Search Results (2,017)

Automatic Identification System (AIS) trajectories provide valuable spatiotemporal information for maritime route structure mining, but robust extraction of key route nodes remains difficult because raw data are noisy, turning behaviors are easily masked by local fluctuations, and conventional Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is sensitive to fixed parameters and ignores heading differences. To address these issues, this study proposes a key route node extraction framework based on multi-constraint turning-point identification and heading-aware adaptive DBSCAN (HA-DBSCAN). Raw AIS data are first cleaned, segmented, and compressed using a heading-aware Douglas–Peucker strategy to reduce redundancy while preserving geometric and directional characteristics. Valid turning points are then identified by jointly considering heading change rate, geometric curvature, and temporal stability. Finally, HA-DBSCAN integrates a heading-aware distance metric, adaptive neighborhood estimation, and density-aware MinPts optimization to cluster turning points and extract representative route nodes. Experiments using AIS data from the Ningbo–Zhoushan Port area retained 287,614 valid records and 754 continuous trajectory segments, from which 1710 turning points were identified. The proposed method generated 45 stable clusters with a noise ratio of 0.0450 and route coverage of 95.5%. These results indicate that, within the current study setting, the framework can distinguish crossing routes, adapt to heterogeneous traffic densities, and provide an interpretable intermediate layer for subsequent maritime route-structure modeling. Supplementary validation on the same AIS dataset further showed that, compared with DBSCAN, Ordering Points To Identify the Clustering Structure (OPTICS), and HDBSCAN baselines as well as several pipeline ablations, the full framework achieved a more balanced performance in terms of coverage, noise suppression, and avoidance of cluster over-fragmentation. Full article

Under the strategic context of building a transportation powerhouse in China, the transportation sector faces the dual challenge of reducing emissions while improving efficiency. This study constructs a two-dimensional regional classification framework based on the “economic-carbon” dimension and systematically investigates the coordinated evolution of the development level (TD) and carbon emission intensity (TCEI) of the transportation systems in 31 provinces of China from 2014 to 2023, using methods such as entropy weight TOPSIS, the coupling coordination degree (CCD) model, kernel density estimation (KDE), spatial autocorrelation analysis, and the XGBoost-SHAP explainable machine learning framework based on transfer learning. The study finds that (1) TD shows a fluctuating upward trend, while TCEI continues to decline, with regional imbalances; (2) in terms of time, CCD shows a general upward trend with an N-shaped evolution; spatially, CCD presents a pattern of stronger coordination in the east and weaker in the west, with sustained regional heterogeneity, forming a development pattern of “Region I leading, Region II breaking through, Region III maintaining, Region IV catching up”; and (3) regarding the driving factors, freight volume, transport industry output value, and passenger turnover are the core driving factors of CCD, with significant regional heterogeneity in their mechanisms. This study provides a systematic analytical framework and differentiated policy tools for promoting coordinated regional development of green transportation. Full article

Background: Flatfoot deformity is associated with altered lower extremity biomechanics and functional impairment during gait. However, evidence describing spatio-temporal gait alterations remains heterogeneous and has not been consistently synthesized across studies. Methods: A systematic review was conducted in accordance with PRISMA guidelines. MEDLINE (via PubMed) and Scopus were searched through 24 March 2025 for studies evaluating gait characteristics in individuals with flatfoot or progressive collapsing foot deformity. Studies reporting spatio-temporal parameters in both flatfoot and healthy control cohorts were included in quantitative synthesis. Random-effects meta-analyses were performed to evaluate gait velocity, stance duration, stride length, and cadence. Results: Fifteen studies met inclusion criteria, of which five provided sufficient data for meta-analysis. Compared with healthy controls, individuals with flatfoot demonstrated longer stance duration and shorter stride length. No differences were observed in gait velocity or cadence. Substantial heterogeneity was present across all pooled outcomes (I² > 80%), reflecting variability in study populations, disease characteristics, and gait analysis methodologies. Conclusions: Flatfoot is associated with consistent spatio-temporal gait adaptations characterized by longer stance duration and reduced stride length. Despite heterogeneity among included studies, these findings suggest consistent spatio-temporal gait adaptations that may serve as clinically relevant markers of altered gait mechanics and functional impairment. Further studies with standardized protocols are needed to refine the role of gait analysis in the assessment and management of flatfoot. Full article

