatial data. Five physical and social variables were considered: critical area, vegetation cover, fire history, slope, and population density. Expert-derived weights were incorporated into two program-specific models. Implementation priority levels were generated using standard deviation classification at both municipal and regional scales. The results reveal marked territorial contrasts and strong intra-municipal variability, particularly in heterogeneous landscapes. A high degree of convergence between the two programs was observed (79–90%), although 10–21% of villages shifted between priority classes. The dual-scale analysis shows how a small number of high-hazard municipalities disproportionately shape the overall priority structure. The proposed framework supports more transparent, consistent, and risk-informed prioritization, strengthening territorial wildfire governance and complementing national mitigation programs such as “Safe Villages” and “Safe People” and “Condominium of Villages”. Full article

Accurate monitoring of aboveground biomass (AGB) is essential for forest carbon accounting and climate change mitigation, yet signal saturation and the treatment of forest landscapes as biophysically homogeneous entities remain significant barriers to high-fidelity mapping. This study implements an ecologically integrated model that leverages forest-type specific (coniferous vs. broadleaf) to enhance regional AGB retrieval. By refining established data fusion techniques with structural and compositional parameters, this approach seeks to mitigate systematic biases often found in generic regional assessments. Compared with 360 geo-referenced subplots, our stratified Support Vector Regression (SVR) model significantly outperformed non-classified counterparts, achieving an R² of 0.76 and a reduced RMSE of 18.48 Mg/ha. This refined precision enabled a nuanced time-series analysis (2013–2020), revealing that while regional AGB increased from 157.13 to 192.23 Mg/ha, this trajectory was punctuated by a distinct sub-regional growth plateau between 2016 and 2018. By correlating these trends with disturbance data, we identified a 11.27% biomass decline in southwestern sectors linked to a tripling of burned area, pinpointing intensified fire regimes as the primary driver overriding recovery-driven carbon gains. These findings demonstrate that harmonizing multi-sensor signals with functional forest differentiation provides the necessary sensitivity to track carbon resilience, offering a scalable and robust tool for operational forest management and global carbon cycle research. Full article

Wildfires pose increasing challenges for Mediterranean landscapes, making rapid and reliable mapping of burn severity essential for management and recovery planning. This study applies an integrated geospatial workflow to wildfires that occurred in Greece during the 2024 summer season. Sentinel-2-derived dNBR and RBR indices were used to map burn severity, while CORINE Land Cover and Tree Cover Density datasets provided complementary context for interpreting how severity varied across different vegetation types and canopy-density conditions. A one-way ANOVA was used to summarize differences in burned area among severity classes. The results show that low and moderate-low severity levels dominated most fire perimeters, whereas high-severity patches were spatially limited and typically coincided with densely forested areas. Validation against Copernicus Emergency Management Service data yielded an overall agreement of approximately 94%, indicating that the applied multispectral workflow produced severity extents broadly consistent with independent operational products. By applying a consistent methodology across multiple fire events, this study demonstrates the value of combining spectral indices with land-cover information for interpreting severity patterns and supporting post-fire management. The findings highlight the usefulness of freely accessible remote sensing data for timely fire assessment in Mediterranean environments and provide a basis for future multi-regional and multi-year comparisons. Full article

The acoustic technique emerges as a highly promising, cutting-edge solution that can be effectively employed for extinguishing flames in locations where the access to classical fire-protection measures is limited, the available extinguishing agent is severely restricted, or the burning materials are difficult to suppress using currently known methods. The results of the experimental attempts confirmed that low-frequency acoustic waves containing higher even harmonics from the tenth to the sixteenth order (inclusive) can successfully extinguish flames, demonstrating both the feasibility and the novelty of the acoustic technique for fire protection. Moreover, statistical analysis was applied to identify operational boundary values and assess their variability, supporting the optimal selection of system parameters required for rapid and effective flame extinguishing. By integrating an acoustic extinguisher with optional intelligent sensors, including artificial vision, it becomes possible to rapidly detect flames at much greater distances than with conventional smoke and temperature sensors, as well as to automatically extinguish them. In this context, an integrated solution combining acoustic waves with an artificial intelligence module (smart sensor) may be employed for comprehensive fire management, encompassing both fire detection and flame extinguishing. Full article

