Search Results (7,283)

Quantifying dynamic urban resilience is critical for climate adaptation. This study assesses the spatiotemporal resilience of 6838 flood-affected communities across 39 Chinese cities using high-resolution human activity data. By establishing a multi-phase framework, we extract six metrics characterizing resistance and recovery trajectories. Results reveal a distinct resilience paradox: coastal cities, despite suffering deeper instantaneous shocks from typhoons, exhibit superior adaptive capacity compared to inland cities, which face chronic recovery deficits under rainstorm stress. Unsupervised clustering identifies 12 distinct resilience phenotypes, ranging from brittle collapse to adaptive growth. Structural analysis confirms a Matthew Effect where functional diversity and economic vitality enable resource-rich communities to bounce forward, while peripheral areas remain trapped in vulnerability. These findings underscore the need for resilience-based regeneration policies that prioritize spatial justice and resource optimization over static engineering standards. Full article

Gaze estimation plays a crucial role in human-computer interaction and behavior analysis. However, in dynamic scenes, rigid head movements and rapid gaze shifts pose significant challenges to accurate gaze prediction. Most existing methods either process single-frame images independently or rely on long video sequences, making it difficult to simultaneously achieve strong performance and high computational efficiency. To address this issue, we propose DGAGaze, a gaze estimation framework based on a difference-driven spatiotemporal attention mechanism. This framework uses a geometry-aware temporal alignment module to mitigate interference from rigid head movements, compensating for them through pose estimation and affine feature warping, thereby achieving explicit decoupling between global head motion and local eye motion. Based on the aligned features, inter-frame differences are used to adjust spatial and channel attention weights, enhancing motion-sensitive representations without introducing an additional temporal modeling layer. Extensive experiments on the EyeDiap and Gaze360 datasets demonstrate the effectiveness of the proposed approach. DGAGaze achieves improved gaze estimation accuracy while maintaining a lightweight architecture based on a ResNet-18 backbone, outperforming existing state-of-the-art methods. Full article

Resource-based cities face unique land use challenges due to resource dependence and path lock-in, yet the driving mechanisms and future trajectories of their land use transitions remain underexplored. This study examines the Huaihai Economic Zone (HEZ), a representative coal-rich region in eastern China, to analyze land use changes from 2000 to 2023 and simulate 2036 scenarios under different development pathways. Using land use transfer matrices, dynamic degree metrics, and the Patch-generating Land Use Simulation (PLUS) model, we systematically identified spatiotemporal evolution patterns, quantified the contributions of driving factors, and projected multi-scenario future land use patterns. Results reveal that land use change in the study area was dominated by the conversion of cultivated land to construction land, alongside spatial restructuring from a monocentric to a polycentric network pattern. Notably, construction land expansion was least evident in the central Mining-Affected Zone, where land use changes remained relatively sluggish compared to other sub-regions. Driving factor analysis indicates that socio-economic factors primarily influenced changes in construction and cultivated land, while natural factors strongly affected ecological land and unused land. Multi-scenario simulations for 2036 demonstrate diverging trajectories: an urban development scenario would accelerate cultivated land loss and unused land expansion; a natural development scenario would maintain current pressures; and an ecological protection scenario would effectively curb urban sprawl while actively promoting ecological land recovery. This study concludes that transcending simple land use control to actively orchestrate “mining-urban-rural-ecological” spatial synergy is critical for achieving a sustainable transition in resource-based regions facing similar transformation pressures. Full article

The study presents a comprehensive spatio-temporal assessment of meteorological drought in the Upper Vistula basin, a region located in southern Poland. The analysis was based on monthly precipitation data from 30 meteorological stations covering the period 1961–2022. These data were used to calculate the Standardized Precipitation Index (SPI) for accumulation periods of 3, 6, 9, 12, 24, and 48 months. Drought events were identified using run theory, adopting a threshold of SPI < −1 for all accumulation periods. On this basis, drought characteristics were determined, including the number of identified drought episodes (N), average drought duration (ADD), average drought severity (ADS), and average drought intensity (ADI). The multi-scale analysis revealed a clear dependence of drought characteristics on the time scale. Short-term droughts (SPI-3 and SPI-6) occurred frequently and were characterized by high monthly intensity but short duration. In contrast, long-term droughts (SPI-24 and SPI-48) occurred less frequently, but were marked by much longer duration and greater cumulative severity, despite lower average intensity. Spatial analyses showed substantial heterogeneity of drought characteristics within the Upper Vistula basin. The western and south-western parts of the region were particularly exposed to frequent short-term droughts, whereas long-term droughts were less frequent, but more regional in nature and resulted from accumulated, multi-year precipitation deficits affecting groundwater resources and catchment retention. The presented findings provide valuable information for improving drought monitoring systems and adaptation strategies in the Upper Vistula basin and in other climatically diverse regions of Central Europe. Full article