This paper investigates the powder spreading process in a Z-direction multi-material fabrication system utilizing a blade. Focusing on 316L stainless steel and CuCrZr, a discrete element model was developed to simulate powder spreading at the heterogeneous material interface. The effects of spreading speed and theoretical layer thickness on the resulting powder bed quality were systematically examined. The results reveal that during spreading over a heterogeneous bed, the underlying powder exhibits an unsteady “forward-surging and rearward-suppressing” motion pattern, with inter-particle force chains displaying significant spatiotemporal fluctuations. Increasing the spreading speed exacerbates the disturbance and removal of the underlying powder, leading to a reduction in the deposited mass of CuCrZr and a deterioration in its distribution uniformity. Conversely, increasing the layer thickness effectively mitigates the mechanical disturbance of the underlying powder by the blade, significantly enhancing both the deposited mass of CuCrZr and its distribution uniformity. Further investigation demonstrates that employing a higher spreading speed in combination with a larger layer thickness can achieve a favorable powder bed quality while maintaining high spreading efficiency, thereby enabling a synergistic optimization of productivity and bed quality. This work elucidates the mesoscopic dynamic mechanisms governing the powder spreading process at Z-direction heterogeneous interfaces and provides a theoretical foundation for process optimization in multi-material laser powder bed fusion. Full article

As a key component of ecosystems, the synergistic relationship between wetland carbon storage and habitat quality is vital for maintaining ecological functions, and its evolution is profoundly influence by changes in wetlands. This study focuses on wetlands in western Jilin Province. Based on four sets of land use data from 2010 to 2023 and utilizing the InVEST model, combined with methods such as spatial autocorrelation, the Coupled Coordination Degree Model, and the GeoDetector, the study analyzed the co-variation of carbon storage and habitat quality, as well as their response to landscape patterns. The study found that between 2010 and 2023, the wetland area increased by a net 858.13 km², and landscape fragmentation was generally alleviated, although local connectivity continued to degrade. Regional carbon storage increased by 68.1%, totaling 7.43 × 10⁶ Mg, while the habitat quality index exhibited high spatiotemporal stability, fluctuating marginally between 0.609 and 0.621. Spatially, high-value areas remained primarily concentrated within nature reserves. Results of bivariate spatial autocorrelation analysis revealed a strengthening of spatial positive autocorrelation between carbon storage and habitat quality, with Moran’s I increasing from 0.410 to 0.501. The coupled coordination degree model further confirmed that the level of synergy between the two services exhibited a pattern of higher values in the north and lower values in the south, and that areas of high coordination expanded significantly outward following restoration projects. GeoDetector analysis indicates that the largest patch index is the core factor driving the synergistic development of ecosystem services. The results also suggest that the integrity of core wetland patches and a heterogeneous landscape pattern can promote the synergistic improvement of carbon storage and habitat quality through boundary effects and habitat complementarity. Full article

The digital preservation and dissemination of historical and cultural heritage is a pivotal area at the intersection of digital humanities and geographic information science. To address the challenges of multi-source heterogeneity, limited dimensionality, and inadequate public engagement, this study designed and implemented an interactive visualization platform using modern Web technologies. Taking the Leshan Confucian Temple (religious heritage) and the former site of Wuhan University (educational heritage) as case studies, the platform integrates four types of heterogeneous data (geospatial coordinates, architectural attributes, visitor behavioral records, and multimedia imagery) into a unified spatiotemporal information model. Core technical implementations are built upon a lightweight front-end stack including the Gaode Map JavaScript API for geographic visualization, ECharts for dynamic statistical charting, and the Tailwind CSS framework for a fully responsive front-end interface. Key interactive features encompass linked map markers with contextual information windows, user-driven chart filtering, and paginated loading of cultural relic cards. Evaluation results demonstrate that the platform achieves cross-device response delay ≤3 s, supports spatially grounded, dynamic, and presentation of cultural heritage information, and attains a System Usability Scale (SUS) score of 82.5. This work offers a lightweight, scalable technical solution for advancing digital recording and public communication of historical and cultural heritage, while contributing to the theoretical discourse on spatial narrative and multi-source data integration in digital humanities. Full article

