Search Results (668)

Emerging technologies are transforming Facilities Management (FM), enabling more efficient and accurate building inspections and maintenance. Mixed Reality (MR), which integrates virtual content into real-world environments, has shown potential for improving operational performance and technician training. This study presents the development and evaluation of an MR-assisted system designed to support facility operations in academic buildings. The system was tested across three case scenarios, namely plumbing, lighting, and fire sprinkler systems, using Microsoft HoloLens^®. A mixed-methods approach combined a post-use questionnaire and semi-structured interviews with twelve FM professionals, including technicians, inspectors, and managers. Results indicated that 66.67% of participants found the MR interface highly effective in visualizing systems and guiding maintenance steps. 83.33% agreed that checklist integration enhanced accuracy and learning. Technical challenges, including model drift, latency, and occasional software crashes, were also observed. Overall, the study confirms the feasibility of MR for FM training and inspection, offering a foundation for broader implementation and future research. The findings provide valuable insights into how MR-based visualization and interaction tools can enhance efficiency, learning, and communication in facility operations. Full article

Land abandonment and recurrent wildfires are major drivers of landscape transformation in Mediterranean Europe, creating favorable conditions for the spread of non-native invasive woody species. Among these, Australian wattles (genus Acacia) are particularly widespread and problematic in Portugal. This work analyzed the spatiotemporal dynamics of Acacia spp. in two municipalities of central Portugal (Sertã and Pedrógão-Grande) by combining multitemporal photointerpretation of aerial imagery (2004–2021), generalized additive models (GAMs), and local perception surveys. Results reveal a 417% increase in occupied area over the last two decades. Modeling outcomes indicate a temporal shift in invasion drivers: from an establishment phase (2004–2010), mainly constrained by altitude and proximity to primary introduction sites, to a disturbance-driven expansion phase (2010–2021), influenced by fire recurrence, slope, and land-use context. Spatial clustering persisted throughout, underscoring the role of founder populations. Surveys confirmed high public awareness of Acacia invasiveness and identified abandonment and wildfire as the main perceived triggers of spread. By integrating ecological and social dimensions, this study provides a socioecological perspective on Acacia spp. expansion in Mediterranean rural landscapes and highlights the urgent need for integrated, landscape-scale management strategies. Full article

Peatlands play a critical role in global and regional climate regulation by functioning as long-term carbon sinks, regulating hydrology, and modulating land–atmosphere energy exchange. Intact peat ecosystems store large amounts of organic carbon and stabilize local climate through high water retention and evapotranspiration, whereas peatland degradation disrupts these functions and can transform peatlands into significant sources of greenhouse gas emissions and climate extremes such as drought and fire. Indonesia contains approximately 13.6–40.5 Gt of carbon, around 40% of which is stored on the island of Sumatra. However, tropical peatlands in this region are highly vulnerable to climate anomalies and land-use change. This study investigates the impacts of major climate anomalies—specifically El Niño and positive Indian Ocean Dipole (pIOD) events in 1997/1998, 2015/2016, and 2019—on peatland cover change across South Sumatra, Jambi, Riau, and the Riau Islands. Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager/Thermal Infrared Sensor imagery were analyzed using a Random Forest machine learning classification approach. Climate anomaly periods were identified using El Niño-Southern Oscillation (ENSO) and IOD indices from the National Oceanic and Atmospheric Administration. To enhance classification accuracy and detect vegetation and hydrological stress, spectral indices including the Normalized Difference Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index (MSAVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI) were integrated. The results show classification accuracies of 89–92%, with kappa values of 0.85–0.90. The 2015/2016 El Niño caused the most severe peatland degradation (>51%), followed by the 1997/1998 El Niño (23–38%), while impacts from the 2019 pIOD were comparatively limited. These findings emphasize the importance of peatlands in climate regulation and highlight the need for climate-informed monitoring and management strategies to mitigate peatland degradation and associated climate risks. Full article

