Search Results (48,670)

Reference-driven humanoid motion tracking aims to reproduce a source motion on a target humanoid while preserving physical executability under actuation limits and changing contact conditions. The problem becomes particularly challenging for dynamic motions involving rapid support transitions, landing impacts, mixed hand–foot contacts, and moderate topology-preserving morphology variation. Existing pipelines often rely heavily on morphology-specific world-frame targets or treat balance and contact quality only indirectly during execution, which limits their reliability under dynamic contact variation. This paper presents a task and cost formulation for reference-driven humanoid tracking within the residual-based MuJoCo model predictive control (MPC) framework. The source motion is decomposed into a pelvis-centered canonical local reference, pelvis height and tilt references, and a pelvis-derived horizontal center-of-mass (CoM) velocity intent, and is tracked online with a zero moment point (ZMP)-aware contact-conditioned residual design including slip, penetration, posture, and control regularization. The formulation is compatible with standard MuJoCo MPC planners, and the evaluation is conducted under a shared iterative linear quadratic Gaussian (iLQG) setting on nominal and morphology-varied humanoids against tracking-only and two-stage inverse-kinematics (IK)-based baselines. The proposed formulation improves success rate, support quality, slip reduction, and progression accuracy, with the clearest gains on contact-sensitive motions; for example, success rate increases from 56.7% to 76.7% on Jump–Turn and from 46.7% to 70.0% on Cartwheel relative to the tracking-only MPC baseline. These results support the use of execution-oriented reference representation and contact-conditioned residual design for physically reliable reference-driven humanoid tracking. Full article

Under the context of global change, forest cover, as a critical component of terrestrial ecosystems, exerts a profound influence on regional ecological security and sustainable development through its spatiotemporal evolution. Current research on forest cover change primarily focuses on pattern description and single-factor driver analysis, with insufficient in-depth exploration of the interactions among multiple factors and their associated nonlinear mechanisms. To address this gap, this study focuses on the Wumeng Mountain area, a typical ecologically fragile karst region in Southwest China. By comprehensively employing methods such as Theil–Sen Median trend analysis, land use transfer matrix, standard deviation ellipse, and spatial autocorrelation analysis, this study systematically reveals the spatiotemporal evolution characteristics of forest cover from 1985 to 2024. On this basis, an integrated eXtreme Gradient Boosting–SHapley Additive exPlanations (XGBoost-SHAP) model is introduced to construct an indicator system comprising 16 driving variables, including elevation, slope, aspect, temperature, precipitation, soil type, soil pH, soil thickness, soil organic matter, soil moisture content, GDP, population, distance from water, distance from railway, distance from grade highway, and distance from government. This model quantifies the influence intensity of each driving factor on forest change. The main findings are as follows: (1) From 1985 to 2024, the forest cover rate in the Wumeng Mountain area significantly increased from 54.7% to 60.2%, exhibiting a “high-low-high” heterogeneous spatial distribution pattern along the northeast-southwest axis; (2) Forest increase primarily originated from the conversion of cropland and grassland, with contribution rates reaching 93.58% and 5.9%, respectively, indicating an overall trend of “increase in low-value areas and decrease in high-value areas”; (3) Forest cover change is driven by both natural and anthropogenic factors, with dominant driving factors exhibiting phased replacement over time. Overall, this is manifested as long-term stable constraints exerted by natural background factors, alongside strong disturbances from anthropogenic factors such as social-economic, and transportation-related activities. Natural factors remain the primary driving force behind changes in forest cover. The core findings of this study elucidate the complex driving factors of forest change in karst mountainous areas, thereby providing scientific support for the precise management of regional forest resources, the planning of ecological restoration projects, and the implementation of sustainable development strategies. Full article

This study investigates the spatiotemporal evolution and migration path coupling of the “water–land–energy–carbon” nexus system in the Beijing–Tianjin–Hebei region from 2002 to 2023 using multi-source data. The Coefficient of Variation and Shannon entropy were employed to assess the stability of elements, while Dynamic Time Warping (DTW) was applied to couple their migration paths. The results reveal the following: (1) Terrestrial water and groundwater exhibited similar evolution patterns, though groundwater showed greater volatility. Land use remained stable, with primary conversion being cropland to impervious. Nighttime light intensity increased significantly in urban areas, reflecting growth in energy consumption. Carbon emissions increased in most areas but decreased in some urban centers. (2) Element centroids displayed differentiated migration: water resources and cropland shifted southwest, and ecological land expanded northwest, while impervious, carbon emissions, and nighttime light concentrated in the southeast and northeast. (3) Two strongly coupled paths were identified: “terrestrial water–groundwater–cropland,” reflecting agricultural dependence on water resources, and “impervious –nighttime light–carbon emissions,” revealing the linkage between urban expansion, energy consumption, and carbon emissions. This study reveals the migration patterns of factors driven by both natural factors and human activities, providing quantitative support for resource optimization and low-carbon development policies in the Beijing–Tianjin–Hebei region. Full article

