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The betel palm is a critical economic crop on Hainan Island. Accurate and timely maps of betel palms are fundamental for the industry’s management and ecological environment evaluation. To date, mapping the spatial distribution of betel palms across a large regional scale remains a significant challenge. In this study, we propose an integrated framework that combines knowledge-based and machine learning approaches to produce a map of betel palms at 10 m spatial resolution based on Sentinel-1/2 data and Google Earth Engine (GEE) for 2023 on Hainan Island, which accounts for 95% of betel nut acreage in China. The forest map was initially delineated based on signature information and the Green Normalized Difference Vegetation Index (GNDVI) acquired from Sentinel-1 and Sentinel-2 data, respectively. Subsequently, patches of betel palms were extracted from the forest map using a random forest classifier and feature selection method via logistic regression (LR). The resultant 10 m betel palm map achieved user’s, producer’s, and overall accuracy of 86.89%, 88.81%, and 97.51%, respectively. According to the betel palm map in 2023, the total planted area was 189,805 hectares (ha), exhibiting high consistency with statistical data (R² = 0.74). The spatial distribution was primarily concentrated in eastern Hainan, reflecting favorable climatic and topographic conditions. The results demonstrate the significant potential of Sentinel-1/2 data for identifying betel palms in complex tropical regions characterized by diverse land cover types, fragmented cultivated land, and frequent cloud and rain interference. This study provides a reference framework for mapping tropical crops, and the findings are crucial for tropical agricultural management and optimization. Full article

Global food security is increasingly challenged by climate change and the availability of arable land. This situation calls for improved crop monitoring and management strategies. Rice is a staple food for nearly half of the world’s population and a significant source of calories. Accurately identifying rice varieties is crucial for maintaining varietal purity, planning agricultural activities, and enhancing genetic improvement strategies. This study evaluates the effectiveness of machine learning algorithms to identify the most effective approach to predicting rice varieties, using multitemporal Sentinel-2 images in the Marismas del Guadalquivir of Sevilla, Spain. Spectral reflectance data were collected from ten Sentinel-2 bands, which include visible, red-edge, near-infrared, and shortwave infrared regions, at two key phenological stages: tillering and reproduction. The models were trained on pixel-level data from the growing seasons of 2021 and 2024, and they were evaluated using a test set from 2022. Four classifiers were compared: random forest, XGBoost, K-nearest neighbors, and logistic regression. Performance was assessed based on accuracy, precision, recall, specificity and F1 score. Non-linear models outperformed linear ones. The highest performance was achieved with the Random Forest classifier during the reproduction phase, reaching an exceptional accuracy of 0.94 using all bands or only the most informative subset (red edge, NIR, and SWIR). This classifier also maintained excellent accuracy (0.93 and 0.92) during the initial tillering phase. This fact demonstrates that it is possible to perform reliable varietal mapping in the early stages of the growing season. Full article

This study assesses the impact of heatwaves on hospital admissions in the Matam region of Senegal by combining climatic indices with machine learning methods. Using daily maximum temperature (TMAX) and heat index (HI), heatwave events were identified from 2017 to 2022. Hospital data from Ourossogui Regional Hospital were analyzed, and three predictive models, Random Forest (RF), Extreme Gradient Boosting (XGB), and Generalized Additive Models (GAMs), were compared. A bootstrapping approach with 1000 iterations was used to evaluate model robustness. The findings reveal a significant delayed effect of heatwaves, with increased hospitalizations occurring three to five days after the event. RF outperformed the other models with R² values ranging from 0.51 to 0.72. These findings highlight the need to enhance heatwave monitoring and promote the integration of impact-based climate forecasting into health early warning systems, particularly to protect vulnerable groups such as the elderly, children, and outdoor workers. Full article