Fly ash–based geopolymers have emerged as a promising alternative to ordinary Portland cement, offering high mechanical strength and reduced environmental footprint. However, they are often limited by significant shrinkage and strength degradation when subjected to elevated temperatures. To enhance their thermomechanical performance and thermal stability, this study investigates the effects of mix proportioning parameters, alkali activator type, and thermal shock on performance deterioration. Compressive strength was evaluated for sodium- and potassium-activated fly ash geopolymer composites as a function of alkaline activator (AA) ratios, both under ambient curing and after exposure to the ISO 834 standard fire curve for 1 and 2 h. Volume change, mass loss, and density variation were analysed to interpret mechanical behaviour and relate it to structural transformations, while XRF, XRD, SEM, and particle size distribution were employed for material characterisation. Results indicate that rapid temperature changes, whether from thermal shock or high fire-heating rates, induced notable additional thermal degradation. Sodium activation achieved the highest compressive strength retention of 145% at one hour of firing, while potassium activation showed superior thermal stability with delayed densification, reaching 154% strength retention at two hours. Furthermore, SiO₂/M₂O ratio exerted the strongest influence on both mechanical and thermomechanical performance. Overall, the findings highlight that the activator type, SiO₂/M₂O ratio, and rapid temperature changes collectively exert strong control over the thermomechanical and thermophysical response of fly ash geopolymers at elevated temperatures. Full article

Wildfire regimes are undergoing rapid transformation under anthropogenic climate change, with major implications for biodiversity, carbon cycling, and ecosystem resilience. This systematic review synthesizes findings from 42 studies across global, continental, and regional scales to assess emerging patterns in fire frequency, intensity, and seasonality, and to identify climatic, ecological, and anthropogenic drivers shaping these changes. Across biomes, evidence shows increasingly fire-conducive conditions driven by rising temperatures, vapor-pressure deficit, and intensifying drought, with climate model projections indicating amplification of extreme fire weather this century. Boreal ecosystems show heightened fire danger and carbon-cycle vulnerability; Mediterranean and Iberian regions face extended fire seasons and faster spread rates; tropical forests, particularly the Amazon, are shifting toward more flammable states due to drought–fragmentation interactions; and savannas display divergent moisture- and fuel-limited dynamics influenced by climate and land use. These results highlight the emergence of biome-specific fire–climate–fuel feedback that may push certain ecosystems toward alternative stable states. The review underscores the need for improved attribution frameworks, integration of fire–vegetation–carbon feedback into Earth system models, and development of adaptive, regionally tailored fire-management strategies. Full article

Forest resources are among the most important ecosystems on the earth. The semantic segmentation and accurate positioning of ground objects in forest remote sensing (RS) imagery are crucial to the emergency treatment of forest natural disasters, especially forest fires. Currently, most existing methods for image semantic segmentation are built upon convolutional neural networks (CNNs). Nevertheless, these techniques face difficulties in directly accessing global contextual information and accurately detecting geometric transformations within the image’s target regions. This limitation stems from the inherent locality of convolution operations, which are restricted to processing data structured in Euclidean space and confined to square-shaped regions. Inspired by the graph convolution network (GCN) with robust capabilities in processing irregular and complex targets, as well as Swin Transformers renowned for exceptional global context modeling, we present a hybrid semantic segmentation framework for forest RS imagery termed GSwin–Unet. This framework embeds the GCN model into Swin–Unet architecture to address the issue of low semantic segmentation accuracy of RS imagery in forest scenarios, which is caused by the complex texture features, diverse shapes, and unclear boundaries of land objects. GSwin–Unet features a parallel dual-encoder architecture of GCN and Swin Transformer. First, we integrate the Zero-DCE (Zero-Reference Deep Curve Estimation) algorithm into GSwin–Unet to enhance forest RS image feature representation. Second, a feature aggregation module (FAM) is proposed to bridge the dual encoders by fusing GCN-derived local aggregated features with Swin Transformer-extracted features. Our study demonstrates that, compared with the baseline models TransUnet, Swin–Unet, Unet, and DeepLab V3+, the GSwin–Unet achieves improvements of 7.07%, 5.12%, 8.94%, and 2.69% in the mean Intersection over Union (MIoU) and 3.19%, 1.72%, 4.3%, and 3.69% in the average F1 score (Ave.F1), respectively, on the RGB forest RS dataset. On the NIRGB forest RS dataset, the improvements in MIoU are 5.75%, 3.38%, 6.79%, and 2.44%, and the improvements in Ave.F1 are 4.02%, 2.38%, 4.72%, and 1.67%, respectively. Meanwhile, GSwin–Unet shows excellent adaptability on the selected GID dataset with high forest coverage, where the MIoU and Ave.F1 reach 72.92% and 84.3%, respectively. Full article