Wildfires are a major natural hazard causing extensive ecological damage and endangering human survival. Previous studies on wildfires in China have mostly focused on specific regions or individual drivers, with limited systematic assessments at the long-term and national scales. The spatiotemporal patterns of wildfires and their multiple driving mechanisms under China’s diverse climatic regimes remain insufficiently understood. To bridge this gap, we combined MCD64A1 burned area data (2001–2023) with multi-source natural (meteorological, vegetation, and topographic) and anthropogenic factors, using random forest models at both the national and regional scales to examine the spatiotemporal patterns, dominant drivers, and response mechanisms of wildfires in China. The results revealed that: (1) Spatially, wildfires were concentrated in northeastern and southern China, which accounted for 86.20% of the total burned area. Temporally, northern wildfires were primarily a spring-dominated fire regime, with peak activity in March and April, whereas southern wildfires were winter-dominated, peaking in February. (2) At the national scale, elevation was the key topographic factor influencing wildfire occurrence (relative importance = 0.49), with low-elevation and gentle-slope areas being more fire-prone. At the regional scale, the driving factors exhibit spatial differentiation, forming a spatial pattern of topography-dominated and climate-dominated. (3) Partial dependence plot analysis revealed nonlinear and threshold responses. Fire probability increases rapidly when the soil moisture is below 20 mm, while extremely high land surface temperatures in arid regions suppress fire occurrence due to fuel limitations. This study enhances the understanding of spatially heterogeneous wildfire drivers in China and provides a scientific basis for region-specific wildfire prevention and management strategies. Full article

As complex system engineering involving multiple stakeholders, multi-objective collaboration, and multi-spatiotemporal scales, the components, logical structure, and functional mechanisms of production equipment operation management and control (PEOMC) can be generalized through functional modelling to support dynamic analysis and intelligent decision-making of PEOMC in the industrial internet environment. To address the diversity of scenarios and objectives of PEOMC, a hierarchical construction method for the functional model of PEOMC based on IDEF0 is proposed. By analysing relevant international standards, such as ISO 55010, ISO/IEC 62264, and OSA-CBM, the generic functional modules for the first and second layers of the functional model are identified and defined. On the basis of semi-supervised machine learning, topic clustering is used to extract the components, functional mechanisms, and logical relationships of production equipment operation management and control from approximately 200 standard texts and to construct a reference resource pool for the third-layer functional module. On this basis, an interface matching and recursive traversal algorithm for functional modules is designed, and a composition and orchestration strategy of functional modules for specific scenarios is provided to support the flexible construction of diversified and personalized PEOMC scenarios. The proposed construction and application method was validated through an engineering case study in an aero-engine transmission unit manufacturing workshop: the average process capability index of the enterprise’s production equipment steadily increased from 1.28 to approximately 1.60, the mean time to repair (MTTR) of production equipment failures significantly decreased from 8 h to 3 h, and the average overall equipment effectiveness (OEE) increased from 56.43% to a stable 68.57%, demonstrating its effectiveness and practicality. Full article

Although satellite sensors provide global observations, factors such as cloud interference and narrow swath widths frequently result in partial data gaps which constrain the continuous spatiotemporal analysis of the column-averaged dry air mole fraction of CO₂ (XCO₂). To address this challenge, this study develops a novel multi-stage fusion framework that integrates GOSAT and OCO-2 data using inverse error variance weighting and a dynamic bias correction technique, generating a seamless monthly XCO₂ dataset for East Asia (2016–2024). Validation against TCCON measurements (RMSE = 1.22 ppm; R² = 0.96) and WDCGG data (RMSE = 2.85 ppm; R² = 0.76) demonstrates the high accuracy of the product. The results show that the growth rate consistently exceeds 2.2 ppm/year, with clear seasonal patterns characterized by spring maxima and summer minima. Spatially, the locus of rapid growth has shifted toward central and western China, reflecting patterns of regional economic development, while substantial concentrations still persist in the industrialized regions of eastern China, Japan, and South Korea. This study provides new insights into regional atmospheric CO₂ dynamics and emphasizes the efficacy of dynamic bias correction in data fusion. Full article

