Search Results (6,878)

Ischemic stroke remains a leading cause of death and disability worldwide. While thrombolysis and endovascular thrombectomy are current mainstays of treatment, their clinical efficacy is often undermined by ischemia–reperfusion injury (I/R). This injury induces secondary brain damage, primarily via disruption of the blood–brain barrier (BBB). No approved therapies directly target BBB protection. This review reinterprets the pathophysiological mechanism of BBB disruption after stroke through a dynamic spatiotemporal framework. The pathological cascade reaction is clearly divided into two core stages: the ischemic phase is mainly driven by energy failure and calcium overload; the reperfusion phase is further divided into four consecutive and progressive sub-stages, namely, oxidative stress burst, amplification of inflammatory response, matrix metalloproteinase 9 (MMP-9)-mediated barrier degradation and programmed cell death. This review critically assesses current therapies and identifies major clinical translation gaps, including a temporal mismatch between preclinical and clinical windows, unacceptable toxicity, lack of durable efficacy and biphasic effects. Matching specific interventions to the different pathophysiological stages of blood–brain barrier disruption is essential for optimizing clinical outcomes. 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

In the context of escalating global warming and the urban heat island effects, recurrent extreme heat events will increase the exposure risk of cyclists, which will have a detrimental effect on both health and the sustainability of active mobility. Nevertheless, this risk has not been given sufficient attention. To accurately quantify the levels of solar radiation exposure experienced by cyclists in high-temperature conditions and the impact of the built environment on these levels, this study focuses on central Shanghai as a case study. The integration of Mobike trajectories, street view imagery, and solar radiation data sets enabled the quantification of trip-level cumulative radiation exposure and per-minute exposure levels. Subsequently, the XGBoost–SHAP interpretability framework was employed to decipher the mechanisms of the built environment. The following key findings have been identified: (1) Spatiotemporally, the radiation exposure level of cyclists exhibited an inverted U-shaped pattern, peaking at midday (10:00–15:00), with per-minute values of 862–943 W/m². This intensity significantly exceeded that observed during the morning (407 W/m²) and evening (253 W/m²). (2) It was determined that geometric factors dominated the radiative exposure level. The shading index demonstrated a critical influence (57% contribution), with exposure reduction intensifying beyond 0.41 yet exhibiting diminishing marginal effects after 0.6. The sky view factor and building height elevated exposure risk by amplifying direct solar radiation. (3) Socioeconomic factors had divergent effects on the radiation exposure level of cyclists: commercial/business densities reduced exposure through continuous building shade, whereas transportation facility density increased exposure due to low-shaded layouts. Consequently, this study proposes “shaded corridors” as a core mitigation strategy, establishing a tripartite intervention framework (spatial-facility-governance) for radiation exposure reduction. The present study provides scientific foundations for the targeted enhancement of heat resilience in active mobility. Full article

As modern energy systems become increasingly complex and multi-source integrated, efficient coordination between diverse energy carriers and dynamic demand is essential. This study identifies a structural parallel between integrated energy system (IES) scheduling and the weapon-target assignment (WTA) problem in joint command and control, and proposes a quantity–quality–spatiotemporal (QQST) framework to model multi-dimensional supply–demand matching. The QQST framework formulates scheduling as a coupled optimization problem integrating quantity balance, energy quality (exergy), spatial distribution, and temporal dynamics. A real-world industrial IES case, involving 60 textile enterprises and a 62 km steam network, is used for validation. The proposed model is benchmarked against a conventional mixed-integer linear programming-based scheduling approach under identical system configurations. Results show that QQST improves overall exergy efficiency by 8.4% and reduces energy quality mismatch by 18.2%, as measured by an exergy-based index. Sensitivity analysis under varying load conditions further confirms the robustness of the approach. These findings demonstrate that the QQST framework provides a structured and effective methodology for enhancing multi-dimensional coordination in complex energy systems. Full article

This paper is based on data from 121 cities in China’s ecologically fragile regions from 2008 to 2022; it constructs an indicator system for the efficiency of pollution control and carbon reduction in agricultural practices. This system includes expenditures on agriculture, forestry, and water affairs, arable land area, agricultural laborers, total agricultural output value, agricultural carbon emissions, and agricultural non-point source pollution. It uses a super-efficiency SBM model that incorporates non-desirable outputs to measure the synergistic efficiency and analyzes its dynamic evolution using the Malmquist–Luenberger index to reveal the spatiotemporal characteristics of the synergistic efficiency. A Tobit model identifies the influence of factors, such as the level of rural economic development, crop planting structure, the strength of fiscal support for agriculture, rural education level, urbanization rate, and mechanization level on the synergistic efficiency. The results show that, from a temporal perspective, the average synergistic efficiency was only 0.58, significantly below the effective value of 1, indicating substantial room for overall improvement. Only 10 cities met the benchmark, with distinctly different reasons for compliance, while the remaining 111 cities remained inefficient. Regarding influencing factors, crop planting structure, the strength of fiscal support for agriculture, and urbanization rate significantly and positively drive efficiency; the level of rural economic development and mechanization level significantly inhibit efficiency, and rural education level shows no significant impact. These findings provide targeted policy recommendations for the synergy effect in ecologically fragile areas, as well as for low-carbon agricultural development. 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