The health risks of PM_2.5-bound metals highlight the need for burden assessment, metal prioritization, and key factor analysis to support effective air quality management, yet relevant studies remain limited. Shandong Province is one of the most polluted regions in northern China, providing an ideal setting for this investigation. We monitored 17 PM_2.5-bound metals for three years across Shandong, China and performed disease burden assessment based on disability-adjusted life years (DALYs). Furthermore, key influencing factors contributing to high-hazard metals were identified through explainable machine learning. The results showed that PM_2.5-bound metal concentrations were generally higher in inland areas than in coastal regions, with Ni concentrations elevated in coastal areas. K, Ca, Zn, and Mn exhibited the highest three-year average concentrations among the metals, while Cr averaged 6.12 ng/m³, significantly exceeding the recommended annual limit of 0.025 ng/m³ set by Chinese Ambient Air Quality Standards. Jinan carried the greatest burden at 4.67 DALYs per 1000 people, followed by Zibo (3.78), Weifang (2.98), and Rizhao (2.80). CKD, interstitial pneumonia, and chronic respiratory diseases account for the highest DALYs from PM_2.5-bound metals in Shandong Province. Industrial emissions are the largest contributors to the disease burden (>34%), with Cr, Cd, and Pb as the primary contributing metals requiring priority control. Fractional vegetation cover was identified as the key factor contributing to the reduction in their concentrations. These results underscore that prioritizing the regulation of industrial combustion, particularly concerning Cr, Cd, and Pb, and enhancing fractional vegetation cover could reduce disease burden and provide public health benefits. Full article

Additive manufacturing (AM) enables the production of complex components but often results in poor surface quality due to its layer-by-layer deposition process. To improve surface finish, postprocessing methods like abrasive flow machining (AFM) are necessary. However, conventional AFM struggles with achieving uniform polishing in intricate regions, especially at internal corners. This study proposes a liquid metal-driven abrasive flow (LM-AF) strategy designed for polishing complex internal channels in AM parts. By combining experimental and numerical simulations, the research investigates surface roughness variations, particularly focusing on the Sa (Arithmetic Average Surface Roughness) parameter. Experimental results show that conventional AFM leaves significant roughness at internal corners compared to adjacent areas. To address this, a hybrid GA-NN-GA (Genetic Algorithm–Neural Network-Genetic Algorithm) optimization model was developed. The model uses a neural network to predict Sa based on key parameters, with genetic algorithms applied for training and optimization. The optimal process parameters identified include a NaOH concentration of $1\mathrm{m}\mathrm{o}\mathrm{l}/\mathrm{L}$, a voltage of 50 V, abrasive concentration of $10\mathrm{\%}$, and a frequency of 428.3 Hz. With these parameters, LM-AF significantly reduced roughness at internal corners of flow channels, achieving uniformity with Sa values reduced from $25.365 \mathsf{\mu }\mathrm{m}$ to $15.780 \mathsf{\mu }\mathrm{m}$, from 22.950 $\mathsf{\mu }\mathrm{m}$ to $15.718 \mathsf{\mu }\mathrm{m}$, and from $10.933 \mathsf{\mu }\mathrm{m}$ to $10.055 \mathsf{\mu }\mathrm{m}$, outperforming traditional AFM methods. Full article

Mueller matrix polarimetry is recently attracting more and more attention for its diagnostic potentials. However, for prevalently used division of time Mueller matrix polarimeter based on dual-rotating retarder scheme, beam drift induced by rotating polarizers and waveplates introduces spatial misalignment and pseudo-edge artifacts in imaging results, hindering following accurate microstructural features characterization. In this paper, we quantitatively analyze the beam drift phenomenon in dual-rotating retarder Mueller matrix microscopy and its impact on linear retardance measurement, which is frequently used to reflect tissue fiber arrangement. It is demonstrated that polarizer rotation induces larger beam drift than waveplate rotation due to surface non-uniformity and stress deformation. Furthermore, for waveplates rotated constantly in dual-rotating retarder scheme, their tilt within polarization state analyzer can result in more drift and throughput loss than those within polarization state generator. Finally, phantom and tissue experiments confirm that beam drift, rather than inherent optical path changes, dominates the systematic overestimation of linear retardance in boundary image regions. The findings highlight beam drift as a dominant error source for quantifying linear retardance, necessitating careful optical design alignment and a reliable registration algorithm to obtain highly accurate polarization data for training machine learning models of pathological diagnosis using Mueller matrix microscopy. Full article