Mountainous megacities face a distinctive form of pluvial waterlogging in which terrain-controlled flow convergence, accelerating imperviousness, and aging drainage interact to produce chronic, spatially clustered failures rather than stochastic events. Existing frameworks, such as hydrodynamic modeling, data-driven machine learning, and multi-criteria composite indexing, carry distinctive failure modes at the municipal scale. This study develops and externally validates a city-wide, grid-based assessment framework for Chongqing, China, through three integrated choices. First, resilience is reformulated as a stabilized adaptation-to-risk ratio and subjected to an explicit falsification test against independent waterlogging observations. Second, multi-source hydroclimatic, topographic–hydrologic, land-cover, and service-accessibility indicators are integrated on a 500 m fishnet (22,500 cells) through within-component CRITIC–Entropy weighting and TOPSIS, with robustness diagnosed by a 500-iteration Monte Carlo weight-perturbation analysis. Third, a spatially grouped LightGBM classifier with SHAP interpretation serves both as an independent validation layer and as a mechanistic lens on non-linear driver thresholds. The composite risk surface achieves ROC-AUC values of 0.834 and 0.873 against two independent waterlogging registries, is strongly spatially clustered (Moran’s I = 0.81, p < 0.001), and preserves its ranking under aggressive weight perturbation (Spearman ρ ≥ 0.95 in 95% of scenarios). A counterintuitive finding emerges from the falsification test as resilience yields ROC-AUC below 0.5 on both point sets, indicating that accessibility-based capacity proxies systematically capture urban centrality rather than drainage robustness, like a diagnosable measurement problem affecting the wider resilience-index literature. LightGBM concentrates 88.0% of waterlogging cells within the top 10% of scored grids, and SHAP-derived thresholds align with saturation-ponding, well-drained, and convergence–hotspot regimes of classical hydrology. Together, these results reframe waterlogging assessment in complex terrain from a cartographic exercise into a falsifiable, resource-aware prioritization framework, and clarify why capacity maps and risk maps should be published as complementary instruments of flood governance. Full article

As a critical component of urban ecological infrastructure, urban forests play a pivotal role in regulating regional climate and mitigating the urban heat island (UHI) effect. However, existing studies have predominantly focused on single temporal snapshots or aggregate spatial scales, with limited attention to the seasonal dynamics of urban forest landscape patterns and a lack of systematic quantification of their nonlinear regulatory mechanisms. Empirical evidence from subtropical cities remains particularly scarce. In this study, Hangzhou was selected as the study area. Land Surface Temperature (LST) was retrieved using the Google Earth Engine (GEE) platform, and the Thermal Field Variance Index was employed to classify UHI intensity. Six representative forest landscape indices were selected to construct an evaluation framework. Pearson correlation analysis and the XGBoost model were further applied to quantify the relationships between landscape patterns and seasonal LST variations. The results reveal that: (1) LST in Hangzhou exhibits pronounced seasonal variability, following the order of summer > spring > autumn > winter. Areas without UHI effects dominate in spring, summer, and autumn, whereas the extent of strong UHI zones increases markedly in winter. (2) All landscape indices are significantly correlated with seasonal LST; forest ratio and forest largest patch index show negative correlations, while forest patch density, forest landscape shape index, number of patches, and landscape division index (DIVISION) are positively correlated. (3) The XGBoost model indicates that DIVISION consistently exhibits high contribution across all seasons, identifying it as a key determinant of LST variation. These findings provide a scientific basis for optimizing urban forest landscape configuration and developing effective UHI mitigation strategies. Full article

