Search Results (10,570)

A waterspout was sighted in the offshore waters of Hong Kong in mid-October 2025, the second-latest occurrence of this weather phenomenon in a single year since 1959. Due to the close proximity of the phenomenon to Lamma Island in Hong Kong, detailed sighting information and photographs of the waterspout are available for analysis. This paper investigates the meteorological background of the event, the stability of the atmosphere, and weather radar images from two dual-polarization weather radar stations within the territory to determine the type and intensity of the observed waterspout and its formation mechanism. At that time, the atmosphere was rather unstable, with high values for CAPE and bulk Richardson number, along with an upper-level divergence area that provided updraft momentum for convective development. Detailed observations from these weather radar images showed that the waterspout was a rather weak system with relatively low radar reflectivity and generally weak Doppler velocities, although the velocity signatures, such as Doppler velocity couplets, and azimuthal shear were quite clear. The potential for an operational 2-kilometer ensemble prediction system (EPS) from the Hong Kong Observatory to indicate a favorable environment for waterspout development was also investigated. While the EPS cannot be expected to resolve the waterspout problem or reproduce its exact location and timing, it can capture weak low-level cyclonic anomalies and convergences near Lamma Island that would provide favorable conditions for the formation of waterspouts and are broadly consistent with the observed mesoscale environment. Full article

Temperature and related thermal comfort metrics at a representative 9-member ensemble of airports in Europe are presented using a combination of historical (1985–2014) and future projection (2035–2064) timescales under a variety of forcing scenarios. Data are shown for summer (June–July–August) and the nine sites are further grouped into `oceanic’, `continentally influenced’, and `Mediterranean coastal’ climate types, which ameliorates visualisation and provides more generalised policy-relevant results. Using the Humidex metric, it is shown that some airports in southern Europe may enter a `dangerous’ (>45 C) regime of human discomfort. This would be accompanied by economic impacts related to longer mandated rest periods for ground workers, as well as increased water intake and changes to health and safety training. The coincidence of the 38 C flash point of kerosene jet fuel with perturbed daily maximum temperature occurrence thresholds at some sites will likely also have knock-on effects on safety practices since some sites may experience 70% of future summer days with temperatures exceeding this value. Using an 18 C threshold for defining cooling and heating `degree days’, increases in cooling requirements are projected to be larger than reductions in heating for continental and Mediterranean sites, and heatwave occurrence (3 or more days at or above the 95th historical percentile) may increase by a factor of 10. From a building and infrastructure services perspective, increased temperature variability around larger average values has the potential to reduce safe runway lifetimes and increase structural fatigue in large-span steel terminal buildings. Full article

Timely access to soil moisture conditions in farmland crops is the foundation and key to achieving precise irrigation. Due to their high spatiotemporal resolution, unmanned aerial vehicle (UAV) remote sensing has become an important method for monitoring soil moisture. This study addresses soil moisture retrieval in alfalfa fields across different growth stages. Based on UAV multispectral images, a multi-source feature set was constructed by integrating spectral and texture features. The performance of three machine learning models—random forest regression (RFR), K-nearest neighbors regression (KNN), and XG-Boost—as well as two ensemble learning models, Voting and Stacking, was systematically compared. The results indicate the following: (1) The integrated learning models generally outperform individual machine learning models, with the Voting model performing best across all growth stages, achieving a maximum R² of 0.874 and an RMSE of 0.005; among the machine learning models, the optimal model varies with growth stage, with XG-Boost being the best during the branching and early flowering stages (maximum R² of 0.836), while RFR performs better during the budding stage (R² of 0.790). (2) The fusion of multi-source features significantly improved inversion accuracy. Taking the Voting model as an example, the accuracy of the fused features (R² = 0.874) increased by 0.065 compared to using single-texture features (R² = 0.809), and the RMSE decreased from 0.012 to 0.005. (3) In terms of inversion depth, the optimal inversion depth for the branching stage and budding stage is 40–60 cm, while the optimal depth for the early flowering stage is 20–40 cm. In summary, the method that integrates multi-source feature fusion and ensemble learning significantly improves the accuracy and stability of alfalfa soil moisture inversion, providing an effective technical approach for precise water management of artificial grasslands in arid regions. Full article