Accurate estimation of vehicle sideslip angle and lateral velocity is essential for the stability control of Advanced Driver Assistance Systems (ADASs). Traditional physics-based observers often exhibit dynamic response distortions under stability-limit conditions due to unmodeled tire relaxation effects, while data-driven methods lack physical interpretability. This paper proposes a Physics-Driven Hybrid Estimation Framework (PD-HEF) to bridge this gap. First, a nonlinear nominal model is constructed as a physical skeleton, and dynamic residual equations are derived to define learning targets. Second, a Spatio-Temporal Feature Coupled Residual Network is designed to capture time-domain phase lag and compensate for spatial nonlinear deviations. Furthermore, a hybrid unscented Kalman filter is developed to inject predicted residuals into the sigma-point evolution. A Dual-Layer Adaptive Mechanism is also introduced to regulate trust weights based on innovation statistics. Joint simulations demonstrate that the proposed framework reduces the root mean square error by over 60% compared to traditional observers while satisfying real-time constraints. Full article

Financial time series in turbulent markets exhibit complex long-memory dynamics and multifractal features that traditional deep learning models fail to capture due to inherent exponential forgetting mechanisms. To address this, we propose Frac-Attn-GL, a novel Fractional-order Spatiotemporal Attention-based GRU-LSTM framework. Grounded in the Fractal Market Hypothesis, the model embeds Grünwald–Letnikov fractional-order operators into a dual-channel architecture (FracLSTM and FracGRU) to characterize long-range memory with rigorous power-law decay priors. Furthermore, an extreme-aware asymmetric loss function is designed to drive a dynamic spatiotemporal routing mechanism, enabling adaptive shifts between long-term macro trends and short-term micro shocks. Empirical tests on major U.S. stock indices reveal three significant findings. First, the Frac-Attn-GL framework substantially reduces prediction errors, achieving up to a 93.1% RMSE reduction on the highly volatile NASDAQ index compared to standard baselines. Second, the adaptively learned fractional-order parameters exhibit a consistent quantitative alignment with the market’s empirical multifractal singularity spectrum, supporting the physical interpretability of the model’s endogenous memory mechanism. Finally, hybrid residual multifractal diagnostics indicate that the framework effectively captures deep long-range correlations, reducing the Hurst exponent of the prediction residuals from ~0.83 to approximately 0.50, a level consistent with the absence of significant long-range dependence. Full article

Coastal ecosystems, particularly semi-enclosed gulfs, are increasing anthropogenic pressures from urbanization and industrialization with profound impacts on biodiversity maintenance, energy transfer, and biogeochemical cycling. However, how metazoan communities—key components of marine food webs—respond to spatiotemporal variability and human disturbance remains insufficiently understood. This study applied eDNA metabarcoding targeting the mitochondrial COI gene to investigate metazoan communities across 68 stations in the Beibu Gulf, spanning bay, coastal, and island regions, during wet and dry seasons. In total, 878 metazoan ASVs from 13 phyla were detected. Arthropoda dominated both seasons (wet: 85%; dry: 55%), whereas Chordata increased during the dry season (wet: 0.16%; dry: 37%). At the α-diversity level, diversity peaked in the bay region during the dry season and shifted toward the coastal region during the wet season. At the β-diversity level, community composition differed significantly between seasons and spatial regions, with seasonal variation exerting a stronger influence than spatial differentiation. Co-occurrence networks revealed higher complexity during the dry season. β-diversity was overwhelmingly driven by species turnover (94.4%). The island region exhibited the highest community uniqueness, while the human-impacted bay region showed reduced distinctiveness. Redundancy analysis further identified anthropogenically influenced inorganic nitrogen, together with water temperature, transparency, and salinity, as key environmental drivers shaping community structure. βNTI analysis indicated that community assembly was governed by the combined effects of deterministic and stochastic processes. Overall, this study highlights how environmental gradients and human pressures jointly regulate metazoan dynamics, providing insights for biodiversity conservation in human-impacted coastal seas. Full article

Accurate regional wind speed forecasting is critical yet challenging due to inherent spatiotemporal correlations and data non-stationarity. This paper proposes a hybrid framework combining Principal Component Analysis (PCA), Variational Mode Decomposition (VMD), and Long Short-Term Memory (LSTM) networks. First, PCA extracts dominant spatial features from a regional wind field (9 × 9 grid), retaining 99.5% of the information to reduce redundancy. Next, an adaptive VMD strategy, optimized by the Sparrow Search Algorithm (SSA), decomposes these components to mitigate temporal non-stationarity. High-correlation sub-signals are then fed into the LSTM predictor. Experimental results demonstrate that the framework achieves an average coefficient of determination (R²) of approximately 0.41 in the first forecasting step. Crucially, it significantly mitigates error accumulation in multi-step forecasting, maintaining a stable R² of 0.39 in the third step. Conversely, complex spatiotemporal models like ConvLSTM achieve high initial accuracy but suffer severe degradation (R² dropping from 0.70 to 0.24) alongside significantly higher computational overhead. The proposed strategy effectively prevents overfitting to high-frequency noise, ensuring a computationally efficient and robust solution for multi-step regional wind forecasting. Full article

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

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