Search Results (984)

Addressing national goals for ecological conservation in the Yellow River Basin, this study focuses on its Henan segment (HYRB). We developed a VOR-SQ assessment framework by augmenting the classic Vitality–Organization–Resilience model with ecosystem services and an enhanced ecological quality indicator. Using multi-source remote sensing and statistical data, we examine the spatiotemporal evolution of ecosystem health in the HYRB from 2000 to 2020. The XGBoost-SHAP algorithm was applied to identify nonlinear drivers and threshold effects. Key findings indicate (1) a persistent “high west, low east” health gradient with an overall declining trend; western mountains remain healthy, while eastern plains, urban, and intensive agricultural areas show degradation. (2) Natural factors—evapotranspiration (ET), elevation, NDVI, and slope—dominate health dynamics, with critical thresholds (~1153 mm, ~457 m, ~0.76, ~10.5°, respectively) beyond which their impacts shift markedly. (3) Anthropogenic factors (GDP, population/road density) contribute less globally but cause strong local negative disturbances in plains. For instance, road density > 434 km/km² or population density > 159 persons/km² reverses their effects from positive to negative. Accordingly, we propose tailored strategies: western conservation, central farmland optimization, and eastern development control. By coupling the VOR-SQ framework with XGBoost-SHAP, this study offers a robust diagnostic tool for ecosystem health and adaptive governance in fragile socio-ecological systems. Full article

Industrial robots are widely used in critical tasks such as assembly, welding, and material handling as core components of modern manufacturing systems. For the reliable operation of these systems, early and accurate detection of execution failures is crucial. In this study, a comprehensive comparison of machine learning and deep learning methods is conducted for the classification of robot execution failures using data acquired from force–torque sensors. Three different feature engineering approaches are proposed. The first is a Baseline approach that includes 90 raw time-series features. The second is the Domain-6 approach, which consists of 6 basic statistical features per sensor (36 in total). The third is the Domain-12 approach, which comprises 12 comprehensive statistical features per sensor (72 in total). The domain features include the mean, standard deviation, minimum, maximum, range, slope, median, skewness, kurtosis, RMS, energy, and IQR. In total, ten classification algorithms are evaluated, including eight machine learning methods and two deep learning models: Support Vector Machines (SVM), Random Forest (RF), k-Nearest Neighbors (KNN), Artificial Neural Network (ANN), Naive Bayes (NB), Decision Trees (DT), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM-LGBM), as well as a One-Dimensional Convolutional Neural Network (CNN-1D) and Long Short-Term Memory (LSTM). For traditional machine learning algorithms, 5 × 5 nested cross-validation is used, whereas for deep learning models, 5-fold cross-validation with a 20% validation split is employed. To ensure statistical reliability, all experiments are repeated over 30 independent runs. The experimental results demonstrate that feature engineering has a decisive impact on classification performance. In addition, regardless of the feature set, the highest accuracy (93.85% ± 0.90) is achieved by the Naive Bayes classifier using the Baseline features. The Domain-12 feature set provides consistent improvements across many algorithms, with substantial performance gains. The results are reported using accuracy, precision, recall, and F1-score metrics and are supported by confusion matrices. Finally, permutation feature importance analysis indicates that the skewness features of the Fx and Fy sensors are the most critical variables for failure detection. Overall, these findings show that time-domain statistical features offer an effective approach for robot failure classification. Full article

To enhance the accuracy of wind shear identification by coherent Doppler wind lidar (CDWL), it is necessary to clarify the variation characteristics of CDWL detection results under typical airflow disturbance conditions. This study first numerically simulated typical wind shear fields and generated the Plane Position Indication (PPI) results of CDWL through coordinate projection. Then, it compared the performance of the double-slope algorithm and the least squares algorithm on wind shear identification from the PPI data. The results showed that for wind fields with significant peak characteristics, the double-slope algorithm can more sensitively identify wind shear near the peak, compared with the least square algorithm. In contrast, for wind fields with stable, continuous and linear gradient characteristics, the least squares algorithm can better suppress noise and fit the wind speed gradient changes. Finally, a self-developed long-range CDWL was used to conduct wind shear detection experiments at a plateau airport. After the CDWL beam position was calibrated, its data were compared with those from the anemometer. The “least square + double-slope” scheme was adopted to analyze the typical wind shear case, and the effectiveness and reliability of the identification scheme were verified in combination with an aircraft crew report. Full article