The mapping of vineyard cultivars presents a substantial challenge in digital agriculture due to the crop’s high intra-class heterogeneity and low inter-class variability. High-dimensional spectral datasets, such as hyperspectral or spectrometry data, can overcome these difficulties. However, research has yet to fully address the need for optimal spectral feature subsets tailored for grapevine cultivar discrimination, while few studies have systematically examined waveband subsets that transfer effectively across different learning algorithms. This study sets out to address these gaps by introducing a Partial Least Squares (PLS)-based ensemble feature selection framework with Weighted Borda Count aggregation for cultivar discrimination. Using in-field spectrometry data, collected for six cultivars, and 18 PLS-based feature selection methods spanning filter, wrapper, and hybrid approaches, the PLS–ensemble identified 100 wavebands most relevant for cultivar discrimination, reducing dimensionality by ~95%. The efficacy and transferability of this subset were evaluated using five classification algorithms: Oblique Random Forest (oRF), Multinomial Logistic Regression (Multinom), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and a 1D Convolutional Neural Network (CNN). For oRF, Multinom, SVM, and MLP, the PLS–ensemble subset improved accuracy by 0.3–12% compared with using all wavebands. The subset was not optimal for the 1D-CNN, where accuracy decreased by up to 5.7%. Additionally, this study investigated waveband binning to transform narrow hyperspectral bands into broadband spectral features. Using feature multicollinearity and wavelength position, the 100 selected wavebands were condensed into 10 broadband features, which improved accuracy over both the full dataset and the original subset, delivering gains of 4.5–19.1%. The SVM model with this 10-feature subset outperformed all other models (F1: 1.00; BACC: 0.98; MCC: 0.78; AUC: 0.95). Full article

Interferometric Synthetic Aperture Radar (InSAR) is an advanced imaging geodesy technique for detecting and characterizing surface deformation with high spatial resolution and broad spatial coverage. However, as an inherently post-event observation method, InSAR suffers from limited capability for near-real-time and short-term updates of deformation time series. In this paper, we proposed a data-driven adaptive framework for deformation prediction based on a hybrid deep learning method to accurately predict the InSAR-derived deformation time series and take the Xi’erguazi−Mawo landslide complex (XMLC) as a case study. The InSAR-derived time series was initially decomposed into trend and periodic components with a two-step decomposition process, which were thereafter modeled separately to enhance the characterization of motion kinematics and prediction accuracy. After retrieving the observations from the multi-temporal InSAR method, two-step signal decomposition was then performed using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variational Mode Decomposition (VMD). The decomposed trend and periodic components were further evaluated using statistical hypothesis testing to verify their significance and reliability. Compared with the single-decomposition model, the further decomposition leads to an overall improvement in prediction accuracy, i.e., the Mean Absolute Errors (MAEs) and the Root Mean Square Errors (RMSEs) are reduced by 40–49% and 36–42%, respectively. Subsequently, the Radial Basis Function (RBF) neural network and the proposed CNN-BiLSTM-SelfAttention (CBS) models were constructed to predict the trend and periodic variations, respectively. The CNN and self-attention help to extract local features in time series and strengthen the ability to capture global dependencies and key fluctuation patterns. Compared with the single network model in prediction, the MAEs and RMSEs are reduced by 22–57% and 4–33%, respectively. Finally, the two predicted components were integrated to generate the fused deformation prediction results. Ablation experiments and comparative experiments show that the proposed method has superior ability. Through rapid and accurate prediction of InSAR-derived deformation time series, this research could contribute to the early-warning systems of slope instabilities. Full article

Epilepsy affects over 50 million people worldwide, yet automated seizure detection systems either achieve moderate sensitivity with excessive false alarms or rely on uninterpretable deep networks. This study presents a patient-independent EEG-based seizure detection framework that achieved zero false alarms in 24 h with 95% sensitivity in a retrospective evaluation on a CHB–MIT pediatric cohort (n = 6 seizure-positive patients). The pipeline extracts 27 time-, frequency-, and nonlinear-domain features from 5 s windows and trains five ensemble classifiers (XGBoost, CatBoost, LightGBM, Extra Trees, Random Forest) using strict leave-one-subject-out cross-validation. All models achieved segment-level AUC ≥ 0.99. Under zero-false-alarm constraints, XGBoost attained perfect specificity with 0.922 sensitivity. SHAP and LIME analyses suggested candidate EEG biomarkers that appear consistent with known ictal signatures, including temporo-parietal theta-band power, amplitude variability (IQR, RMS), and Hjorth activity. External validation on the Siena Scalp EEG Database (12 adult patients, 37 seizures) demonstrated cross-dataset generalization with 95% event-level sensitivity (Extra Trees) and AUC of 0.86 (Random Forest). Temporal lobe channels dominated feature importance in both datasets, confirming consistent biomarker identification across pediatric and adult populations. These findings demonstrate that calibrated gradient-boosted ensembles using interpretable EEG features achieve clinically safe seizure detection with cross-dataset generalizability. Full article