The island of Gran Canaria (Spain) has undergone a significant transformation in wildfire dynamics over the past two decades, characterized by a decline in wildfire frequency but a marked increase in the severity and spatial impact of extreme events, particularly within the wildland–urban interface (WUI). This study analyzes wildfire activity between 2000 and 2020 using official datasets and statistical trend analyses, incorporating robust severity indicators and measures of burned area concentration. Results show a statistically significant decreasing trend in the number of wildfires, while burned area is extremely concentrated in a small number of high-intensity events, with four large wildfires accounting for more than 97% of the total affected area. Climatic influences on wildfire activity were assessed through the analysis of long-term meteorological indicators, focusing on trends in extreme heat days and precipitation as proxies for thermal stress and fuel moisture availability. The results indicate a substantial modification of the background climatic framework under which wildfires develop, although no direct causal relationships are inferred. In parallel, territorial processes—such as rural abandonment, increased fuel continuity, and the expansion of dispersed housing beyond consolidated settlements—act as key amplifiers of wildfire risk. Overall, the findings highlight a transition from emergency-oriented fire suppression toward resilience-based wildfire management, emphasizing the need to integrate climate adaptation, territorial planning, and stricter land-use regulation in WUI areas. Full article

Unmanned helicopters play a critical role in various fields including defense, disaster response, and infrastructure inspection. Military platforms such as the MQ-8C Fire Scout represent high-value assets exceeding $40 million per unit including development costs, particularly when compared to expendable multicopter drones costing approximately $500–2000 per unit. Unexpected failures of these high-value assets can lead to substantial economic losses and mission failures, making the implementation of Health and Usage Monitoring Systems (HUMS) essential. However, the scarcity of failure data in unmanned helicopters presents significant challenges for HUMS development, while the economic feasibility of investing resources comparable to manned helicopter programs remains questionable. This study presents a novel cross-scale transfer learning framework for vibration-based anomaly detection in unmanned helicopters. The framework successfully transfers knowledge from a source domain (Airbus large manned helicopter) using publicly available data to a target domain (Stanford small RC helicopter), achieving excellent anomaly detection performance without labeled target domain data. The approach consists of three key processes. First, we developed a multi-task learning transformer model achieving an F-β score of 0.963 (β = 0.3) using only Airbus vibration data. Second, we applied CORAL (Correlation Alignment) domain adaptation techniques to reduce the distribution discrepancy between source and target domains by 79.7%. Third, we developed a Control Effort Score (CES) based on control input data as a proxy labeling metric for 20 flight maneuvers in the target domain, achieving a Spearman correlation coefficient ρ of 0.903 between the CES and the Anomaly Index measured by the transfer-learned model. This represents a 95.5% improvement compared to the non-transfer learning baseline of 0.462. Full article