Landslides occurring on reservoir banks in steep, high-gradient canyon areas pose a significant risk of surge disasters when they slide into the water. This can endanger the lives and property of downstream residents and damage coastal infrastructure. Therefore, researching the formation mechanisms, disaster evolution, and risk assessment of the landslide-surge disaster chain in such areas is essential. This paper takes the Laowuchang landslide in the Shuibuya Reservoir area of the Qingjiang River, China, as its research object. Using GeoStudio 2018 software, it evaluates the landslide’s stability under varying reservoir water levels and rainfall conditions. For potential unstable scenarios identified, a full-chain numerical simulation of the landslide–tsunami disaster was conducted based on the Tsunami Squares method, with a focus on analyzing the wave characteristics during generation, propagation, and run-up processes. Furthermore, the paper assesses the risk of landslide–tsunami disasters in the Laowuchang landslide area. The research findings indicate that: (1) Under the long-term continuous river incision, limestone of the Triassic Daye Formation slides along weak interlayers, inducing large-scale collapses. Subsequently, part of the landslide mass is transported by water, while most accumulates in the near-shore area of the Qingjiang River, ultimately shaping the present morphology of the landslide. (2) The Laowuchang landslide is stable under static water levels of 375 m and 400 m, with corresponding safety factors of 1.137 and 1.167, respectively. Under combined static water level and heavy rainfall conditions, the slope stability decreases significantly, with safety factors of 1.034 and 1.064, respectively. Under reservoir drawdown conditions, the slope tends to be unstable, with a safety factor of 1.047. (3) Numerical simulation results indicate that if the Laowuchang landslide fails into water by the speed of 12 m/s and with a volume of 2 million m³, the maximum initial wave height can reach 15.9 m. The tsunami’s affected range spans 10 km upstream and downstream from the landslide mass, with four houses and one substation within a 2 km up and downstream falling into high-risk areas. If abnormal increases in landslide displacement occur, relocation and risk avoidance measures should be implemented. The findings of this study provide a scientific basis for the prevention and response to landslide–tsunami disasters in similar high and steep canyon terrains. Full article

Urban hydrological risks are endangering vulnerable populations, particularly in densely populated metropolitan areas undergoing rapid land use transformation. This study uses geospatial analysis to identify zones in the Athens metropolitan area that are prone to surface water accumulation and stream flow development during extreme rainfall events. Two spatial indices were developed by integrating digital elevation models, flow accumulation, slope, aspect, the topographic wetness index, and classified road network data: a Surface Water Accumulation Index and a Stream flow Pathway Index. Roads were categorized based on their orientation relative to the direction of the slope, which allowed for an assessment of their influence on hydrological flow. Both indices were classified into five risk levels representing gradients of hydrological vulnerability. The spatial patterns revealed by this analysis show strong correlations with flood-prone areas and natural drainage systems. These insights are essential for guiding urban planning efforts aimed at reducing hydrological hazards, particularly for at-risk groups such as the homeless. This approach offers a valuable tool for promoting sustainable, socially inclusive landscape management. Full article

The accelerated pace of urbanization has significant effects on the community composition, structure, regional distribution, and diversity characteristics of vegetation within urban river corridors. Spontaneous plants have strong environmental adaptability, high plasticity, and shorter life cycles; they also operate largely independently of human control. As a result, they are widely distributed throughout urban river corridors, and their ability to respond rapidly to heterogeneous habitats within these corridors makes them an ideal subject for studying the reciprocal mechanisms between rapid urbanization and riverine biodiversity. Based on a survey of 208 plots across six river corridors in Changchun, China, we found that the hydrological gradient was the strongest predictor of spontaneous herbaceous community distribution among the environmental factors examined. A total of 181 native herbaceous plant species, belonging to 55 families and 140 genera, were recorded. The Asteraceae, Poaceae, Fabaceae, Lamiaceae, and Polygonaceae families dominated. TWINSPAN classification divided the native herbaceous plant communities into 11 types, with the dominant species being predominantly low-growing perennial herbaceous plants. Canonical correspondence analysis (CCA) ordination confirmed this pattern, showing that the community distribution from aquatic to terrestrial habitats primarily aligned along the first CCA axis (defined by water depth and canopy cover), while the second axis reflected gradients in anthropogenic disturbance and slope. Thus, even in intensively managed urban rivers, natural hydrological processes remain pivotal in shaping riparian plant community composition and enhancing biodiversity. This study provides a scientific foundation for the conservation and sustainable utilization of plant resources in urban river corridors. Full article

