Search Results (6,268)

Time-Sensitive Networking (TSN) with Cyclic Queuing and Forwarding (CQF) is a critical mechanism for ensuring deterministic forwarding. However, existing incremental schedulers typically rely on single-dimensional heuristics, which fail to address the coupled impact of traffic characteristics and spatiotemporal resource distribution. This limitation leads to suboptimal scheduling success, especially under complex topologies and high network loads. To address this, we propose TMCOA–MFS, a joint incremental scheduling framework that integrates the Triple-Mode Cooperative Optimization Algorithm (TMCOA) with a Multi-Feature Scheduling (MFS) strategy. The logic of our approach is twofold: First, to balance spatial resource distribution, we introduce the TMCOA—inspired by table-tennis offensive–defensive behaviors—to optimize path selection by minimizing port-load variance and escaping local optima through a three-mode population partition. Second, building upon the optimized spatial paths, the MFS strategy is employed to resolve temporal scheduling conflicts. By computing a composite priority score that accounts for path hops, offset configuration difficulty, and flow size, MFS enables a robust incremental offset search with integrated feasibility checking. Extensive simulations on benchmark functions and diverse TSN scenarios demonstrate that the TMCOA offers superior convergence and stability. More importantly, the integrated TMCOA–MFS framework significantly enhances scheduling success rates and load balancing, effectively overcoming the bottlenecks of high-load and topologically complex environments. Full article

This study evaluated the spatial and temporal variability of rainfall erosivity (R factor) and its implications for soil loss in the Velhas River Basin, Minas Gerais, Brazil. Rainfall erosivity was estimated from 49 rain gauge stations and CHIRPS precipitation data using empirical equations-based on monthly and annual precipitation totals. Soil loss was estimated using the RUSLE model for the years of minimum and maximum erosivity. Between 2014 and 2024, annual R values ranged from approximately 3900 to more than 9000 MJ mm ha⁻¹ h⁻¹ yr⁻¹, with the lowest values recorded in 2014 and the highest in 2022. Although 2020 had the highest annual rainfall, 2022 showed the highest erosivity, indicating that rainfall intensity and temporal concentration were more important than total rainfall volume. Furthermore, the comparison of erosivity was estimated from ANA stations and derived from CHIRPS agreement for paired station-year observations (r = 0.7196), although CHIRPS slightly underestimated erosivity values (mean bias −5.74%). Estimated soil loss ranged from 0.60 to 274.17 Mg ha⁻¹ yr⁻¹, with the highest values occurring mainly in exposed soil and agricultural areas. These findings highlight the importance of rainfall temporal distribution in erosion risk and support the use of satellite-derived precipitation products for regional-scale erosion assessments in data-scarce tropical basins. Full article

Background: Accurate brain tumor segmentation from Magnetic Resonance Imaging (MRI) depends on the fusion of multiple complementary modalities. However, clinical practice often faces incomplete modality sets due to acquisition failures, patient contraindications, or protocol variations. Current methods either treat each modality feature extractor in isolation or depend on computationally expensive teacher networks for cross-modal knowledge transfer. Objective: This paper presents Hierarchical Adaptive Group Self-Support Learning with Boundary-Aware Calibration (HAGSS), a framework that overcomes three key limitations of existing group self-support methods: static group formation that ignores temporal prediction quality, uniform treatment of boundary and interior voxels, and distribution mismatch across heterogeneous modality logits. Methods: We propose a hierarchical adaptive group formation mechanism that reassigns group leader roles at each epoch based on voxel-level prediction confidence scores instead of fixed sensitivity priors. We also introduce a boundary-aware calibration module that applies spatially varied distillation weights with greater emphasis on tumor boundary regions. In addition, we design a cross-scale consistency regularization term that enforces agreement between multi-resolution predictions to stabilize the self-support target. Results: Experiments on BraTS2020, BraTS2018, and BraTS2021 datasets show that HAGSS achieves consistent improvements over state-of-the-art baselines. The average Dice gains across the whole tumor, tumor core, and enhancing tumor regions reach 1.30% on BraTS2020 and 1.61% on BraTS2021 compared to existing methods. All improvements are statistically significant ($p<0.05$). Conclusions: HAGSS operates exclusively during training, adds no parameters or inference cost, and can be applied as a plug-in module to any multi-encoder incomplete multi-modal segmentation architecture. Code is publicly available at GitHub. Full article

