Search Results (100)

In this contribution, we propose and study long-time behaviors of a new class of N-dimensional delayed Kirchhoff–Carrier-type problems with variable transfer coefficients involving a logarithmic nonlinearity. We take into account the dependence of diffusion and damping coefficients on the position and direction, as well as the presence of different types of delays. This class of nonlocal anisotropic and nonlinear wave-type equations with multiple time-varying mixed delays and dampings, of a fairly general form, containing several arbitrary functions and free parameters, is of the following form: ${\displaystyle \frac{{\partial }^{2}u}{\partial t^2}-{div\left(\mathcal{K}\right(\Vert \sigma \nabla u\Vert }_{L^2\left(\Omega \right)}^2)A_{\sigma }{\left(\mathbf{x}\right)\nabla u)+\mathcal{M}(\Vert u\Vert }_{L^2\left(\Omega \right)}^2)u-div(\zeta \left(t\right)A_{\sigma }\left(\mathbf{x}\right)\nabla \frac{\partial u}{\partial t})+d_0\left(t\right)\frac{\partial u}{\partial t}+{\mathbb{D}}_r(\mathbf{x},t;\frac{\partial u}{\partial t})=\mathbb{G}\left(u\right),}$ where $u(\mathbf{x},t)$ is the state function, $\mathcal{M}$ and $\mathcal{K}$ are the nonlocal Kirchhoff operators and the nonlinear operator $\mathbb{G}\left(u\right)$ corresponds to a logarithmic source term. The symmetric tensor $A_{\sigma }$ describes the anisotropic behavior and processes of the system, and the operator ${\mathbb{D}}_r$ represents the multiple time-varying mixed delays related to velocity ${\displaystyle \frac{\partial u}{\partial t}}$. Our problem, which encompasses numerous equations already studied in the literature, is relevant to a wide range of practical and concrete applications. It not only considers anisotropy in diffusion, but it also assumes that the strong damping can be totally anisotropic (a phenomenon that has received very little mathematical attention in the literature). We begin with the reformulation of the problem into a nonlinear system coupling a nonlocal wave-type equation with ordinary differential equations, with the help of auxiliary functions. Afterward, we study the local existence and some necessary regularity results of the solutions by using the Faedo–Galerkin approximation, combining some energy estimates and the logarithmic Sobolev inequality. Next, by virtue of the potential well method combined with the Nehari manifold, conditions for global in-time existence are given. Finally, subject to certain conditions, the exponential decay of global solutions is established by applying a perturbed energy method. Many of the obtained results can be extended to the case of other nonlinear source terms. Full article

Blueberries (Vaccinium spp.) are highly sensitive to winter chilling fulfillment, growing degree days above 7 °C (GDD7), and water balance (WB). By integrating a climate-based natural suitability index (CNSI), three-dimensional kernel density estimation, traditional and spatial Markov chains, and optimal geographic detector analysis, this study examines the spatiotemporal evolution and driving mechanisms of blueberry climatic suitability realization in 19 major producing provinces in China during 2008–2023. Results show that CNSI exhibits a stable and moderately right-skewed distribution, with partial convergence and a narrowing interprovincial gap. Suitability realization is highest in the middle and lower Yangtze River rice-growing belt, whereas the northern dryland belt and the southern subtropical mountainous belt show persistent mismatches between climatic potential and production advantages. Markov results reveal path dependence and moderate mobility, with “low–low lock-in” and “high–high club” phenomena reinforced under neighborhood effects. GeoDetector results indicate that effective facility irrigation and fertilizer input are dominant factors explaining spatial variation in CNSI, while comprehensive transportation accessibility and agricultural labor act as stable complements. Interaction analysis suggests that multi-factor synergies, particularly irrigation-centered combinations, yield strong dual-factor enhancement and near-nonlinear enhancement. These findings highlight the importance of aligning climatic suitability with adaptive infrastructure investment and region-specific management to promote sustainable production-share advantages in China’s blueberry industry. Full article