Accurate runoff forecasting is vital yet challenged by the increasing non-stationarity of hydrological systems, which often exceeds the capacity of traditional single models. Ensemble forecasting, as an effective approach, integrates multiple models’ information to enhance forecasting performance and assess uncertainty. However, existing methods (such as Bayesian Model Averaging and BMA) still have limitations in dealing with complex hydrological scenarios, particularly in the construction and optimization of forecast intervals. This paper proposes a novel hydrological ensemble interval forecasting method based on constrained multi-model weight optimization (Constrained Multi-Model Weight Optimization, CMWO). CMWO utilizes a set of heterogeneous deterministic models to generate members, assigns dynamic optimization weight intervals to enhance flexibility, and employs a multi-objective framework to minimize interval width and errors subject to a ≥95% coverage constraint. Taking the Huangjinxia Reservoir in the upper reaches of the Hanjiang River as a case study, the CMWO method was systematically applied and evaluated for decadal-scale runoff forecasting and comprehensively compared with widely used BMA methods and individual models. The results show that CMWO significantly outperforms in improving point forecast accuracy (measured by RMSE, KGE, etc.) and interval forecast quality (evaluated by PICP, PIAW, CRPS, etc.), especially in generating narrower, more informative prediction intervals while ensuring high reliability. The CMWO method proposed in this study provides a competitive new tool for the effective management of forecasting uncertainty in complex hydrological systems. Full article

Abdominal aortic aneurysm refers to the irreversible abnormal dilation of the aorta at the abdominal level, and it is acknowledged as one of the leading causes of mortality on a global scale. Most abdominal aortic aneurysms are asymptomatic until they approach the point of rupture; thus, it is essential to establish an efficient workflow for the accurate detection of this condition to enhance clinical outcomes. The incorporation of artificial intelligence learning algorithms into healthcare workflows holds the prospect of significantly improving the accuracy of decision-making related to patient mortality risk. Since the potential surgical repair of an aortic aneurysm depends upon the maximum external diameter of the aneurysm, this study aims to develop an end-to-end algorithmic method for classifying low-risk and high-risk cases based on abdominal aortic aneurysm data. To perform the predictive analysis, we adopt neuro-fuzzy systems, ensembles of neuro-fuzzy systems, and hybrid evolutionary-based fuzzy classifiers. The models are trained using features extracted from the radiomics framework and exhibit high generalisation performance, as measured by the adopted metrics, and estimated on a K-fold cross-validation basis. Numerical studies further reveal that the hybrid evolutionary-based fuzzy system exhibits exceptional accuracy in distinguishing between the two identified classes. Full article

This paper examines how data preparation affects machine-learning classifiers for diabetes-risk prediction using the Pima Indians Diabetes Database. Three preprocessing methods are considered: imputing invalid zeros, handling outliers, and data scaling. Nine algorithms are evaluated on this dataset: linear/probabilistic baselines (Logistic Regression, Gaussian Naive Bayes), distance-based methods (KNN, Support Vector Machines), a single tree-based model (Decision Tree), and tree ensembles (Random Forest, Gradient Boosting, XGBClassifier, LightGBM). Median imputation of invalid zeros yields the largest and most consistent gains in accuracy and AUC. Outlier handling uses interquartile-range filtering, with Local Outlier Factor as an auxiliary indicator; effects are modest for accuracy and small, model-dependent for AUC. Scaling offers targeted benefits: for KNN, robust scaling can slightly alter performance and may reduce AUC relative to median-only imputation in this setup; SVM shows modest gains, while tree ensembles are comparatively insensitive overall. Ensembles achieve the highest performance and remain robust under minimal preparation, while simpler models benefit most from pipelines combining median imputation, careful outlier handling, and appropriate scaling. Hyperparameter tuning yields small to substantial gains—large for Decision Trees—while leaving ensemble rankings largely unchanged. Overall, results highlight the centrality of median imputation and the selective value of scaling for distance-based classifiers in diabetes-risk prediction. Full article