Post-mining surface uplift has affected the northeastern part of Pécs, Hungary, since the closure of underground coal mines in the 1990s. This study synthesises 30 years of SAR data (ERS, Envisat, and Sentinel-1) with geodetic surveys, groundwater monitoring, and over 900 residential damage reports to investigate the spatiotemporal evolution of this deformation. In densely built urban environments, Persistent Scatterer Interferometry (PS-InSAR) provides spatially detailed complementary data measurements to traditional levelling, particularly where survey lines offer limited coverage. The performed combined analysis tracked deformation from initial uplift through stabilisation, revealing a clear transition: while early lower-order measurements showed limited correlation, modern Sentinel-1 data and high-order geodetic surveys (post-2014) demonstrate a robust correlation (R = 0.65). The cross-correlation of InSAR results with geodetic and hydrogeological records revealed that aquifer recovery by the 2010s coincided with the onset of surface stability. While over 90% of 1990s residential damage claims fell within measured deformation zones, this relationship weakened over time, with recent claims showing little spatial connection with ground movements. This highlights the complementary strengths of InSAR and geodetic techniques. It demonstrates the value of integrating geotechnical and socio-economic datasets, providing a transferable framework for reliable deformation monitoring and risk management in post-mining urban environments. Full article

Using She County, a national new-type urbanization comprehensive pilot area, as a case study, this research develops a multi-layered “static–dynamic–driver” analytical framework based on rural settlement data. By integrating GIS spatial overlay, landscape pattern indices, average nearest neighbor analysis, kernel density estimation, and cold–hotspot analysis, the study systematically characterizes the spatiotemporal evolution and driving mechanisms of rural settlements from 1980 to 2020. The results reveal that: (1) settlement evolution exhibits distinct phase-specific patterns, encompassing four primary types of transformation: localized expansion and consolidation, individual disappearance, rapid expansion, and the emergence of new settlements with peripheral extension; (2) landscape pattern and aggregation analyses indicate continuous growth in both total area and number of settlements, accompanied by increasing irregularity and fragmentation of patches; settlement size aggregation shows a fluctuating decline followed by recovery, overall spatial clustering intensity trends upward, and high-density kernel areas shift from the central–western to the northwestern region; (3) under multi-factor interactions, settlement layouts transitioned from an early “survival–location dependent” pattern dominated by natural constraints and transportation accessibility, to a mid-stage rapid aggregation driven by economic development and public service provision, ultimately evolving into a composite pattern balancing economic drivers and ecological constraints. The findings underscore the nonlinear superimposed effects of natural environment, economic development, transportation accessibility, public service availability, and ecological carrying capacity, providing a robust scientific basis for optimizing rural settlement spatial arrangements and informing rural development policy under the context of national new-type urbanization. Full article

Sea surface temperature (SST) serves as a fundamental driver of ocean–atmosphere interactions and global climate variability, exhibiting strong nonstationarity, multiscale dynamics, and cross-variable coupling. However, current deep learning models often fail to capture these complex characteristics, limiting their ability to support accurate and physically consistent long-term SST prediction. To address these issues, we propose PAMSTnet, a unified deep learning framework for physics-informed adaptive multiscale spatiotemporal prediction. PAMSTnet leverages three-dimensional empirical wavelet transform (3DEWT) to learn interpretable multiscale spatiotemporal dynamics from raw observations, and applies multivariate spatiotemporal empirical orthogonal function (MSTEOF) to identify dominant cross-variable coupled modes. These physically meaningful representations are integrated into a deep ConvLSTM predictive network (DCPN) to support coordinated multiscale dynamical learning. Furthermore, PAMSTnet introduces physics-informed consistency learning (PICL) to enforce thermodynamic and dynamic constraints, enhancing physical consistency and interpretability. Extensive experiments demonstrate that PAMSTnet achieves superior performance against state-of-the-art baselines in long-term global SST prediction, reducing RMSE by 8.1% and improving ACC by 2.8% compared with the best-performing baseline, particularly under extreme climate events. Interpretation insights further highlight PAMSTnet’s adaptive representation of variable contributions and regional physical drivers. These findings position PAMSTnet as a promising paradigm for developing intelligent ocean prediction systems with enhanced physical consistency and interpretability. Full article

