Search Results (4,571)

High DG/BESS penetration reshapes power-flow patterns in distribution networks, amplifying line-loss (LL) volatility and stressing conventional planning and operation. We present ML-STELLO (Multi-Layer Spatio-Temporal nEtwork Learning and Line-loss Optimization), a multi-layer framework that unifies: (i) data governance for 96-point feeder curves via RODDPSO-enhanced FCM imputation and coefficient-of-variation improved Isolation Forest (CV-I Forest); (ii) multi-scale spatial-temporal (ST) learning using CNN-LSTM with attention for LL estimation; (iii) operation-time control coupling BESS dispatch and local VAR; and (iv) hierarchical planning and control across five time-scales—from multi-year investment to 5–15 min MPC—extending two-layer models driven by WGAN-GP scenarios and solved with IWOA. On IEEE-33 and a provincial feeder, ML-STELLO reduces LL and voltage violations relative to two-layer baselines while retaining robustness to missing/noisy data. Our design distills and extends advances in LL analysis, ST modeling, and uncertainty-aware planning. Full article

Cave microbiota comprise metabolically diverse organisms, including microalgae, among which Bacillariophyta (diatoms) represent one of the most prominent groups, inhabiting a wide range of substrates within cave ecosystems. In contrast to aerophytic cave habitats, aquatic cave environments remain poorly studied. Therefore, the main aims of this study were to determine the diversity, spatial distribution, and seasonal dynamics of diatom assemblages in the Ponorac Stream flowing through Ravništarka Cave, and to assess the influence of environmental variables on diatom diversity and distribution. Samples were collected from six sites along the Ponorac stream in May and November 2023. Physical and chemical water parameters showed only minor variation among sampling sites. In total, 148 diatom taxa belonging to 54 genera were recorded, including several rare diatom taxa. Diatom assemblages in the Ponorac stream were characterized by high taxonomic richness, high α-diversity, and pronounced community heterogeneity. Many taxa occurred in both seasons and across multiple sites, whereas several were restricted to a single season or exhibited clear site specificity. Most diatom index values indicated generally high ecological status. This study highlights the importance of aquatic cave habitats as reservoirs of diatom diversity and their value in studying temporal and spatial variation of their communities. Full article

Target tracking for uncrewed aerial vehicles (UAVs) demands both low-latency, real-time inference and robust, long-term temporal consistency. Current approaches often face a trade-off between efficiency and stability in practice. This tension is particularly pronounced in resource-limited UAV platforms: computationally heavy architectures can exceed onboard processing capacity and energy budgets, whereas overly lightweight models degrade temporal state fidelity—leading to cumulative drift under challenging conditions such as occlusion, motion blur, rapid scale variation, and cluttered backgrounds. To address this challenge, we propose SPM-Track, a lightweight yet temporally consistent tracking framework grounded in explicit state maintenance. It introduces a dual-loop judgment-calibration architecture comprising three coordinated components: (1) the content-aware state encoder, which employs input-gate modulation, selectively models temporal dynamics to suppress noise propagation into the state; (2) the hierarchical state manager enhances robustness against long-term occlusions and appearance variations by coordinating short-term state updates with a long-term reliable snapshot library via dual-path cooperation; (3) the adaptive feature recalibration module applies joint spatial-channel discriminative weighting before response map generation, effectively enhancing target distinctiveness and mitigating background clutter interference. Experiments on UAV123, DTB70, UAVTrack112, and LaSOT show that SPM-Track outperforms lightweight baselines and remains competitive with several Transformer-based trackers, demonstrating a favorable trade-off between edge-deployable efficiency and long-term robustness in UAV-based tracking. Full article

This study presents a comprehensive multi-parameter analysis of seismo-ionospheric responses to the Mw 7.7 Myanmar earthquake on 28 March 2025, using GNSS-based Total Electron Content (TEC) data, seismic b-value trends, and acoustic gravity wave (AGW) signatures. A significant negative TEC anomaly (~30 TECU below the statistical threshold) was detected on 25 March, three days before the mainshock under geomagnetically quiet conditions, indicating a lithospheric origin. Concurrent variations in the Ionospheric Disturbance Index (IDI) and Rate of TEC Index (ROTI) indicate pronounced background departures and enhanced short-term variability during the preparation phase. Temporal b-value analysis shows a consistent decline from 1.12 to 0.58 across the 30-year to 6-month windows, with the lowest values clustering near the epicenter, indicating progressive stress accumulation. Spatial b-value mapping further reveals a low b-value zone overlapping the region of TEC depletion, while the Relative Seismic Hazard Index (RSHI) highlights high-hazard zones aligned with the epicentral area. Kernel density estimation (KDE) supports this coupling by showing a dominant low-b, low-vTEC cluster, consistent with linked lithospheric stress and ionospheric depletion. Overall, the integrated GNSS and seismic analyses demonstrate the value of multi-domain observations for characterizing earthquake preparation processes, highlighting a coherent physical linkage between crustal stress accumulation and ionospheric depletion that can enhance short-term seismic hazard assessment. Full article