Research traditionally examines the spatial distribution of urban parks through the lens of spatial equity, overlooking the intricate interaction between the physical foundation of park construction and historical processes. Grounded in the theory of material geography, we investigate the mechanisms underlying the spatio-temporal evolution of urban parks in Shenzhen. We conduct topographical analysis and examine relevant historical policy texts to explore the ‘production of nature’ in China’s post-Mao urbanisation. We find that the distribution of urban parks in Shenzhen is not merely a result of social choice but a product of the interplay between material natural endowments—centred on topography—and urban spatial policies across historical stages. During rapid urbanisation, government-led spatial policies functionally reorganised and assigned symbolic meanings to diverse topographical features, such as plains, hills, and coastal areas, transforming them into urban parks that support capital accumulation and urban upgrading. The proposed ‘topography–policy’ synergistic framework transcends neutral spatial descriptions, revealing the nexus between the commodification of nature and urban governance. We clarify the rationale for the creation of contemporary urban green spaces in China and offer novel theoretical and empirical insights into sustainable urban transformation worldwide. Full article

High penetration of wind and photovoltaic (PV) generation increases renewable uncertainty and real-time balancing pressure in active distribution networks. To address this problem, this paper proposes a two-stage robust optimization method for day-ahead and real-time scheduling of a flexibly interconnected multi-microgrid (MMG) system enabled by a flexible interconnection device (FID). The proposed framework jointly optimizes power purchase from the upper-level distribution network, micro-gas turbine output, energy storage system (ESS) operation, and FID-based bidirectional power exchange, thereby coordinating local temporal flexibility and inter-microgrid spatial flexibility. A polyhedral uncertainty set is used to model wind and PV forecast errors, and the problem is solved by the column-and-constraint generation (C&CG) algorithm. Case studies on a two-microgrid system show that, compared with independent operation under traditional robust optimization, the proposed method reduces real-time balancing cost, wind and PV curtailment, and total operating cost by 98.96%, 95.84%, and 0.59%, respectively. Sensitivity analysis further verifies the economy–robustness trade-off under different uncertainty budgets and forecast deviation levels. Full article

Using surface observations, ADTD lightning data, and radar reflectivity from April-September 2022–2024 in the Xi’an terminal area, this study classified severe convective events into four categories: ordinary thunderstorms, short-duration heavy precipitation, convective wind gust, and hail events. Their temporal variability, spatial distribution, life cycle characteristics, and propagation pathways were systematically analyzed. The results reveal significant differences among convective event types across multiple temporal and spatial scales. Convective wind gust events exhibited the strongest interannual variability, with a decrease of 44% from 2023 to 2024. Hail events occurred relatively infrequently, totaling only 16 cases from 2022 to 2024. Seasonally, convective wind gusts were concentrated in April-May, while ordinary thunderstorms and short-duration heavy precipitation events mainly occurred in July–August. Most events initiated during the afternoon and intensified toward evening, with short-duration heavy precipitation events showing a bimodal diurnal variation. Ordinary thunderstorms were dominated by short-lived events lasting 30–60 min, whereas heavy precipitation, convective wind gust, and hail events were primarily associated with long-lived convective systems exceeding 180 min. Spatially, severe convective weather generally initiated in the western part of the terminal area and propagated eastward. Lightning activity was more concentrated in the southeastern sector, indicating greater impacts on the SHX waypoint. Propagation paths were predominantly oriented toward the east-northeast. Full article

Accurate estimation of the internal spatial-temporal temperature distribution is crucial for the safety and performance management of lithium-ion batteries. However, traditional lumped parameter models overlook spatial gradients, while numerical methods for partial differential equations (PDEs) incur high computational costs. This paper proposes a hard constraint physics-informed neural network (HCPINN) framework for the real-time reconstruction of the axial temperature field in 18,650 cylindrical batteries. By restructuring the neural network’s solution space through distance functions, the Robin boundary conditions are strictly embedded as hard constraints, ensuring exact satisfaction of the prescribed Robin boundary conditions within the mathematical model and eliminating boundary loss terms. An electro-thermal coupled model considering the Arrhenius effect and state-of-charge (SOC) dependent internal resistance is integrated into the loss function to capture the nonlinear heat generation dynamics. Experimental validation across discharge rates from 1C to 4C demonstrates that the HCPINN achieves high estimation accuracy with a mean absolute error (MAE) below 0.34 °C. Furthermore, by leveraging the continuous differentiability of the model, this study quantifies the evolution of spatial temperature gradients and reveals the ideal heat transfer coefficients required for thermal equilibrium are inverted, providing a quantitative basis for the design of advanced battery thermal management systems (BTMS). Full article

