Search Results (2,544)

Long-term operation of drip emitters under sediment-laden water conditions readily induces particle deposition and clogging, leading to discharge reduction and deterioration of irrigation uniformity. To clarify the temporal evolution and spatial distribution of clogging and to support structure-oriented anti-clogging improvement, three integrated drip tape emitters with different labyrinth-channel geometries were tested at sediment concentrations of 1, 2, and 3 g·L⁻¹ under a constant pressure of 100 kPa. The average relative discharge ratio (Dra) and Christiansen’s uniformity coefficient (CU) were continuously monitored, and cross-sectional observation and numerical simulation were combined to identify dominant deposition hotspot regions within the labyrinth channel. The results showed that increasing sediment concentration significantly accelerated clogging development and shortened operating lifetime. At 1 g·L⁻¹, the times required for the three emitter types to reach the clogging criterion of Dra < 75% were 120, 81, and 107 h, respectively, whereas at 3 g·L⁻¹ these values decreased to 39, 42, and 39 h. CU continuously declined with operating time and, in some treatments, responded earlier than Dra to system deterioration. Sediment deposition was mainly concentrated in the inlet section and bend regions, indicating that these locations were the dominant hotspots for clogging initiation and propagation. These findings demonstrate that clogging in drip emitters is jointly regulated by sediment load and labyrinth-channel geometry, and that hotspot-based structural optimization provides an effective basis for improving anti-clogging performance under sediment-laden water conditions. Full article

This study utilizes digital transformation and patent data from A-share listed companies on the Shanghai and Shenzhen stock exchanges in China between 2011 and 2021 to examine the influence of digital transformation on the quality of cross-regional collaborative innovation. The findings reveal that the cooperative innovation network exhibits pronounced small-world characteristics. In terms of spatio-temporal evolution, China’s urban collaborative innovation network demonstrates a notable quadrilateral spatial structure and has evolved toward a multicenter pattern. Moreover, the advancement of digital transformation positively contributes to both the quality and quantity of cross-regional cooperative innovation. By enhancing the relational embeddedness among cities, digital transformation facilitates improved outcomes in collaborative innovation. Furthermore, when the volume of digital patent applications surpasses a certain threshold, its positive effect on the quality of cross-regional collaborative innovation accelerates. These results provide empirical evidence from a major emerging economy, offering insights that can inform policies and strategies in other regions undergoing digital transition. The mechanisms identified, such as network structure evolution and relational embeddedness, contribute to a broader understanding of how digital transformation shapes innovation dynamics across geographical boundaries in a globalized knowledge economy. Full article

The Camino de Santiago is one of the most important cultural routes in the world and a privileged laboratory for analysing the challenges of sustainability in long-distance heritage destinations. The aim of this research is to understand the underlying dynamics of the Way, as well as its degree of sustainability. To achieve this, we examine the recent evolution of tourist demand for the Way from a territorial and sustainability perspective, integrating official statistical data with digital interest indicators from Google Trends (2004–2025). The methodology combines quantitative analyses of trends, seasonality, spatial diversification and internationalisation of demand, applying robust techniques such as the Theil–Sen slope and the Mann–Kendall test. The results show structural growth and high resilience of the Jacobean tourism system, even after the disruption caused by COVID-19, together with a growing internationalisation of flows. However, this tourism success is accompanied by strong spatial and temporal imbalances, with a marked concentration on the French Way and in the summer months, which increases environmental and social pressure on the most travelled territories. The analysis of digital interest also reveals a progressive decline in the importance of Holy Years as a driving force for attraction, especially in international markets. Full article

Continuous monitoring of sitting posture is crucial for ergonomic assessment and fatigue prevention, yet many existing approaches rely on vision-based systems or single-modality sensing that are limited in capturing spatial and temporal biomechanical dynamics. This paper presents a multimodal smart-skin sensing system for spatial and temporal ergonomic fatigue analysis in sitting postures. The proposed platform integrates 42 distributed pressure, temperature, and vibration sensors arranged in 14 trimodal sensing nodes embedded across anatomical seating and back regions to enable real-time multimodal acquisition of human–chair interaction patterns. The study introduces an analytical framework combining anatomical heatmap visualization, temporal evolution analysis, delta pressure mapping, fatigue intensity estimation, and hotspot detection to characterize dynamic pressure redistribution during prolonged sitting. Experimental evaluations were conducted using a biomechanical mannequin and a single human participant with identical anthropometric characteristics (165 cm height and 62 kg body mass) across nine seated conditions, including neutral sitting, reclining, leaning, periodic shifting, and vibration-induced motion. Each posture condition was recorded as a time-series session and segmented into temporal phases to analyze fatigue evolution during prolonged sitting. Statistical analysis of pressure redistribution dynamics indicates significantly higher pressure drift in human measurements compared with the mechanically stable mannequin baseline (p < 0.001). The proposed framework provides a scalable sensing approach for ergonomic monitoring, intelligent seating systems, and human–machine interface applications. Full article

This study explores the coupling relationship between wetland ecotourism and resource–environmental carrying capacity in the Dongting Lake region. By constructing a comprehensive index system and utilizing a coupling coordination degree model, we analyzed the temporal and spatial evolution characteristics across 24 districts and counties from 2014 to 2022. The results indicate the following: (1) The quality of both ecotourism and environmental carrying capacity has steadily improved, though significant regional disparities remain. (2) The coupling coordination degree exhibits a “high in the center, low in the periphery” spatial pattern, showing a positive correlation between ecotourism levels and environmental capacity. (3) The region comprises three development types: balanced coordination, well-matched, and lagging. These findings provide a scientific basis for optimizing ecotourism pathways and achieving high-quality regional sustainable development. Full article