In the context of carbon neutrality, the large-scale integration of renewable energy sources has led to frequent load changes in coal-fired boilers. These fluctuations cause key operational parameters to deviate significantly from their design values, undermining combustion stability and reducing operational efficiency. To address this issue, we introduce a novel predictive control method to enhance the control precision of key parameters under complex variable-load conditions, which integrates a coupled predictive model and real-time optimization. The predictive model is based on a coupled Transformer-gated recurrent unit (GRU) architecture, which demonstrates strong adaptability to load fluctuations and achieves high prediction accuracy, with a mean absolute error of 0.095% and a coefficient of determination of 0.966 for oxygen content (OC); 0.0163 kPa and 0.987 for bed pressure (BP); and 0.300 °C and 0.927 for main steam temperature (MST). These results represent substantial improvements over lone implementations of GRU, LSTM, and Transformer models. Based on these multi-step predictions, a WOA-based real-time optimization strategy determines coordinated adjustments of secondary fan frequency, slag discharger frequency, and desuperheating water valves before deviations occur. Field validation on a 300 t/h boiler over a representative 24 h load cycle shows that the method reduces fluctuations in OC, BP, and MST by 62.07%, 50.95%, and 40.43%, respectively, relative to the original control method. By suppressing parameter variability and maintaining key parameters near operational targets, the method enhances boiler thermal efficiency and steam quality. Based on the performance gain measured during the typical operating day, the corresponding annual gain is estimated at ~1.77%, with an associated CO₂ reduction exceeding 6846 t. Full article

The possibility of transforming debris from a ceramic clinker into high quality foam granulate is discussed. The foaming process, which was carried out at temperatures 150–200 °C higher than the production process, was studied by HSM and DTA-TG coupled with MS. Phase and structural transformations were investigated by XRD and SEM, respectively. The results highlight that the foaming mechanism is related to the release of oxygen due to a reduction in Fe³⁺ to Fe²⁺ after the melting of hematite and the dissolution of pseudobrookite present in clinker waste. Granules obtained after 30 min of holding at 1280 °C are impermeable to water and, depending on the cooling applied, have a density between 0.4 and 0.7 g/cm³, porosity between 70 and 85 vol %, and compressive strength between 0.7 and 1.1 MPa. These results meet the requirements for high-quality fire-resistance lightweight aggregates. Full article

In the context of green transition and digital transformation, forestry is becoming a strategic area of application of current modern technologies. The Internet of Things (IoT), artificial intelligence (AI), big data analysis (Big Data) and Digital Twins define the basic infrastructure of smart forestry. By connecting sensors, drones and satellites, IoT allows for continuous monitoring of forest ecosystems, risk anticipation and decision optimization in real-time. The purpose of this study is to perform a comprehensive narrative analysis of the relevant scientific literature from the recent period (2020–2025) regarding the application of IoT in forestry, highlighting the conceptual, technological and institutional developments. Based on a selection of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (29 full-text articles), four major axes are analyzed: (A) forest fire detection and prevention; (B) climate-smart forestry and carbon accounting; (C) forest digitalization through the concepts of Forest 4.0, Forest 5.0 and Digital Twins; (D) sustainability and digital forest policies. The results show that IoT is a catalyst for the sustainable transformation of the forest sector, supporting carbon accounting, climate-risk reduction and data-driven governance. The analysis highlights four major developments: the consolidation of IoT–AI architectures, the integration of IoT and remote sensing, the emergence of Forest 4.0/5.0 and Digital Twins and the growing role of governance and data standards. These findings align with the objectives of the EU Forest Strategy 2030 and the European Green Deal. Full article

This article analyzes prohibitions against the destruction of flora in the calendrical regulations of the late Western Han period and in the millenarian cosmological discourses in the Taiping jing 太平經 (Canon of Great Peace). The study focuses on the “Zhaoshu sishi yueling wushi tiao” 詔書四時月令五十條 (“Edict of Monthly Ordinances for the Four Seasons in Fifty Articles”) which was promulgated by Wang Mang in 5 CE. The Edict prohibited setting fire to forests and was intended to restore cosmic harmony. At the time, natural disasters and celestial anomalies were interpreted as signs of the loss of the Mandate of Heaven. Heavenly patterns and hemerology play a central role here by enabling environmental regulations to be incorporated into a political logic of legitimization. The Canon of Great Peace reinterprets these norms by replacing seasonal cycles with an interpretation of balance between yin and yang and by giving environmental prohibitions eschatological significance. Thus, calendrical regulations for natural resource management transform into an apocalyptic discourse in which the natural environment becomes the setting for cosmic disorder that must be avoided. Full article