Drone-based Urban Air Mobility (UAM) shows immense potential in urban logistics and emergency response; however, evidence regarding its systemic sustainability remains fragmented. In a systematic review using the PRISMA methodology, this study analyzes 301 core articles to construct an evaluation framework spanning environmental, economic, social, and systemic effectiveness dimensions. Given technical similarities, electric Vertical Take-off and Landing (eVTOL) findings are integrated to anticipate operational challenges. Results highlight a clear consensus: drone delivery is time-efficient in high-sensitivity scenarios, though noise, equity, and safety remain critical bottlenecks. Meanwhile, deep controversies persist across some dimensions. Environmental benefits are highly context-dependent, contingent on operating models, battery life cycles, and clean energy proportions from a Life Cycle Assessment (LCA) perspective. Economically, a mismatch between high costs and low willingness to pay (WTP) necessitates optimized pricing strategies. Socially, public acceptance is sensitive to the balance between perceived benefits and risks. Furthermore, systemic effectiveness depends on the coupling between vertiports and ground infrastructure. Concluding that sustainable drone-based UAM is a multistakeholder systemic endeavor, we urge future research to prioritize LCA, pricing strategies, public acceptance surveys, and integrated air-ground coordination to resolve controversies and foster sustainable systems. Full article

Semantic segmentation of high-resolution remote-sensing images is critical for urban planning, land-cover mapping, and ecological monitoring. However, existing methods face limitations in handling complex land-cover types, multi-scale objects, and modeling long-range dependencies. To address these challenges, we propose RFA2Net, an enhanced semantic segmentation model based on the DeepLabv3+ framework. The key innovations include the integration of the RFCSA-Conv module into the ResNet101 backbone to enhance feature representation and long-range dependency modeling, the design of the RFA-DASPP structure built upon the Dense ASPP framework with the novel RFCA-DConv dilated convolution module to reduce information loss during multi-scale feature fusion and enhance the model’s ability to perceive long-range directional structures, and the introduction of a Dual-Branch Fusion Network to improve segmentation accuracy for small-scale objects. Experimental results on the ISPRS Potsdam and LoveDA datasets demonstrate that RFA2Net outperforms several CNN and Transformer-based models, achieving 78.94% and 59.46% mean intersection over union (mIoU) on the ISPRS Potsdam and LoveDA datasets, respectively, with improvements of 3.19% and 3.08% over the original DeepLabv3+. Ablation studies and comparative experiments further confirm the model’s effectiveness, robustness, and practical applicability in high-resolution remote-sensing image segmentation, with particular relevance to environmental monitoring and sustainable energy applications. Full article

This study analyzes how dynamic disturbances influence the decisions made during the human supervision of autonomous unmanned aerial vehicles. While previous research has primarily focused on control algorithms and system stability, the effect of disturbances originating from system dynamics on operator intervention behavior has been less extensively investigated. To examine this problem, a hardware-in-the-loop (HIL) experimental framework was developed, which is based on a previously validated unmanned aerial vehicles (UAVs) test platform and was adapted in this study to enable the investigation of human supervisory decision-making. Participants observed the behavior of an autonomously operating system under controlled disturbances and were provided with the possibility to intervene by activating an emergency landing mechanism. The results indicate that the disturbance intensity had a significant effect on intervention decisions, while the reaction times did not show notable differences. This finding suggests that supervisory behavior is primarily determined by the evaluation of the system state rather than by timing characteristics. It also identifies that subjective risk perception plays a decisive role in the formation of intervention decisions, indicating the presence of an implicit decision threshold for participant behavior. The research findings offer a novel approach to the interpretation of human–UAV interaction by emphasizing the role of system dynamics in shaping user decisions. The presented method may provide a foundation for the development of predictive and adaptive supervisory systems that take into account the characteristics of human decision-making, thereby contributing to the design of safer and more efficient autonomous systems. Full article

