Search Results (1,806)

The spatial pattern of building damage produced by an earthquake carries information that classical building-by-building vulnerability indices cannot capture. This study presents one of the first frameworks to use Topological Data Analysis (TDA), a set of methods that quantify the “shape” of data, to characterise the spatial topology of seismic damage across an urban building inventory. Using the geo-referenced centroids of buildings as a point cloud, a sequence of connectivity graphs (a Vietoris–Rips filtration) is built at increasing distance scales, and persistent homology is used to track which spatial features appear and disappear. From this we extract four interpretable descriptors: Betti numbers (the numbers of connected building clusters and of enclosed gaps), persistence entropy (a measure of how disordered the damage pattern is), total persistence (the combined lifespan of all topological features), and the Wasserstein-2 distance (how far the post-earthquake pattern has moved from the intact pre-earthquake pattern). These descriptors form a physics-informed feature vector that is used to predict the building-cluster damage state. The developed framework was trained, tested, and validated on 1490 buildings over seven post-earthquake scenarios. Lognormal fragility parameters were estimated with maximum likelihood estimation, and an Artificial Neural Network (ANN) and a Random Forest (RF) were retrained on the same 593-building training dataset for comparison. On the 847-building benchmark, the TDA framework reached 93.3% accuracy (95% CI: 91.4–94.9%), F1 = 0.921 (0.902–0.940), and AUC = 0.933, using a stratified 70/15/15 split (training = 593, validation = 127, test = 127). This is a 6.0-percentage-point gain over the retrained ANN and a 12.1-percentage-point gain over the HAZUS-MH index (McNemar p = 0.017). Damage was recorded on the six EMS-98 states DS0–DS5, with DS4 and DS5 merged into a single class to give a five-class taxonomy, and building-type-specific inter-storey drift ratio thresholds were validated against EN 1998-3 (Eurocode 8 Part 3). Exact Rips computation is practical only for clusters up to about 2000 buildings; for larger populations, a CGAL (Computational Geometry Algorithms Library)-based sparse approximation with O(N log N) cost is recommended. It seems that the topological descriptions of the damage field may provide predictive information above and beyond that given by density and ground motion intensity and offer a reproducible tool for post-earthquake screening. Full article

Large sensor arrays are an increasingly popular sampling method among ecologists. To last in the field, sensor housing needs to be resistant to damage from both weather and animals. The popular AudioMoth acoustic recorder does not have integral weather-resistant housing and is deployed by users in a wide variety of protective cases. One inexpensive way to protect AudioMoths is to deploy them in plastic bags, which offer moderate weather resistance but are susceptible to chewing damage from small mammals. In this study, we test the effectiveness of commercially available mammal deterrents in preventing such chewing damage. We deployed 115 treatment-control pairs across two grids in temperate forests in Pennsylvania. Bag treatments consisted of Liquid Fence, Bonide, and a cayenne and Vaseline mixture. For all deterrents, there was no statistically significant difference in the proportion or severity of mammal chewing damage between treatments and controls. Counter to expectations, for all three treatments, more of the bags treated with a deterrent were damaged by mammal chewing than the paired control bags. Our results strongly suggest that single applications of these three deterrents have no useful effect on preventing mammal chewing damage to sensor housing in the field. Full article

Understanding olive tree metabolism and its interactions with biotic and abiotic factors is crucial for the sustainability and resilience of olive cultivation in a changing agroclimatic context. In response to biotic stress, plants activate complex signaling pathways that trigger the production of specialized metabolites, particularly volatile organic compounds (VOCs). This study investigates the volatolomic profile naturally emitted by whole olive fruits using an integrated metabolomic strategy that combines design of experiments (DoE), targeted and untargeted analyses, and multivariate statistics. Optimal headspace solid-phase microextraction (HS-SPME) conditions were established using 30 g of sample, a 50 °C extraction temperature, a 50 min extraction time, and a 3 min injection at 250 °C, identifying extraction time and temperature as the most critical factors influencing VOC recovery. The data demonstrated significant cultivar-dependent variation in the volatile emissions from healthy olive fruit among six representative varieties. Furthermore, robust partial least squares-discriminant analysis (PLS-DA) and random forest models provided a clear separation between healthy and damaged olive fruits, achieving high predictive accuracy (90%) and identifying key volatile biomarkers derived from the lipoxygenase (LOX) pathway. This novel multivariate optimization approach (SPME–GC/MS) represents a powerful tool for establishing a reliable chemical fingerprint of the olive fruit “volatilome” under evolving agroclimatic challenges. Full article