Accurate monitoring of canopy nitrogen content in wolfberry (Lycium barbarum L.) is essential for optimizing fertilization management, improving crop yield, and promoting sustainable agriculture. However, the sparse, architecturally complex canopy of this perennial shrub—featuring coexisting branches, leaves, flowers, and fruits across maturity stages—poses significant challenges for canopy spectral-based nitrogen assessment. This study integrates methods across canopy spectral acquisition, transformation, feature spectral selection, and model construction, and specifically explores the potential of hyperspectral remote sensing, integrated with spectral mathematical transformations and machine learning algorithms, for predicting canopy nitrogen content in wolfberry. The overarching goal is to establish a feasible technical framework and predictive model for monitoring canopy nitrogen in wolfberry. In this study, canopy spectral measurements are systematically collected from densely overlapping leaf regions within the east, south, west, and north orientations of the wolfberry canopy. Spectral data undergo mathematical transformation using first-derivative (FD) and continuum-removal (CR) techniques. Optimal spectral variables are identified through correlation analysis combined with Recursive Feature Elimination (RFE). Subsequently, predictive models are constructed using five machine learning algorithms and three linear regression methods. Key results demonstrate that (1) FD and CR transformations enhance the correlation with nitrogen content (max correlation coefficient (r) = −0.577 and 0.522, respectively; p < 0.01), surpassing original spectra (OS, −0.411), while concurrently improving model predictive capability. Validation tests yield maximum R² values of 0.712 (FD) and 0.521 (CR) versus 0.407 for OS, confirming FD’s superior performance enhancement. (2) Nonlinear machine learning models, by capturing complex canopy-light interactions, outperform linear methods and exhibit superior predictive performance, achieving R² values ranging from 0.768 to 0.976 in the training set—significantly outperforming linear regression models (R² = 0.107–0.669). (3) The Random Forest (RF) model trained on FD-processed spectra achieves the highest accuracy, with R² values of 0.914 (training set) and 0.712 (validation set), along with an RPD of 1.772. This study demonstrates the efficacy of spectral transformations and nonlinear regression methods in enhancing nitrogen content estimation. It establishes the first effective field monitoring strategy and optimal predictive model for canopy nitrogen content in wolfberry. Full article

China’s sustained economic growth and industrialisation have led to increasingly serious problems of resource consumption and environmental pressure, making green development an inevitable choice for the country’s transformation and development. Green finance policies are becoming an increasingly important tool for increasing the use of green energy in cities. Using a dual machine learning (DML) model, this paper assesses the specific impact of green finance policies on green energy efficiency in Chinese cities, the mechanism of action, and regional disparities. The analysis is based on objective and scientific measurement of the level of green finance policies and green energy efficiency in 282 Chinese cities at prefecture level and above from 2006 to 2022. Benchmark regression results show that green finance policies significantly promote green energy efficiency in Chinese cities, passing a rigorous robustness test. Green bond policies are found to have the greatest promotional effect, whereas green support policies are found to have no significant effect. The results of the heterogeneity analysis suggest that green finance policies are more effective in promoting green energy efficiency in resource-based cities, cities with established industrial bases, and more developed cities. The results of the impact mechanism suggest that green finance policies can promote green energy efficiency by allocating the three internal urban factors of labour, capital and technology. The results of the analysis of regional disparities demonstrate that green finance policies effectively reduce disparities in urban green energy efficiency at the national level, between the north and south, and between coastal and inland regions. However, they also widen the disparities between central and peripheral cities within each province, hindering balanced regional development. This paper makes relevant policy recommendations based on this. Full article

