Search Results (1,293)

In order to improve the fluidity, pumpability, and strength of separation-layer grouting fire-protecting filling material and reliability with multiple parameters and factors in traditional orthogonal tests, the coupling theory of the response surface-satisfaction function is applied to optimize the ratio of separation-layer grouting fire-protecting filling material. Cement content, the ash–gangue ratio, slurry concentration, and admixture were selected as the influencing factors for the ratio optimization of separation-layer grouting fire-protecting filling material and slump, with the bleeding rate and compressive strength selected as the evaluation indexes of material properties. The Box–Behnken experimental design method was applied to conduct 25 groups of experiments with different material ratios, and the response surface functions of various material performance evaluation indexes were constructed. The relationship between the influencing factors of fire protecting and filling material ratios and the target responsiveness was studied, as well as the optimal ratio of separation-layer grouting fire-protecting filling materials under multi-objective conditions. The results show that the influence of the slurry concentration and cement content on the degree of collapse is significant. The cement content and slurry concentration had significant influence on the compressive strength. The ash–gangue ratio has a significant impact on bleeding rate. Meanwhile, the interaction of the ash–gangue ratio, slurry concentration, and cement content also has a significant impact on the bleeding rate. For waste rock cementation abscission-layer grouting fire protecting and filling material, the optimal ratio is an ash and gangue ratio of 1:2, the cement content is 12.12%, the admixture is 1.49%, and the slurry concentration is 52%. The ratio of the corresponding response under the condition of prediction result is a slurry slump of 28.5 cm, bleeding rate of 2.36%, and filling body strength of 4.62 MPa, which basically coincide with the experimental results and verification and provide evidence for the abscission layer grouting field industrial test. Full article

Fly ash from coal-fired power plants is a promising precursor for zeolite synthesis due to its aluminosilicate-rich composition. However, its direct utilization is often limited by impurities and a low silicon-to-aluminum ratio (SAR). This study demonstrates the conversion of Class C fly ash from the Soma thermal power plant (Turkey) into FAU- and CHA-type zeolites through optimized acid leaching and hydrothermal synthesis. Acid treatment increased the SAR from 1.33 to 2.85 and effectively reduced calcium-, sulfur-, and iron-bearing impurities. The SAR enhancement by acid leaching was found to be reproducible among Class C fly ashes, whereas Class F materials exhibited a limited response due to their acid-resistant framework. Subsequent optimization of alkaline fusion-assisted synthesis enabled selective crystallization of FAU and CHA, while GIS and MER appeared under prolonged crystallization or higher alkalinity. SEM revealed distinct morphologies, with MER forming rod-shaped clusters, and CHA exhibiting disc-like aggregates. Water sorption analysis showed superior uptake for metastable FAU (~23 wt%) and CHA (~18 wt%) compared to stable GIS and MER (~12–13 wt%). Overall, this study establishes a scalable and sustainable route for producing high-performance zeolites from industrial fly ash waste, offering significant potential for adsorption-based applications in dehumidification, heat pumps, and gas separation. Full article

Pyrolysis of 24 samples of foliage from three U.S. regions with frequent wildland fires (Southeastern U.S., northern Utah and Southern California) was studied in a fuel-rich flat-flame burner system at 765 °C (for Southeastern U.S. samples) and 725 °C (for northern Utah and Southern California species), with a heating rate of approximately 180 °C/s. These conditions were selected to mimic the conditions of wildland fires. Individual plant samples were introduced to the high temperature zone in a flat-flame burner and pyrolysis products were collected. Tar was extracted and later analyzed by GC/MS. Light gases were collected and analyzed by GC/TCD. The estimated range for the average yields of tar and light gases were 48 to 62 wt% and 18 to 31 wt%, respectively. Apart from Eastwood’s manzanita (Arctostaphylos glandulosa Eastw.), aromatics were the major constituents of tar. The variations in the concentrations of tar compounds likely resulted from differences in biomass composition and physical characteristics of the foliage. The four major components of light gases from pyrolysis (wt% basis) were CO, CO₂, CH₄ and H₂. Tar contributed more than 82% of the high heating value of volatiles. These data can be used to improve physical-based fire propagation models. Full article

