Search Results (2,444)

To comprehensively assess the emissions of flue gas pollutants from municipal solid waste incineration (MSWI) in China and their socioeconomic driving factors, this study employs a bottom-up approach to develop an integrated carbon and air pollutant emission inventory for 1016 MSWI plants in 2024. We apply a Random Forest (RF) model to analyze the underlying drivers. Results indicate that for air pollutants, NO_x has the highest emissions, whereas mercury (Hg) and dioxins (polychlorinated dibenzo-p-dioxins and dibenzofurans, PCDD/Fs) are identified as priority control pollutants due to their high toxicity. Spatially, emissions display a distinct “high in the east, low in the west” pattern, concentrated in eastern coastal provinces, with characteristic pollutants being prominent in specific regions. Meanwhile, among greenhouse gases (GHGs), CO₂ dominates mass emissions, while N₂O exhibits significant global warming potential. Driver analysis reveals that Gross Domestic Product (GDP) and MSWI treatment capacity are key common drivers, showing stable positive and negative contributions, respectively. The number of invention patent applications is specifically and strongly associated with NO_x and heavy metal emissions. This study provides a national-scale integrated quantification of MSWI emissions and a quantitative analysis of their driving mechanisms using RF, offering a critical data foundation and scientific basis for supporting synergistic pollution and carbon reduction. Full article

In acidic tea soil, boron (B) adsorption and desorption processes are dominated by the complex relationship between soil acidity, mineralogy, and organic matter. This study investigated B adsorption–desorption behavior in five acidic tea soils (pH 3.8–5.6) collected from the Eastern Black Sea region of Türkiye and evaluated the potential of machine learning (ML) algorithms to predict B desorption. Laboratory batch experiments were conducted using five initial B concentrations, and adsorption data were interpreted using the Langmuir isotherm model. Adsorption experiments indicated that B interacted with Fe/Al-oxide-containing clay minerals, which had low but favorable binding affinity, as indicated by Langmuir maximum adsorption capacities (Qmax) ranging from 46.5 to 181.8 mg kg⁻¹. Desorption experiments revealed a high degree of reversibility, particularly in soils with lower adsorption capacities, ensuring potential B leaching. To capture the governing B desorption, six machine learning (ML) algorithms—Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Regression (SVR), Gaussian Process Regression (GP), Elastic Net Regression (EN), and Multivariate Adaptive Regression Splines (MARS)—were trained on 75 data points. Among the tested models, Elastic Net showed the highest predictive accuracy (R² = 0.735). This model does not replace adsorption experiments. It offers a within-assay determination of desorption given measured adsorption, which may reduce the requirement for separate desorption equilibration and analyses. Permutation importance analysis identified B_ads as the dominant predictor of B desorption, with smaller contributions from pH_ads and EC_ads. The results demonstrate that integrating laboratory experiments with machine learning provides an effective framework for predicting B mobility in acidic tea soils, offering a parameterized experimental framework for describing boron desorption behavior in acidic tea soils. Full article

To accurately understand and investigate carbon fluxes in cropland ecosystems, this study adopted a machine learning ensemble model for estimation. Focusing on the Jinzhou station of the ChinaFLUX, we integrated eddy covariance carbon flux observations with multi-source satellite remote sensing data to construct a machine learning-based cropland carbon flux estimation model. For environmental driver selection, a strategy combining correlation analysis with ecological mechanism understanding was employed to screen LST, NDVI, and NDMI as model input variables, effectively avoiding multicollinearity issues. Using footprint-weighted integrated data from 2005 to 2014 for model training and validation, a Stacking ensemble model was constructed with the RF model serving as the meta-learner to stack the predictions of RF, CART, and GBM. The ensemble model further reduced the prediction error (RMSE = 39.82), maintaining an R² > 0.9 in most years and effectively improving predictive performance during anomalous years where single models underperformed. Based on these findings, the model was applied to analyze the spatiotemporal evolution of NEE in Jinzhou croplands from 2005 to 2014. The analysis revealed that while the region functioned overall as a carbon sink, it exhibited significant spatiotemporal heterogeneity. Spatially, the distribution followed a pattern of “strong intensity in the northeast and center, and weak intensity in the northwest and southwest.” Temporally, the sink intensity underwent significant interannual oscillations characterized by a “strengthening–weakening–re-strengthening–declining” trajectory. The high-precision prediction method proposed in this study is of great significance for revealing spatiotemporal variations in carbon sources/sinks, guiding green agricultural development, and supporting relevant policy formulation. Full article