The occurrence of marine heatwaves (MHWs) and phytoplankton blooms is accelerating under climate change, yet the frequency and drivers of their compound co-occurrence remain poorly understood. Using coastal-optimized satellite observations from 2003–2020, we mapped global compound MHW–phytoplankton bloom (MHW-PB) events across coastal large marine ecosystems and quantified their spatiotemporal trends and environmental predictors. Compound events are increasing at 4.8% yr⁻¹, driven primarily by a 6.5% yr⁻¹ rise in MHW frequency; a temporal shuffle test confirms this trend falls below random co-occurrence expectation, indicating biological suppression actively constrains compound event growth. The compound independence factor (CIF) reveals latitudinal heterogeneity: low-latitude upwelling systems show MHW–PB mutual exclusivity, while high-latitude and eutrophic coastal regions show positive co-occurrence tendency. Interpretable machine learning further shows that nutrient availability dominates bloom responses at low latitudes whereas light dominates at high latitudes, with MHW intensity exhibiting nutrient-dependent non-linear associations with bloom probability. Paradoxically, compound frequency accelerates nearly twice as fast in low latitudes (6.1% yr⁻¹) as in high latitudes (3.5% yr⁻¹), driven by rapid tropical MHW acceleration. These diverging regimes signal dual ecological risks: trophic mismatches in upwelling systems and escalating hypoxia and harmful algal bloom hazards in eutrophic coastal waters. Full article

As natural nanoscale intercellular messengers, exosomes exhibit considerable potential in modulating inflammation, angiogenesis, immunoregulation, and tissue remodeling, making them attractive candidates for regenerative medicine. However, their clinical translation remains limited by rapid systemic clearance, nonspecific biodistribution, insufficient lesion retention, and functional attenuation in hostile pathological microenvironments. In this review, we propose that biomaterial engineering should evolve from providing passive exosome carriers to constructing active regulatory platforms capable of precise spatiotemporal control. We summarize engineering strategies along two complementary dimensions. In the temporal dimension, biomaterials can enable sustained, sequential, or microenvironment-responsive release to match the dynamic phases of tissue repair. In the spatial dimension, biomaterials can improve local retention, tissue anchoring, structural guidance, endogenous cell recruitment, and lesion-specific delivery. Using cutaneous wound healing, osteochondral regeneration, myocardial repair, and neural regeneration as representative examples, we further analyze these strategies through a “clinical challenge–engineering strategy–biological mechanism” framework, with particular attention to how engineered systems influence key signaling pathways such as PI3K/Akt, Wnt/β-catenin, NF-κB, and PTEN/PI3K/Akt/mTOR. We also discuss translational barriers, including exosome heterogeneity, safety concerns inherited from parental cells, large-scale GMP-compliant manufacturing, product standardization, storage stability, and regulatory classification of exosome–biomaterial hybrids. Finally, we highlight emerging directions, including multi-mechanism combinational systems, closed-loop responsive platforms, and artificial intelligence-assisted design for personalized exosome therapeutics. This review provides a design-oriented framework to accelerate the bench-to-bedside development of biomaterial-enabled precision exosome therapy. Full article

Frequent extreme heatwaves (HWs) have significantly exacerbated urban thermal risks, yet the regulatory mechanisms of urban morphology remain poorly understood. This study focuses on the core urban areas of Beijing and develops a Local Climate Zone (LCZ)-constrained spatiotemporal data fusion model (LCZ-FSDAF) to generate high-resolution Land Surface Temperature (LST) datasets from 2015 to 2024. By integrating urban–rural gradient analysis with the XGBoost-SHAP model, this study quantitatively resolves the spatiotemporal evolution of land surface temperature during heatwaves and the nonlinear threshold effects of urban morphological parameters, using a representative extreme heatwave event in July 2023 as a case study. The results indicate that the LCZ-FSDAF model achieves high precision across complex urban underlying surfaces (up to 0.946, RMSE as low as 0.762 K), effectively capturing the spatial heterogeneity of the urban thermal environment. Over the past decade, heatwave events in Beijing have exhibited a significant trend of increasing frequency, duration, and intensity. During these events, LST displays a concentric core-high, periphery-low structure; however, the peak temperature shifts toward high-density built-up areas in the sub-core, manifesting a distinct heat island core shift phenomenon. Furthermore, the impact of urban morphology on LST is characterized by significant nonlinearity, with the Normalized Difference Vegetation Index (NDVI) and Mean Building Height (MBH) identified as dominant factors. Notably, Building Coverage (BC) and Sky View Factor (SVF) exhibit pronounced threshold effects across different thermal indicators. Findings of this study are useful for guiding urban planning, optimizing spatial configurations, formulating urban heat island mitigation policies under heatwaves, and promoting the Sustainable Development Goals (SDGs) of cities and communities. Full article