Active voltage control (AVC) in active distribution networks coordinates the reactive power outputs of distributed inverters to maintain bus voltages within secure limits. Although multi-agent reinforcement learning (MARL) shows promise for AVC, current methods face three main limitations: graph topologies rely on unweighted adjacency, ignoring physical parameters like line impedance and electrical distance; centralized critics output a single global Q-value, leading to coarse spatial credit assignment; and temporal critic modules suffer from vanishing gradients and representation drift. To address these issues, we propose physics-informed spatiotemporal multi-agent value learning (PST-MA), a physics-informed spatiotemporal value-learning framework integrating three coupled designs: a physics-informed graph attention mechanism with electrical-distance-aware sparsification; node-conditional value outputs utilizing a replicated-graph diagonal-extraction strategy; and a temporal latent compression module featuring a gated bypass and late action fusion. Experiments on the IEEE 33-bus and 141-bus systems validate the effectiveness of the proposed PST-MA method. Results demonstrate that it consistently achieves a higher controllable ratio than baseline methods for coordinated voltage regulation under uncertainty. Full article

Land subsidence is an important challenge faced by coastal cities under rapid urban development. This study focuses on the Haikou–Laocheng area and conducts time-series monitoring of land subsidence using PS-InSAR and SBAS-InSAR based on 42 Sentinel-1 SAR scenes acquired from April 2023 to April 2025, thereby deriving the spatial distribution of cumulative subsidence rates and the evolution patterns of multi-temporal cumulative subsidence. Because only ascending-orbit Sentinel-1 data were used, the reported deformation values are vertical-projected estimates converted from line-of-sight (LOS) displacement under the assumption that horizontal motion is negligible. The reliability of the monitoring results is evaluated through cross-validation between the two methods, assessing their inter-method consistency. The results indicate that the study area is dominated by slight subsidence, with vertical-projected subsidence rates mainly ranging from −6 to 3.7 mm/y, while a few uplift points are locally observed, forming an overall “stable with localized anomalies” deformation pattern. PS-InSAR and SBAS-InSAR show good consistency in overall trends, and both identify a pronounced subsidence bowl in the southwestern part of the study area, where the peak vertical-projected subsidence rates reach −25.1 mm/y and −35.1 mm/y, respectively, with outward banded attenuation. The results suggest that land subsidence in the study area is influenced by both natural factors and human activities. Specifically, rainfall shows a non-synchronous, stage-wise modulation relationship with subsidence evolution, and most high-subsidence zones are distributed in impervious surfaces such as built-up land and transportation corridors, or in low-elevation areas such as farmland. In terms of geological factors, thick, highly compressible soft soils are the primary geological control on the continued development of subsidence. These findings can provide scientific references for the prevention and control of abnormal subsidence and for urban planning and development in the Haikou–Laocheng area. The strengthened discussion clarifies the research gap, planning significance, and limitations of applying dual time-series InSAR in a data-scarce tropical coastal soft-soil setting. Full article

