Search Results (639)

Normal alcohols (n-alcohols) exhibit contrasting effects on neuronal excitability; specifically, ethanol enhances neuronal firing, while hexanol suppresses it. Both compounds are known to inhibit sodium currents, yet the mechanisms behind their differing effects remain unclear. Our previous studies demonstrated that Kv7 channels are modulated differently by alcohol chain length, prompting investigation into their role in these contrasting effects. We conducted whole-cell patch clamp recordings on neonatal (P5-P7) rat superior cervical ganglion neurons to assess alcohol impacts on action potential firing and ionic currents, utilizing tetrodotoxin (TTX), XE991, and retigabine (RTG). Ethanol (100 mM) increased action potential frequency, whereas hexanol (3 mM) decreased it, despite both inhibiting sodium currents by 12% and 45%, respectively. Notably, ethanol inhibited Kv7 currents by 16%, while hexanol enhanced them by 29%. TTX alone did not affect firing frequency until sodium current inhibition exceeded 76%, indicating moderate sodium channel blockade cannot fully explain the effects of alcohol. XE991 increased firing frequency and depolarized the resting membrane potential, while retigabine produced opposite effects. The combination of TTX with Kv7 modulators replicated the effects observed with each alcohol. These findings suggest Kv7 channel modulation plays an important role in the chain length-dependent effects of alcohol on neuronal excitability. Full article

The present study investigates the influence of atomizing air-to-liquid mass ratio (ALR) on the near-field spray characteristics and stability of a novel twin-fluid injector that integrates bubble-bursting for primary atomization and shear-induced secondary atomization. Unlike conventional injectors, the novel design generates ultra-fine sprays at the exit with low sensitivity to liquid properties. The previous version improved secondary atomization even for highly viscous liquids, showing strong potential in hydrogel-based fire suppression. The current design improves primary atomization, leading to more stable and finer sprays. The near-field spray characteristics are quantified using a high-speed shadowgraph across ALRs ranging from 1.25 to 2.00. This study found that stable and finely atomized sprays are produced across all the tested ALRs. Increasing ALR reduces droplet size, while the spray is the widest at 1.25. Sauter Mean Diameter (SMD) contours show larger droplets at the edges and smaller ones toward the center, with ALR 2.00 yielding the most uniform size distribution. As per the atomization efficiency, ALR of 1.25 shows the best performance. Overall, an optimum ALR of 1.75 is identified, offering balanced droplet size distribution, stability, and atomization efficiency, making the injector potentially suitable for fire suppression and liquid-fueled gas turbines requiring high stability and fuel flexibility. Full article

Building-Integrated Photovoltaics (BIPV), which are used for building exteriors such as walls, roofs, balconies, and awnings, play a significant role in reducing greenhouse gas emissions. However, since the back sheet, sealant, junction box, and cable of BIPV modules are made of flammable plastic materials, fire protection technologies are needed to ensure fire safety. The aim of this work is to evaluate the fire safety performance of BIPV modules coated with fire-resistant (FRs) and flame-retardant (FRt) materials. The test results show that the performance of the FRs coating was excellent in terms of fire blocking, physical properties, and durability, compared to the FRt coating. Surface damage, such as cracks and blisters, was observed on the FRt coating during the impact and acid resistance tests, whereas the FRs coating demonstrated superior durability without any defects. Specifically, aluminum hydroxide (ATH, 5–10 wt%) added to the FRs coating promoted an endothermic reaction that lowered the flame temperature, released H₂O, and stably formed an Al₂O₃ heat-shielding layer. Due to this reaction, the suppression of the fire spread by the BIPV modules was the best compared to that of Mg, Ti, and Si-based additives. Full article

Fires remain a critical threat to the resilience and safety of the built environment, yet current research is often fragmented across building types, technologies, and functions. This study investigates typology-specific gaps in fire safety by conducting a scientometric review of peer-reviewed articles published between 2010 and 2025. Following a PRISMA-guided protocol, a total of 83 studies indexed in the Web of Science were systematically screened and analyzed using VOSviewer (v1.6.19) and the R-based Bibliometrix package (version 4.2.1). The dataset was classified according to building typologies, fire safety functions—detection, suppression, and evacuation—and applied technologies such as BIM, simulation platforms, and AI-based models. The results show a strong research bias toward evacuation modeling in high-rise and general-purpose buildings, while critical typologies including healthcare facilities, heritage structures, and informal settlements remain underexplored. Suppression systems and real-time detection technologies are rarely integrated, and technological applications are often fragmented rather than interoperable. A conceptual matrix is proposed to align tools with typology-specific risk profiles, highlighting mismatches between research priorities and building functions. These findings emphasize the need for integrated, data-driven, and context-sensitive fire safety strategies that bridge methodological innovation with practical application, offering a roadmap for advancing resilient and adaptive fire safety in diverse urban settings. Full article

