Search Results (20,188)

Using panel data from 11 prefecture-level cities in Zhejiang Province (2014–2023), this study applies the entropy method, spatial autocorrelation analysis, and an obstacle-factor diagnosis model to examine the spatiotemporal evolution, regional disparities, and constraints on urban–rural integration. The results show a steady upward trend in urban–rural integration alongside significant regional disparities. This reveals a complex pattern marked by the coexistence of convergence and divergence. Spatially, a clear “northeast–high, southwest–low” pattern is observed, with local adjustments within a stable framework, reflecting a “stable core and entrenched low-value areas.” Spatial agglomeration is characterized by “dual-core agglomeration with a predominantly non-significant periphery,” dominated by homogeneous “high–high” and “low–low” clusters, with no statistically significant spatial outliers. Obstacle factor diagnosis indicates markedly uneven constraining effects across subsystems, with spatial integration exhibiting the highest degree of obstacles. The composition of primary obstacle factors is highly stable, and obstacle structures differ significantly across city tiers. These findings elucidate the spatiotemporal evolution and core constraints of urban–rural integration in Zhejiang, offering a theoretical and decision-making basis for advancing high-quality urban–rural integration in the region. Full article

Coastal port ecosystems serve as critical interfaces between marine environments and anthropogenic activities, yet zooplankton community dynamics in these transitional zones remain poorly understood. This study investigated seasonal variations in zooplankton assemblages and their environmental drivers in the coastal waters surrounding Lanshan Port, northern Yellow Sea, through quarterly field surveys spanning spring to winter. A total of 33 zooplankton species and 16 planktonic larval categories were identified, with Hydromedusa, Copepoda, and planktonic larvae comprising the three dominant groups. Marked seasonal disparities were observed in species richness (spring: 21 species and 11 larvae categories; winter: 8 species and 3 larvae categories), biomass (autumn: 333.7 mg/m³; winter: 34.0 mg/m³), and abundance (spring: 185.3 ind/m³; winter: 25.7 ind/m³). Notably, Aidanosagitta crassa maintained perennial dominance across all seasons. Principal component analysis of dominant zooplankton taxa across seasons indicated that the first two principal components explained 70.05% and 15.97% of the total variance in zooplankton community structure, respectively, with distinct seasonal clustering of sampling sites along PC1 reflecting pronounced seasonal succession in community composition. Redundancy analysis revealed seasonal-specific correlations between dominant taxa and nutrients: nitrate concentration was negatively correlated with the relative abundance of Sergestidae in both spring and summer, whereas ammonium concentration was negatively correlated with Hydromedusa; by contrast, the abundances of Chaetognatha and Tunicata exhibited a significant positive correlation with nitrate. We also found water temperature only drove communities in autumn, while salinity had little effect. These findings elucidate the mechanisms structuring zooplankton communities in temperate coastal port ecosystems and underscore the necessity of seasonally resolved monitoring frameworks for effective marine environmental management. Full article

The complex ocean dynamics in the Northwest Pacific high-seas fishing grounds shape phytoplankton communities, which serve as the foundation for commercially pelagic species. This study investigates how mesoscale eddies modulate phytoplankton groups’ structures by analyzing the spatiotemporal evolution of eight phytoplankton functional types (PFTs) from 2014 to 2023. Utilizing high-resolution AI-driven model data (AIGD-PFT) and a normalized radial distance grid (0–2 R), we quantified PFTs concentrations within cyclonic (CE) and anticyclonic (AE) eddies, validated by Biogeochemical Argo (BGC-Argo) and in situ measurements. Results reveal that diatoms and dinoflagellates dominate the region, accounting for 88.7% of phytoplankton with distinct seasonal peaks in spring and autumn, respectively. CE significantly enhance diatom and dinoflagellate concentration, particularly within the 0.4 R–1.2 R dynamic ring, while AE favor the aggregation of picophytoplankton, such as Prochlorococcus, in mid-to-low latitudes. Correlation analysis indicates that diatom abundance is strongly linked to dissolved oxygen and negatively correlated with sea surface height. We conclude that mesoscale eddies drive the spatial remodeling of phytoplankton communities by altering local physical and nutrient conditions. These findings provide a critical ecological context for assessing the habitat distribution and sustainable management of North Pacific fisheries across different trophic levels. 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