Urban competitiveness encapsulates a city’s comprehensive capacity for development. Utilizing panel data for 282 prefecture-level cities from 2012 to 2020, this study constructs an evaluation index system of urban competitiveness and applies kernel density estimation, standard deviational ellipse, trend-surface analysis, and Dagum Gini coefficient decomposition to examine its spatiotemporal evolution and regional disparities. The results indicate that: (1) urban competitiveness exhibits a V-shaped recovery, with intensified polarization after 2016, widening innovation advantages in the East, and persistent decline in the Northeast; (2) the spatial configuration follows a “dual-gradient, polycentric” structure, characterized by an inverted-U pattern along the east–west axis and an expanding gradient gap along the north–south axis; (3) club convergence and hierarchical entrenchment coexist, as polarization deepens in the East while the Northeast tends toward internal balance; and (4) the competitiveness center shifts southeastward, accompanied by a pronounced fragmentation trend in the Northeast. Based on these findings, the paper proposes differentiated spatial governance, the development of multi-tier innovation networks, and the promotion of green and sustainable development as integrated strategies to systematically enhance urban competitiveness. Full article

Forest fires pose escalating threats to ecological security and public safety in Guangdong Province. This study presents a novel machine learning framework for fire occurrence prediction by synergistically integrating multi-source geospatial data. Utilizing Moderate-resolution Imaging Spectroradiometer (MODIS) active fire detections from 2014 to 2023, we quantified historical fire patterns and incorporated four categories of predisposing factors: meteorological variables, topographic attributes, vegetation characteristics, and anthropogenic activities. Spatiotemporal clustering dynamics were characterized via kernel density estimation and spatial autocorrelation analysis. An XGBoost classifier, hyperparameter-optimized through the Sparrow Search Algorithm (SSA), achieved a predictive accuracy of 90.4%, with performance evaluated through precision, recall, and $F_1$-score. Risk zoning maps generated from predicted probabilities were validated against independent fire records from 2019 to 2024. Results reveal pronounced spatial heterogeneity, with high-risk zones concentrated in northern and western mountainous areas, constituting 29% of the provincial territory. Critical driving factors include slope gradient, proximity to roads and rivers, temperature, population density, and elevation. This robust predictive framework furnishes a scientific foundation for spatially-explicit fire prevention strategies and optimized resource allocation in key high-risk jurisdictions, notably Qingyuan, Shaoguan, Zhanjiang, and Zhaoqing. Full article

The deep integration of the health industry and the digital economy represents a crucial pathway toward a sustainable and resilient future, as it enhances the competitiveness and promotes the orderly expansion of the health sector. Utilizing provincial panel data of 30 provinces in China from 2011 to 2022, this study employs the entropy method and a coupling coordination model to quantify the coupling coordination degree between these sectors. Kernel density estimation and center of gravity–standard deviational ellipse analysis reveal spatiotemporal evolutionary patterns. Key findings include: ① Significant regional disparities exist in the development levels of both the health industry and the digital economy, with notable intra-regional variations among provinces. ② The coupling and coordination level of the health industry and digital economy development across China and within each region have shown a continuous growth trend. The regional levels are in the order of East > West > Central > Northeast, while the regional growth rates are East > Central > West > Northeast. Moreover, a polarization trend has emerged in the central and western regions. ③ The center of gravity of the spatial coupling coordination degree across the entire territory of China shows a clustering trend of moving towards the southeast. The spatial distribution pattern of the coupling coordination degree is in an east-northwest to west-southeast direction. The eastern and northeastern regions, respectively, show a dispersed and clustered trend of moving towards the southwest, while the central and western regions all show a clustered trend of moving towards the southeast. Based on this, policy suggestions are put forward for the deep integration and coordinated development of the health industry and the digital economy, with the aim of leveraging digital innovation to build a health sector that is socially inclusive, economically viable, and environmentally sustainable in the long term. Full article

With increasing emphasis on ecological conservation and food security, cultivated land issues have become more prominent. This study focuses on Jilin Province and uses nine prefecture-level administrative units and prefectures as the basic analytical units. Using continuous data for 2000–2023, this study analyzes the spatiotemporal evolution of cultivated land use efficiency (CLUE). By 2023, most regions had achieved ecological safety (ES), examined through their coupling and coordination. The Super-Efficiency SBM-DEA model and the Malmquist–Luenberger (ML) index were used to evaluate the static and dynamic changes in CLUE. A DPSIR–PLS-SEM integrated framework was applied to identify causal mechanisms influencing ES, while the TOPSIS method was employed to assess overall evolutionary trends. In addition, the coupling coordination degree (CCD) model combined with kernel density estimation (KDE) was used to characterize the interaction between CLUE and ES and their spatial evolution. Results indicated the following: (1) From 2000 to 2023, overall CLUE in Jilin Province showed an upward trend with fluctuations, while regional disparities narrowed and spatial distribution became more balanced. (2) The composite ES index increased from 0.3009 to 0.7900, accompanied by a marked expansion of areas classified as secure. (3) The CCD improved from a basic level to a high-quality coordination level, indicating enhanced synergistic development. Higher coordination was observed in central and eastern regions, whereas western and peripheral areas lagged. This study integrates multi-dimensional modeling approaches to systematically assess the coupled dynamics on cultivated land use efficiency and ecological safety, providing insights for land management and policy formulation. Full article