Gold deposits of the Iron Quadrangle are highly heterogeneous, requiring integrated studies to optimize processing. This study presents a geometallurgical assessment of the Lamego orogenic gold deposit, located in the Iron Quadrangle, Brazil. Eleven composite samples representing four lithotypes, namely metandesite, banded iron formation (BIF), smoky quartz, and carbonaceous phyllite, were analyzed through QEMSCAN, fire assay, and Leco methods. Samples underwent gravity separation and flotation tests to evaluate mineralogical variability and its metallurgical implications. The results show that sulfide-rich lithotypes, particularly those containing pyrite and arsenopyrite, achieved higher gold and sulfur recoveries, especially in flotation. In contrast, samples with high concentrations of muscovite or reactive carbonates such as ankerite and dolomite showed reduced selectivity due to reagent competition and flotation interference. Grinding behavior varied among lithologies, with smoky quartz requiring the highest energy input (10.32 kWh/t) and displaying the lowest breakage parameter (K = 0.120), reflecting its high hardness and fine mineral intergrowths. Strong correlations were established between ore mineralogy and process performance; for instance, sulfide abundance directly predicted flotation recovery, while quartz content correlated with higher grinding energy consumption. These findings underscore the importance of incorporating detailed mineralogical characterization into process design. Geometallurgical tools enable more accurate prediction of metallurgical performance and support the development of lithotype-specific flowsheets for improved recovery, reduced energy consumption, and more efficient gold processing in complex ore systems such as Lamego. Full article

Eco-friendly flame retardants are becoming a popular alternative to traditional fire retardants, many of which contain toxic halogens. These modern additives, which are based on phosphorus, nitrogen, or silicon compounds, minimize the emission of harmful gases during combustion, making them safer for the environment and human health. This study aimed to synthesize and analyze poly(vinyl chloride) (PVC) composites using a newly synthesized hybrid fire retardant, boehmite derivative (aluminium dibutyl phosphonate), as an environmentally friendly additive. The fire-retardant properties of chitosan, which is derived from the natural biopolymer chitin, have also been tested. The chemical structure of the synthesized compounds was confirmed using ATR/FTIR spectroscopy and SEM-EDX analysis. Next, PVC-based dry blends were prepared with the addition of a stabilizer, plasticiser, chalk, and selected flame retardants (aluminium dibutyl phosphonate or chitosan) at concentrations of 10 wt%, 30 wt%, and 50 wt%, resulting in homogeneous materials intended for evaluating fire performance, thermal stability (DSC, TGA), and mechanical resistance. Full article

Wireless Sensor Networks (WSNs) are vital to a wide range of applications, spanning from environmental monitoring to safety-critical systems. Ensuring dependable operation in these networks critically depends on selecting an optimal node scheduling algorithm; however, this remains a major challenge since no single approach performs best under all conditions. To address this issue, this paper proposes an AI-driven framework that evaluates scenario-specific functional requirements—such as coverage, connectivity, and network lifetime—to identify the optimal node scheduling algorithm from a pool that includes Hidden Markov Models (HMMs), BAT, Bird Flocking, Self-Organizing Maps (SOFMs), and Long Short-Term Memory (LSTM) networks. The framework was evaluated using a neural network trained on simulated data and tested across five real-world scenarios: healthcare monitoring, military operations, industrial IoT, forest fire detection, and disaster recovery. The results clearly demonstrate the effectiveness of the proposed framework in identifying the most suitable algorithm for each scenario. Notably, the LSTM algorithm frequently achieved near-optimal performance, excelling in critical objectives such as network lifetime, connectivity, and coverage. The framework also revealed the complementary strengths of other algorithms—HMM proved superior for maintaining connectivity, while Bird Flocking excelled in extending network lifetime. Consequently, this work validates that a scenario-aware selection strategy is essential for maximizing WSN dependability, as it leverages the unique advantages of diverse algorithms. Full article