The Timed Up and Go (TUG) test is a clinical gold standard for assessing elderly mobility, yet its automated deployment in home-monitoring and resource-limited areas is hindered by high hardware costs and expert calibration requirements. This study introduces a Physics–Data Hybrid framework specifically designed for uncalibrated consumer-grade CMOS cameras, enabling a “plug-and-play” solution for early Parkinson’s disease (PD) risk screening. The proposed pipeline integrates learning-based pose perception with a self-evolving physics model to recover absolute metric-scale motion without manual checkerboard calibration. A noise-adaptive fusion strategy is implemented to reconcile 2D pixel dynamics with 3D kinematic consistency, overcoming the inherent scale ambiguity of monocular vision. Crucially, this framework enables the extraction of high-dimensional spatiotemporal parameters—such as stride length coefficient of variation and mean gait velocity—which provide a finer diagnostic resolution for capturing subtle motor fluctuations than conventional timing-only systems. Results from our pilot study with a cohort of 10 subjects demonstrate that these extracted metric features serve as decisive markers for risk staging simulated by dual-task-induced cognitive-motor-interference, achieving 98% screening accuracy and an overall classification accuracy of 87.32%. This framework provides a robust, low-cost tool for ubiquitous telehealth, potentially supporting early PD risk assessment in underserved populations. Full article

This study presents the development and implementation of a web-based geospatial platform for the quantitative assessment of land use and land cover change (LULCC) based on multispectral satellite images. The system operationalizes the Rao spectral diversity metric (Rao’s Q) to detect and quantify LULCC resulting from different environmental agents. The platform supports single-band (classic mode) or multi-band (multidimensional mode) processing. Its main functionalities include the interactive de-limitation of areas of interest (AOI) and calendar-based temporal selection, allowing analyses to be performed at discrete time points or at defined intervals. Among the tools available in the application are the automated calculation of Rao’s Q surfaces and maps of change between pairs of dates. Additionally, the platform allows the selection of several spectral indices, with the aim of supporting ecosystem monitoring and the characterization of the Earth’s surface. In the use case demonstration (Reykjanes Peninsula volcanic eruption of February 2024), the Rao’s Q method applied to Sentinel-2 SWIR imagery demonstrated strong performance in lava flow detection, with the multidimensional approach (bands 11 + 12) achieving the most balanced results (OA = 83.0%, PA = 84.0%, UA = 82.4%), while band 11 alone yielded the highest precision (UA = 97.4%). By integrating spatiotemporal analysis, spectral diversity metrics, and spectral indices into an accessible and extensible framework, the platform constitutes a robust tool for monitoring LULCC and assessing environmental impacts. Full article

Owing to the intricate mechanical coupling characteristics and the considerable difficulty in extracting synergistic spatio-temporal features from high-dimensional sensor data under fluctuating alternating loads, this study proposes a robust anomaly detection framework that combines Normalized Mutual Information (NMI) and Spatio-Temporal Graph Neural Networks (STGNN). First, NMI is utilized to quantify the nonlinear physical coupling intensity among multi-source sensors, thereby filtering out weakly correlated noise and reconstructing the spatial topological structure of the coupler system. Subsequently, a deep learning architecture incorporating Graph Convolutional Networks (GCN), Gated Recurrent Units (GRU), and one-dimensional convolutional residual connections is developed to capture the dynamic evolutionary characteristics of equipment states across both spatial interactions and temporal sequences. Finally, based on the model’s health-state predictions, a moving average algorithm is introduced to smooth the residual sequences, and an anomaly early-warning baseline is established in conjunction with the 3σ criterion. Experimental validation conducted using field service data from heavy-haul trains demonstrates that, compared to conventional serial CNN and Long Short-Term Memory (LSTM) models, the proposed method exhibits superior fitting performance and robustness against noise, effectively reducing the false alarm rate within normal working intervals. In a real-world case study, the method successfully identified variations in spatial linkage features induced by local damage and triggered timely alerts. Notably, the spatial alarm nodes were highly consistent with the fatigue crack initiation sites identified through on-site magnetic particle inspection. This study provides a viable data-driven analytical framework for the condition monitoring and anomaly identification of critical load-bearing components in heavy-haul trains. Full article