To systematically reveal the influence mechanisms and spatial effects of industrial intellectual property (IP) reform strategies and manufacturing craftsmanship spirit on regional energy intensity, this study aims to provide theoretical support and practical pathways for emerging market economies pursuing dual goals of energy efficiency governance and manufacturing transformation. Based on a “technology–culture synergistic innovation ecology” theoretical framework, the study deepens the understanding of energy intensity governance and introduces two spatial weight matrices—the economic distance matrix and the nested economic–geographic matrix—to uncover the spatial heterogeneity of policy and cultural effects. Using panel data from 30 Chinese provinces from 2010 to 2022 (excluding Tibet, Hong Kong, Macao, and Taiwan), we construct an index of manufacturing craftsmanship spirit (CSM) and its four dimensions—excellence in detail, persistent dedication, breakthrough orientation, and innovation inheritance—via the entropy method. Empirical analysis is conducted through Spatial Difference-in-Differences (SDID) and Double Machine Learning (DML) models. The results show that: (1) Industrial IP reform strategies significantly reduce local energy intensity through improved property rights definition and technology transaction mechanisms, but may increase energy intensity in economically proximate regions due to intensified technological competition. (2) All four dimensions of craftsmanship spirit indirectly mitigate regional energy intensity via distinct pathways, with particularly strong mediating effects from persistent dedication and innovation inheritance. In contrast, breakthrough orientation shows no significant impact, possibly due to limitations from the current stage of the technology lifecycle. (3) Spatial spillover effects are heterogeneous: under the nested economic–geographic matrix, IP reform strategies reduce neighboring regions’ energy intensity through synergistic effects, while under the economic distance matrix, competitive spillovers lead to an increase in adjacent energy intensity. Based on these findings, we propose the following: deepening IP reform strategies to build a technology–culture synergistic ecosystem; enhancing regional policy coordination to avoid technology lock-in; systematically cultivating the core of craftsmanship spirit; and establishing a dynamic incentive mechanism for breakthrough orientation. These measures can jointly drive systemic improvements in regional energy efficiency. Full article

Exposure to PM_2.5 poses severe risks to public health and sustainable development, with exposure inequalities exacerbated by variations in atmospheric activity and uneven regional development. However, the urban-rural inequalities and natural-human driving mechanisms underlying PM_2.5 exposure inequalities within urban agglomerations are poorly understood. Taking the Central Plains Urban Agglomeration (CPUA) in China as an example, this study investigated the spatio-temporal variations of PM_2.5 and considered its future trends. The Theil index was employed to quantify PM_2.5 exposure inequalities. An interpretable machine learning model (RF-SHAP) was applied to identify the raster natural and socioeconomic driving factors. We found that 99.68% of the CPUA exhibited a decreasing trend in ground-level PM_2.5. The overall Theil index decreased from 0.168 to 0.142, with a rural decline from 0.115 to 0.084, suggesting an overall reduction in air pollution inequalities, particularly in rural areas. Conversely, the urban Theil index increased from 0.096 to 0.208, highlighting an increasing inequality in urban PM_2.5 exposure. Resource-based cities, such as Changzhi, Jincheng, and Jiaozuo, exhibited the largest PM_2.5 exposure inequality. Elevation was identified as the dominant factor influencing overall and rural PM_2.5 exposure inequalities, while population density was the primary driver of urban inequalities. This study highlighted the differences in urban−rural PM_2.5 inequalities and their drivers at the city agglomeration scale. The aims were to mitigate PM_2.5 exposure inequalities through socio-environmental systems, provide evidence for the integrated management of PM_2.5 exposure inequalities in city agglomerations, and support regional sustainable development. Full article

