Search Results (682)

A substantial proportion of wildfires in Mediterranean regions continue to be recorded without information about the cause or source of ignition, limiting our ability to understand ignition drivers and design effective prevention strategies. In this study, we develop a spatially harmonised wildfire database for mainland Spain by integrating ignition records from the Spanish General Fire Statistics (EGIF) with fire perimeters generated from satellite images. We then apply a Random Forest classifier to infer ignition causes for events lacking cause attribution. To interpret model behaviour, we use Shapley Additive Explanation (SHAP) values at both global and local scales. Results indicate that human-caused ignitions are dominant, with intentional and negligence-related fires accounting for 52.13% of all known events, although they are associated with contrasting climatic and land-use settings. Negligence-related fires tend to occur under hot, dry and windy conditions, often in agricultural interfaces, whereas intentional fires are more frequent under cooler and wetter conditions and in areas with higher population density and land-use change. Lightning-caused fires represent a small fraction of total ignitions (3%) but exhibit a distinct climatic signature, occurring primarily in sparsely populated areas, under intermediate moisture conditions, and often leading to larger burned areas. Despite strong overall model performance (F1-score = 0.82), minority classes (e.g., lightning and fire rekindling, 0.17%) remain challenging to classify, reflecting both data imbalance and uncertainty in causal attribution. Overall, the combined use of machine learning and explainable AI provides a coherent spatial characterisation of wildfire ignition drivers across mainland Spain, highlights systematic differences among ignition causes, and identifies key limitations in existing fire cause records. This framework represents a practical step towards improving fire cause information by integrating remote sensing products with field-based fire reports, thereby supporting more targeted and evidence-based fire risk management. Full article

Artificial intelligence (AI)-enabled unmanned aerial vehicles (UAVs) are rapidly emerging as transformative technologies for sustainable healthcare logistics, particularly in remote and infrastructure-constrained regions. Despite growing implementation, the environmental, social, and governance (ESG) implications of these systems remain insufficiently synthesized in the literature. This study conducts a PRISMA-ScR-guided Systematic Review of 37 peer-reviewed studies selected from 333 records across six major scientific databases (2015–2026). The analysis reveals a sharp acceleration of research after 2021, with over 80% of publications produced between 2021 and 2024, indicating increasing global interest in AI-supported autonomous medical logistics. Evidence demonstrates that AI-enabled drones can substantially reduce delivery times; expand access to blood, vaccines, and essential medicines; and enhance emergency response capacity in rural and disaster-affected environments. From a sustainability perspective, AI-driven route optimization and autonomous navigation may reduce transport-related emissions, supporting climate-responsive healthcare supply chains. However, large-scale deployment remains constrained by regulatory fragmentation, cybersecurity risks, operational limitations, and challenges with social acceptance. This review proposes an ESG-oriented framework linking technological innovation, ethical governance, and equitable healthcare access while identifying key research gaps in lifecycle sustainability assessment, cost-effectiveness modeling, and real-world implementation aligned with the Sustainable Development Goals (SDGs). Full article

Underwater micro-bubble detection entails multiple challenges, including diminutive target sizes, sparse pixel information, pronounced specular highlights and water scattering, indistinct bubble boundaries, and adhesion or overlap between instances. To address these issues, we propose HSMD-YOLO, an improved detector tailored for high-resolution micro-bubble detection and built upon YOLOv11. The model incorporates three novel components: the Scale Switch Block (SSB), a scale-transformation module that suppresses artifacts and background noise, thereby stabilizing edges in thin-walled bubble regions and enhancing sensitivity to geometric contours; the Global Local Refine Block (GLRB), which achieves efficient global relationship modeling with an asymptotic linear complexity ($O\left(N\right)$) in spatial dimensions while further refining local features, thereby strengthening boundary perception and improving bubble–background separability; and the Bidirectional Exponential Moving Attention Fusion (BEMAF), which accommodates the multi-scale nature of bubbles by employing a parallel multi-kernel architecture to extract spatial features across scales, coupled with a multi-stage EMA based attention mechanism to enhance detection robustness under weak boundaries and complex backgrounds. Experiments conducted on an Side-Illuminated Light Field Bubble Database (SILB-DB) and a public gas–liquid two-phase flow dataset (GTFD) demonstrate that HSMD-YOLO achieves mAP@50 scores of 0.911 and 0.854, respectively, surpassing mainstream detection methods. Ablation studies indicate that SSB, GLRB, and BEMAF contribute performance gains of 1.3%, 2.0%, and 0.4%, respectively, thereby corroborating the effectiveness of each module for micro-scale object detection. Full article