Background: Ambulatory blood pressure monitoring (ABPM) offers a comprehensive assessment of mean blood pressure (BP), circadian patterns, and out-of-office hypertension phenotypes. We evaluated the association of ABPM parameters with left ventricular hypertrophy (LVH) and hypertensive retinopathy (HRP) in children with hypertension. Methods: This retrospective analysis included 269 children who underwent ABPM at a tertiary care facility. Multivariable logistic regression was used to evaluate associations between BP Z scores and target organ damage (TOD), adjusting for age, sex, and body mass index standard deviation score. Machine learning techniques (logistic regression, random forest, and multilayer perceptron) were also evaluated using 10-fold cross-validation across exploratory combinations of the 90th or 95th percentiles and BP-load thresholds of 25% or 50%. Results: LVH was detected in 45/247 patients (18%) and HRP was observed in 22/240 patients (9%). The 24 h systolic BP Z score was independently associated with LVH (adjusted OR 1.44; 95% CI 1.16–1.80; p = 0.001). Cohort-derived systolic Z-score thresholds for LVH were 1.31 for 24 h systolic BP and 1.47 for nighttime systolic BP, corresponding to approximately the 90.6th and 93rd percentiles, respectively. No variable evaluated was independently associated with retinopathy. Logistic regression using the 90th percentile/25% load dataset showed the highest performance among the evaluated classifiers, but discrimination was limited (ROC area 0.603; MCC 0.041). Conclusions: Higher ambulatory systolic BP was independently associated with LVH but not with clinically detected hypertensive retinopathy. ABPM values at or above the 90th percentile and a systolic load of ≥25% may help identify children at increased risk of TOD, although these thresholds remain exploratory and require prospective external validation. Full article

Accurate wildfire prediction increasingly determines whether emergency resources arrive before a disaster becomes uncontrollable, yet the dominant paradigm reduces the problem to binary occurrence, offering no estimate of the severity that drives suppression planning. This study develops a machine-learning framework for four-class wildfire severity prediction, conditional on ignition, from weather-station observations and calendar terms alone. We construct a composite severity index (CSI) by applying principal component analysis to five damage dimensions (burned area, suppression equipment, personnel, duration, and property loss) recorded for 868 wildfires in Gangwon Province, South Korea (2011–2022) and pair standard observations with effective humidity and six indices of the Canadian Forest Fire Weather Index (FWI) System. Under a leakage-safe protocol, the strongest tree ensembles reach a macro F1 of 0.46 to 0.50 (recommended configuration: 0.41 ± 0.03 across 20 repeated splits) against a four-class chance level of 0.25, and the recommended Random Forest attains an extreme-class recall of 0.474; the CSI target outperforms burned area by 5.5 macro-F1 points under identical inputs. A weather-only screen separates extreme from non-extreme events with an ROC AUC of 0.758, capturing 47% of extreme events at a 20% alert budget. We also quantify how oversampling misplaced before the train-test split inflates the macro F1 to 0.65–0.83, a cause for caution for the severity-prediction literature. Full article

Sustainable development in wood technology demands eco-friendly alternatives to conventional preservatives, positioning nanotechnology as a promising frontier for green wood protection. The antifungal potential of silver nanoparticles (AgNPs) against the brown-rot agent Fomitopsis pinicola was investigated in vitro, alongside an assessment of their protective performance on Scots pine wood. AgNPs were tested at 5, 25, 50, and 100 ppm in vitro, with an additional 200 ppm concentration included in the wood-decay test. In the in vitro assays, lower concentrations (5 and 25 ppm) stimulated radial mycelial growth, 50 ppm showed no effect, and 100 ppm caused growth inhibition. In contrast, all tested concentrations enhanced the decay resistance of pine wood. Average wood mass loss ranged from 4.9% to 8.1% after 2 months and from 10.9% to 18.6% after 4 months, with the maximum protective effect observed at 50 ppm. While AgNPs did not entirely prevent F. pinicola-induced decay, they significantly mitigated mass loss. These findings highlight the potential of AgNPs for the short-term preservation of freshly harvested timber, particularly storm- or disaster-damaged wood stored in forests or log yards prior to processing. Full article