In densely populated and traffic-congested major cities, medical emergency rescue incidents occur frequently, making the use of drones for emergency medical supplies delivery a new emergency distribution method. However, establishing drone transportation networks in urban areas requires balancing spatiotemporal fluctuations in emergency needs, meeting hospitals’ mandatory constraints on response time, and addressing factors like airspace restrictions and weather impacts. By analyzing the spatiotemporal distribution characteristics of medical emergency logistics in large cities, this study constructs a drone base station location optimization model integrating dynamic and static factors. The model combines multi-source data including emergency needs, geographic information, and airspace limitations. It employs kernel density estimation to identify hotspot areas, uses DBSCAN clustering to detect long-term stable demand hotspots, and applies LSTM methods to predict short-term and sudden demand fluctuations. The model optimizes coverage rate, response time, and cost budget control for drone transportation networks through a multi-objective genetic algorithm. Using Guangzhou as a case study, the results demonstrate that through “dynamic-static” collaborative deployment and multi-model drone coordination, the network achieves 96.18% demand coverage with an average response time of 673.38 s, significantly outperforming traditional vehicle transportation. Sensitivity analysis and robustness testing further validate the model’s effectiveness in handling demand fluctuations, weather changes, and airspace restrictions. This research provides theoretical support and decision-making basis for scientific planning of urban medical emergency drone transportation networks, offering practical significance for enhancing urban emergency rescue capabilities. Full article

Rapid urbanization has intensified resource and population agglomeration while exacerbating urban-rural disparities. To address the long-standing dual structure, China advocates urban-rural integrated development (URID) to achieve common prosperity. However, the long-term evolutionary patterns and explanatory mechanisms of URID remain insufficiently explored, particularly at the county (district)-level in western China. This study constructed an entropy-weighted TOPSIS evaluation system combined with kernel density estimation and an optimal parameters-based geographical detector (OPGD) model to analyze the spatiotemporal evolution and explanatory mechanisms of URID in 26 counties (districts) of the Xi’an metropolitan area from 2010 to 2022. The results showed that: (1) URID levels increased steadily over the study period, forming a pronounced core-periphery gradient with faster improvement in national URID pilot counties. (2) Factor associations evolved from being dominated by a few dimensions to multidimensional coupling. Socioeconomic and geographical factors remained dominant and relatively stable, demographic influences were clearly stage specific, and the interaction between forest coverage and economic variables weakened over time. (3) Enhancing regional transport accessibility, optimizing land use efficiency, and fostering positive population-industry interaction are key pathways for promoting URID in the study area. Methodologically, this study introduces a “significance testing followed by threshold verification” logic into the OPGD model, refining the parameter-setting process and improving the robustness and q-value of factor detection. The findings enrich URID theory, provide county (district)-scale evidence for western China, and offer policy implications for optimizing factor allocation and promoting coordinated regional development. Full article

Analyzing the spatiotemporal evolution and drivers of multifunctional territorial spatial utilization efficiency (TSE) is essential for guiding the sustainable use of territorial space. This study develops an evaluation system integrating urban, agricultural, and ecological spatial utilization, and investigates the Yangtze River Delta (YRD) from 2000 to 2023 using kernel density estimation and the XGBoost–SHAP model. The main findings are as follows: (1) TSE in the YRD exhibits a sustained upward trajectory and a distinct east–west gradient. At the sub-dimensional scale, urban spatial utilization efficiency is clustered in southeastern core cities, agricultural spatial utilization efficiency is concentrated in the central transition zone, and ecological spatial utilization efficiency is highest in the northern areas. (2) The overall regional disparity in multifunctional TSE shows a fluctuating yet declining trend, indicating a gradual reduction in spatial inequality. The inter-provincial imbalance in development is identified as the primary cause of spatial differentiation in the YRD. (3) Topography, economic density, and population density are the leading determinants of TSE, while their interactions with socioeconomic variables generate nonlinear effects on efficiency improvement. These conclusions provide empirical support for spatial planning and efficiency-oriented territorial governance in the YRD. Full article