Ground subsidence and shaft lining deformation caused by compressed dewatered bottom aquifers in deep unconsolidated strata mining areas are critical engineering challenges, making the study of the seepage–soil deformation coupling mechanism during groundwater injection remediation vital. This study built a visual cylindrical model (1025 mm × 150 mm); formulated well-graded analogous materials based on the D₂₀ principle to simulate sandy gravel layers; embedded FBG sensors at 200/400/600 mm depths, combined with a dial indicator on the model top; and conducted two water injection–dewatering cycles. Results indicate: water injection generates excess pore water pressure, placing the entire model in a tensile stress state with top rebound; post-injection vertical stress redistributes (tension above the injection point, compression below, and an interlaced transitional band), validating the necessity of full-section injection; during the second injection–dewatering cycle, tensile strain at the upper monitoring point reaches 597.77 με, while compressive strain at lower depths reaches −253.90 με, internal deformation stabilizes within 6.5–10.0 days, injection improves the in situ stress state by reducing effective stress, and the deformation of the field strata remains in a stabilization period, with the stabilization time decreasing as the depth of the strata increases. This study clarifies the temporal evolution and representative spatial variation in internal strain at monitored depths during injection, providing theoretical and design references for optimizing water injection schemes to mitigate coal mine shaft damage. Full article

This study presents a comprehensive investigation of OPE residues, distribution patterns, and dietary exposure risks in crustaceans from southeast China. OPEs were detected in over 90% of samples, with mean total concentrations (ΣOPEs) of 5.80 μg/kg wet weight (ww) in freshwater shrimp, 6.52 μg/kg ww in marine prawn, and 1.25 μg/kg ww in marine crab. Tributyl phosphate (TiBP), triethyl phosphate (TEP), and tris(2-chloroethyl) phosphate (TCEP) emerged as the dominant congeners, accounting for 68.1% of ΣOPEs, which indicates inputs from industrial emissions, plastic waste leaching, and aquaculture equipment. Spatial analysis revealed striking regional differences: coastal industrial cities (Zhoushan, Taizhou) exhibited ΣOPE levels up to 12-fold higher than inland mountainous areas (Quzhou, Lishui), while no significant temporal variations were observed. Human health risk evaluation, based on estimated daily intake (EDI) and target hazard quotient (THQ), demonstrated negligible non-carcinogenic risks for the general population (HI < 1), though children and frequent seafood consumers have slightly elevated exposure. These findings indicate the value of crustaceans as bioindicators for OPE contamination and require long-term monitoring of emerging OPEs and their synergistic effects with co-occurring pollutants. Full article

Soil moisture (SM) in the mountains is critical for agropastoral productivity, and it is subject to both large-scale climate gradients and fine-scale effects of terrain, vegetation and soil. However, how the climate, topography, soil and vegetation factors impact surface SM spatiotemporal dynamics remains elusive in mountainous terrains, due to their complex interactions. Based on multi-source datasets, this study employs the structural equation model to investigate the impact pathways of climate and vegetation factors on annual surface SM dynamics from the year 2000 to 2022 in the Tianshan Mountains of China (TS). We also utilize the factor and interaction detectors of Geographical Detector to explore the individual and interactive effects of climate, topography, soil and vegetation factors on the spatial pattern of the annual surface SM. Moreover, their integrated impacts on the spatiotemporal dynamics of annual surface SM were investigated based on the explanatory power from the factor detector and total effects from structural equation modeling. The results showed that the multi-year average surface SM was 0.21 m³·m⁻³ for the whole region, with greater values in areas with dense vegetation and high elevation. Annual surface SM exhibited significant increasing trends across different land cover classifications and elevation zones, which was directly influenced by vegetation greenness enhancement. Precipitation (PRE) and relative humidity (RH) also significantly influenced the temporal variations in surface SM through their indirect effect on vegetation greenness, while these indirect effects were much lower than the direct effect of vegetation greenness. RH, PRE and surface net solar radiation (SSR) showed strong individual and interactive effects on the spatial distribution of surface SM, particularly the interactive effects of RH and PRE with wind speed (WS). Surface SM was highly sensitive to RH and PRE in the central TS. Overall, vegetation greenness, PRE and RH were the main drivers of surface SM variations across both temporal and spatial scales, while SSR, total evaporation and WS primarily shaped its spatial distribution. These insights enhance our understanding of land–atmosphere interactions in mountainous areas and provide scientific references for sustainable agropastoral water resource management under global warming. Full article