Earthquakes, fires, and climate change-related hazards increasingly threaten cultural heritage. Documenting and identifying the significance of heritage sites before disasters is essential for archival purposes and for guiding post-disaster interventions such as consolidation, reconstruction, or redesign. Although various post-disaster strategies exist in the literature, they often lack consideration of pre-disaster values and authentic qualities, limiting their effectiveness in value-based regeneration. This study proposes a framework for managing post-disaster interventions grounded in pre-disaster documentation of heritage values, authenticity, and integrity. The methodology includes seven phases: case selection; site survey and documentation; thematic analysis and mapping; quantification of qualitative data; synthesis of pre-disaster analysis results to define values, problems, and potentials; post-disaster assessment using aerial and terrestrial imagery; and development of targeted intervention strategies. This study focuses on two areas in Antakya, Türkiye: Kurtuluş Street and Kuyulu Neighborhood, affected by the 2023 earthquake (M 7.7). These areas represent different historical layers: a Hellenistic grid plan with French-style buildings, and an organic Ottoman settlement morphology, respectively. Conservation data collected in 2019 inform the analysis. Mapping techniques evaluate attributes such as spatial characteristics, typologies, and structural systems. The study concludes that traces of pre-disaster spatial patterns and building features should inform post-disaster designs, ensuring sustainable, earthquake-resistant, and value-based interventions. 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

Between three and six years of age, children become better able to detect smaller differences in non-symbolic visual quantities. This includes judging which set of objects are greater in number and area. These findings suggest that the underlying approximate magnitude representations, which facilitate the estimation of these quantities, become more precise with age into adulthood. Such parallels in developmental trends raise questions about the extent to which they are observed across different non-symbolic quantities. The present study addressed this question using a novel quantity: visual fire intensity. Like number and area, fire intensity can be estimated using visual cues and has real-world implications. However, it is unclear whether young children accurately compare fire intensities and if there are age-related differences in performance. The present study investigated the developmental trend of young children’s visual perception of fire intensity. Over 70 three- to six-year-olds completed a comparison task where they judged which of two train engines had a more intense fire. Based on non-symbolic quantity research, the ratio (relative difference) between the intensity of two simulated fires was varied across trials to be smaller versus larger. Significant ratio and age effects were observed: children were more likely to select the train with the greater-intensity fire as being ‘more’ the larger the ratio and the older the child. These results suggest that young children are sensitive to differences in fire intensity using visual cues and have increasingly precise estimates by six years of age. This developmental pattern indicates that approximate magnitude representations support perceptions of ecologically relevant, dynamic quantities such as fire intensity. Full article

This article examines how the time of exposure (0, 10, 20 and 30 min) to fire affects the optimal design of Howe timber trusses. The study integrates experimental characterization, thermal modeling (Eurocode 5 1995-1-2), and the bio-inspired Firefly Algorithm (FA). Five Brazilian species (Cambará-rosa, Cupiúba, Angelim-pedra, Garapa, and Jatobá) were assessed in spans of 6, 9, 12, and 15 m. Each configuration was optimized 30 times with 120 agents, 600 iterations, and penalty treatments. In ambient conditions, Angelim-pedra and Garapa produced the lightest trusses, while under fire, simulated trusses with Jatobá wood properties provided the best performances, resulting in up to 35% mass reduction compared to trusses optimized with denser species under equivalent fire scenarios. Safety margins, defined through the Gross Mass Increase (GMI) index, quantify the additional structural mass required under fire in relation to the ambient design. GMI values ranged between 22% and 140% across the analyzed cases, quantifying the additional section demand under fire conditions relative to ambient design. To predict overdesign, regression equations were fitted using symbolic regression for the Index of Gross Area Correction Index (GACI), based on fire exposure time and resistant parameters, achieving R² above 0.85. The study provides guidelines for species selection, span sizing, and fire safety design. Overall, combining thermal analysis, bio-inspired optimization, and symbolic regression highlights the potential of timber trusses for efficient, safe, and sustainable roof structures. In addition, this study demonstrates the scientific novelty of integrating experimental characterization, Eurocode 5 thermal modeling, and metaheuristic optimization with symbolic regression, providing analytical indices such as the Gross Mass Increase (GMI) and Gross Area Correction Index (GACI). These results also offer practical guidelines for species selection, span sizing, and fire safety design, reinforcing the applicability of the methodology for engineers and designers of timber roof systems. Full article