Oasis regions in arid northwestern China represent critical interfaces for watershed ecological security and rural sustainable development. However, under escalating resource constraints and intensifying human–land conflicts, the disorderly expansion of rural settlements has increasingly constrained high-quality territorial development. Liangzhou District, located in the transitional zone of the upper Heihe River Basin at the eastern end of the Hexi Corridor, provides a representative case for examining the spatial evolution of rural settlements in oasis environments. Using multi-temporal land-use data from 2000 to 2023, this study integrates landscape pattern metrics, kernel density estimation, and nearest-neighbor analysis to characterize the spatiotemporal evolution of rural settlements. The Markov–CLUE-S model is further applied to simulate land-use changes under three scenarios for 2035: natural development, new urbanization, and ecological protection. Results indicate that the number of rural settlement patches increased from 1598 to 3009, while their total area expanded from 10,321.83 hm² to 20,828.34 hm², demonstrating a sustained expansion trend and a transition from scattered distribution to increasingly clustered patterns along urban centers and major transportation corridors. Scenario simulations suggest that rural settlement areas will decline by 5.27 km², 12.13 km², and 11.68 km² under the three respective scenarios, predominantly converting to cropland, grassland, and urban construction land. Model validation yields a Kappa coefficient of 0.88, confirming high simulation accuracy. This study develops an integrated “pattern evolution–driving mechanism–scenario response” analytical framework for rural settlement dynamics in arid oasis regions, highlighting the combined influences of environmental constraints and socio-economic drivers. The findings provide a scientific basis for rural spatial optimization and watershed-scale territorial governance in arid regions. Full article

The tunneling face poses a significant risk for gas disaster in coal mining due to the complex interplay of geological conditions, ventilation strategies, and construction techniques, resulting in nonlinear and spatiotemporal dynamics in gas concentration distribution. Accurate prediction of gas levels is crucial for ensuring the safety of coal mining operations. This study introduces a novel approach for gas concentration forecasting at the tunneling face by integrating the Kolmogorov–Arnold Network (KAN) with an enhanced iTransformer model, leveraging multi-source sensor data for enhanced predictive capabilities. KAN improves the feature extraction ability due to flexible mapping kernel function that is capable of capturing complicated nonlinearities between gas emission volume and environmental variables. iTransformer, with concentrated attention mechanism and sparsity pattern, can further model very long-term sequence dependencies and learn to capture multi-scale features. As a whole, they address the problem of gradient vanishing and insufficient feature extraction in the temporal sequential prediction models based on deep learning methods with long sequences input, leading to significant improvements in prediction accuracy and model stability. Experiments on site monitoring datasets demonstrate that the proposed KAN + iTransformer model achieved better fitting and generalization capacity than two baseline models (iTransformer, Transformer) for gas concentration prediction. Full article

Against the backdrop of China’s “carbon peaking” and “carbon neutrality” goals, as well as the advancement of new industrialization, the oil and gas industry is undergoing a critical transformation from resource-dependent growth toward innovation-driven, low-carbon, and high-quality development. The integrated advancement of high-end, intelligent, and green transformation—collectively referred to as the “Three Modernizations”—has become a vital pathway for promoting industrial upgrading and sustainable growth. Based on panel data from 30 Chinese provinces from 2009 to 2023, this study constructs a comprehensive evaluation index system covering 19 secondary indicators across three dimensions: high-end, intelligent, and green development. Using the entropy-weighted TOPSIS method, kernel density estimation, Dagum Gini coefficient decomposition, and σ–β convergence models, the study examines the spatiotemporal evolution, regional disparities, and convergence characteristics of HIG integration, and further explores its driving mechanisms through a two-way fixed effects model and mediation effect analysis. The results show that (1) the overall HIG integration index rose from 0.34 in 2009 to 0.46 in 2023, forming a spatial pattern of “high in the east, low in the west, stable in the center, and fluctuating in the northeast”; (2) regional disparities narrowed significantly, with the Gini coefficient declining from 0.093 to 0.058 and σ decreasing from 7.114 to 6.350; and (3) oil and gas resource endowment, policy support, technological innovation, and carbon emission constraints all positively promote integration, with regression coefficients of 0.152, 0.349, 0.263, and 0.118, respectively. Heterogeneity analysis reveals an increasing integration level from upstream to downstream, with eastern regions leading in innovation-driven development. Based on these findings, the study recommends strengthening policy and institutional support, accelerating technological innovation, improving intelligent infrastructure, deepening green and low-carbon transformation, promoting regional coordination, and establishing a long-term monitoring mechanism to advance the integrated high-quality development of China’s oil and gas industry. Overall, this study deepens the understanding of the internal logic and spatial dynamics of the “Three Modernizations” integration in China’s oil and gas industry, providing empirical evidence and policy insights for accelerating the construction of a low-carbon, secure, and efficient modern energy system. Full article