Under the macro background of the rural revitalization strategy and urban-rural integrated development, rural settlements are undergoing a profound transformation from physical morphology to functional connotation. However, existing studies mainly focus on the expansion of single land elements, lacking long-term quantitative monitoring of the coupling relationship between rural development and policy texts. Taking Fuzhou City as a case study, this research selects long-term Global Human Settlement Layer (GHSL) and Night-Time Light (NTL) data from 1990 to 2025, combined with policy text quantification methods. Based on rural development units, the Coupling Coordination Degree Model (CCDM), Macro-Micro Matching Index (MMI), and gravity center migration analysis are employed to systematically reveal the spatiotemporal evolution characteristics and driving mechanisms of rural settlement morphology under policy institutional changes. The research results indicate that: (1) Fuzhou’s rural settlements exhibit relatively stable gravity centers of construction land, while the gravity center of economic vitality has significantly shifted toward the southeastern coastal area under policy guidance; (2) The coupling coordination degree of rural human–land relationships has generally increased, but with significant spatial heterogeneity, forming a pattern of high-quality coordination in coastal areas and low-efficiency lag in mountainous regions; (3) The shift in policy orientation from scale expansion to functional enhancement has driven economic factors to concentrate in key policy areas ahead of physical spatial expansion. The analytical framework combining remote sensing monitoring and policy quantification constructed in this study reveals the precedence of factor flow and the lag of physical space driven by policies, providing a scientific basis for the differentiated governance of rural areas in coastal mountainous cities. Full article

Identifying the heterogeneous characteristics of habitat quality (HQ) trajectories is a key prerequisite for refined ecological spatial management. We used kernel Normalized Difference Vegetation Index (kNDVI) to correct the highly sensitive parameters, validated the correction results based on their consistency with the prior study findings, developed a framework for the evolution of HQ using Sen+MK and Pettitt’s tests, and utilized XGBoost and partial correlation analysis to identify the primary drivers of dynamic changes in HQ from both spatiotemporal perspectives. Our findings include the following: (1) between 2000 and 2023, the average annual rate of change in the HQ index was 0.0037 per year, indicating a continuous improvement in HQ. Compared with the period from 2011 to 2023 (0.0026 per year), the rate of improvement in HQ was faster during 2000–2011 (0.0047 per year). (2) Mutational improvement and progressive improvement were the main evolutionary trajectories, accounting for over 50.33% of the total. (3) Precipitation, land-use intensity (LUI), temperature, and elevation show a strong correlation with HQ distribution. The magnitude of HQ variation is related to HQ status, LUI, precipitation, and elevation. This study establishes a scientific foundation for developing differentiated regulatory strategies for YRB. Full article

Wind energy is a cornerstone of Saudi Arabia’s renewable energy transition under Vision 2030, yet national-scale wind resource assessment remains constrained by sparse and unevenly distributed ground observations. This study evaluates the spatiotemporal variability of near-surface wind speed and direction across Saudi Arabia using Global Ensemble Forecast System Reforecast (GEFSv12 Reforecast) wind fields integrated with a GIS 10.8-based processing workflow. Wind vectors (U and V) were extracted from NetCDF files, converted to wind speed and meteorological wind direction, and analyzed at 183 grid-cell “virtual stations” covering the Kingdom for a five-year period (2018–2022) at four synoptic time steps (6-hourly). The resulting database comprises approximately 1,336,632 records. A practical verification using five airport stations matched to nearest virtual stations shows strong agreement between GEFS-derived and observed wind speeds (RMSE = 1.823; R² = 0.879), supporting the dataset’s suitability for regional screening. Results reveal pronounced spatial heterogeneity and diurnal structure: northern, northeastern, central, and eastern Saudi Arabia consistently exhibit moderate-to-high winds (often >5.5 m/s) with persistent northwesterly–westerly flow, while western and southwestern coastal zones show stronger diurnal variability associated with thermal and sea-breeze influences. Peak, spatially coherent winds occur during the late-day synoptic period, forming a broad high-wind corridor across central and eastern regions. Given the ~1° (~110 km) resolution, findings are intended to be used for macro-scale wind-resource screening and the prioritization of high-wind zones for follow-up assessment. Full article