Sea ice classification is of fundamental importance for polar monitoring and global climate research. Global Navigation Satellite System Reflectometry (GNSS-R) has emerged as a frontier technology in polar remote sensing due to its high spatiotemporal resolution and cost-effectiveness. Based on BeiDou System Reflectometry (BDS-R) data acquired from the Fengyun-3E (FY-3E) satellite, this study introduces a classification approach that integrates multi-dimensional sea ice information. A comprehensive feature set was constructed by integrating the Spectral Entropy (SE) of the Normalized Integrated Delay Waveform (NIDW) First-order Differential Curve to characterize the oscillatory complexity of the trailing edge power decay process as a scattering dynamic property, the Root Mean Square height (RMS) to characterize the attenuation magnitude of scattering intensity arising from surface roughness and related factors as a scattering intensity attenuation property, and salinity (S) and L-band brightness temperature (TB) data from SMOS to describe dielectric and radiative properties. These novel features are combined with traditional GNSS-R features. After selecting the optimal feature set via an ablation study, the features were used to train a Random Forest (RF) classifier for sea ice classification. Validated against Ocean and Sea Ice Satellite Application Facility (OSI SAF) sea ice type products, the proposed method yielded an overall accuracy of 93.86% and a Kappa coefficient of 0.8061. The integration of multi-dimensional features notably improved the identification of Multi-Year Ice (MYI), achieving a Recall of 85.11% and an F1-score of 84.43%. These results indicate that the proposed multi-dimensional feature set provides an effective solution for GNSS-R-based sea ice classification. Full article

Background/Objectives: Ankle–foot orthoses (AFOs) are commonly used to improve gait in children with cerebral palsy (CP), but their effect on specific gait patterns is underreported. This study evaluates the utilization of the Gait Pattern Classification System for Children with Spastic CP (GaP-CP) to investigate the effects of ankle–foot orthoses on gait kinematics, spatio-temporal parameters and the energy cost of walking. Methods: In this retrospective study, 66 ambulatory children with spastic CP underwent 3D gait analysis with and without AFOs or functional electrical stimulation. Gait patterns were classified according to GaP-CP. AFOs were articulated, flexible, or rigid. Thirty-six children also performed a 5 min walk test with gas exchange measurements. Step length, walking speed, and the energy cost of walking were calculated. Gait kinematics were analyzed with statistical nonparametric mapping. Non-parametric statistics were used to investigate orthotic effects for the total group and for each gait pattern. Results: Ankle kinematics improved in swing phase and initial contact (10 degrees less plantarflexion, p < 0.05) for the total group, dropfoot and genu recurvatum. During the stance phase, reduced knee extension in genu recurvatum (by 3 degrees, p < 0.05) and increased knee extension in crouch (by 3 degrees, p < 0.05) were observed. Median changes in non-dimensional step length were clinically significant (>0.039, p ≤ 0.02, effect size ≥ 0.55) for the total group and the dropfoot, genu recurvatum, and crouch subgroups, while changes in most gait indices, walking speed and the energy cost of walking were not clinically significant. Conclusions: The combined use of GaP-CP and kinematic analysis provided new insights into the effects of ankle–foot orthoses on gait. Articulated and flexible orthoses may not have provided adequate support for genu recurvatum and crouch gait, showing a potential value in gait pattern specific orthotic design to optimize gait kinematics. Full article