To effectively extract fault characteristics from complex vibration signals and improve the diagnostic performance of deep learning networks, this paper introduces a wind turbine blade fault diagnosis method that combines Multi-scale Enhanced Hierarchical Fuzzy Entropy (MEHFE), Isolation Forest, and the Grey Wolf Optimization (GWO) algorithm for optimizing the Gated Recurrent Unit (GRU). Initially, the MEHFE algorithm is applied to decompose and reconstruct three-directional vibration signals at the blade root, thereby extracting “scale-frequency” dual-dimensional features that represent the evolution of fault frequency structure and complexity across multiple scales. Subsequently, Isolation Forest is employed to assess and filter feature importance, constructing an optimal feature subset to mitigate redundancy and noise interference. Finally, the optimal features are fed into the GRU network for fault pattern recognition, and the GWO algorithm is utilized to adaptively optimize network hyperparameters, thereby enhancing classification accuracy and noise resilience. Simulation experiments on typical wind turbine blade faults reveal that when GRU serves as the classifier, the diagnostic accuracy of MEHFE exceeds 76%. After feature optimization with Isolation Forest and network parameter optimization with GWO, the diagnostic accuracy surpasses 93%, demonstrating notable advantages in both classification capability and stability. Even under conditions of noise interference, the accuracy remains above 90%. The research substantiates that the proposed method can effectively extract pattern information indicative of blade structural damage from vibration data, achieving high fault recognition accuracy and robustness. Full article

Rockburst risk in seismically active mines poses a significant threat to underground safety. This study aims to improve the prediction of rockburst-induced damage by addressing the challenge of class imbalance, which is commonly encountered in rockburst datasets. Various data-balancing techniques, including the Synthetic Minority Oversampling Technique (SMOTE) and its variants, namely SMOTE-Tomek, KM-SMOTE, SMOTE-ENN, SVM-SMOTE, Borderline-SMOTE, and Adaptive Synthetic (ADASYN) sampling, were applied to a dataset containing rockburst damage scales and selected influencing parameters. The data were collected from Canadian and Australian underground mining operations affected by mining-induced seismicity. Three machine learning classifiers, namely Random Forest (RF), CatBoost (CB), and Gradient Boosting (GB), were trained and evaluated using accuracy, recall, precision, and F1-score metrics. The five best-performing models were SMOTE-ENN-GB, SMOTE-ENN-CB, SMOTE-ENN-RF, KM-SMOTE-CB, and Borderline-SMOTE-CB, achieving testing accuracies ranging from 70% to 88%. SHAP analysis further revealed that stress conditions and peak particle velocity (PPV) are the dominant factors controlling rockburst severity, while geological factors and support conditions act as secondary contributing factors. Compared with previous studies using the same dataset, the proposed approach achieved substantial improvements in predictive performance, particularly for the minority and severe rockburst classes. It is concluded that SMOTE-based balancing techniques, when combined with ensemble learning algorithms, can significantly improve rockburst damage prediction and contribute to safer and more effective risk management in deep, seismically active mining environments. Full article

The Republic of Korea experiences over 500 forest fires annually, consuming more than 4000 ha. Helicopters are the primary resource for initial attack, but effectively dispatching these limited resources during multiple simultaneous fires poses a significant challenge, as these incidents compete for the same pool of helicopter resources. To support real-time, operational-level helicopter dispatch decisions, an interactive decision support framework was developed that integrates information gathering, fire prioritization, and dispatch optimization. This framework employs an integer linear programming (ILP) approach to minimize the weighted sum of suppression costs and resulting burn perimeters, while allowing for uncontained fires when fire spread rates exceed the cumulative suppression capacity of available helicopters. The framework was applied to two test cases: (1) five hypothetical simultaneous fire incidents, and (2) four actual simultaneous fire incidents recorded on 22 March 2025, with the resulting solutions compared against manual dispatch decisions made by the Korea Forest Service (KFS). The results demonstrate the framework’s capability to analyze diverse fire suppression scenarios and generate a range of effective dispatch options. By integrating real-time fire behavior simulation and optimization, incorporating fire damage potential, and replicating the Republic of Korea’s unique suppression practices, this framework aims to enhance real-time helicopter dispatch decision-making, contributing to the KFS’s ongoing efforts to integrate scientific knowledge into forest fire suppression and management. Full article