The increasing challenges posed by climate change demand holistic approaches to mitigate ecosystem degradation. In Mediterranean-type regions—biodiversity hotspots facing intensified droughts, fires, and biological invasions—such strategies are particularly relevant. Among invasive species, Acacia longifolia produces substantial woody and leafy biomass when removed, offering an opportunity for reuse as soil-improving material after adequate processing. This study aimed to evaluate the potential of invasive A. longifolia Green-waste compost (Gwc) as a soil amendment to promote soil recovery and native plant establishment after fire. A field experiment was carried out in a Mediterranean ecosystem using Arbutus unedo, Pinus pinea, and Quercus suber planted in control and soils treated with Gwc. Rhizospheric soils were sampled one year after plantation, in Spring and Autumn, to assess physicochemical parameters and microbial community composition (using composite samples) through Next-Generation Sequencing. Our study showed that Gwc-treated soils exhibited higher moisture content and nutrient availability, which translated into improved plant growth and increased microbial richness and diversity when compared with control soils. Together, these results demonstrate that A. longifolia Gwc enhances soil quality, supports increased plant fitness, and promotes a more diverse microbiome, ultimately contributing to faster ecosystem recovery. Transforming invasive biomass into a valuable resource could offer a sustainable, win–win solution for ecological rehabilitation in fire-affected Mediterranean environments, enhancing soil and ecosystem functioning. Full article

Lithium-ion batteries are extensively employed in electric vehicles (EVs) and energy storage systems (ESSs) owing to their high energy density, long cyclability, and cost-effectiveness. However, the use of flammable electrolytes makes them inherently susceptible to thermal runaway (TR), which can lead to ignition, explosion, and large-scale fires. Accordingly, early detection of defect internal conditions that precede thermal events is essential for ensuring battery safety. This study proposes a time-resolved partial discharge (TRPD)-based diagnostic method for identifying early electrical precursors of fire hazards in lithium-ion batteries. Both destructive (ex situ) and non-destructive (in situ) experiments were performed to collect defect signal data under physical deformation and accelerated degradation conditions. Through fast fourier transform (FFT) analysis of the acquired signals, specific frequency-domain characteristics associated with micro internal short circuits (MISC) were identified, particularly within the 3.9 MHz, 11.9 MHz, and 19 MHz bands. Defect signals were clearly distinguishable from background common-mode voltage (CMV) noise, confirming the diagnostic sensitivity of the proposed approach. The results demonstrate that the TRPD-based technique enables early recognition of latent insulation degradation and internal short-circuit phenomena before thermal runaway occurs. This work bridges the gap between conventional insulation monitoring and battery safety diagnostics, providing a scalable framework for integrating high-frequency signal analysis into EV and ESS battery management systems for fire prevention. Full article

This study addresses the drawbacks of traditional dispersed fire retardants—such as anisotropy, reduced strength, and poor filler impregnability—by developing in situ-formed hybrid epoxy composites. The materials, based on diglycidyl ether of bisphenol A and triethylenetetramine, were modified with a solution of zinc sulfate heptahydrate in orthophosphoric acid. This approach yielded near-spherical microparticles (6–16 µm) within the polymer matrix. The scientific novelty lies in investigating how such in situ particle formation affects material properties. The modification significantly enhanced fire resistance: char residue increased 1.7–2.2-fold, while total heat release, peak heat release rate, and smoke release were reduced by up to 60.5%, 40.2%, and 70%, respectively. The observed increase in the mass loss rate suggests that accelerated thermal-oxidative degradation promotes char formation. These findings, supported by scanning electron microscopy, energy-dispersive X-ray spectroscopy, and Fourier-transform infrared spectroscopy data, demonstrate the efficacy of the in situ strategy for creating high-performance, fire-safe epoxy composites. Full article