This study proposes and evaluates a data-driven framework for short-term System Marginal Price (SMP) forecasting in the Chilean National Electric System (NES), a power system characterized by high penetration of variable renewable generation and persistent transmission congestion. Using publicly available hourly operational data for 2024, multiple machine learning regressors including Linear Regression (base case), Bayesian Ridge, Automatic Relevance Determination, Decision Trees, Random Forests, and Support Vector Regression are implemented under a node-specific modeling strategy. Two alternative approaches for predictor selection are compared: a system-wide methodology that exploits lagged SMP information from all network nodes; and a spatially filtered methodology that restricts SMP inputs to correlated subsystems identified through nodal correlation analysis. Model robustness is explicitly assessed by reserving January and July as out-of-sample test periods, capturing contrasting summer and winter operating conditions. Forecasting performance is analyzed for representative nodes located in the northern, central, and southern zones of the NES, which exhibit markedly different congestion levels and generation mixes. Results indicate that non-linear and ensemble models, particularly Random Forest and Support Vector Regression, provide the most accurate forecasts in well-connected areas, achieving mean absolute errors close to 10 USD/MWh. In contrast, forecast errors increase substantially in highly congested southern zones, reflecting the structural influence of transmission constraints on price formation. While average performance differences between M1 and M2 are modest, a paired Wilcoxon signed-rank test reveals statistically significant improvements with M2 in highly congested zones, where M2 yields lower absolute errors for most models, despite relying on fewer inputs. These findings highlight the importance of congestion-aware feature selection for reliable price forecasting in renewable-intensive systems. Full article

This paper presents an updatable stochastic geometallurgical framework that integrates geochemical compositions and processing-related variables within a unified spatial modeling and data assimilation workflow. The framework combines multivariate geostatistical simulation with real-time updating based on the Ensemble Kalman Filter, allowing stochastic realizations to be sequentially adjusted as new production data become available. The methodology accounts for geological uncertainty, compositional constraints, and multivariate dependencies. This is achieved by combining the isometric log-ratio transformation with flow anamorphosis within a multivariate Gaussian framework. As a result, compositional geochemical variables and metallurgical responses can be updated consistently while preserving their physical and statistical relationships. The framework is demonstrated using the Gol Gohar iron ore deposit as a case study. Exploration drill hole data and production-scale blast hole measurements are assimilated within an ore control context. The results indicate that the update-enabled simulation approach reduces prediction errors and spatial uncertainty, while capturing complex, non-linear relationships among geometallurgical variables. The framework is generic and can be applied to other deposits where real-time integration of geological, geochemical, and processing information is needed to support operational decision-making. Full article

This study presents a hybrid approach integrating metaheuristic optimization and machine learning methods to quickly and reliably estimate the optimum design parameters of dome-shaped axially symmetric cylindrical reinforced concrete (RC) walls. A comprehensive dataset was created using the Jaya algorithm to minimize total material cost for hinged and fixed support conditions. For each optimized design case, total wall height (H), dome height (Hd), dome thickness (hd), and fluid unit weight (γ) were considered as input parameters; optimum wall thickness (hw) and total cost were determined as output parameters. Using the obtained dataset, a total of thirteen different regression-based machine learning algorithms, including linear regression-based models, tree-based ensemble methods, and neural network models, were trained and tested. Hyperparameter adjustments for all models were performed using the Optuna framework, and model performances were evaluated using a ten-fold cross-validation method and holdout dataset results. The results showed that machine learning models can learn the optimum design space obtained from metaheuristic optimization outputs with high accuracy. In optimum wall thickness estimation, Gradient Boosting-based models provided the highest accuracy under both hinged and fixed support conditions. In total cost estimation, the Gradient Boosting model stood out under hinged support conditions, while the XGBoost model yielded the most successful results for fixed support conditions. The findings clearly show that no single machine learning model exhibits the best performance for all output parameters and support conditions. The proposed approach offers significantly higher computational efficiency compared to traditional iterative optimization processes and allows for rapid estimation of optimum design parameters without the need for any iterations. In this respect, this study provides an effective decision support tool that can be used especially in the preliminary design phases and contributes to sustainable, cost-effective reinforced concrete structure design. Full article