The efficient exploration and development of mineral resources rely on deep mining and correlation analysis of massive, multi-source, and unstructured geological data. Knowledge graph technology provides a structured solution for integrating fragmented knowledge in the field of mineral resources. This study takes the iron ore resources in Linyi City, Shandong Province as a typical case and proposes a method framework for automatically constructing a regional mineral resource knowledge graph from unstructured text. Firstly, seven types of mineral entities (location, ore body, scale, type, attitude, alteration, development degree) and five semantic relationships (type, scale, location, inclusion, development) were defined, and a high-quality Chinese annotation corpus containing 10,434 entities and 6660 relationships was constructed through domain ontology design. Secondly, BiLSTM-CRF, BiGRU-CRF, and various BERT based models were compared in the named entity recognition task, and it was found that the optimized BERT-CRF model achieved the best performance (F1 score: 82.8%). The BERT based model significantly outperforms traditional PCNN and BiGRU models, achieving an F1 score of 98.14%, which was found in relation extraction tasks. Finally, based on the extracted triples, a visual knowledge graph of iron ore resources in Linyi City was constructed using the Neo4j graph database, in order to achieve knowledge association queries and visual navigation. Full article

Dynamic characterization of calcareous (carbonate) sands is essential for performance-based design of offshore foundations, coastal reclamation, and marine infrastructure in tropical and subtropical regions. In contrast to silica sands, carbonate sediments are biogenic and typically comprise angular, irregular grains with intra-particle voids and fragile skeletal microstructure. These traits promote grain crushing and fabric evolution at relatively low-to-moderate confinement, leading to pronounced stress dependency, strong nonlinearity with strain amplitude, and substantial scatter in laboratory stiffness and damping measurements. Consequently, empirical correlations calibrated primarily on quartz sands may yield biased estimates when transferred to carbonate environments. This study presents an ML-driven, leakage-aware benchmarking framework for predicting two key dynamic parameters of biogenic calcareous sands, damping ratio $D$ and shear modulus $G$, using standard tabular descriptors commonly available in geotechnical practice. Two consolidated experimental databases were curated from resonant column and cyclic triaxial measurements ($D$: $n=890$; $G$: $n=966$), spanning mean effective confining stress $25 \le {\sigma }_m^{\prime }\le 1600 \mathrm{k}$Pa and a wide range of density and gradation conditions. To emphasize transferability, explicit deposit/site labels were excluded, and missingness arising from heterogeneous reporting was handled through a consistent preprocessing pipeline (training-only imputation, categorical encoding, and scaling). Eleven regression algorithms were evaluated, covering linear baselines, regularized regression, neighborhood learning, single trees, bagging and boosting ensembles, kernel regression, and a feedforward neural network. Performance was assessed using $R^2$, RMSE, and MAE on training/validation/test splits, and engineering credibility was supported through explainability-based diagnostics to verify mechanically plausible sensitivities. Results show that ensemble-tree models (Extra Trees and Random Forest) provide the most reliable accuracy–robustness balance across both targets, consistently outperforming linear models and the tested SVR configuration and exhibiting stable validation-to-test behavior. The explainability audit confirms physically meaningful separation of governing controls: stiffness is primarily stress-controlled (${\sigma }_m^{\prime }$ dominant for $G$), whereas damping is primarily strain-controlled ($\gamma$ dominant for $D$). The proposed framework supports practical deployment as a fast surrogate for generating $G\hspace{0.17em}-\hspace{0.17em}\gamma$ and $D\hspace{0.17em}-\hspace{0.17em}\gamma$ curves within the training domain and for guiding targeted laboratory test planning in carbonate settings. Full article