Structural collapses are a major threat to urban safety and infrastructure resilience and as such there is growing research interest in understanding the causes and improving the prediction of risk to prevent human and material losses. Whether caused by fires, earthquakes or progressive failures due to overloads and displacements, these events have been the focus of investigation over the past 15 years. This systematic literature review looks at the use of formal risk analysis models in structural failures between 2010 and 2025 to map methodological trends, assess model effectiveness and identify future research pathways. From an initial database of 139 documented collapse incidents, only 42 were investigated using structured risk analysis frameworks. A systematic screening of 417 related publications yielded 101 peer-reviewed studies that met our inclusion criteria—specifically, the application of a formal analytical model. This discrepancy highlights a significant gap between the occurrence of structural failures and the use of rigorous, model-based investigation methods. The review shows a clear shift from single-method approaches (e.g., Fault Tree Analysis (FTA) or Finite Element Analysis (FEA)) to hybrid, integrated models that combine computational, qualitative and data-driven techniques. This reflects the growing recognition of structural failures as socio-technical phenomena that require multi-methodological analysis. A key contribution is the development of a strategic framework that classifies models by complexity, data requirements and cost based on patterns observed across the reviewed papers. This framework can be used as a practical decision support tool for researchers and practitioners to select the right model for the context and highlight the strengths and limitations of the existing approaches. The findings show that the future of structural safety is not about one single “best” model but about intelligent integration of complementary context-specific methods. This review will inform future practice by showing how different models can be combined to improve the depth, accuracy and applicability of structural failure investigations. Full article

Gepirone, a selective 5-hydroxytryptamine (serotonin) 1A (5-HT_1A) receptor agonist, offers a promising strategy for treating mood and anxiety disorders. The therapeutic importance of 5-HT_1A modulation is well established, as these receptors regulate serotonergic neurotransmission both presynaptically, in the somatodendritic regions of raphe neurons, and postsynaptically, in structures including the hippocampus, neocortex, septum, amygdala, and hypothalamus. Gepirone exhibits a distinctive pharmacological profile, acting as a full agonist at presynaptic autoreceptors and a partial agonist at postsynaptic receptors, with high affinity for 5-HT_1A and much lower affinity for 5-HT_2A receptors. Its effects on serotonergic signaling are time-dependent. Acute administration suppresses serotonergic firing through autoreceptor activation, while chronic treatment induces autoreceptor desensitization, leading to enhanced 5-HT release in projection areas. This process is complemented by partial agonism at postsynaptic 5-HT_1A receptors, which further supports long-term neuromodulation. This article provides an integrated overview of gepirone’s mechanism of action, bridging receptor pharmacology, neurophysiological adaptations, and therapeutic implications. Particular emphasis is placed on the compound’s unique dual role in regulating serotonergic tone over time, a feature that differentiates it from other 5-HT_1A-targeting agents. By linking molecular mechanisms to clinical outcomes, we highlight gepirone’s potential advantages in efficacy, safety, and tolerability compared with conventional antidepressants. This comprehensive perspective underscores gepirone as a paradigmatic example of selective 5-HT_1A modulation and offers novel insights into the development of targeted treatments for depression and anxiety. Full article