Tropical mountain watersheds contain heterogeneous land cover and land use (LCLU) mosaics, yet the relationship between these mosaics and landslide morphometry and occurrence at the watershed scale remains unclear. We compiled landslide inventory from 2002 to 2023 for the 152.3 km² Upper Ciliwung Watershed, West Java, Indonesia. We mapped morphometry for a subset of 84 landslides, classified the events into seven LCLU classes, and compared landslide size–frequency distributions across vegetation groups. Principal component analysis (PCA) revealed that LCLU type influences landslide size and mobility. Forested terrain produced narrower, longer-runout landslides on steeper slopes, whereas agricultural and other herbaceous-dominated terrain generated wider landslides on gentler slopes. Clarifying landslides by vegetation characteristics as either tree- or herbaceous-dominated areas (including urban areas) revealed distinct size–frequency patterns, especially for small landslides (tree-dominated: 133 m², herbaceous-dominated and other: 97 m²; overall 112 m²), which are consistent with the contrasting vegetation structures and hydrological responses. PCA supported these patterns, with PC1 describing a morphometric axis and PC2 capturing gradients in event rainfall and antecedent wetness. Together, these results support the conclusion that vegetation structure and land-use conditions influence slope stability by affecting soil reinforcement and hydrological responses. This provides a foundation for land–use–specific geohazard mitigation and vegetation-based slope stability planning. Full article

Forest carbon sinks are central to climate change mitigation, and prior work has established a solid basis for assessing carbon sinks at regional scales. At the basin scale, however, forest carbon density (vegetation biomass carbon density, i.e., aboveground + belowground biomass carbon; t C ha⁻¹) often shows pronounced spatial clustering and inequality, while its temporal evolution and underlying mechanisms remain poorly quantified and interpreted for management-relevant units such as townships. Using the Xiuhe River Basin as a case study and townships as the basic analytical units, this study identifies the clustered spatial structure and inequality characteristics of forest carbon density and clarifies the joint effects of natural constraints and human disturbances, including potential threshold responses. We first assessed global spatial autocorrelation within a spatial weights framework using Global Moran’s I with permutation tests, and delineated local clustering by classifying local indicators of spatial association (LISA) types based on Local Moran’s I. We then measured the magnitude and stage-wise evolution of inter-township disparities using the Gini coefficient and the Theil T index. Finally, we applied GeoDetector factor, interaction, and risk detection to identify dominant drivers, interaction enhancement, and class-based contrasts. The results show significant and persistent positive spatial autocorrelation in forest carbon density from 2002 to 2024, with Moran’s I ranging from 0.68786 to 0.73849 (p < 0.01). Significant LISA units account for 40.74%–45.37% of townships, and the pattern is dominated by high–high (HH) and low–low (LL) clusters. Inequality follows a stage-wise trajectory: it expanded slightly during 2002–2019, converged markedly during 2019–2021, and rebounded modestly by 2024, while remaining below the levels observed in 2002 and 2019. Strong type-based differentiation is evident in 2024: mean carbon density is 46.06 t C ha⁻¹ in HH areas versus 17.64 t C ha⁻¹ in LL areas; HH areas contribute 38.44% of total carbon stock, whereas LL areas contribute only 5.08%. In terms of drivers, natural and human factors jointly shape the spatial pattern and commonly exhibit interaction enhancement. Elevation (q = 0.7832), slope (q = 0.7133), and NPP (q = 0.6373) are the leading natural constraints, while population density (q = 0.6054) and the built-up land ratio (q = 0.5374) are key indicators of human disturbance. Risk detection further indicates a stable negative gradient for the built-up land ratio and nonlinear class differences for population density, implying that once disturbance intensity reaches higher levels, low-value clustering is more likely to persist. By linking clustered spatial structure, stage-wise inequality, and disturbance-related threshold signals, our results support basin-scale zoning and differentiated management at the township level. Specifically, HH clusters should be prioritized for conservation and connectivity maintenance, whereas LL clusters warrant stricter control of built-up expansion and fragmentation to reduce the risk of persistent low-carbon locking under high disturbance. By linking spatial structure, inequality dynamics, and threshold responses, this study provides a quantitative basis for basin-scale zoning to enhance carbon sinks and for implementing differentiated spatial controls. Full article