This study develops a multiregional forecasting framework for road traffic accidents in Ecuador, addressing a critical limitation in existing predictive approaches that rely predominantly on point error metrics without validating the statistical assumptions underlying forecast uncertainty. Although the analysis is conducted at the provincial level, the spatial dimension is used primarily for cross-regional comparison and risk classification rather than for explicit spatial interaction modeling. Using a dataset of 27,648 monthly observations covering all 24 provinces from 2014 to 2025, the study applies the Prophet model within a Design Science Research paradigm and a CRISP-DM implementation cycle. Separate provincial models are estimated with a 24-month forecasting horizon, and methodological rigor is ensured through systematic residual diagnostics using the Shapiro–Wilk test for normality and the Ljung–Box test for temporal independence. Empirical results indicate that the Prophet-based artifact outperforms a naïve seasonal benchmark in 70.8% of the provinces, demonstrating excellent predictive accuracy in structurally stable regions such as Tungurahua (MAPE = 10.9%). At the same time, the framework enables the identification of critical emerging risks in provinces such as Santo Domingo and Cotopaxi, where projected increases exceed 49% despite acceptable point forecasts. The findings confirm that point accuracy alone does not guarantee the validity of confidence intervals and that residual validation is essential for trustworthy uncertainty quantification. Overall, the proposed approach provides a robust foundation for a predictive surveillance system capable of supporting differentiated, evidence-based road safety policies in territorially heterogeneous contexts. Full article

The coupling coordination between Urbanization Quality (UQ) and Water Ecological Carrying Capacity (WECC) represents a critical nexus for sustainable regional development within the Yangtze River Economic Belt (YREB). Focusing on 16 cities in Hubei Province over the period 2020–2024, this study constructed comprehensive indicator systems for UQ and WECC, Spatial Autocorrelation Analysis and Key Factor Analysis are then applied to analyze spatiotemporal evolution, identify key influencing factors. The results reveal that: (1) Both UQ and WECC demonstrated upward trajectories, with UQ increasing from 0.369 to 0.409, although WECC exhibited fluctuating patterns; (2) Spatial analysis identified pronounced “core–periphery” clustering effects with Wuhan as the dominant center, confirmed by the positive Global Moran’s I; (3) Hubei’s CCD advanced from 0.626 to 0.661, progressing toward initially coordinated stages, with Wuhan pioneering this transition, while 81.25% of cities remained at the moderately coordinated stage; (4) Grey relational analysis identified aquatic biological resources as the principal constraint, with piscivore biomass ratios and pension insurance participation rates (γ = 0.752) emerging as key biophysical and socioeconomic drivers, respectively. These findings provide empirical evidence for targeted interventions promoting balanced urban–water ecological development in the YREB, while contributing a novel analytical framework for examining UQ-WECC interactions in rapidly urbanizing regions globally. Full article

Background: Knee osteoarthritis (KOA) is a major cause of pain, mobility limitation, and increased fall risk among older adults. Gait dysfunction, characterized by spatiotemporal and kinematic alterations, is a key functional consequence of KOA. While sagittal-plane gait deviations are well-established, multiplanar kinematic changes—particularly in the frontal and transverse planes—remain less clearly understood. This study aimed to compare three-dimensional gait characteristics between older adults with and without KOA. Methods: Ninety older adults (45 with KOA and 45 controls) completed gait assessments using a VICON™ motion capture system. Participants walked at a self-selected speed along a straight walkway without turning movements during data collection. Spatiotemporal parameters and lower-limb joint kinematics (hip, knee, and ankle) were recorded during key gait phases: initial contact, mid-stance, toe-off, and mid-swing. Group comparisons were performed using independent t-tests with statistical significance set at p < 0.05. Results: Compared with controls, participants with KOA demonstrated significantly slower gait velocity (p = 0.001), reduced cadence (p = 0.020), shorter stride length (p = 0.011), increased step time (p = 0.006), prolonged double support time (p = 0.009), and reduced single support time (p = 0.012). Kinematic analysis revealed greater knee adduction at initial contact (p = 0.001), reduced hip adduction (p = 0.002) and greater knee adduction (p = 0.003) during mid-stance, and increased ankle plantarflexion at toe-off (p = 0.004) in the KOA group. No significant between-group differences were observed during the mid-swing phase. Conclusions: Older adults with KOA exhibit distinct spatiotemporal and multiplanar kinematic gait alterations, particularly during weight-bearing phases. These changes may reflect adaptive gait patterns associated with joint dysfunction rather than definitive compensatory mechanisms. Three-dimensional gait analysis may provide valuable biomechanical insights to support early identification of mobility impairments and inform targeted rehabilitation planning in individuals with KOA. Full article