Environmental loading is a major driver of nonlinear GNSS vertical displacements, yet its spatiotemporal heterogeneity remains insufficiently understood in regions with complex topography. In this study, we investigate the environmental loading effects on GNSS vertical motions across Southwest China using observations from a network of 66 stations. Singular Spectrum Analysis (SSA) and Empirical Orthogonal Function (EOF) analysis were applied to extract annual signals, while component-wise RMS reduction quantified hydrological and atmospheric loading contributions. Spatial statistical analysis, cross-wavelet transform, and k-means clustering examined correlation patterns and phase hysteresis between GNSS observations and modeled loads. Results show that hydrological loading dominates seasonal vertical oscillations, but crustal responses exhibit pronounced spatial heterogeneity controlled by regional topography and hydro-climatic gradients. EOF analysis reveals a dipole pattern induced by the Hengduan Mountains’moisture-blocking effect. Atmospheric loading anomalously dominates the eastern Sichuan Basin, whereas Yunnan displays strong amplitudes with high heterogeneity due to karst hydrogeology. Phase analysis identifies three distinct regimes: a rapid elastic response on the Tibetan Plateau, (with the lag of ~20 ± 5 days, correlation coefficient R ≈ 0.65), intermediate delays in Yunnan (~60 ± 5 days, R ≈ 0.58), and pronounced hysteresis in the Sichuan Basin (~105 ± 5 days, R ≈ 0.38) linked to slow groundwater diffusion and poroelastic processes. These findings highlight the critical role of local hydrogeological dynamics in modulating GNSS vertical deformation and provide new insights for improving environmental loading corrections in complex mountainous regions. Full article

As a key link between short-distance urban mobility and public transport, dockless bike-sharing (DBS) systems have expanded rapidly in recent years. However, existing studies are limited by insufficient factor coverage, incomplete temporal analysis, and inadequate assessment of spatial-scale effects. To address these gaps, this study uses Shenzhen as a case study, integrating 40 million DBS trip records from August 2021 with multi-source geospatial data to develop a spatiotemporal analytical framework. First, it examines differences in riding patterns between weekdays and weekends, further segmenting trips into six time periods to capture intra-day temporal variations. Through multicollinearity and spatial autocorrelation tests, a 700-m grid was identified as the optimal analysis unit. Subsequently, a Multi-scale Geographically Weighted Regression (MGWR) model quantified how multiple sources of factors collectively shape DBS usage behavior. Results indicate that higher frequency, faster speeds, and longer distances during peak periods characterize weekday trips. Office POIs and transit accessibility positively affect DBS usage during weekday peaks, whereas Residential POIs and Convenience Service POIs have a greater influence on weekend trips. Population density and land-use mix consistently promote DBS use across all periods. Younger residents (<30 years) were the main users, especially during weekday peak and weekend no-peak periods, whereas gender and education had limited impact. These findings provide empirical evidence to optimize bike-sharing deployment, enhance multimodal transport integration, and support sustainable urban mobility planning. Full article

Accurate air quality forecasting requires the effective integration of heterogeneous data sources that vary in spatial coverage, temporal resolution, and sensing reliability. This paper presents a unified spatio-temporal data processing framework designed to support multi-source air quality forecasting by jointly leveraging regulatory monitoring stations of the Central Pollution Control Board (CPCB) as reference-grade anchors and community-driven Internet of Things (IoT) sensing platforms for spatial densification. The proposed end-to-end workflow addresses key challenges associated with heterogeneity, data quality, and interoperability through systematic schema harmonization, multi-stage data cleaning, and robust missing data imputation using a Robocentric Iterated Extended Kalman Filter (RIEKF). The processed data are temporally aligned to a uniform sampling grid and enriched with spatial descriptors, including geospatial coordinates, administrative boundaries, and proximity-based emission features. These enriched observations are subsequently fused into a unified spatio-temporal representation that captures both spatial dependencies and temporal dynamics across the sensor network. Dynamic graphs constructed from this representation are processed using a Mobility-Aware Peripheral-Enhanced Graph Neural Network to forecast pollutant concentrations and generate categorical air quality indices. The framework is evaluated using regression metrics reported as RMSE/MAE in µg/m³ and MAPE in %, together with standard AQI classification metrics, demonstrating its effectiveness in improving predictive accuracy and robustness for real-world air quality forecasting applications. Full article