In recent years, many countries have increased their investment in the field of humanoid robots, promoting significant technological development. This study aims to enable humanoid robots to better adapt to various complex environments, enhancing the robustness of their motion systems and the generalization ability of their motion strategies. Using reinforcement learning algorithms, training on varied terrain is a critical factor for developing adaptable humanoid robots. This paper takes the humanoid robot G1 as the research platform. First, it completes the training, transfer verification, and real-machine deployment of a flat-ground walking model. Then, using fuzzy logic control and a phased training strategy, walking models for ascending/descending stairs and traversing slopes are trained. By systematically varying the stair height and slope gradient, the convergence of the reward function and the task completion success rate are analyzed. Furthermore, the dynamic stability of the robot on complex terrains is validated through qualitative kinematic analysis. The research concludes that as the single-step height and slope gradient increase, the reward value initially rises with more iterations but converges more slowly and at a lower final value. Statistical analysis shows that the success rates of phased training for stair and slope terrains are higher than 86% and 92%, respectively. Full article

This study examines how longitudinal road gradients affect the energy consumption and driving range of a Tesla electric vehicle using dynamometer measurements and Simulink simulations. Tests performed on slopes from 0% to 4% show a strong inverse relationship between gradient and range, with more than a 62% reduction at a 4% incline. The Simulink model accurately reproduces these trends despite the tested vehicle’s age and battery degradation. Shifting from driving range to energy consumption metrics provides a more robust assessment of vehicle efficiency, revealing that uphill segments substantially increase consumption, while downhill segments enable significant recuperation. When averaged, these effects nearly cancel out for moderate slopes, especially at higher speeds where aerodynamic drag dominates. Constant-speed simulations confirm that slope has minimal net impact at highway speeds but strongly affects consumption at urban speeds, with increases of up to 17% at a 4% gradient. Overall, the findings highlight road gradients as a key factor in EV energy modelling and emphasize the need to incorporate terrain and driving environment into predictive range estimation and eco-routing strategies. Full article

Numerical weather prediction (NWP) models are essential for precipitation forecasting but are constrained by coarse spatial resolutions (10–50 km), which fail to capture fine-scale variations required for regional disaster prevention, particularly in complex terrain. While statistical and machine learning downscaling methods have been developed to bridge this resolution gap, they predominantly operate as “black boxes” without explicit physical guidance, leading to predictions that violate meteorological principles and systematic underestimation of extreme precipitation events. To address these limitations, this study aims to develop a Physics-Informed Machine Learning framework that explicitly integrates multi-scale topographic modulation and physical consistency constraints into precipitation downscaling. Specifically, a Random Forest model enhanced with Multi-Scale Structural Similarity (MS-SSIM) loss and Physical Constraint Enhancement (MSSSIM-PCE-RF) was constructed. The model introduces elevation gradient weights at low-resolution layers and micro-topographic parameters (slope, surface roughness) at high-resolution layers, while enforcing physical consistency between precipitation intensity, radar reflectivity, and ground observations via the Z-R relationship. Based on hourly data from 2252 meteorological stations in Jiangxi Province (2021–2022), coupled with topographic factors (DEM, slope, aspect) and Normalized Difference Vegetation Index (NDVI), a technical framework of “data fusion–feature synergy–machine learning–spatial reconstruction” was established. Results demonstrate that the MSSSIM-PCE-RF model achieves a validation R² of 0.9465 and RMSE of 0.1865 mm, significantly outperforming the conventional RF model (R² = 0.9272). Notably, errors in high-altitude, steep-slope, and high-vegetation areas are reduced by 45.3%, 42.0%, and 43.1%, respectively, with peak precipitation period errors decreasing by 37.2%. Multi-scale topographic analysis reveals significant orographic lifting effects at 250–1000 m elevations, peak precipitation at 12–15° slopes, and abundant precipitation on south/southeast aspects. By explicitly embedding topographic modulation and physical consistency constraints, the model effectively alleviates systematic underestimation of extreme precipitation in complex terrain, providing high-resolution data support for transmission line disaster prevention and micro-meteorological risk assessment. Full article