Tracking structural behavior is critically important to reduce maintenance and repair costs. Structural Health Monitoring (SHM) aims to evaluate the structural integrity, detect damage or abnormalities, and estimate overall safety. The integration of Machine Learning techniques has significantly advanced SHM by enabling the identification of deterioration patterns through sensor data analysis. This study focuses on classifying different vibration patterns recorded under various excitation scenarios (ambient, transient, and forced) using sensors installed directly on a 3-DoF structure. The proposed approach used a two-dimensional convolutional neural network (2D-CNN) trained on vibration image patterns generated from vibration signal scalogram images. To address dataset imbalance, stratified 5 × 3 Nested cross-validation and multiple performance metrics were computed to ensure robust evaluation. The proposed method was compared with single-sensor scalogram approaches and baseline models, including Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), One-Dimensional Convolutional Neural Network (1D-CNN), and Long Short-Term Memory (LSTM) models, incorporating class-weighting strategies. Additionally, the contribution of the Total Energy Delivered by Sensor (TES) feature was evaluated for SVM, RF, and XGBoost models. The 2D-CNN model achieved superior performance in identifying excitation types associated with structural dynamic behavior, highlighting its effectiveness for structural vibration pattern recognition in SHM applications. Full article

Forest fire occurrence prediction remains challenging despite advances in operational fire danger rating systems. In South Korea, the Korea Forest Fire Danger Rating Index (KFDRI) incorporates meteorological conditions, terrain (elevation, aspect), and forest type to assess regional fire danger. While KFDRI successfully assesses environmental fire danger at the pixel level, it does not explicitly account for human activity patterns that create substantial occurrence variability among locations with similar environmental conditions. This limitation is critical in human-dominated landscapes where where the main source of fire occurrence is anthropogenic. This study developed a Random Forest (RF) model to predict forest fire occurrence probability and propose management priorities during the forest fire prevention season (November–May) along the eastern coast of Korea, explicitly integrating human proximity variables (distance to agricultural areas and roads) with topographical (elevation, slope, aspect), surface fuel load, and meteorological variables (SMAP soil moisture, cumulative precipitation). Using forest fire occurrence records (1112 fire occurrence records) and background samples from 2015 to 2024, the model was trained with monthly stratified sampling and 10-fold cross-validation. The model achieved stable classification performance, with an overall F1-score of 0.515 and accuracy of 0.733. According to the SHAP (SHapley Additive exPlanations) analysis, distance to agricultural areas, elevation, slope, aspect, 5-day cumulative precipitation, and forest type were the most influential predictors. In particular, occurrence probability tended to increase in areas close to agricultural land (<180 m), at low elevations (≤200 m), on moderately steep slopes (≥8°), on south- and west-facing aspects, and under dried conditions. These results emphasize that fire occurrence risk is primarily structured by human proximity within areas of similar environmental danger. We propose an operational integration in which the RF model provides a 30 m “where-to-focus” occurrence layer that is used alongside KFDRI’s daily danger rating to prioritize prevention and patrol efforts. Full article

Accurate classification of salt marsh vegetation is crucial for coastal wetland monitoring, but fine-grained species discrimination remains difficult, particularly when only limited training data are available for deep learning approaches. To address this challenge, this paper presents a transfer learning-based framework for classifying salt marsh vegetation using UAV multispectral imagery, focusing on a seven-class taxonomy representative of dominant species and water surfaces. Multispectral orthophotos acquired with a MicaSense Dual-Camera system (10 spectral bands) are combined with five vegetation indices to create rich multi-channel inputs. A classification architecture inspired by heterogeneous transfer learning is developed, where a feature-encoding branch compresses the 15-channel input into three channels before processing through a VGG-16 Convolutional Neural Network (CNN), pre-trained on RGB imagery. By leveraging transfer learning from VGG-16, the proposed model achieves high classification accuracy even with limited training data. Performance is compared with traditional machine learning classifiers, namely Support Vector Machines (SVMs) and Random Forest (RF). Results show that the deep learning approach significantly outperforms SVM and RF, achieving an overall accuracy of 98.4% when jointly using spectral bands and vegetation indices. These findings demonstrate the potential of integrating multispectral UAV data and CNN-based classification to support accurate mapping of heterogeneous salt marsh communities for ecological monitoring and coastal management. Full article