Live fuel moisture content (LFMC) is a critical variable influencing wildfire behavior, ignition potential, and suppression difficulty, yet it remains challenging to monitor consistently across landscapes due to sparse field observations, rapid temporal changes, and vegetation heterogeneity. This study presents a comprehensive review of satellite-based approaches for estimating LFMC, with emphasis on methods applicable to New Zealand, where wildfire risk is increasing due to climate change. We assess the suitability of different remote sensing data sources, including multispectral, thermal, and microwave sensors, and evaluate their integration for characterizing both LFMC and fuel types. Particular attention is given to the trade-offs between data resolution, revisit frequency, and spectral sensitivity. As knowledge of fuel type and structure is critical for understanding wildfire behavior and LFMC, the review also outlines key limitations in existing land cover products for fuel classification and highlights opportunities for improving fuel mapping using remotely sensed data. This review lays the groundwork for the development of an operational LFMC prediction system in New Zealand, with broader relevance to fire-prone regions globally. Such a system would support real-time wildfire risk assessment and enhance decision-making in fire management and emergency response. Full article

In real-world fire scenarios, complex lighting conditions and smoke interference significantly challenge the accuracy and robustness of traditional fire detection systems. Fusion of complementary modalities, such as visible light (RGB) and infrared (IR), is essential to enhance detection robustness. However, spatial shifts and geometric distortions occur in multi-modal image pairs collected by multi-source sensors due to installation deviations and inconsistent intrinsic parameters. Existing multi-modal fire detection frameworks typically depend on pre-registered data, which struggles to handle modal misalignment in practical deployment. To overcome this limitation, we propose an end-to-end multi-modal Fire Salient Object Detection framework capable of dynamically fusing cross-modal features without pre-registration. Specifically, the Channel Cross-enhancement Module (CCM) facilitates semantic interaction across modalities in salient regions, suppressing noise from spatial misalignment. The Deformable Alignment Module (DAM) achieves adaptive correction of geometric deviations through cascaded deformation compensation and dynamic offset learning. For validation, we constructed an unregistered indoor fire dataset (Indoor-Fire) covering common fire scenarios. Generalizability was further evaluated on an outdoor dataset (RGB-T Wildfire). To fully validate the effectiveness of the method in complex building fire scenarios, we conducted experiments using the Fire in historic buildings (Fire in historic buildings) dataset. Experimental results demonstrate that the F1-score reaches 83% on both datasets, with the IoU maintained above 70%. Notably, while maintaining high accuracy, the number of parameters (91.91 M) is only 28.1% of the second-best SACNet (327 M). This method provides a robust solution for unaligned or weakly aligned modal fusion caused by sensor differences and is highly suitable for deployment in intelligent firefighting systems. Full article

The neutral point of a 10 kV distribution network often adopts an arc suppression coil or high resistance grounding mode to ensure the reliability of the power supply. The single-phase grounding fault current is below 10 A, and the distribution network can continue to operate with the fault for up to 2 h. However, long-time arc faults may ignite cables and cause electrical fires, causing further damage to adjacent cables and seriously affecting the safety of the power grid. To study the combustion characteristics of a single-phase grounding fault of a distribution network cable under the action of a long-term small current arc, the cable fault ignition test was carried out by using the arc ignition method of welding tin wire fuses. Then, the temperature distribution of the cable channel in an electrical fire was simulated, based on an FDS simulation, and the damage of adjacent cables under typical layout was further analyzed. The results show that the 10 kV cable was quickly ignited by the high temperature arc within 0.04 s after the breakdown and damage of the cable. Flammable XLPE insulation melted or even dripped off at a high temperature in fire. Thus, the fire spread to both ends when burning. Under the condition of 4–10 A, the maximum flame temperatures above the arc fault point reached 725 °C, 792 °C, 812 °C and 907 °C, respectively. According to the network structure, some protection, such as fireproof tape, needs to be applied directly above the faulty cable when the fault current exceeds 6 A. Full article