Background: Wild jujube (Ziziphus jujuba var. spinosa) exhibits remarkable tolerance to saline-alkali stress, yet its molecular mechanisms remain poorly understood. 3-ketoacyl-CoA synthase (KCS) is a key enzyme involved in the biosynthesis of very-long-chain fatty acids (VLCFAs), which constitute pivotal precursors for membrane lipids involved in stress adaptation. Methods: Through genome-wide analysis and molecular biology techniques, 20 ZjKCS genes were identified. Results: The ZjKCS genes were grouped into nine subfamilies, exhibiting highly conserved gene structures, motifs, and functional domains within each subfamily. Two pairs of collinear gene pairs were identified, with the ZjKCS12-ZjKCS18 pair retaining core conserved functions despite intense purifying selection. ZjKCS genes are rich in cis-acting elements associated with light transduction, phytohormone responses, and abiotic stress adaptation. Tissue-specific expression patterns of ZjKCS under light, ABA (abscisic acid), and MeJA (methyl jasmonate) treatments were analyzed by quantitative real-time PCR (qRT-PCR). Under saline-alkali stress, ZjKCS genes were significantly upregulated, with most showing strong sustained induction during later treatment stages. Conclusions: These findings indicate that the ZjKCS family participates in saline-alkali stress and abiotic stress adaptation, potentially by enhancing VLCFA synthesis to reinforce and remodel membrane lipid structure. This study provides a foundation for elucidating lipid-mediated stress resistance mechanisms in stress-tolerant fruit trees. Full article

Objectives: While the previous literature has established that meteorological conditions are associated with COVID-19 mortality fluctuations, the relative effect of each of these highly correlated factors remains unclear. This study aims to conduct a comparative analysis to determine which of three main meteorological variables—Ambient Temperature, Ultraviolet (UV) Index, and Sunlight Duration—have the strongest negative association with COVID-19 mortality. The objective is to quantify and rank their impact over a 7-to-21-day biological exposure window. Methods: We conducted retrospective spatiotemporal analyses in the form of panel Poisson Distributed Lag Models (PDLMs) regression using daily data from 21 January 2020 to 10 January 2023, spanning 129 distinct geographical regions worldwide. To ensure a direct and fair comparison of effect sizes, all meteorological and environmental variables were Z-score standardized. We estimated three independent PDLMs—each focusing separately on UV Index, Ambient Temperature, and Sunlight Duration—with lags ranging from 7 to 21 days. These models controlled for overarching time trends and utilized a categorical variable to account for Region Fixed Effects modeling time-invariant regional health and socioeconomic determinants (e.g., obesity, age demographics, healthcare capacity). Furthermore, distributed lags of daily PM2.5 (air pollution) and relative humidity were explicitly included in each model as dynamic confounders. Results: The comparison of PDLM results reveals that the UV Index has the strongest negative association with COVID-19 mortality. A one standard deviation increase in the UV Index corresponds to a massive, highly significant cumulative reduction in deaths observed 1 to 3 weeks later (p < 0.001). Sunlight Duration is the second-strongest protective meteorological factor, whereas Ambient Temperature has the weakest effect. The distributed lags of particulate matter (PM2.5) and relative humidity were found to be statistically insignificant when modeled alongside the meteorological variables. Conclusions: After standardizing variables and controlling for dynamic environmental confounders like air pollution and humidity, the study findings provide robust empirical evidence that meteorological conditions have a strong significant association with COVID-19 mortality fluctuation with a temporal delay, overcoming the confounding effects of merely dry or clear-air conditions. Full article