Accurate forecasting of aggregated demand response (DR) potential is critical for load aggregators, yet remains challenging under severe data scarcity and domain shift conditions. This paper proposes a domain-adaptive transfer learning framework based on an ensemble of Random Vector Functional-Link (RVFL) neural networks for DR potential prediction without requiring any labeled target-domain data. By integrating domain adaptation layers and Maximum Mean Discrepancy (MMD) regularization, the proposed method explicitly reduces marginal feature distribution discrepancies between source and target domains, enabling effective knowledge transfer across heterogeneous operating scenarios. Compared with deep learning architectures, the RVFL-based framework offers favorable theoretical and practical properties for this application, including closed-form least-squares training, reduced risk of overfitting under limited data, and stable generalization under distribution shifts due to its direct-link structure and randomized hidden representations. These characteristics lead to significantly lower computational complexity and training cost than gradient-based deep models, while maintaining strong predictive capability. Case studies using real-world residential consumption data from the Pecan Street dataset demonstrate that the proposed approach consistently outperforms benchmark methods, including SVR, RF, and LSTM, across both intra-year and cross-year transfer scenarios. Reliable prediction accuracy is achieved even when only 10% of source-domain data are available, indicating strong data efficiency and scalability for practical aggregator deployment in day-ahead DR planning. Full article

Despite the enormous amounts of waste textiles produced by the world’s textile industry’s explosive growth, resource utilization rates are still poor. Cotton/polyester blended waste fabrics make up a sizable share, and sorting them precisely is essential to increasing recycling value and promoting the circular economy in the textile industry. Traditional mechanical and human sorting techniques are ineffective and inaccurate; current spectral analysis algorithms mainly concentrate on quantitative composition prediction and are insufficiently capable of differentiating between waste fabrics with comparable content gradients. To address these challenges, this paper proposes an improved 1DCNN model (Dual-1DCNN-Residual-SE) integrated with Near-Infrared (NIR) hyperspectral imaging technology. This model takes raw spectral data and Savitzky-Golay (SG) smoothing data as dual-channel inputs, introducing residual connections to capture subtle spectral differences between similar fabric categories, and employs SE attention mechanisms to adaptively enhance key features. Comparative experiments with four traditional algorithms—KNN, RF, SVM, and PLS—demonstrate that the proposed model achieves a classification accuracy of 95.94%, surpassing the best traditional algorithm SVM (88.12%) by 7.82%. Ablation experiments confirm each enhanced module’s efficacy. This study achieves high-precision classification of cotton/polyester blended waste fabrics, providing technical support for intelligent sorting of industrial waste fabrics. Full article

In recent years, with the widespread application of shortwave infrared (SWIR) spectroscopy in mineral identification and hydrothermal alteration studies, an increasing number of studies have attempted to integrate SWIR spectral data with machine learning approaches to fully exploit mineralization-related discriminative information embedded in high-dimensional spectral datasets. In this study, the Zhunuo porphyry copper deposit in Tibet was selected as the research target. SWIR drill core spectral data were systematically acquired, and a random forest (RF) machine learning model was applied to full-band SWIR spectra (1300–2500 nm) to conduct integrated analyses of copper grade regression and mineralization discrimination. A total of 2140 drill core samples were measured, with three replicate measurements per sample, yielding 6420 spectra. After standardized preprocessing and interpolation resampling, a unified spectral feature dataset was constructed for regression and classification analyses. SWIR spectral data are characterized by a large number of bands, strong inter-band correlations, and relatively limited sample sizes; under such conditions, model generalization ability and stability become critical factors in method selection. Based on ensemble learning, the random forest model constructs multiple decision trees and aggregates their predictions through voting or averaging, effectively reducing model variance and mitigating overfitting, and is therefore well suited for high-dimensional, small-sample, and highly correlated geological spectral datasets. In porphyry copper systems, the spectral characteristics of hydrothermal alteration minerals and mineralization intensity commonly exhibit complex nonlinear relationships, which can be effectively captured by random forest models without requiring predefined functional forms. The regression results indicate that accurate quantitative prediction of copper grade based solely on SWIR spectral data remains limited. In contrast, when a threshold-based binary classification was introduced using an industrial cutoff grade of 0.2% Cu, the model achieved an overall accuracy of 75%, an F1 score of 0.69, and an area under the ROC curve (AUC) of 0.80, demonstrating strong mineralization discrimination capability and stability. Overall, the integration of SWIR spectroscopy with machine learning methods provides an efficient, reliable, and geologically interpretable technical approach for early-stage exploration and detailed drill core interpretation in porphyry copper deposits. Full article