Objectives: This study aimed to develop and validate an explainable machine learning (ML) model to predict the risk of sarcopenia in older European adults with arthritis, providing a practical tool for early and precise screening in clinical settings. Methods: We analyzed data from the English Longitudinal Study of Aging (ELSA) and the Survey of Health, Aging and Retirement in Europe (SHARE). The final analysis included 1959 participants aged ≥65 years. The ELSA dataset was divided into a training set (n = 1371) and an internal validation set (n = 588), while the SHARE dataset (n = 1001) served as an independent external test cohort. From an initial pool of 33 variables, nine core predictors were identified using ensemble feature selection techniques. Six ML algorithms were compared, with model performance evaluated using the Area Under the Curve (AUC) and calibration analysis. Model interpretability was enhanced via SHapley Additive exPlanations (SHAP). Results: The Decision Tree model demonstrated the optimal balance between performance and interpretability. It achieved an AUC of 0.921 (95% CI: 0.848–0.988) in the internal validation set and maintained robust generalizability in the external SHARE cohort with an AUC of 0.958 (95% CI: 0.931–0.985). The nine key predictors identified were stroke history, BMI, HDL, loneliness, walking speed, disease duration, age, recall summary score, and total cholesterol. SHAP analysis visualized the specific contribution of these features to individual risk. Conclusions: This study successfully developed a high-performance, explainable, lightweight ML model for sarcopenia risk prediction. By inputting only nine readily available clinical indicators via an online tool, individualized risk assessment can be generated. This facilitates early identification and risk stratification of sarcopenia in older European arthritis patients, thereby providing valuable decision support for implementing precision interventions. Full article

Static, machine learning-based malware detection is widely used in Android security products, where even small increases in false-positive rates can impose significant burdens on analysts and cause unacceptable disruptions for end users. Both tabular features and image-based representations have been explored for Android malware detection. However, existing public benchmark datasets do not provide paired tabular and image representations for the same samples, limiting direct comparisons between tabular models and vision-based models. This work investigates whether carefully engineered, domain-specific tabular features can match or surpass the performance of state-of-the-art deep vision models under strict false-positive-rate constraints, and whether ensemble approaches justify their additional complexity. To enable this analysis, we construct a large corpus of Android applications with paired static representations and evaluate six popular machine learning models on the exact same samples: two tabular models using EMBER features, two tabular models using extended EMBER features, and two vision-based models using malware images. Our results show that a LightGBM model trained on extended EMBER features outperforms all other evaluated models, as well as a state-of-the-art approach trained on a much larger dataset. Furthermore, we develop an ensemble model combining both tabular and vision-based detectors, which yields a modest performance improvement but at the cost of substantial additional computational and engineering overhead. Full article

Reconstructing original cusp dimensions in worn molars represents a fundamental challenge across dentistry, anthropology, and paleontology, as dental wear obscures critical morphological information. In this proof-of-concept study, we present a standardized machine learning pipeline for predicting original cusp height, specifically the horn tips of the enamel–dentine junction (EDJ), in worn lower molars using three-dimensional morphometric data from micro-computed tomography (micro-CT). We analyzed 40 permanent lower first (M1) and second (M2) molars from four hominin groups, systematically evaluated across three wear stages: original, moderately worn (worn1), and severely worn (worn2). Morphometric variables including height, area, and volume were quantified for each cusp, with Random Forest and multiple linear regression models developed individually and combined through ensemble methods. To mimic realistic reconstruction scenarios while preserving a known ground truth, models were trained on unworn specimens (original EDJ morphology) and tested on other teeth after digitally simulated wear (worn1 and worn2). Predictive performance was evaluated using root mean square error (RMSE) and coefficient of determination (R²). Our results demonstrate that under moderate wear (worn1), the ensemble models achieved normalized RMSE values between 11% and 17%. Absolute errors typically below 0.25 mm for most cusps, with R² values up to ~0.69. Performance deteriorated under severe wear (worn2), particularly for morphologically variable cusps such as the hypoconid and entoconid, but generally remained within sub-millimetric error ranges for several structures. Random Forests and linear models showed complementary strengths, and the ensemble generally offered the most stable performance across cusps and wear states. To enhance transparency and accessibility, we provide a comprehensive, user-friendly software pipeline including pre-trained models, automated prediction scripts, standardized data templates, and detailed documentation. This implementation allows researchers without advanced machine learning expertise to explore EDJ-based reconstruction from standard morphometric measurements in new datasets, while explicitly acknowledging the limitations imposed by our modest and taxonomically unbalanced sample. More broadly, the framework represents an initial step toward predicting complete crown morphology, including enamel thickness, in worn or damaged teeth. As such, it offers a validated methodological foundation for future developments in cusp and crown reconstruction in both clinical and evolutionary dental research. Full article