Groundwater level (GWL) variations in the arid regions of Northwest China are driven by both natural processes and human activities. Identifying causal links between hydrological variables is fundamental to understanding groundwater evolution and conducting dynamic simulations. This study integrates the Mann–Kendall test, Seasonal-Trend decomposition using Loess, and the Peter and Clark Momentum-threshold and Momentary Conditional Independence (PCMCI) causal inference to analyze GWL variation characteristics and causal response processes across seven sub-basins in the Tarim Basin using multi-source remote sensing data. Results show an overall decline in GWL, primarily in the north-central part of the basin, with the Kaidu–Konqi River Basin reaching a maximum rate of 0.51 m/year. The trend components reveal localized depletion alongside broad stability, while seasonal components exhibit three types of temporal shifts in fluctuations. A mismatch exists between the prevalence of environmental influences and their causal strength. Daytime land surface temperature (LST_D), surface runoff (RO), and evapotranspiration (ET) show the highest detection frequencies, yet volumetric soil water in layers 2 (SWVL₂) and RO exhibit the largest ranges in strength and drive variations at specific sites. Response times are asymmetric. Negative effects from ET on GWL transmit quickly, while positive recovery is slow. Conversely, positive recharge from volumetric soil water in layer 1 (SWVL₁) is faster than its negative lag. At the basin scale, surface processes recharge GWL while mediating indirect influences from other variables. Climate and agricultural irrigation act as direct sinks. Depending on local conditions, three regional patterns emerge: direct climate-driven depletion, obstructed shallow water retention, and indirect compensation from agricultural water use. Causal networks indicate that RO and SWVL₁ have the highest centrality and dominate water output, whereas SWVL₂ acts as a passive receiver. Pathways from the surface to GWL are also asymmetric. The most frequent path involves step-by-step infiltration along RO → ET → SWVL₁ → SWVL₂ → GWL. In contrast, the paths with the highest cumulative strength are shorter and faster, specifically RO → ET → GWL and RO → SWVL₁ → GWL. The identified pathways and lag parameters provide a direct basis for groundwater dynamic modeling and water resource management in the basin. Full article

We present and discuss time-lapse gravity variations recorded by a large-scale absolute gravity network operating in Central Italy. The network comprises four stations distributed across the Lazio, Umbria, and Abruzzo regions, areas affected by the significant seismic activity of 2009 and 2016–2017. From 2018 to 2023, six campaigns were carefully conducted using an FG5 absolute gravimeter. We detected significant gravity decreases around 2020 reaching between −15 and −20 μGal in three sites and approximately −37 μGal at the fourth. The Sentinel-1 time series of permanent scatterers (PS) allowed us to exclude significant contribution from vertical deformations to the observed gravity changes. We analyzed both ground-based data (rainfall gauges and well water levels) and satellite-based observations (the Gravity Recovery and Climate Experiment-Follow-On, GRACE-FO, mission) together with the Global Land Data Assimilation System (GLDAS) and precipitation models. The results reveal a significant decrease in the regional groundwater content from 2018 to the end of 2020, which coincides temporally with the observed gravity decrease. We show that the absolute gravity variation trends observed at all stations are consistent with regional-scale hydrological processes, pointing to a significant decrease in terrestrial water storage (TWS) during the same time interval. At L’Aquila (AQUI), the gravity anomaly is larger than expected from regional hydrological products alone, suggesting an additional local component possibly related to the hydrogeological response of the fractured karst system undergoing significant post-seismic activity. Full article

The Source Region of the Yangtze River is a high-altitude area with extensive permafrost on the Tibetan Plateau. While temperature, precipitation, and radiation significantly affect vegetation phenology, the influence of permafrost changes remains unclear. Using the daily Long-term Seamless NOAA AVHRR NDVI Dataset of China (2003–2022), we extracted the start (SOS) and end (EOS) of the growing season in the Source Region of the Yangtze River (SRYR). Soil thawing date (SOT) was obtained from freeze–thaw state products, while active layer thickness (ALT) was estimated using the Stefan model based on MODIS land surface temperature (LST). Partial least squares regression and mediation analysis quantified the direct and indirect effects of permafrost degradation. Results show: (1) The end of the growing season (EOS) became significantly earlier in 64.33% of the region, while the start of the growing season (SOS) showed little change. (2) The effect of SOT on SOS depends on moisture conditions. Earlier SOT leads to earlier SOS in wetter areas by supplying meltwater, but delays SOS in cold–dry areas by increasing soil water loss. (3) Thicker ALT strongly promotes earlier EOS, accounting for up to 42.61% of EOS variation in cold–dry zones, because a deeper active layer potentially promotes downward movement of water, which may further lead to the potential leaching of nutrients from the shallow root zone, limiting resources for shallow-rooted plants. (4) Alpine meadows respond more strongly to permafrost changes than alpine grasslands. Overall, water loss caused by permafrost degradation may reduce the potential lengthening of the growing season under climate warming, highlighting the key role of soil water in linking permafrost and vegetation dynamics. Full article