The rapid deployment of electric vehicle (EV) charging infrastructure, coupled with the integration of 5G/6G and Internet of Vehicles (IoV) technologies, has transformed charging stations into cyber–physical systems that rely on wireless communication for authentication, control, and grid coordination. While existing security standards such as ISO 15118 provide cryptographic protection at upper layers, they are insufficient to address physical-layer threats inherent to wireless connectivity. In particular, wireless active eavesdropping attacks can corrupt channel estimation during the authentication phase, enabling impersonation, unauthorized charging, and disruption of grid operations. This paper proposes a machine learning-based physical layer security (PLS) framework for detecting active eavesdropping attacks in 5G/6G-enabled EV charging systems. By modeling malicious EVs as pilot-spoofing attackers, three discriminative features, namely mean power, power ratio, and angle-based feature, are extracted from received pilot signals at the charging station. Three classifiers are evaluated: single-class support vector machine (SC-SVM), Random Forest (RF), and DNN. Simulation results demonstrate that the SC-SVM maintains a stable accuracy between 94% and 96% across all attacker power levels, while RF and DNN significantly outperform it under stronger attack conditions. Specifically, under strong attacker conditions, RF achieves an accuracy of 99.9%, and DNN reaches 99.8%, both exceeding 99% detection accuracy. By preventing pilot-spoofing-based impersonation during authentication, the proposed framework enhances charging availability, billing integrity, and grid-aware scheduling in intelligent EV charging infrastructure. Full article

Rice blast disease is one of the most contagious and destructive diseases affecting rice, posing a serious threat to global rice production and the agricultural economy. To enable accurate early detection under field conditions, this study proposes an integrated feature sorting algorithm (IFSA). The algorithm integrates five spectral feature selection methods—partial least squares, successive projections algorithm (SPA), principal component analysis loading (PCA-Loading), genetic algorithm (GA), and random forest (RF)—and employs the Borda count method for comprehensive feature ranking and selection. Field experiments were conducted in Haicheng, Anshan, Liaoning Province, China, using the rice cultivar Yanfeng 47. A total of 4893 hyperspectral samples were collected under natural field conditions. The results demonstrate that IFSA effectively identifies key spectral wavelengths for the early diagnosis of rice blast disease, achieving significantly higher detection accuracy than conventional single-method dimensionality reduction approaches. Based on the IFSA-selected wavelengths, an early detection model (Inception-CBAM) was further developed by integrating a multi-channel convolutional neural network with a convolutional block attention module, thereby enhancing the extraction and recognition of early disease-related features. Compared with six baseline models (InceptionV4, ResNet, BiGRU, RF, support vector machine, and extreme learning machine), Inception-CBAM achieved an overall accuracy of 95.44 ± 0.50% and a Kappa coefficient of 93.92 ± 0.67% for early rice blast disease detection, outperforming all competing methods. This study confirms the effectiveness of IFSA for hyperspectral feature selection and demonstrates that the proposed Inception-CBAM model provides strong capability for early disease detection. Nevertheless, the data were collected from a single cultivar and a single region; therefore, the model’s generalization performance across broader environments requires further improvement. Future work will extend the evaluation to multi-cultivar and multi-region scenarios to facilitate practical deployment for real-time field diagnosis. Full article

Accurate quantification of mitral regurgitation (MR) is central to perioperative decision-making, yet the agreement and interchangeability of commonly used echocardiographic methods remain uncertain. This study evaluated quantitative MR parameters individually and within a multiparametric framework using three-dimensional (3D) vena contracta area (VCA) as an intraoperative reference. In this single-center retrospective analysis, intraoperative echocardiographic data from 85 patients undergoing mitral valve intervention between July 2024 and June 2025 were analyzed. Regurgitant volume (RVol) and regurgitant fraction (RF) were quantified using two-dimensional proximal isovelocity surface area (PISA), a 3D volumetric method, and a Doppler-based continuity equation. Agreement was assessed by Bland–Altman analysis, and categorical concordance was assessed by Cohen’s kappa for individual and multiparametric grading strategies. Agreement between individual quantitative methods was limited, with substantial bias and wide limits of agreement for both RVol and RF, resulting in poor-to-fair concordance for MR severity classification. Incorporation of RVol and RF into multiparametric grading strategies improved concordance. Compared with 3D VCA, multiparametric integration incorporating PISA-derived measures showed the best overall performance, with high accuracy and sensitivity and moderate specificity. These findings indicate limited interchangeability of standalone quantitative echocardiographic methods and support reporting the applied technique and using a multiparametric approach anchored to 3D VCA when cardiac magnetic resonance imaging is unavailable. Full article