Anoplophora glabripennis (ALB) is an invasive wood-boring cerambycid that causes extensive damage to hardwood host trees through sequential tissue penetration from the bark to the sapwood. Developmental biology of ALB is well established. However, interactions among its life cycle, environmental contaminants, and fungal associates remain poorly understood. In particular, the ecological relationships among microplastics, entomopathogenic fungi, and the fungal symbiont Fusarium solani (FSSC) within ALB-associated woody tissues remain largely uncharacterized. This review develops a conceptual anatomical framework integrating ALB developmental biology, fungal associations, frass deposition pathways, and potential microplastic interactions within woody host tissues. The framework was constructed through ecological literature synthesis and anatomical reconstruction. To our knowledge, this represents the first conceptual framework integrating ALB developmental anatomy, fungal symbiosis, and potential microplastic interactions within host tree gallery systems. A longitudinal cross-sectional model was developed to illustrate oviposition, larval gallery formation, pupation, and adult emergence in relation to the outer bark, cambium/phloem, sapwood, and heartwood. FSSC isolates previously documented on ALB egg surfaces following oviposition and within ALB frass were examined, thereby positioning the fungal symbiont both within and outside galleries produced by ALB throughout its life cycle. Previous studies have demonstrated that microplastics can be taken up by plant stem tissues and accumulate on the forest floor through atmospheric deposition. This widespread presence suggests that micro- and nanoplastics may penetrate sapwood and heartwood galleries through xylem and phloem flow. These transport pathways may overlap with regions where late-instar larvae actively forage. The integrative framework presented here highlights potential ecological interactions within the gallery microhabitat and provides a foundation for future experimental investigations into contaminant–pathogen–host dynamics in xylophagous insects. While we refrain from proposing specific management strategies, we present a conceptual framework to elucidate how microplastics may serve as incidental contact points for cerambycid anatomy and fungal propagules. We hypothesize that these interactions link microplastic pollution to invertebrate ecology. Microplastics may function as substrates for fungal spores within forest canopies and gallery systems, potentially influencing fungal persistence, contaminant transport, and ecological dynamics within infested forest habitats. Full article

Traditional methods for wine bottle packaging leakage detection often suffer from low efficiency, high false-positive rates, or an inability to detect micro-leakages. This paper proposes a near-infrared laser leakage detection system based on tunable diode laser absorption spectroscopy at 1392 nm, combined with a LightGBM machine learning model. The system detects gaseous ethanol vapor escaping from leaking bottles, addressing the spectral interference caused by ambient water vapor. A total of 1410 samples were collected, and each raw 2000-point spectral contour was compressed into a 200-dimensional feature vector through baseline correction, Z-score normalization, and uniform down-sampling. A two-stage hyperparameter optimization strategy yielded the optimal LightGBM configuration with a 5-fold cross-validation. For the binary classification task, the model achieved an AUC of 0.9949 and an inference speed of 0.0058 ms per sample on a CPU, outperforming Random Forest, PLS, and four deep learning models. For the regression task, the model achieved an R² of 0.5854 ± 0.0919. An anti-interference experiment on 422 samples under varying flow rates, temperatures, and commercial wine types confirmed the model’s robustness, achieving an overall accuracy of 0.94 and an alcohol recall of 0.99. To further validate the system under realistic conditions, a simulated micro-leakage test was conducted using a negative-pressure extraction method: 320 samples were collected from artificially damaged commercial wine bottles placed in a custom-built acrylic vacuum chamber that replicates the production line enclosure. The model achieved an accuracy of 0.95 with zero false negatives. The complete detection cycle takes no more than 5 s per bottle, enabling non-destructive, rapid, and online packaging integrity assessment. The results demonstrate that the proposed system provides a low-cost and reliable solution for wine bottle leakage detection suitable for industrial deployment. Full article

Flash floods are among the most destructive natural hazards, often causing substantial loss of life and severe damage to infrastructure and property. Predicting flood-prone areas remains challenging because flood generation is controlled by complex interactions among topographic, hydrological, climatic, and environmental factors. In this study, six machine learning algorithms—Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree Classifier (DTC), AdaBoost, and Artificial Neural Network (ANN)—were developed to predict flash-flood inundation locations using satellite-derived flood inventories from two major rainfall events in Wadi El-Darb and Wadi El-Allaqi, Egypt. Model performance was evaluated using accuracy, precision, recall, and F1-score. During model development, Random Forest and Decision Tree Classifier achieved the highest prediction accuracy (94%), followed by AdaBoost and ANN (92%), while Logistic Regression (89%) and SVM (88%) also produced satisfactory results. To evaluate model generalization, the trained models were independently validated using a rainfall event in Wadi Hodein (Egypt) and a major flash-flood event that occurred in Oman during April 2024. The external validation showed that AdaBoost achieved the highest predictive performance in both validation basins, with accuracies of 87% for Wadi Hodein and 83% for Oman, providing encouraging initial evidence of applicability across hydrologically similar arid watersheds, While AdaBoost and Logistic Regression maintained satisfactory performance during external validation, other algorithms exhibited noticeable reductions in recall and F1-score, particularly in the Oman case study, indicating variability in model generalization across independent watersheds These findings suggest that the proposed framework may support flood susceptibility assessment in ungauged arid environments with comparable hydrological characteristics, although further validation across a wider range of climatic and geological settings is needed. Overall, the results highlight the value of integrating satellite remote sensing with machine learning to support flood hazard assessment, disaster preparedness, early warning systems, and flood risk management in data-scarce regions. Full article