Over the past decade, the rapid development of geospatial tools has significantly expanded the scope of archaeobotanical research, enabling unprecedented insights into ancient plant domestication, agricultural practices, and human-environment interactions. Within the Chinese context, where rich archaeobotanical records intersect with complex socio-ecological histories, GIS-driven approaches have revealed nuanced patterns of crop dispersal, settlement dynamics, and landscape modification. However, despite these advances, current applications remain largely exploratory, constrained by fragmented datasets and underutilized spatial-statistical methods. This paper argues that a more robust integration of large-scale archaeobotanical datasets with advanced ArcGIS functionalities—such as kernel density estimation, least-cost path analysis, and predictive modelling—is essential to address persistent gaps in the field. By synthesizing case studies from key Chinese Neolithic and Bronze Age sites, we demonstrate how spatial analytics can elucidate (1) spatiotemporal trends in plant use, (2) anthropogenic impacts on vegetation, and (3) the feedback loops between subsistence strategies and landscape evolution. Furthermore, we highlight the challenges of data standardization, scale dependency, and interdisciplinary collaboration in archaeobotanical ArcGIS. Ultimately, this study underscores the imperative for methodological harmonization and computational innovation to unravel the intricate relationships between ancient societies, agroecological systems, and long-term environmental change. Full article

Studying the coordination between urban economic resilience (ER) and high-quality tourism development (HQTD) is crucial to understanding tourism’s role in responding to economic shifts and driving urban economic transformation. Using 2010–2023 panel data from the Yangtze River Economic Belt (YREB) and a “measurement—evolution—disparity—diagnosis” framework, this study examines their coupling coordination via the coupling coordination degree (CCD) model, kernel density estimation, Gini coefficient decomposition, and influence coordination force index, elucidating spatiotemporal evolution, regional disparities, and drivers. The results show: (1) YREB synergies strengthened significantly, with ER and HQTD increasingly reinforcing each other; (2) Eastern coordination levels markedly exceeded central and western ones, reflecting persistent regional imbalances; (3) Coupling coordination converged toward higher levels, with inter-city gaps narrowing. Recommendations include enhancing regional coordination, balancing ecology and economy, fostering industrial innovation, and promoting social participation. This study provides empirical support for integrated, sustainable regional economic-tourism development. Full article

Conventional power load forecasting frameworks face limitations in dynamic spatial topology capture and long-term dependency modeling. To address these issues, this study proposes a hybrid GAT-CNN-LSTM architecture for enhanced short-term power load forecasting. The model integrates three core components synergistically: Graph Attention Network (GAT) dynamically captures spatial correlations via adaptive node weighting, resolving static topology constraints; a CNN-LSTM module extracts multi-scale temporal features—convolutional kernels decompose load fluctuations, while bidirectional LSTM layers model long-term trends; and a gated fusion mechanism adaptively weights and fuses spatio-temporal features, suppressing noise and enhancing sensitivity to critical load periods. Experimental validations on multi-city datasets show significant improvements: the model outperforms baseline models by a notable margin in error reduction, exhibits stronger robustness under extreme weather, and maintains superior stability in multi-step forecasting. This study concludes that the hybrid model balances spatial topological analysis and temporal trend modeling, providing higher accuracy and adaptability for STLF in complex power grid environments. Full article