Accurate and timely mapping of paddy rice is essential for agricultural management, food security, and climate-resilient policy. However, high-precision mapping remains challenging in subtropical monsoon regions due to persistent cloud cover, long revisit intervals, and striping noise, which compromise satellite data quality and availability. To address these limitations, a rice mapping framework suitable for different geographical environments was developed based on a random forest (RF) by combining time-series harmonic analysis (HANTS) with Sentinel-1 and Sentinel-2 multi-source data. To address these limitations, a rice mapping classification algorithm for different geographical environments was developed by combining Harmonic Analysis of Time Series (HANTS) with Sentinel-1/2 multi-source data. The research obtained annual maps of single-season and double-season rice in the research area from 2019 to 2024, with a spatial resolution of 10 m. The results indicated that the Sentinel-1, Sentinel-2, GEE, and HANTS algorithm can effectively support the yearly mapping of single- and double-season paddy rice in Anhui Province, China. The resultant paddy rice map has a high accuracy with overall accuracies exceeding 92% and Kappa coefficients above 0.84. HANTS effectively captures key phenological features of paddy rice, and it can especially enhance the discrimination between single- and double-season rice; compared to existing rice mapping products, the proposed approach reduces classification errors by an average of 3.92% in six major rice-producing cities, each with cultivation areas exceeding 1 million hectares; spatial correlation analysis indicates substantial heterogeneity in rice cultivation patterns across northern, central, and southern Anhui, associated with both biophysical and anthropogenic factors. These results indicate that integrating phenological data with machine learning can enhance the accuracy of long-term, high-resolution crop monitoring, and annual rice maps will offer valuable support for food security assessment, water resource management, and policy planning. Full article

We developed an indoor positioning system combining Bluetooth direction-finding antennas with machine learning to improve localization accuracy and stability cost-effectively. It integrates existing indoor positioning and lighting control with a Bluetooth angle of arrival (AoA)-dongle, compatible with current mesh networks, using the message queuing telemetry transport protocol for data transmission to a server. The system, developed with nRF5340 and u-blox AoA antenna boards, was evaluated in an experimental field with 12 positioning points arranged in a grid. Datasets categorized by AoA antenna quantity and data preprocessing were used to train K-nearest neighbors, support vector machine (SVM), random forest, and multilayer perceptron models. Optimal parameters were identified using grid search, and models were validated using confusion matrices and F1-scores. Results indicated significant accuracy improvements of 11.11–30.51% without preprocessing and 1.17–6.32% with preprocessing when incorporating AoA features. Real-time tests revealed SVM as the best-performing model, achieving up to 96.58% accuracy, significantly enhancing positioning stability. The results of this study underscore Bluetooth direction-finding combined with machine learning as a promising solution for the Internet of Things applications. Full article

Climate variability threatens smallholder maize production in semi-arid Southern Africa, necessitating accurate irrigation management. We developed an Earth Observation–machine learning framework integrating Sentinel-1 SAR, TU Wien retrievals, and meteorological data to generate daily 10 m resolution root-zone soil moisture estimates (0–100 cm) for South Africa’s Vhembe District (2017–2022). Five algorithms—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), and Multivariate Adaptive Regression Splines (MARS)—were calibrated using ~50,000 observations from two monitoring stations across six depths and five growing seasons. RF and XGBoost achieved highest accuracy (R² = 0.96–0.97, RMSE < 0.025 cm³/cm³), detecting critical irrigation thresholds (management allowable depletion = 0.23 cm³/cm³, field capacity = 0.35 cm³/cm³) with operational precision (nRMSE < 0.05). Depth-stratified validation revealed strong SAR surface correlations (r = 0.84–0.85 at 10 cm) declining systematically with depth (r < 0.2 below 40 cm), confirming ML models integrate satellite observations at shallow layers with meteorological gap-filling at depth. District mapping showed 79–94% of maize areas required irrigation during dry years (2017–2019, 2021–2022) versus 32% in wet 2020–2021. The framework provides a transferable pathway for precision irrigation in smallholder systems, pending vegetation-corrected retrievals and expanded validation. Full article