Guaranteeing the ecological security of the Dongting Lake Basin is of paramount importance for national-scale programs, such as the Yangtze River Economic Belt and aquatic conservation projects. Within this framework, carbon storage and its determining drivers act as essential indicators of regional ecological stability. However, the historical trajectory of carbon pools and their response to future multi-scenario land-use transitions remain insufficiently understood. Therefore, this study aims to quantify the spatiotemporal evolution of carbon storage in the Dongting Lake Basin from 2000 to 2020 and project its future dynamics under diverse development pathways. This study, utilizing land use data from 2000 to 2020 and the carbon density database of the Dongting Lake Basin, assessed land use changes over two decades and determined the spatiotemporal distribution of carbon storage. Additionally, using 17 driving factors and various spatial policies, the study projected the land use and land cover changes (LUCC) for 2030 under four scenarios: natural development, ecological protection, economic development, and planned development. The spatiotemporal distribution of carbon storage and its response mechanisms were analyzed for each scenario. The results showed that carbon storage was directly impacted by LUCC, with an overall “decrease-increase-decrease” trend from 2000 to 2020, resulting in a net increase of 3.685 × 10⁶ t. By 2030, the changes in carbon storage under the natural development, ecological protection scenario, economic development, and planned development scenarios were projected to be −1.008 × 10⁷ t, 1.276 × 10⁷ t, 3.292 × 10⁸ t, and −1.200 × 10⁵ t, respectively. Notably, the ecological protection scenario showed a significant positive growth in carbon storage, primarily driven by an increase in forest and wetland areas. Additionally, the spatial distribution of carbon storage exhibited a pattern of “high in the west and low in the east”. These results imply that to achieve the “Dual Carbon Strategy”, future land use planning in the Dongting Lake Basin should prioritize ecological protection and planned development models, including strict control of construction land expansion, increasing ecological land area, and enhancing carbon storage. Full article

The increasing scale and structural flexibility of modern wind turbine rotors have made real-time monitoring and active control of blade tip deflection a critical requirement for ensuring operational safety, particularly regarding blade-tower clearance. Since direct measurement through physical sensors is often impractical due to high costs, installation difficulties and maintenance challenges, this work proposes a data-based framework for out-of-plane blade tip deflection estimation. The approach introduces a systematic and hierarchical input selection framework that evaluates sensor signal groups, ranging from standard SCADA measurements to configurations including auxiliary nacelle/tower sensors and dedicated blade-root instrumentation. By combining Spearman correlation and spectral coherence, the proposed framework ensures consistent representation of key turbine dynamics across all operating regions. This framework provides a structured trade-off between implementation feasibility and estimation fidelity, enabling tailored solutions for applications such as structural health monitoring and safety-critical active control. Compact Feedforward Neural Network (FNN) and Time-Delay Neural Network (TDNN) architectures, whose hyperparameters are optimized via Bayesian optimization, are employed to achieve high estimation accuracy while preserving computational efficiency. Evaluated through high-fidelity aeroelastic simulations of the NREL 5 MW turbine using the industry-standard FAST (Fatigue, Aerodynamics, Structures, and Turbulence) tool across all operating conditions, the approach achieves $R^2=0.894$ using SCADA-only inputs, $R^2=0.973$ when augmented with nacelle and tower-top sensors and a peak fidelity of $R^2=0.989$ using blade-root bending moment data. These results demonstrate that high-fidelity virtual sensing is attainable without blade instrumentation, providing a viable pathway for real-time tip clearance monitoring and fatigue mitigation. This directly enhances the operational resilience of wind energy systems and their contribution to the stability of renewable-dominated power grids. Full article

Heritage Building Information Modeling (HBIM) is accelerating the transition from reactive restoration to preventive conservation in architectural heritage management. Nevertheless, research at the heritage-cluster scale remains limited, particularly in terms of multi-source data integration, dynamic value–risk coupling, and lifecycle-oriented decision support. This study proposes an intelligent HBIM-based framework designed to support integrated data processing, automated value–risk assessment, and preventive intervention planning for masonry heritage clusters. The framework is validated through its application to the Suopo Ancient Watchtower Complex in Danba, Sichuan, consisting of 84 polygonal stepped-in stone towers. By integrating 3D laser scanning, unmanned aerial vehicle (UAV) oblique photogrammetry, and historical archival data, a closed-loop workflow is established, spanning data acquisition, parametric semantic modeling, and intervention prioritization. A dedicated parametric component library and hierarchical semantic database tailored to irregular polygonal masonry significantly enhance modeling consistency, semantic coherence, and cross-building reusability. Leveraging the Revit Application Programming Interface (API) and Dynamo, the framework embeds a value–risk model (P = V × R), enabling automated component-level evaluation, real-time visualization of conservation priorities, and one-click generation of intervention lists. Results demonstrate improved modeling accuracy, efficiency, and decision reliability compared with conventional manual workflows. The framework offers a scalable and replicable pathway for sustainable conservation of masonry heritage clusters in high-seismic regions and provides a foundation for future integration with IoT-enabled digital twin systems. Full article