Soil erosion intensifies the redistribution and loss of soil nutrients. However, how erosion–deposition processes shape the spatial patterns of soil total nitrogen to total phosphorus (TN:TP) ratio in long-term eroded landscapes remains poorly understood. In this study, we examined the spatial variability of TN, TP, and the N:P ratio and their driving factors across a 7-ha sloping black-soil cropland in northeast China. Results showed that mean topsoil concentrations of TN and TP were 1.7 and 0.7 g kg⁻¹, respectively, and the corresponding N:P ratio averaged 5.2, which was 46.6% lower than the Chinese average. Erosion–deposition effects were strongly depth dependent. In the 20–40 cm soil layer, all three variables declined in strongly eroded zones but increased in depositional areas, whereas in the 0–20 cm layer they were lower in depositional zones than in weakly eroded zones, indicating a vertical decoupling of nutrient redistribution under prolonged erosion. Notably, variability in soil N:P was closely associated with TN, soil organic carbon, and silt content, with TN emerging as the dominant control, as reflected by its stronger correlation with N:P (p ≤ 0.001) and higher variability (CV = 21.7–35.8%) relative to TP. Although elevation and slope gradient both influenced N:P spatial variability, only elevation showed a significant negative correlation (p ≤ 0.05). These findings indicate that, compared with TP, TN is more sensitive to long-term soil erosion and deposition and dominates the spatial pattern of the N:P ratio. The enhanced role of TN may increase the risk of N limitation in eroded farmlands. This study provides insights into the mechanisms of nutrient imbalance in eroded black soil regions and offers a scientific basis for formulating targeted soil conservation and fertility enhancement measures. Full article

The conservation status of the Colombian Caribbean dune system was assessed considering the influence of natural and anthropogenic factors. The study took place in five locations with a gradient of human disturbance. In total, 198 plots and 22 transects were established, three transects in Gairaca and Costa Verde; four in Lipe, and six in Mendihuaca and Salguero. Environmental variables such as dune height, slope, sediment physical–chemical attributes, and anthropogenic impact were assessed in each site, while species composition, frequency, and plant cover were determined for each plot. The results show a correlation between natural and anthropogenic factors and the composition and structure of plant communities growing on the beach and coastal dunes. Human disturbances (urbanized areas, construction, burning, debris, trampling, logging, tourism, groins, sewage, roads, garbage, and sediment extraction) were particularly relevant. Plant cover and species diversity were inversely related to human impact and disturbance. Furthermore, community structure varied among sites: trees and vines were more frequent in the preserved locations, while shrubs and parasitic plants were more abundant in the disturbed sites. Management alternatives should consider the environmental factors (natural and anthropogenic) affecting vegetation to improve the conservation of plant diversity on coastal dunes along the Colombian Caribbean coast. Full article

To find out the combined effect of seismic action, seepage, and sandy and argillaceous interlayers on the seabed slope stability, the safety factors of seabed slopes, which include sandy and argillaceous interlayers, under different hydraulic gradients and seismic loads, were calculated using the geotechnical simulation software Geo-Studio 2012. Results demonstrate that both seismic action and seepage exert significant impacts on seabed slope stability: seismic loads play a dominant role in governing slope stability, while seepage acts as a key triggering factor for slope failure. With the gradual increase in seismic load magnitude, the influence of seepage hydraulic gradient on slope safety factor decreases progressively. For homogeneous segregated slopes, which consist of silty clay, a higher seepage hydraulic gradient reduces the magnitude of critical seismic load that induces slope instability. Under identical seismic load and hydraulic gradient conditions, seabed slopes with sandy interlayers exhibit higher stability compared to homogeneous soil slopes, whereas slopes with argillaceous interlayers show reduced stability. Full article

Climate change influences the agronomic behaviour of fruit trees. It is necessary to determine which cultivars adapt best to conditions in which water supplies are becoming increasingly scarce. This study analyses different phenological, morphological, physiological, agronomic and productive parameters to evaluate water stress tolerance in six late-blooming almond cultivars widely grown in Spain (‘Ferragnès’, ’Francolí’, ‘Masbovera’, ‘Glorieta’, ’Guara’ and ‘Lauranne’). Two different plots were analysed: one under regulated deficit irrigation, at Les Borges Blanques, Lleida, with a water deficit (146.2 mm/year) and the other under rainfed conditions, at Mas Bové, Constantí, Tarragona, with a water deficit (284.5 mm/year). Parameters, including an increase in canopy volume, leaf-to-air thermal gradient, and slope between leaf water potential and level of leaf saturation, have proven to be good indicators of resistance to water stress. Yield variation and leaf temperature variation between rainfed and irrigated conditions also perform quite well. An assessment of leaf chlorophyll content, measured using SPAD-502, suggested the presence of a collateral effect resulting from the opacity of the biomass, as well as to chlorophyll-related cuticular colouring. Finally, under the experimental conditions, ‘Guara’ and ‘Masbovera’ proved the most resistant cultivars; ‘Glorieta’ and ‘Francolí’ exhibited an intermediate level, and ‘Lauranne’ and ‘Ferragnès’ were the least resistant cultivars. Full article