To investigate the temporal and spatial variations in smoking activities in Shanghai, atmospheric fine particles (aerodynamic diameter ≤ 2.5 μm) were collected at four sites in different functional zones, a central urban site (XJH), an urban site (PD), a suburban site (BS), and a rural site (QP), between 2012 and 2020 with the concentration of nicotine measured by GC-MS. The results showed that smoking activities in Shanghai decreased significantly from 2012 to 2020. The average concentration of nicotine in fine particles at XJH (2012–2013) was 13.86 ng m⁻³, while it was 3.39 ng m⁻³ at BS (2017–2018), and 1.13 ng m⁻³ and 0.58 ng m⁻³ at PD and QP during 2018–2020. Nicotine concentration in Shanghai showed strong spatial variability but generally followed a seasonal trend of high in winter and low in summer. At XJH and BS, where higher nicotine concentrations were detected, positive correlations between nicotine and organic carbon in fine particles were observed, but not at PD and QP. A negative correlation between nicotine and ozone was found at QP, suggesting the influence of transported nicotine at the rural site. In general, the concentration of nicotine in atmospheric fine particles is primarily governed by local smoking activities, but is also influenced by meteorological conditions. Full article

To investigate the spatiotemporal variations in particulate matter (PM) retention by common urban greening species, six tree species were studied across different functional zones in Urumqi, China, which includes traffic area (TA), residential area (RA), park area (PA), and landscape ecological forest (LA) at varying altitudes. We measured the retention of PM_0.2–3, PM_3–10, PM_>10, and PM_total for Pinus sylvestris, Picea asperata, Ulmus pumila, Ligustrum obtusifolium, Ulmus densa, and Fraxinus rhynchophylla. Results showed significant differences (p < 0.05) among functional zones, with retention capacity following the order that evergreen trees > deciduous shrubs > deciduous trees. Specifically, P. sylvestris and Picea asperata exhibited the highest overall PM retention. Temporally, PM accumulation increased over time, reaching a minimum 3 days after heavy rainfall (>20.4 mm) and a maximum after 23 days. Spatially, retention was highest in the TA and lowest in the PA. On Yamalike Mountain, PM_3–10 and PM_>10 retention by Ulmus pumila increased significantly with altitude, while other fractions showed no clear trend. These findings suggest that the spatiotemporal differences in PM retention are distinct, and the strategic selection and management of species in specific urban environments can significantly enhance the regulation of atmospheric particulate pollution. Full article

Numerous studies have demonstrated the potential of Synthetic Aperture Radar (SAR) in monitoring glacier velocity. However, owing to the complex dynamics of glaciers and the variability of their surface features, velocity fields derived from even short-interval SAR image pairs often exhibit missing parts. This study proposes a missing glacier velocity recovery method based on a spatiotemporal hybrid neural network to solve the above problem. Considering the spatiotemporal characteristics of glacier velocity fields, a hybrid network combining an Artificial Neural Network (ANN) and a Denoising Autoencoder (DAE) is developed. The ANN is first employed to capture spatial correlations associated with missing values, after which it is integrated with the DAE to model temporal variations using a time-aware loss function. An iterative weighting strategy adaptively balances spatial and temporal features during training. The method is applied to SAR–derived velocity fields of Petermann Glacier. Experimental results show that the method significantly improves the performance of glacier velocity recovery compared to traditional methods. Additionally, the study compares and analyzes the velocity of Petermann Glacier in different seasons, and the findings indicate that the glacier exhibits more pronounced seasonal differences in the accumulation zone. Full article