Electrocardiography (ECG) is a key biosensing technique for assessing cardiac function and autonomic activity. Accurate detection of R-peaks and precise calculation of R-R intervals (RRIs) are essential for heart rate variability (HRV) analysis; however, automated detection algorithms remain vulnerable to local misdetections, such as false positives or missed beats (false negatives), caused by noise, baseline fluctuations, or waveform variability. Conventional correction approaches based on filter or threshold adjustment may introduce new errors outside the target region, highlighting the need for an intuitive and localized manual correction capability. To address this issue, we developed a prototype graphical user interface (GUI)-based ECG viewer implemented in Fortran for high computational efficiency. The system enables interactive insertion and deletion of detected R-peaks, with recalculation of the RRI time series and automatic updating of related analyses, including power spectral density, histograms, Lorenz plots, and polar plots. Validation using synthetic ECG signals at four sampling frequencies (125–1000 Hz) and three display time scales (2, 5, and 10 s) demonstrated correction errors below 0.7% and stable update times within 20–30 ms. When applied to real ECG recordings from the MIT-BIH Arrhythmia Database (records 115, 122, and 209; MLII lead), the GUI-derived RRIs achieved accuracies exceeding 0.985 at a strict ±10 ms tolerance and reached 1.000 at ±20 ms or higher, including recordings with frequent atrial premature contractions. These results indicate that the proposed system provides reliable feedback for localized correction of R-peak misdetections without altering the underlying ECG signal. The proposed algorithm may support future research and experimental applications in biosignal processing. Full article

Accurately predicting the ultimate tensile strain of full-scale pipelines with unequal wall thickness containing cracked girth weld joints is essential for strain-based design, structural integrity assessment, and safe operation. However, many existing limit state prediction methods for full-scale girth welds are developed for equal wall thickness configurations or idealized geometries, and their applicability to unequal wall thickness conditions remains limited. To address this gap, this paper develops a limit state prediction model for the ultimate tensile strain of cracked girth welded joints in full-scale pipelines with unequal wall thickness. The model is established using a numerical database generated from finite element simulations, incorporating realistic pipe geometry, material properties, wall thickness mismatch, and representative crack defect characteristics. By considering the stress and strain concentration effects induced by geometric non-uniformity in the weld region, the proposed model provides a practical and efficient tool for limit state evaluation. During pipeline construction, it supports the formulation of quantitative requirements for key design and fabrication parameters, such as the strength matching level. During stable operation, it enables reliable prediction of the strain capacity of existing girth welds in pipelines with unequal wall thickness, thereby supporting integrity management and decision making for safe service. Full article