Burned area, fire severity, and suppression expenditures have increased in British Columbia in recent decades with climate change. Approximately 80% of suppression expenditures are attributable to wildfires near the Wildland–Urban Interface (WUI). Evaluating the potential for fuel management to reduce suppression expenditures is essential to mitigating demands on fire response resources and reducing impacts on communities. One management approach is to increase the proportion of deciduous tree species, which have a lower propensity for crown fire. Using fire suppression expenditure data from 1981 to 2014, we applied the machine learning method causal forests (CFs) to estimate the effect of the proportion of conifer forest cover on suppression expenditures for WUI fires and how these effects varied with other influential factors (i.e., heterogenous treatment effects). Across all fires, the effect of conifer cover on suppression expenditures was stronger on private land compared to public land, under high fire danger measured by daily severity ratings (DSRs), which reflect wind speed and fuel moisture, and for fires igniting earlier in the calendar year, based on Julian day. These findings provide insights into prioritizing wildland fuel treatment when budgets are limited. The CFs approach demonstrates potential for broader applications in fire risk mitigation and analysis beyond the scope of the current data. CFs may also be valuable in other areas of forest research where heterogenous treatment effects are common. Full article

Forest ecosystems are vital to sustainable development, contributing to economic, environmental and social well-being. However, the increasing frequency and severity of wildfires threaten these ecosystems, demanding more effective and integrated fire management (IFM) strategies. Current suppression efforts face limitations due to high resource demands and the need for timely, informed decision-making under uncertain conditions. This paper presents the SILVANUS project’s approach to developing an advanced Decision Support System (DSS) designed to assist incident commanders in optimizing resource allocation during wildfire events. Leveraging Geographic Information Systems (GIS), real-time data collection, AI-enhanced analytics and multicriteria optimization algorithms, the SILVANUS DSS component integrates diverse data sources to support dynamic, risk-informed decisions. The system operates within a cloud-edge infrastructure to ensure scalability, interoperability and secure data management. We detail the formalization of the resource allocation problem, describe the implementation of the DSS within the SILVANUS platform, and evaluate its performance in both controlled simulations and real-world pilot scenarios. The results demonstrate the system’s potential to enhance situational awareness and improve the effectiveness of wildfire response operations. Full article

In the context of climate change, increasingly severe forest fires present a significant threat to lives, property, ecosystem functionality, and the sustainable development of forest resources. As a result, there is an urgent need for rapid, efficient, and environmentally friendly technologies for fire suppression and containment. This paper begins by reviewing the current research on forest fire extinguishing agents and materials that hold promise for effective fire suppression. Among these agents, gaseous and foam extinguishing materials exhibit drawbacks such as low efficiency or significant environmental hazards. In contrast, natural polymer hydrogels, which are high in water content, environmentally friendly, and biodegradable, show significant potential for developing clean and efficient extinguishing materials. Furthermore, this paper discusses existing environmental assessment standards for fire extinguishing agents, as well as the assessment systems proposed in various studies. It finds that, while universal assessment standards are fairly well-established, current research primarily focuses on enhancing fire suppression performance. However, the environmental performance assessment of forest fire extinguishing agents—often used in large quantities—remains inadequate. Therefore, there is an urgent need to establish a comprehensive and systematic environmental assessment system to address this theoretical and practical gap. Full article

The spread of contemporary artificial intelligence technologies, particularly machine learning, has significantly enhanced the capacity to predict asymmetrical natural disasters. Wildfires constitute a prominent example, as machine learning can be employed to forecast not only their spatial extent but also their environmental and socio-economic impacts, propagation dynamics, symmetrical or asymmetrical patterns, and even their duration. Such predictive capabilities are of critical importance for effective wildfire management, as they inform the strategic allocation of material resources, and the optimal deployment of human personnel in the field. Beyond that, examination of symmetrical or asymmetrical patterns in fires helps us to understand the causes and dynamics of their spread. The necessity of leveraging machine learning tools has become imperative in our era, as climate change has disrupted traditional wildfire management models due to prolonged droughts, rising temperatures, asymmetrical patterns, and the increasing frequency of extreme weather events. For this reason, our research seeks to fully exploit the potential of Principal Component Analysis (PCA), Minimum Redundancy Maximum Relevance (MRMR), and Grammatical Evolution, both for constructing Artificial Features and for generating Neural Network Architectures. For this purpose, we utilized the highly detailed and publicly available symmetrical datasets provided by the Hellenic Fire Service for the years 2014–2021, which we further enriched with meteorological data, corresponding to the prevailing conditions at both the onset and the suppression of each wildfire event. The research concluded that the Feature Construction technique, using Grammatical Evolution, combines both symmetrical and asymmetrical conditions, and that weather phenomena may provide and outperform other methods in terms of stability and accuracy. Therefore, the asymmetric phenomenon in our research is defined as the unpredictable outcome of climate change (meteorological data) which prolongs the duration of forest fires over time. Specifically, in the model accuracy of wildfire duration using Feature Construction, the mean error was 8.25%, indicating an overall accuracy of 91.75%. Full article