On 6 June 1944, more than 156,000 Allied troops landed along the heavily fortified beaches of Normandy, France, during Operation Overlord, the largest amphibious assault in modern history. Intensive naval bombardment, ground combat, and subsequent occupation resulted in the introduction and emplacement of substantial quantities of anthropogenic metal into the coastal environment. Previous work has documented the presence of shrapnel and other metallic detritus within Normandy beach sands, with estimates suggesting ~1% of sediment may be derived from wartime activity; however, these observations were based on limited sampling. This study presents the first systematic, coast-wide investigation of sediment across all five Allied landing beaches (Utah, Omaha, Gold, Juno and Sword). A total of 460 surface and subsurface samples were collected in June 2024 and April 2025 and analyzed for metallic grain abundance, grain size, morphology and composition. Metallic grains comprise an average of 0.4 wt.% of the total sediment across the D-Day beaches. These grains are dominantly iron-rich based on geochemical characterization of a representative subset of samples (n = 33), with lower concentrations of aluminum, titanium and trace amounts of other metallic elements. These grains display a range of morphologies indicative of anthropogenic origin, including angular fragments and metallic spherules and rounded grains consistent with primary fragmentation and subsequent reworking. The combined evidence of morphology, magnetic properties, spatial distribution, and regional sediment compartmentalization supports a predominantly anthropogenic origin. Potential contributions from natural magnetite and industrial sources are considered but are unlikely to account for the observed patterns across all sites. Metallic grains are non-uniformly distributed and partitioned along the beach profile, with consistent enrichment within the swash zone relative to supratidal environments. Subsurface profiles show metallic grain persistence to depths exceeding 1 m, with peak concentrations consistently observed at 5–15 cm and 45–75 cm. These results demonstrate that the sedimentary record of Operation Overlord remains preserved within the modern Normandy coastline eighty years after emplacement. This anthropogenic material provides a temporally constrained stratigraphic tracer within a dynamic macrotidal system, offering insight into sediment redistribution, beach aggradation rates, and coastal processes operating on decadal timescales. Full article

In the context of high renewable energy penetration, the demand for power grid flexibility regulation is growing, yet existing load-side flexibility resource research ignores spatio-temporal coupling effects, leading to fragmented mining, insufficient quantification adaptability and low aggregation efficiency. This paper proposes an adaptive mining and quantification method for load-side flexibility resources considering spatio-temporal coupling. First, an adaptive mining framework integrating multi-energy time patterns, regional spatial clustering and spatio-temporal coupling matching is constructed to locate resources accurately. In this framework, the ST-MLCM (spatio-temporal multi-layer chain model) and ST-GMM (spatio-temporal Gaussian mixture model) are mathematically extended with spatial coupling terms, enabling genuine joint modeling of temporal dynamics and spatial dependencies. Second, a quantification model combining AHP-entropy weight method and fuzzy multi-attribute decision-making with spatio-temporal dynamic correction is established to balance weights and heterogeneity. Third, a ST-GNN-based distributed aggregation method is designed to realize high-efficiency aggregation of large-scale resources. Experimental results verify the effectiveness of the method in resource mining, quantitative evaluation and aggregation application, providing technical support for spatio-temporal coordinated dispatching of new power systems. Full article

Demyelinating disorders such as multiple sclerosis are characterized by oligodendrocyte loss and insufficient remyelination. The cuprizone model provides a well-established experimental system for studying these processes. The apelinergic system, including the apelin receptor (APLNR), has been implicated in neuroprotection and central nervous system homeostasis. However, its role in white matter demyelination and repair remains incompletely understood. This study aimed to characterize the spatial and temporal dynamics of APLNR expression in relation to oligodendrocyte lineage cells in the corpus callosum (CC) during demyelination and remyelination. Demyelination was induced in 8-week-old C57BL/6 mice by 0.2% cuprizone supplementation in their drinking water for 5 weeks, followed by 5 weeks remyelination phase after toxin withdrawal. Histological assessment using Luxol Fast Blue/Cresyl violet staining was performed to evaluate structural changes in the CC. Immunohistochemistry and confocal microscopy were used to analyze APLNR expression, GST-π⁺ cells, and NG2⁺ cells, including their spatial distribution and co-localization. Quantitative analyses and correlation tests were conducted to assess relationships between cellular markers and CC area. Demyelination resulted in significant reduction in CC area and a marked decrease in GST-π⁺ cells, accompanied by a robust increase in NG2⁺ cells, while remyelination led to partial structural and cellular recovery. APLNR expression increased progressively from control to demyelination and further during remyelination, exhibiting pronounced regional heterogeneity with higher levels in lateral CC regions. Confocal analysis demonstrated increasing co-localization of APLNR with NG2⁺ cells, particularly during remyelination. Correlation analyses identified GST-π⁺ cell density as the strongest predictor of CC area, whereas APLNR showed phase-dependent associations, including a positive correlation with GST-π⁺ cells during remyelination and a negative relationship with NG2⁺ cells during demyelination. APLNR expression is dynamically regulated during cuprizone-induced demyelination and remyelination and is closely associated with oligodendrocyte lineage cell responses. Its increased expression and enhanced co-localization with NG2⁺ cells during remyelination suggest a potential role in endogenous repair processes. However, as the findings are based on descriptive analyses, further functional studies are required to determine the mechanistic contribution of APLNR signaling and its potential as a therapeutic target in demyelinating diseases. Full article