The use of antibiotics as growth promoters in livestock production has contributed to the emergence and spread of antimicrobial resistance (AMR), posing a significant global public health threat specifically from the projected mortality burden. Although many countries have restricted the non-therapeutic use of antibiotics, practical and effective alternatives are still required to maintain livestock productivity. This scoping review examines the current evidence on non-antibiotic compounds evaluated as growth-promoting agents in livestock production. The primary objective of this search was to generate a comprehensive list of commonly applied alternatives to antibiotics used as growth promoters in livestock systems. A search was conducted in the CAB Abstracts, Web of Science Core Collection, and AGRICOLA databases. Prior to the scoping review, an initial list of alternatives to antibiotic components was generated through a screening of selected scientific sources and subsequently verified using Google Scholar for the period 2010–2025. This list included brief descriptions of each component, which were used to inform the keyword strategy for the scoping review. Eligible studies were screened in accordance with PRISMA-ScR guidelines, and data were extracted on compound type, livestock species, geographic region, and reported performance outcomes. The alternatives identified included probiotics and prebiotics, phytogenic compounds and essential oils, enzymes and organic acids, vaccines and immunostimulants, bacteriophages, and competitive exclusion products. A total of 1230 records were retrieved and imported into Zotero for reference management. After removal of duplicate records using Zotero’s built-in deduplication tool, 377 unique records remained for screening. Overall, these compounds demonstrated variable effects on feed efficiency, weight gain, and gut health. However, most studies were limited in scale, duration, and methodological consistency. As a result, comprehensive comparative trials and large-scale field evaluations are needed to support evidence-based policy recommendations and the sustainable implementation of alternatives to antibiotics in livestock production systems. Our findings identified six major categories that represent the most frequently reported alternatives to antibiotic growth promoters. Although probiotics, phytogenic, and organic acids were the most extensively studied, substantial heterogeneity in trial design, dosage, and production systems limited meaningful cross-comparisons. In addition, most studies focused on poultry and swine, with comparatively fewer investigations involving ruminant species. This scoping review was not intended to evaluate the efficacy or practical applicability of these alternatives; such assessments require further standardized and extensive studies before recommendations for their widespread application can be made. Full article

An assessment and prediction system for the quality of public water networks was developed, using Timișoara, Romania, as a case study. This was implemented on a Google Firebase cloud storage system and comprised twelve ML algorithms applied to test samples for drinkability and used in predictions of upcoming samples. The system compares 17 water quality parameters to the World Health Organization and public reports of Timișoara drinking water standards for 804 samples. The system provides real-time data storage, drinkability prediction for the reservoir water system, and rule-based critical water recommendations for elementary treatment in samples. The most accurate and best-calibrated against random forest, gradient boosting, and Logistic Regression algorithms was the decision tree algorithm of the ML models. The experimental findings also determine the regions of the worst and best water quality and propose respective treatment. In contrast to previous research and structures, the paper demonstrates an approved stable solution for smart water monitoring, correlating practical deployment with sophisticated data-based conclusions. The results contribute to enhancing public health, enhancing water management measures, and upscaling the system for larger-scale applications. Full article

Wildfire monitoring in tropical regions requires robust frameworks capable of transforming heterogeneous satellite detections into consistent, event-level information suitable for decision support. This study presents the DescrEVE Fogo (Descrição de Eventos de Fogo) framework, a relational and scalable system that models daily fire events in Brazil by integrating Advanced Very High Resolution Radiometer (AVHRR), Moderate-Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) active-fire detections within a unified Structured Query Language (SQL)/PostGIS environment. The framework formalizes a mathematical and computational model that defines and tracks fire fronts and multi-day fire events based on explicit spatio-temporal rules and geometry-based operations. Using database-native functions, DescrEVE Fogo aggregates daily fronts into events and computes intrinsic and environmental descriptors, including duration, incremental area, Fire Radiative Power (FRP), number of fronts, rainless days, and fire risk. Applied to the 2003–2025 archive of the Brazilian National Institute for Space Research (INPE) Queimadas Program, the framework reveals that the integration of VIIRS increases the fraction of multi-front events and enhances detectability of larger and longer-lived events, while the overall regime remains dominated by small, short-lived occurrences. A simple, prototype fire-type rule distinguishes new isolated fire events, possible incipient wildfires, and wildfires, indicating that fewer than 10% of events account for more than 40% of the area proxy and nearly 60% of maximum FRP. For the 2025 operational year, daily ignition counts show strong temporal coherence with the Global Fire Emissions Database version 5 (GFEDv5), albeit with a systematic positive bias reflecting differences in sensors and event definitions. A case study of the 2020 Pantanal wildfire illustrates how front-level metrics and environmental indicators can be combined to characterize persistence, spread, and climatic coupling. Overall, the database-native design provides a transparent and reproducible basis for large-scale, near-real-time wildfire analysis in Brazil, while current limitations in sensor homogeneity, typology, and validation point to clear avenues for future refinement and operational integration. Full article