Fire safety in high-rise talent apartments, which is vital for safeguarding strategic human resources, was investigated to enhance evacuation resilience. A coupled fire-evacuation model was established using PyroSim and Pathfinder. This study analyzed multi-factor management strategies, including occupant vertical distribution, evacuation speed, evacuation priority settings, panic psychology, and guide intervention. Additionally, an Artificial Neural Network (ANN) model was developed using simulation data obtained under non-panic conditions to predict evacuation time and explore intelligent algorithms. Results show that the evacuation stairwells are the primary bottlenecks. Panic psychology significantly reduces evacuation efficiency, with severe panic increasing total evacuation time by up to 71.1%. The combined strategy CS4, integrating Pyramidal Vertical Distribution (VD7) and Organized Segmented Speed Control (ES6), reduced evacuation time by 17.42%. Guide intervention effectively mitigates the negative impact of panic. The ANN model achieved a coefficient of determination (R²) of 0.8695, confirming its predictive capability. Visibility was identified as the key parameter determining the Available Safe Egress Time (ASET). This study demonstrates that an integrated “hard–soft combination” strategy, complemented by intelligent modeling, offers an effective approach to building a resilient evacuation system for high-rise talent apartments. Full article

The extensive integration of lithium-ion batteries (LIBs) into modern technologies—including portable electronics, electric vehicles (EVs), and battery energy storage systems (BESSs)—has created a critical dependency on the supply of raw materials. The ongoing shift toward clean mobility is expected to further intensify this demand. This trend coincides with a projected increase in battery waste: over the next decade, millions of tons of EV and BESS batteries will reach their end-of-life (EOL), alongside the generation of considerable manufacturing scrap. Recycling is essential for recovering critical materials and reducing dependency on primary mining, thereby benefiting the circular economy and environmental sustainability. However, EOL-LIBs are more prone to thermal runaway due to defects and aging-induced degradation, which can lead to fire and explosion incidents, as well as associated environmental and health hazards. Such incidents have been increasingly reported in recent years during transportation, storage, handling, and illegal disposal, resulting in potential loss of life, property damage, and ecological degradation. To ensure the safe design and operation of the battery recycling industry, this work provides an updated overview of the health, safety and environment (HSE) hazards posed by EOL-LIBs and the safety measures required to mitigate these hazards. First, this work outlines the structures, components, and aging mechanisms of LIBs. Second, it summarizes the state-of-the-art recycling pathways and relevant process risks, such as deactivation, dismantling, and mechanical and thermal pretreatments. Third, it reviews recent safety incidents initiated by thermal runaway of EOL-LIBs and recycling intermediates like black mass, with an emphasis on storage and handling. Fourth, recommendations for future work regarding the safe storage and processing of EOL batteries are provided. Finally, conclusions and perspectives on future research directions are presented. Continued research and development in this field are essential to improve recycling methods, optimize processes, and ensure the safe and sustainable management and legislation of EOL lithium-ion batteries. Full article