This study examines the interdependent performance of fire detection systems, automatic sprinklers, and natural smoke ventilation in warehouse environments. Using computational fluid dynamics (CFD) simulations with PyroSim and FDS, four operational scenarios were analyzed to evaluate system activation sequences and suppression effectiveness. Results show that ceiling-only sprinklers may limit fire growth but fail to achieve extinguishment, while integrated in-rack sprinklers with coordinated venting provide full suppression and optimized thermal control. The findings underscore the importance of performance-based, integrated design strategies to enhance fire protection and minimize property losses. Full article

Fire blight, caused by Erwinia amylovora, is a severe disease impacting pome fruit production worldwide, including in Saudi Arabia. This study evaluated antibiotic sensitivity and the potential of chemical and elicitor treatments to suppress E. amylovora isolates collected from various regions in Saudi Arabia. In the in vitro assays, at low antibiotic levels (10 µg/mL streptomycin and 25 µg/mL oxytetracycline), all Saudi Arabian strains exhibited minimal inhibition (zones ≤ 14 mm). Two isolates displayed partial tolerance at an intermediate oxytetracycline concentration (50 µg/mL). True sensitivity (zones > 18 mm) was mainly observed at the highest tested oxytetracycline dose (100 µg/mL). Regarding copper sulfate, all isolates showed no inhibition between 0.02 and 0.08 mM, while all isolates exhibited intermediate susceptibility at 0.16 mM. The second experimental phase examined in planta effects of streptomycin, salicylic acid (SA), and their combination on disease development in artificially inoculated apple (Malus domestica) shoots under greenhouse conditions. Both streptomycin and SA significantly reduced fire blight incidence (by 75%) and symptom severity, while the combined treatment yielded the greatest reduction in shoot necrosis and bacterial load. This is the first report demonstrating that SA, particularly when used in combination with streptomycin, can effectively suppress fire blight in Saudi Arabia. These results stress the importance of integrating resistance inducers into fire blight management strategies to counter the rise in antimicrobial resistance. Full article

This review establishes a comprehensive technical framework for aerogel-based fire-resistant coatings, from fundamental mechanisms to industrial applications. It analyses the multi-mode flame-retardant and thermal insulation mechanisms achieved through aerogels’ synergistic suppression of heat conduction, convection, and radiation, establishing their theoretical basis. The work compares the intrinsic characteristics of silica-based, carbon-based, and bio-based aerogels, providing rational selection criteria for fire protection systems. The study examines key integration challenges: balancing nanopore preservation with interfacial compatibility, inherent mechanical weaknesses, conflicts between high filler loading and workability, and scalability issues. It evaluates targeted strategies including interface engineering, mechanical reinforcement, workability optimization, and low-cost production routes. Application prospects in construction, tunneling, and cable protection are outlined. This review provides a coherent progression from mechanisms and material properties to challenges and solutions, offering theoretical guidance and a technical roadmap for developing next-generation high-performance fire-resistant coatings. Full article

This paper examines the state-of-the-art in fire suppression technologies based on computer vision applications in the subject areas of computer science and engineering. The study involves a two-stage analysis of publications using keywords. This paper presents a bibliographic analysis of scientific literature from the Scopus database using VOSviewer software and the author’s methodological approach. General keywords were used for the initial analysis of the dataset, followed by a more detailed study with additional criteria and specific keywords. The categories considered in the article are as follows: Firefighting Robots, Fire Detection, Fire Suppression, Aerial Vehicles, and Computer Vision. It is shown that the research includes technical aspects of fire robots and systems, as well as the improvement of their software and hardware. The subsequent review highlights the important role of computer vision in improving the efficiency and effectiveness of fire suppression systems. It is noted that key advances include the development of sophisticated fire detection algorithms and the implementation of automated fire suppression systems. The study also discusses the challenges and future directions in this field, emphasizing the need for continuous innovation and interdisciplinary collaboration. This review provides valuable information for researchers, engineers, and practitioners in the field of fire safety by offering a comprehensive overview of state-of-the-art technologies and their applications in fire suppression. Full article