Saline–alkali soil salinization is a global ecological crisis affecting 932 million hectares of land worldwide, posing a severe threat to food security and ecological sustainability. Traditional improvement methods, such as chemical amendments and hydraulic engineering, are limited by high costs and environmental risks, whereas microbial amendments have emerged as eco-friendly and sustainable alternatives due to their ability to regulate soil microenvironments and enhance plant stress resistance. However, a comprehensive synthesis of their core mechanisms, global application progress, and regional adaptation characteristics is still lacking, hindering the standardization and promotion of related technologies. This review, conducted in accordance with PRISMA guidelines, systematically synthesizes 112 core studies (1990–2025) retrieved from Web of Science, Scopus, and CNKI databases, focusing on three core research objects: salt-tolerant microbial communities in saline–alkali soils (dominant taxa, functional genes, metabolic characteristics), development and optimization of microbial amendments (strain screening, composite formulation, carrier selection), and mechanisms and application effects of microbial remediation (soil–plant–microbe interactions, physicochemical improvement, crop growth promotion). Key findings include the following. (1) Dominant microbial taxa (e.g., Proteobacteria, Actinobacteria) exhibit region-specific adaptation strategies, with salt tolerance thresholds and functional characteristics varying by soil type (coastal vs. inland saline–alkali soils). (2) Composite microbial amendments, especially those combined with biochar or organic fertilizers, achieve synergistic effects in desalination, alkali reduction, and fertility improvement. (3) Core mechanisms involve organic acid-mediated pH regulation, EPS-driven ion adsorption, and plant hormone-induced stress tolerance. (4) Microbial remediation technologies have been successfully applied globally (e.g., China, Africa, Americas), resulting in average crop yield increases of 15–42% and soil salinity reductions of 30–50%. This review provides a standardized technical framework for the development and application of microbial amendments, offers theoretical support for region-specific remediation strategies, identifies key challenges (e.g., strain stability, cost control) and future research directions (e.g., gene-edited strains, smart monitoring integration), and thus facilitates the industrialization and large-scale promotion of microbial remediation technologies to address global saline–alkali soil issues. Full article

Cyclonic extreme events have recently undergone an important boost over the Mediterranean Sea, a trend closely linked to ongoing strong climate variations. Several studies are explaining the combination of many different effects that increase the frequency of mesoscale vortices’ intensification, namely Mediterranean tropical-like cyclones (TLCs), until the stage of Medicanes. Among these effects, processes like sea–atmosphere energy exchanges, baroclinic instability, and the release of latent heat lead to the intensification of these systems into fully tropical-like structures. This study investigates the formation and development of Ianos, the most intense Mediterranean tropical-like cyclone recorded in recent years, which affected the Ionian Sea and surrounding regions in September 2020. Using satellite observations and remote sensing data, the study applies a dual approach to characterise the system evolution across the spatial and temporal scales. Firstly, proper orthogonal decomposition (POD) is exploited to assess temperature and pressure fluctuations derived from the geostationary database of Meteosat Second Generation (MSG-11)/SEVIRI. POD allows for the identification of dominant modes of variability and the quantification of energy distribution across different spatial structures during the cyclone’s lifecycle. The decomposition reveals that a small number of orthogonal modes capture a significant proportion of the total variance, highlighting the emergence and persistence of coherent structures associated with the cyclone’s core and peripheral convection. To support scale-dependent energy organisation and dissipation within Ianos, total-period and three-period analyses were carried out, in addition to early-stage intensification patterns and implications for meteorological scale assessments. From the study on the temperatures’ spatio-temporal evolution, a comparison in the POD spectra and of the structures during the peak of intensity was carried out between the Ianos TLC and the Faraji and Freddy tropical cyclones. Additional multi-sensor data from Suomi NPP and Sentinel-3 satellites were integrated to analyse the evolution of the same parameters, also taking into account an evaluation of the vertical temperature gradient, over a 4-day period encompassing the full life cycle of Ianos. The study of the daily evolution helps investigate the spatial trends around the warm core regions, identifying the pressure minima for a comparison with the BOLAM and ERA5 databases of the mean sea level pressure. Overall, this study demonstrates the value of combining dynamic decomposition methods with high-resolution satellite datasets to gain insight into the multiscale structure and convective energetics of Mediterranean tropical-like cyclones. Some significant patterns come out from the spatial organisation of deep convection that seem to be linked to the permanent structures of atmospheric fluctuations near the warm core centre. Full article