Mangrove forests along China’s coastline serve as vital ecological barriers and blue carbon reservoirs. However, pests and diseases have become the primary biotic threats driving stand decline and diminished carbon sequestration capacity. This review synthesizes current knowledge on the major insect pests and plant diseases affecting Chinese coastal mangroves, focusing on their species profiles, characteristic damage symptoms, occurrence dynamics, and integrated control strategies. Fungal pathogens predominate among the diseases, with outbreaks most common during periods of high temperatures and humidity or low temperatures combined with high humidity; these often interact synergistically with insect pests. The dominant insect pests comprise leaf-feeding Lepidoptera, sap-sucking Hemiptera, and wood-boring Coleoptera, which spread through diverse pathways and can rapidly produce extensive “scorched” damage across mangrove stands during epidemic events. Control efforts follow the principle of “prevention first and integrated management,” incorporating cultural practices, chemical interventions, biological control agents, physical trapping methods, and rigorous quarantine-monitoring protocols. When applied in concert, these measures effectively limit damage to acceptably low levels. Recent studies identify pest–disease interactions and climate change as the foremost challenges in current management. Future priorities should include advancing molecular identification techniques, breeding disease-resistant varieties, and developing environmentally friendly biopesticides to establish precision ecological control systems. Such advances will deliver robust scientific support for mangrove conservation and the achievement of China’s dual-carbon goals. Full article

The 6 February 2023 Kahramanmaraş earthquakes caused widespread structural damage and highlighted the need for rapid building-level decision support in post-earthquake assessment. This study presents an explainable ensemble learning framework for seismic damage prediction using 16,611 building-level field observations from Kırıkhan, Hatay, Türkiye. The original damage records were reorganized into three operational classes: No-Damage, Slight–Moderate, and Heavy–Collapse. Eight tree-based ensemble models, LightGBM, CatBoost, XGBoost, Random Forest, Extra Trees, Gradient Boosting Machine, AdaBoost, and HistGradientBoosting, were evaluated under a consistent protocol using class-weighting strategies where supported, with Balanced Accuracy as the primary metric. LightGBM and Random Forest achieved the joint-highest Balanced Accuracy value (0.650). Random Forest produced the strongest agreement-based metrics, while LightGBM remained closely competitive and was selected as the representative model for explainability because of its balanced class-wise behavior. CatBoost achieved the highest Heavy–Collapse recall (0.729), XGBoost achieved the highest Macro-AUC (0.821), and GBM produced the highest Overall Accuracy (0.658), showing that model ranking varied by evaluation criterion. SHapley Additive exPlanations identified building age, lithology, number of floors, structural system, plinth area, and proximity to faults and surface ruptures as key contributors. The remaining classification uncertainty, particularly among adjacent damage states, indicates that the framework is best interpreted as a complementary decision-support tool for preliminary screening and prioritization before final safety decisions or official damage assessment. Full article

Neltuma pallida is a multi-purpose tree species of the seasonally dry tropical forests of northern Peru, where it provides essential ecological and socioeconomic functions. However, recurrent defoliation associated with the cecidomyiid gall midge Enallodiplosis discordis may compromise early seedling establishment and the success of forest restoration programs. This study evaluated the effects of larval infestation on foliar integrity and established quantitative damage thresholds in N. pallida seedlings under dry forest conditions. Insects collected from naturally infested plants were identified using an integrative taxonomic approach that combined classical morphological diagnosis with COI-based DNA barcoding obtained by Sanger sequencing. Morphological assessment assigned the defoliating dipteran to E. discordis, while BLASTn v2.17.0. analysis of the 576-bp partial COI sequence showed 92.6% identity and 100% query coverage with Cecidomyiidae records, supporting its taxonomic placement within this family. Field bioassays conducted over a 17-week period, in which 25 individual seedlings were evaluated (N = 25), revealed a strong and significant positive correlation between larval density and foliar damage percentage (r = 0.872; p < 0.001), with moderate damage levels predominating throughout the evaluation period. Despite sustained larval presence, seedlings did not reach severe damage categories, suggesting potential relative tolerance to partial defoliation under the evaluated field conditions. Temperature and relative humidity were not significantly associated with infestation intensity or foliar damage during the study period. Overall, these findings indicate that E. discordis-associated foliar damage represents a relevant, although not necessarily lethal, biotic constraint for the early regeneration of N. pallida under the field conditions assessed. The quantitative thresholds reported here provide useful criteria for dry forest restoration programs, phytosanitary monitoring, and integrated pest management strategies in the Peruvian dry forest. Full article