Deep learning models are widely used in autonomous vehicle systems for perception, localization, and decision-making. However, their lack of transparency poses significant challenges in safety-critical environments. This systematic review presents a unified mathematical framework for explainable deep learning which integrates multimodal inputs, graph-theoretic road geometry, uncertainty modeling, and intrinsically interpretable representations. Road-structured priors that include lane topology and spatial constraints are incorporated into learning and optimization processes for ensuring model predictions and explanations to remain physically and semantically grounded. The review synthesizes methods across saliency-based, concept-based, causal, and intrinsic explainability, and extends them to vision-language models. This enables language-grounded, human-interpretable reasoning in autonomous vehicle systems. While vision-language models offer a new paradigm for semantic explainability, their limitations such as hallucinations, misgrounding, and reduced reliability under distribution shifts are also critically examined. Along with the role of road priors in improving alignment and robustness, another key contribution of this work is its quantitative evaluation metrics for road-aware explainability. These evaluation metrics link the explanations to spatial consistency, uncertainty alignment, and graph-structured reasoning. The overall framework connects latent representations, predictions, and explanations within a single formulation, enabling systematic comparison and analysis across models. Based on a PRISMA-guided review of 164 studies, this research identifies gaps in real-world reliability, temporal reasoning, and standardized evaluation, and outlines future directions including human-in-the-loop systems, regulatory readiness, and language-based auditing. Overall, this study advances a mathematically grounded and road-aware perspective on explainable vehicle AI which significantly bridges the gap between high-performance models and transparent, trustworthy autonomous systems. Full article

Change detection in open-pit mining areas from remote sensing imagery is of great importance for mining supervision, ecological monitoring, and restoration planning. Nevertheless, mining-related changes usually exhibit multi-scale patterns, irregular boundaries, and fragmented spatial distributions, which make accurate detection difficult. Existing CNN- and Transformer-based methods often cannot effectively balance global context perception and local detail preservation, resulting in incomplete boundary extraction and insufficient sensitivity to subtle changes. To overcome these limitations, we propose GLMECD-Net, a Global–Local Multi-scale Cross-fusion Enhanced Change Detection Network for remote sensing image change detection in open-pit mining areas. Specifically, a Siamese encoder is used to extract hierarchical bi-temporal features, while a Global–Local Feature Mixing Embedding (GLME) module is introduced to jointly capture long-range contextual information and local spatial details. Furthermore, multi-scale feature aggregation and cross-temporal feature fusion are employed to improve change representation and boundary recovery. Experimental results on mining area datasets show that the proposed method achieves 71.66% Precision, 83.78% OA, 77.53% F1-score, and 53.82% IoU. The results demonstrate that GLMECD-Net provides effective and robust performance for detecting complex and subtle changes in open-pit mining areas. Full article

Satellite remote sensing-based soil moisture (SM) retrieval quantifies the spatial and temporal distributions of SM to support Earth system modeling. However, existing SM products, including satellite remote sensing, model-simulated, and land data assimilation products, are plagued by large measurement errors. The Triple Collocation (TC) method can systematically quantify these errors and generate spatially and temporally continuous SM. In this study, we analyzed SM over the Qinghai-Tibetan Plateau (QTP) using three mainstream products: The European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-interim) SM, the National Centers for Environmental Prediction Climate Forecast System version 2 (CFSv2) SM, and the China Meteorological Administration Land Data Assimilation System Version 1.0 (CLDAS-V1.0) SM. Results show that the ERA-interim contributes the largest weight to the TC-blended SM over the QTP, followed by CFSv2, while CLDAS-V1.0 makes the minimum contribution. The three products yield consistent results in the eastern and southern QTP but show significant discrepancies in the northwestern region. The TC-blended SM performs well across most land cover categories in the QTP, except Alpine swamp meadow areas. Our findings confirm that this SM blending technique effectively improves the accuracy of existing SM products, with wide application potential in future research. Full article