Amid China’s pursuit of the “dual carbon” goals, the development and large-scale integration of renewable energy have become a core pillar of the power system transition. However, the intermittency and uncontrollability of wind and photovoltaic (PV) power have intensified peak-regulation conflicts after large-scale grid integration. Traditional coal-fired units lack sufficient flexibility to accommodate renewable energy fluctuations, while their willingness to participate in deep peak shaving remains low due to high associated costs. Addressing these challenges requires both enhanced system-level peak-regulation flexibility and effective market incentives for thermal units. Motivated by the limitations of existing studies that often consider individual flexibility resources or deterministic market mechanisms in isolation, this study investigates a coordinated multi-resource peak-regulation framework combined with an optimized market-clearing mechanism for deep peak-shaving ancillary services. First, flexibility resources are classified, and the peak-regulation mechanisms of source–load–storage coordination and auxiliary service markets are analyzed. Second, a wind–PV–thermal–storage operation cost model is established, followed by a two-layer peak-regulation market-clearing model that explicitly accounts for wind–PV uncertainty. The upper-level model minimizes total system operating costs through the coordinated dispatch of demand response and energy storage, while the lower-level model minimizes power purchase costs under a unified marginal clearing price. In addition, an uncertainty modeling framework based on Information Gap Decision Theory (IGDT) is introduced to manage renewable generation uncertainty and support decision-making under different risk preferences. Case studies are conducted to verify the effectiveness of the proposed framework. The results show that: (1) synergistic peak shaving through energy storage and demand response reduces the system peak–valley difference from 460 MW to 387.87 MW and decreases wind–PV curtailment costs from 355,000 yuan to 15,700 yuan, thereby alleviating thermal unit pressure and improving renewable energy accommodation; (2) the unified marginal clearing price mechanism reduces total system operating costs by 41.07% and significantly lowers the frequency of deep peak shaving for thermal units, enhancing their participation willingness; and (3) the IGDT-based model effectively addresses wind–PV uncertainty by providing optimistic and pessimistic scheduling strategies under different deviation coefficients. These results confirm that the proposed framework offers an effective and flexible solution for coordinated peak shaving in power systems with high renewable energy penetration. Full article

Coal mine fires represent one of the major threats constraining sustainable and safe production in the coal industry. To investigate the mechanisms of accident causation and coupling evolution, this study proposed a fire risk analysis method integrating the N-K model (a model for quantifying interactions among system components) with complex network theory. Seventy-five coal mine fire accident cases were selected as samples to identify the coupling types and coupling mechanisms among human, management, technology, environment, and equipment risk factors. The N-K model was employed to determine accident coupling types and calculate risk coupling values. Based on association rule mining among risk factors, a coal mine fire risk network model was constructed. By integrating accessibility characteristics derived from complex network analysis with the N-K model, the normalized out-degree of network nodes was adjusted using N-K coupling values to better reflect node influence, thereby identifying key risk factors. The results showed that management factors were the dominant dimension driving risk coupling, and an increase in the number of coupled factors significantly affected the level of coal mine fire risk. The top four key risk factors were inadequate safety supervision by regulatory authorities, insufficient safety training and education, illegal production organization, and incomplete safety technical measures. Finally, targeted prevention and control strategies were proposed. The findings provide critical support for advancing sustainable and safe coal mine production by informing targeted safety interventions and optimizing resource allocation in safety management. Full article

Resilience to climate change is a complex concept, especially in metropolitan areas where diverse services and stakeholders interact. Promoting sustainable climate adaptation, a resilience assessment method focused on regional areas and nature-based solutions is presented, along with its open-access, web-based platform, supporting resilience assessment, planning, and monitoring. Floods, droughts, heat or cold waves, windstorms, and forest fires can be assessed. A framework for holistic assessment and other framework, addressing critical infrastructure, are integrated. Four resilience dimensions are assessed: organizational (governance, social aspects, finance); spatial (exposure, impacts, and mapping); functional (service management, interdependencies); and physical (infrastructure robustness, redundancy). Strategic services comprise, e.g., water, waste, and natural areas. Resilience capacities, e.g., to prevent, respond, and recover from disruptions, are also assessed. The paper emphasizes new developments and assessment. Practical step-by-step guidance aligned with assessment purposes is included, aiming to address observed limitations (e.g., fragmented service provision, communication silos, data constraints). Overall results of a Spanish metropolitan area (AMB) and an exploratory application to an Austrian rural case (SLR) are also presented. Following the guidelines, AMB progressed from an essential to a comprehensive assessment. Overall, almost 1/3 of the metrics are advanced or progressing. SLR assessed its resilience capabilities regarding electrical infrastructure. Full article