As an important traffic and transportation roadway, tunnel engineering is widely used in important fields such as highways, railways, water conservancy, subways and mining. It is limited by complex geological conditions, harsh construction environments and poor robustness of the monitoring system. If the construction process and monitoring method are not properly designed, it will often directly induce disasters such as tunnel deformation, collapse, leakage and rockburst. This seriously threatens the safety of tunnel construction and operation and the protection of the regional ecological environment. Therefore, based on distributed fiber optic sensing technology, the full–cycle spatiotemporally continuous sensing information of the tunnel structure is obtained in real time. Accordingly, the health status of the tunnel is dynamically grasped, which is of great significance to ensure the intrinsic safety of the whole life cycle for the tunnel project. Firstly, this manuscript systematically sorts out the development and evolution process of the theory and technology of structural health monitoring in tunnel engineering. The scope of application, advantages and disadvantages of mainstream tunnel engineering monitoring equipment and main optical fiber technology are compared and analyzed from the two dimensions of equipment and technology. This provides a new path for clarifying the key points and difficulties of tunnel engineering monitoring. Secondly, the mechanism of action of four typical optical fiber sensing technologies and their application in tunnel engineering are introduced in detail. On this basis, a spatiotemporal continuous perception method for tunnel engineering based on DFOS is proposed. It provides new ideas for safety monitoring and early warning of tunnel engineering structures throughout the life cycle. Finally, a high–speed rail tunnel in northern China is used as the research object to carry out tunnel structure health monitoring. The dynamic changes in the average strain of the tunnel section measurement points during the pouring and curing period and the backfilling period are compared. The force deformation characteristics of different positions of tunnels in different periods have been mastered. Accordingly, scientific guidance is provided for the dynamic adjustment of tunnel engineering construction plans and disaster emergency prevention and control. At the same time, in view of the development and upgrading of new sensors, large models and support processes, an innovative tunnel engineering monitoring method integrating “acoustic, optical and electromagnetic” model is proposed, combining with various machine learning algorithms to train the long–term monitoring data of tunnel engineering. Based on this, a risk assessment model for potential hazards in tunnel engineering is developed. Thus, the potential and disaster effects of future disasters in tunnel engineering are predicted, and the level of disaster prevention, mitigation and relief of tunnel engineering is continuously improved. Full article

The estimation of rubber plantation aboveground biomass (AGB) is crucial for carbon sequestration assessment and management optimization. Unmanned Aerial Vehicles (UAVs) fitted with multispectral sensors present an economical approach for local-scale AGB monitoring. However, the prevailing studies primarily concentrate on spectral characteristics and algorithmic enhancements, failing to incorporate key ecological parameters such as stand age. Moreover, the current approaches remain constrained to local-scale assessments due to the absence of reliable upscaling methodologies from UAV to satellite platforms, limiting their applicability for regional monitoring. Thus, this study aims to establish an improved estimation model for rubber plantation AGB based on UAV multispectral imagery and stand age, develop an upscaling algorithm to bridge the gap between UAV and satellite scales, and ultimately achieve accurate regional-scale monitoring of rubber forest AGB. Combining optimized multispectral features, Landsat-derived stand age, and machine learning techniques yields the most accurate UAV-scale AGB estimates in this study, with performance metrics of R² = 0.90, an RMSE = 13.24 t/ha, and an MAE = 11.09 t/ha. Notably, the novel ‘UAV-satellite’ upscaling approach proposed in this study enables regional-scale AGB estimation using Sentinel-2 imagery, with remarkable consistency (correlation coefficient of 0.93). The developed framework synergistically combines Landsat-derived stand age data with spectral features, effectively improving rubber plantation AGB estimation accuracy through machine learning and enabling UAVs to replace manual measurements. This cross-scale upscaling framework demonstrates applicability beyond rubber plantation AGB monitoring, while providing novel insights for estimating critical parameters, including regional-scale stock volume and leaf area index, across diverse tree species. Full article