Urban green spaces are increasingly threatened by rapid urban expansion, making their continuous monitoring and prediction essential for sustainable urban management. This study investigates the spatiotemporal dynamics of urban garden landscapes in Shiraz, Iran, by integrating multi-temporal Landsat imagery, GIS analysis, and CA–Markov modeling. Landsat data from 2003, 2013, and 2023 were processed to derive the Normalized Difference Vegetation Index (NDVI), which was classified into four vegetation-density categories to quantify land-cover transitions. A CA–Markov framework implemented in IDRISI TerrSet (Version 20.0) was then employed to simulate spatial dynamics and predict vegetation changes for 2033. Results reveal a significant expansion of non-vegetated areas from 711.93 ha in 2003 to 976.66 ha in 2023, accompanied by a decline in dense vegetation from 403.68 ha to 382.64 ha. Model projections indicate a further reduction in dense vegetation to 239.35 ha by 2033, suggesting ongoing fragmentation of urban green infrastructure driven by development pressures. By combining time-series remote sensing, GIS-based spatial analysis, and predictive modeling, this study provides an integrative framework for detecting, interpreting, and forecasting urban land-cover change. The findings offer evidence-based insights to support sustainable urban planning, green infrastructure protection, and climate-resilient city management in rapidly growing urban environments. Full article

A miniaturized (70 × 7.7 cm) multi-parameter synchronous observation system was developed for in situ ocean turbulence measurement, integrating micro-electromechanical system (MEMS)-based two-dimensional (2D) turbulence, pressure, temperature, conductivity, and attitude sensors. Field tests conducted at a depth of 1800 m in the northern South China Sea validated the system’s accuracy through comparisons with standard CTD (Conductivity, Temperature, and Depth) sensors, dual-probe consistency analysis, and Nasmyth spectrum fitting. The system precisely captured thermoclines, internal waves, and turbulent shear fluctuations at a depth of approximately 125 m, revealing enhanced turbulence near the thermocline due to intensified shear effects. With high spatiotemporal synchronization and reliability, the system provides an effective solution for studying multiscale ocean turbulence and associated dynamic processes. Full article

Accurately monitoring the surface stabilization of waste dumps in open-pit coal mines is critical for hazard prevention and ecological reclamation. In arid and semi-arid regions, traditional optical remote sensing vegetation indices suffer from a systematic “response lag” in assessing physical stability due to the slow establishment of pioneer vegetation. To overcome this biological limitation, this study proposes a quantitative spatiotemporal monitoring framework based on time-series Interferometric Synthetic Aperture Radar (InSAR) coherence to detect early-stage geotechnical stabilization. Using Sentinel-1 imagery of the Balongtu coal mine, a sliding-window detection algorithm was developed to capture the physical transition of surface electromagnetic scattering mechanisms from active disturbance to stable consolidation. The main findings are as follows: (1) Statistical analysis identified a critical geophysical coherence threshold of 0.15, which effectively and objectively distinguishes active dumping disturbance zones from structurally stable areas. (2) The spatiotemporal evolution dynamics of the completed dump areas from 2017 to 2023 were successfully characterized, revealing that 87.6% of the open-pit areas achieved physical stabilization within three years post-mining, with a spatial distribution highly consistent with the objective operational rule of “mining first, dumping later”. (3) Accuracy assessment using 700 spatiotemporally balanced validation points—derived through strict visual interpretation of high-resolution optical imagery—demonstrated high algorithm reliability, achieving overall accuracies (OA) of 87.57% and 90.43% at half-yearly and annual monitoring intervals, respectively. By decoupling physical surface stabilization from optical greenness, this study provides a timely abiotic precursor indicator, offering scientific, quantitative decision support for precision ecological zoning and accelerated land turnover approval in mining areas. Full article

To improve ship AIS trajectory prediction under pronounced spatiotemporal coupling and dynamic maneuvering conditions, this study proposes a Spatio-Temporal-Hybrid-Multi-head Attention model (STHA) integrating multiscale convolution, bidirectional long short-term memory, and multi-head attention. Historical AIS data from the Zhoushan waters in 2024 were preprocessed through screening, cleaning, outlier removal, resampling, and cubic spline interpolation to construct trajectory samples. Comparative experiments were conducted against BP, BiLSTM, and BiGRU using MAPE, RMSE, and R² as evaluation metrics. The results show that STHA achieves the best overall predictive performance, more accurately follows trajectory variations across different vessel types, and exhibits better robustness in scenarios involving turning and speed changes. These findings indicate that the proposed model is effective for high-precision ship trajectory prediction and can provide useful support for subsequent collision risk assessment and navigation safety assistance. Full article