Tick-borne diseases pose a significant threat to global cattle production, with species displacement between ticks compounding this issue. This narrative review synthesises the literature to examine the drivers behind the expansion of the invasive Rhipicephalus microplus and its displacement of the native Rhipicephalus decoloratus in Southern Africa. We analysed the biological, ecological, environmental, and anthropogenic factors by reviewing existing scientific studies and reports. Our findings indicate that R. microplus possesses a competitive advantage due to its shorter life cycle, higher reproductive output, and greater acaricide resistance. Furthermore, anthropogenic activities such as communal grazing practices, unregulated livestock movement, and land-use changes facilitate the spread of this parasite. Climate change and vegetation shifts also create more favourable habitats for this invasive species. The conclusion is that the displacement of R. decoloratus by R. microplus intensifies the burden of tick-borne diseases, leading to substantial economic losses. Effective mitigation requires an integrated tick management approach that combines chemical, biological, and ecological strategies, supported by improved surveillance and farmer education. Full article

Sustainable land use policy planning requires integrated approaches that account for environmental and socio-economic trade-offs of greenhouse gas (GHG) mitigation measures. This study presents a spatial decision-support tool developed to support the evaluation of policy scenarios in non-urban land-use sectors, with application to the land use, land-use change, and forestry (LULUCF) sector in Latvia. The tool enables users to select predefined mitigation measures, apply spatial selection criteria, and generate quantitative and spatially explicit outputs. In addition to estimating GHG mitigation potential, it evaluates impacts on profitability, employment, and habitat quality, allowing the assessment of trade-offs and synergies across multiple dimensions. Scenario results are reported as both absolute and relative impacts, improving transparency and comparability. Developed in Python 3.10 and supported by a PostgreSQL 17/PostGIS 3.5 database, the tool operates through a web-based interface and supports efficient scenario construction and evaluation. While results depend on underlying data and assumptions, the tool provides a transparent framework for exploring policy options and supports evidence-based decision-making in land-use and climate policy planning. Full article

Freshwater ecosystems are increasingly threatened by eutrophication and other anthropogenic and climate-driven pressures that undermine ecological functioning and biodiversity. This study evaluates the transferability of a GIS-based multi-criteria decision analysis (GIS–MCDA) framework with Fuzzy Analytic Hierarchy Process (F-AHP), originally developed for a shallow coastal lake, to a morphologically distinct deep upland lake (Lough Tay, Ireland). Monthly in situ measurements at a single monitoring point in 2024 were analysed together with meteorological variables using Spearman rank correlations. Because spatial interpolation of in-lake water quality parameters was not feasible, eutrophication susceptibility was mapped using four external spatial drivers: distance from water resources (River Cloghoge inflows), land-based nitrogen export potential, distance from environmental pollutants represented by the transportation network, and a wind exposure index derived from a DEM and wind-rose analysis. Criteria were standardized with fuzzy membership functions, weighted using F-AHP (consistency index 0.056), and aggregated using weighted linear combination at 25 m resolution. The resulting Eutrophication Susceptibility Index (ESI) ranged from 0.18 to 0.81, indicating generally moderate to good conditions, with higher ESI values concentrated in the northern lake sector near inflow zones. The results demonstrate that GIS–MCDA can be adapted to lakes with limited monitoring by relying on external drivers, providing a spatial proxy for susceptibility rather than measured trophic status. Full article

Urban morphology, encompassing both horizontal landscape patterns and three-dimensional architectural structures, plays a pivotal role in modulating urban heat distribution. However, conventional models often fail to capture the intricate spatial nonstationarity and nonlinear coupling of these drivers at the block scale. Recognizing that land surface temperature (LST) exhibits distinct diurnal and nocturnal thermal cycles, this study explicitly incorporates spatial heterogeneity analysis to systematically evaluate the relative and local contributions, marginal effects, and interaction mechanisms of multidimensional urban morphology on diurnal LST variations. To achieve this objective, geographically weighted extreme gradient boosting and SHapley Additive exPlanations were employed to decipher these complex driving mechanisms from a morphological perspective. The results indicate the following: (1) Built environment variables predominate the spatial heterogeneity of LST in Xi’an, China, with their governing mechanisms shifting diurnally—characterized by a midday NDVI-induced evapotranspiration cooling effect and an atmospheric back-radiation warming effect associated with PM2.5 during the night and early morning. (2) The driving mechanisms exhibit pronounced spatial nonstationarity; while the northeastern and northern sectors are primarily influenced by the synergistic interaction between surface albedo and PM2.5, the central-western and southern regions are governed by population density and 3D architectural morphology. (3) Significant nonlinear interaction thresholds and non-monotonic response mechanisms were identified across the variables. By resolving localized thermal responses through the lens of spatial heterogeneity, this research provides a robust scientific framework for precision urban planning and the mitigation of the urban heat island effect. Full article