In rural and mountainous regions, power supply reliability remains a persistent challenge due to structural vulnerabilities, data incompleteness, and limited automation. In this study, a data-driven methodology is leveraged, wherein a validated machine learning framework comprising Random Forest (RF), Lasso Regression, and Recursive Feature Elimination (RFE) is applied to analyze outage data. The machine learning models, validated on a held-out test set, demonstrated modest but positive predictive performance, confirming a quantifiable, non-random relationship between grid structure and restoration time. This validation provides a credible foundation for the subsequent feature importance analysis. Through a transparent, consensus-based analysis of these models, the most robust influencing factors were identified. The results reveal that key structural indicators related to network redundancy (e.g., Inter-Bus Loop Rate) and electrical stress (e.g., Peak Daily Load Current, Load Factor) are the most significant predictors of prolonged outages. Furthermore, statistical analyses confirm that increasing structural redundancy and regulating line loads can effectively reduce outage duration. These findings offer practical, data-driven guidance for prioritizing investments in rural grid planning and reinforcement. This study contributes to the broader application of machine learning in energy systems, particularly showcasing a robust methodology for identifying key drivers under data and resource constraints. Full article

As the largest carbon reservoir in terrestrial ecosystems, soil organic carbon (SOC) plays a critical role in the global carbon cycle and climate change mitigation. A promising approach to swiftly procuring geographically dispersed SOC data is the amalgamation of UAV-based multispectral imagery at the local scale and Sentinel-2 satellite imagery at the regional scale. This integrated approach is particularly well-suited for precision agriculture and real-time monitoring. In this study, we evaluated the performance of UAVs and Sentinel-2 imagery in predicting SOC using four machine-learning models: Multiple Linear Regression (MLR), Support Vector Regression (SVR), Random Forest (RF), and Artificial Neural Networks (ANNs). UAV imagery outperformed Sentinel-2, achieving more accurate detection of local SOC variability thanks to its finer spatial resolution (5–10 cm versus 10–20 m). Among the models tested, the Random Forest algorithm achieved the highest accuracy, with an R² of up to 0.85 using UAV data and 0.65 using Sentinel-2 data, along with low RMSE values. All models confirmed the superiority of UAV imagery based on key error metrics (SSE, MSE, RMSE, and NSE). Although Sentinel-2 remains valuable for regional assessments, UAV imagery combined with Random Forest provides the most reliable SOC estimates at local scales. The spatial SOC maps generated from both UAV and Sentinel-2 imagery showed more nuanced spatial variability than standard interpolation techniques. While prediction accuracy using UAV-based models was slightly lower in some cases, UAV imagery provided greater spatial detail in SOC distribution. However, this is associated with higher acquisition and processing costs compared to freely available Sentinel-2 imagery. Given their respective advantages, we recommend using UAV imagery for detailed, site-specific SOC estimations and Sentinel-2 data for broader regional-to-global SOC mapping efforts. Full article

Timely and accurate winter crop distribution maps are crucial for agricultural monitoring, food security, and sustainable land use planning. However, conventional methods relying on field surveys are labor-intensive, costly, and difficult to scale across large regions. To address these limitations, this study presents an automated winter crop mapping framework that integrates phenology-based sample generation and machine learning classification using time-series Sentinel-2 imagery. The Winter Crop Index (WCI) is developed to capture seasonal vegetation dynamics, and the Otsu algorithm is employed to automatically extract reliable training samples. These samples are then used to train three widely used machine learning classifiers—Random Forest (RF), a Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost)—with hyperparameters optimized via Bayesian optimization. The framework was validated in three diverse agricultural regions in China: the Erhai Basin in Yunnan Province, Shenzhou City in Hebei Province, and Jiangling County in Hunan Province. The experimental results demonstrate that the combination of the WCI and Otsu enables a reliable initial classification, facilitating the generation of high-quality training samples. XGBoost achieved the best performance in the Erhai Basin and Shenzhou City, with overall accuracies of 0.9238 and 0.9825 and F1-scores of 0.9233 and 0.9823, respectively. In contrast, the SVM performed best in Jiangling County, yielding an overall accuracy of 0.9574 and an F1-score of 0.9525. The proposed approach enables high-precision winter crop mapping without reliance on manually collected samples, demonstrating strong generalizability and providing a promising solution for large-scale, automated agricultural monitoring. Full article