Climate change is intensifying fire regimes, thereby challenging forest ecosystems and making it more difficult to predict the fate of burned trees. The significant ecological impacts of latent tree mortality remain poorly understood. In this study, we reviewed the scientific literature on latent tree mortality in conifer forests following wildfires or prescribed fires. A total of 2294 papers published between 2000 and 2024 were identified from Scopus and Web of Science databases. Using the PICO selection method, we included 16 relevant studies in the final analysis. These studies are based on field assessment, excluding remote sensing and controlled laboratory conditions. Our research revealed that latent mortality results from multiple forms of damage and environmental stressors that disrupt hydraulic function and carbon allocation, increasing tree vulnerability to secondary biotic and abiotic stressors. The discussion is structured around four thematic areas: physiology, ecophysiology, dendrochronology, and silviculture. This approach contributes to a deeper, interdisciplinary understanding of latent tree mortality. However, predicting it remains difficult, reflecting persistent knowledge gaps. Despite the limited literature on this specific field, our review highlights the need for integrated physiological indicators, such as sap flow, transpiration, nonstructural carbohydrates and glucose concentration, as well as long-term monitoring along many growing seasons to better assess tree survival after fire. Full article

Fire spotting (FS), the process by which firebrands are lofted, transported, and ignite new fires ahead of the main flame front, plays a critical role in escalating extreme wildfire events. This systematic literature review (SLR) analyzes peer-reviewed articles and book chapters published in English from 2000 to 2023 to assess the evolution of FS models, identify prevailing methodologies, and highlight existing gaps. Following a PRISMA-guided approach, 102 studies were selected from Scopus, Web of Science, and Google Scholar, with searches conducted up to December 2023. The results indicate a marked increase in scientific interest after 2010. Thematic and bibliometric analyses reveal a dominant research focus on integrating the FS model within existing and new fire spread models, as well as empirical research and individual FS phases, particularly firebrand transport and ignition. However, generation and ignition FS phases, physics-based FS models (encompassing all FS phases), and integrated operational models remain underexplored. Modeling strategies have advanced from empirical and semi-empirical approaches to machine learning and physical-mechanistic simulations. Despite advancements, most models still struggle to replicate the stochastic and nonlinear nature of spotting. Geographically, research is concentrated in the United States, Australia, and parts of Europe, with notable gaps in representation across the Global South. This review underscores the need for interdisciplinary, data-driven, and regionally inclusive approaches to improve the predictive accuracy and operational applicability of FS models under future climate scenarios. Full article

New energy vehicles face a critical challenge in balancing the thermal safety management of high-specific-energy battery systems with the simultaneous improvement of energy density. With the large-scale application of high-energy-density systems such as silicon-based anodes and solid-state batteries, their inherent thermal runaway risks pose severe challenges to battery thermal management systems (BTMS). Currently, the thermal insulation performance, temperature resistance, and fire protection capabilities of flame-retardant materials (e.g., foam cotton, fiber felts) used in automotive batteries are inadequate to meet the demands of intense combustion and high temperatures generated during thermal failure in high-energy-density batteries. Against this backdrop, thermal insulation and fireproof aerogel materials are emerging as a revolutionary solution for the next generation of power battery thermal protection systems. Leveraging their nanoporous structure’s exceptional thermal insulation properties (thermal conductivity of 0.013–0.018 W/(m·K) at room temperature) and extreme fire resistance (temperature resistance > 1100 °C/UL94 V-0 flame retardancy), aerogels are gaining prominence. This article provides a systematic review of thermal runaway phenomena in automotive batteries and corresponding protective measures. It highlights recent breakthroughs in the selection of material systems, optimization of preparation processes, and fiber–matrix composite technologies for automotive fireproof aerogel composites. The core engineering values of these materials, such as blocking thermal runaway propagation, reducing system weight, and improving volumetric efficiency, are quantitatively validated. Furthermore, the paper explores future research directions, including the development of low-cost aerogel composites and the design of organic–inorganic hybrid composite structures, aiming to provide a foundation and industrial pathway for the research and development of next-generation high-performance battery thermal management systems. Full article