Lakes on the Inner Mongolia–Xinjiang Plateau (IMXP) are increasingly vulnerable to eutrophication under climate change and human pressure, yet long-term monitoring remains limited by sparse field sampling. Here, we reconstruct multi-decadal trophic dynamics across the IMXP using Landsat time series and temporally transferable machine-learning models and further quantify the underlying natural and anthropogenic drivers. We compiled monthly in situ water-quality observations (chlorophyll-a, Chl-a; total phosphorus, TP; total nitrogen, TN; Secchi depth, SD; and permanganate index, COD_Mn;) and calculated the trophic level index (TLI). After rigorous quality control and monthly aggregation, we compiled a dataset of 1345 matched lake–month samples spanning 2000–2024, and divided it into a training set (n = 1076; ≤2019) and an independent test set (n = 269; 2020–2024) to evaluate temporal transferability. We utilized Google Earth Engine to generate monthly surface reflectance composites from Landsat 7 ETM+, Landsat 8 OLI, and Landsat 9 OLI-2. Four supervised regression algorithms—ridge regression (RR), support vector regression (SVR), random forest (RF), and eXtreme Gradient Boosting (XGBoost)—were trained to estimate TLI. On the independent test period, XGBoost performed best (R² = 0.780, RMSE = 3.290, MAE = 1.779), followed by RF (R² = 0.770, RMSE = 3.364), SVR (R² = 0.700, RMSE = 3.842), and RR (R² = 0.630, RMSE = 4.267); we then used XGBoost to reconstruct monthly and yearly TLI for 610 perennial grassland lakes from 2000 to 2024. From 2000 to 2024, the annual mean TLI (48–49) across the IMXP exhibited a statistically significant upward trend (slope = 0.0158 TLI yr⁻¹; 95% confidence interval (CI) = 0.0050–0.0267; p = 0.006). Meanwhile, spatial heterogeneity was distinct (TLI: 41.51–59.70). High values concentrated in endorheic and desert–oasis basins (e.g., Eastern Inner Mongolia Plateau, >51), whereas lower values characterized high-altitude regions (e.g., Yarkant River, <45). Overall, trends ranged from −0.49 to 0.51 yr⁻¹, increasing in 54% of lakes (15.6% significantly) and decreasing in 46% (15.4% significantly). Attribution analyses identified NDVI (33.92%) and temperature (21.67%) as dominant drivers (55.59% combined), followed by precipitation (13.99%) and human proxies (30.42% combined: population 10.66%, grazing 10.31%, built-up 9.45%). Across 53 sub-basins, NDVI was the primary driver in 28, followed by temperature (11), population (7), precipitation (3), grazing (3), and built-up land (1); notably, the top two drivers explained 56.6–87.1% of variations. TWFE estimates revealed bidirectional NDVI effects (significant in 31/53): positive associations in 22 basins were linked to nutrient retention, contrasting with negative effects in nine basins associated with agricultural return flows. Temperature effects were significant in 15 basins and predominantly negative (14/15), except for the Qiangtang Plateau. Overall, eutrophication risk across the IMXP lake region reflects the combined influences of climatic conditions, vegetation conditions, and human activities, with their relative contributions varying among basins. Full article

Motor vehicle encroachment into non-motorized lanes is a common but hard-to-verify violation in urban intersections, especially when monitored from unmanned aerial vehicles (UAVs) or high-mounted overhead views. Existing rule-based solutions built on horizontal bounding boxes and center-point/line-crossing criteria are sensitive to perspective distortion, occlusion, and frame-to-frame jitter, resulting in unstable decisions and low evidential value. This paper presents a cascaded UAV-view system that closes the loop from perception to evidence output through detection–segmentation–recognition–decision. First, we adopt a two-stage detection cascade: a lightweight vehicle detector localizes vehicles using axis-aligned bounding boxes, and a dedicated YOLOv5n-based oriented bounding box (OBB) license plate detector, constructed via architecture grafting and weight transfer, is then applied within each vehicle region of interest (ROI) to localize rotated license plates under large pose variation and small-target conditions. Second, a U-Net lane region segmentation module provides pixel-level spatial constraints to define an enforceable lane occupancy region. Third, a perspective rectification step is integrated with the PP-OCRv4 optical character recognition (OCR) framework to improve license plate recognition reliability for tilted plates. Finally, an area ratio criterion and an N-frame temporal counter are used to suppress transient misdetections and stabilize alarms. On a representative 100-sample controlled encroachment benchmark, the proposed system improves detection accuracy from 67.0% to 92.0% and reduces the false positive rate from 32.35% to 5.88% compared with a baseline horizontal bounding box (HBB)-based rule. The system outputs both violation alarms and license plate evidence, supporting practical deployment for multi-view traffic governance. Full article

Rapid urbanization has intensified the mismatch between urban green space (UGS) and urban spatial vitality (USV), hindering sustainable development. To address this, we developed the Urban Green Space Vitality Adaptation Model (UGSVAM) and analyzed 64 subdistricts in central Nanjing. Specifically, this study asks: Does the mismatch exist? What are its spatiotemporal patterns? What factors drive it? Methodologically, we use the Gini coefficient and Lorenz curve to assess overall UGS-USV adaptation, then construct the Urban Green Space Vitality Density (UGVD) indicator to quantify the match level, classifying units as overloaded, underloaded, or balanced. OLS and GWR reveal global and local influencing mechanisms, while quadrant analysis supports differentiated planning. Results show: (1) UGS-USV adaptation in Nanjing is weak, with Gini coefficients of 0.466 (weekday) and 0.456 (weekend). UGVD exhibits a spatial pattern of a primary overload core in the central city, a secondary core in the southwest, and peripheral decline, with the southeast underloaded. Overloaded units also show notable temporal variation. (2) Globally POI density and intersection density promote UGVD, while excessive transport facilities, air pollution, and high temperatures inhibit it—ecological factors have stronger weekend effects. (3) Locally, the northeast is more sensitive to POI density, the southwest to transport and heat, and the Jiangbei New Area could enhance green space carrying capacity through transport optimization and spatial integration. The UGSVAM integrates spatial diagnosis, mechanism analysis, and planning response, offering a transferable framework for refining green space governance in high-density cities. Full article

Desertification is one of the main threats to high Andean ecosystems, particularly in arid and semi-arid regions subject to increasing climatic and anthropogenic pressures. This study evaluated the spatial-temporal dynamics of desertification in the province of Candarave (Tacna, Peru) by integrating the Remote Sensing-based Desertification Index (RSDI), constructed from a principal component analysis incorporating four biophysical indicators: vegetation greenness, surface moisture, soil grain size, and fraction of solar radiation reflected (albedo), derived from Landsat 5 and 8 satellite images processed in Google Earth Engine. Temporal trends were analyzed using the Mann–Kendall test, while system stability was evaluated using the coefficient of variation, allowing different degrees of stability and environmental degradation to be characterized during the period 2010–2025. The results show that moderate and severe desertification classes predominate in higher altitude areas, covering approximately 92% of the study area, and are characterized by insignificant to weakly significant negative trends associated with high to relatively high temporal volatility. In contrast, stable areas with no significant changes represent 5.3% of the territory, while restoration processes occupy a small proportion, close to 2.7%. The high variability observed in the high Andean sectors is mainly linked to the interaction between reduced water availability, climate variability, and extreme events, as well as anthropogenic pressures, particularly overgrazing and aquifer exploitation. This multitemporal analysis allows us to anticipate the evolution of desertification and highlights the need to strengthen conservation planning in order to reduce the degradation of strategic high Andean ecosystems in the Tacna region. Full article

High-spatiotemporal-resolution land surface temperature (LST) data are crucial for analyzing surface energy balance, modeling temperature-related processes, and monitoring thermal environments. However, despite advancements in multi-source fusion and reconstruction techniques, high-frequency LST data remain susceptible to anomalies such as abrupt changes and outliers due to retrieval uncertainties and varying observation conditions. Conventional statistical outlier detection methods risk misidentifying physically plausible rapid weather changes as data errors, introducing systematic biases. To address this, we propose a two-stage anomaly detection framework that follows a “temporal physical pre-screening first, spatial statistical verification later” logic. First, a piecewise empirical model, based on typical diurnal LST variation characteristics, is constructed to identify points violating physical patterns. Subsequently, a spatial consistency test using median absolute deviation (MAD) is introduced to distinguish real weather-driven fluctuations from genuine data anomalies from a spatial synergy perspective. This sequential design effectively reduces the risk of mis-correcting physically reasonable temperature variations. Validated using hourly seamless LST data (2016–2021) and ground observations in the Heihe River Basin, our method outperformed Seasonal-Trend decomposition using Loess (STL), double standardization methods, and robust Holt–Winters. For over 87% of the detected anomalies, the proposed method demonstrated positive improvement rates in RMSE, MAE, R, and R². The overall average improvement rates reached 23.61%, 18.79%, 16.46%, and 61.33%, respectively, indicating robust performance. The results underscore that explicitly incorporating physical constraints enhances the reliability and interpretability of quality control for high-temporal-resolution remote sensing LST data. Full article