Accurately assessing dynamic water resource carrying capacity (WRCC) is essential and challenging, particularly in regions like the Gansu sections of the Yellow River Basin (GSYRB), a core water source protection zone in the arid northwest of China, due to its pressing challenge of balancing water resources for socioeconomic needs and ecological security. This study proposes a novel integrated computational assessment framework named SD-VIKOR to address the complexities arising from nonlinear interactions within the “water resources–socioeconomic–ecological environment” (W–S–E) system. The core of this framework is the tight coupling of a system dynamics (SD) simulation model with a VIKOR multi-criteria evaluation module, where indicator weights are objectively–subjectively determined via an Analytic Hierarchy Process (AHP)–entropy weight method. This integrated SD-VIKOR engine enables dynamic, scenario-based WRCC trajectory simulation. To move beyond simulation and enable mechanistic insight, the framework further incorporates a diagnostic suite: a Geodetector module quantifies dominant drivers and their interactions; an obstacle degree model pinpoints key limiting factors; and a coupling coordination degree model evaluates subsystem synergies. Together, they form a closed-loop “dynamic simulation → multi-criteria assessment → driving mechanism analysis and constraint diagnosis → subsystem coordination analysis” workflow. Applied to the GSYRB from 2012 to 2030 under five development scenarios, the framework demonstrated high efficacy. It successfully captured path-dependent WRCC evolution, revealing that the ecological-priority scenario (B2), which shifts system drivers from economic-scale expansion to resource-efficiency and environmental governance, yielded optimal WRCC and the highest system coordination. In contrast, business-as-usual and single-minded economic expansion scenarios underperformed. Six key obstacle factors were quantitatively identified, linking WRCC constraints to natural endowments, economic patterns, and domestic demand. The results reveal pronounced spatial–temporal heterogeneity in WRCC across the GSYRB, with socioeconomic development, water resource use efficiency, and ecological conditions acting as the primary joint drivers of WRCC evolution. Critically, several key indicators are identified as persistent constraints on regional water sustainability. In contrast to conventional static evaluations, the integrated framework captures the complex dynamics and multi-subsystem interactions governing WRCC, offering a more robust diagnostic of resource–environment systems. These insights provide a transferable analytical basis for designing sustainable water management strategies in arid river basins. Full article

Robust and transferable approaches for evaluating research capacity—whose measurable expression is reflected in research output—are essential for evidence-based science policy and strategic research management. This study develops an integrated framework to assess global scholarly capacity and regional disparities by combining semantic-similarity-based literature filtering, bibliometric mapping, dynamic performance assessment, and spatial analytical techniques into a coherent and replicable model. A Sentence-BERT model ensures thematic precision and dataset consistency, while CiteSpace 6.1.R3 is used tomap publication trajectories, thematic evolution, and influential contributors. A dynamically weighted TOPSIS model incorporates temporal variation to quantify national research capacity, and spatial analyses—including gravity center analysis, Theil index decomposition, spatial autocorrelation, gray relational analysis, and the Geographical Detector Model—identify disparity patterns and their explanatory associations. Applied to urban ecosystem security research (2001–2023), an emerging interdisciplinary field within sustainability science, the framework shows that China and the United States dominate research output, whereas European journals exert strong academic influence. The field has advanced through three stages, with increasing emphasis on ecosystem services and sustainable development. GDP, environmental pressure, and urbanization rate show the strongest explanatory associations with research capacity, and interactive effects—especially those involving GDP—exceed single-factor explanatory strength. Ecological baseline conditions such as NDVI and climate exhibit only limited associations, functioning mainly as contextual factors. Policy implications highlight four priorities: strengthening interdisciplinary and cross-regional collaboration in developing regions; promoting equity-oriented research agendas in developed regions; establishing unified definitions and validated evaluation frameworks; and advancing dynamic, systems-based approaches to ecosystem security analysis. By shifting attention from ecological status assessment to the dynamics of scientific knowledge production and research capacity, this study advances methodological foundations for research evaluation and enriches analytical approaches in urban ecosystem security, offering a generalizable framework for identifying capacity differences and supporting evidence-informed policy design. Full article

Prolonged water injection in unconsolidated sandstone reservoirs can induce pore rearrangement and modify flow pathways, thereby affecting reservoir performance. However, quantitative characterization of pore evolution in both temporal and spatial dimensions remains limited. This study investigates the mechanisms of pore-structure evolution during extended injection through a series of multi-scale experiments. Scanning electron microscopy and X-ray diffraction analyses were employed to compare mineral composition and microstructural characteristics before and after injection, while in situ nuclear magnetic resonance (NMR) monitoring captured the dynamic evolution process, enabling pore-size classification from T₂ spectra and fractal assessment of structural complexity. Segmented NMR measurements at different distances further resolved spatial heterogeneity. The results show that prolonged water injection reduced permeability by 10.4–32.1%, whereas porosity exhibited only minor variation, indicating that the decline in flow capacity is primarily controlled by pore–throat structural adjustment rather than pore volume loss. Mineralogical redistribution and fine-particle migration decreased the median pore radius by 21.5–51.8% and the micropore fractal dimension by 23.8–76.5%, with stronger responses observed at higher permeabilities, while meso- and macropore fractal dimensions remained nearly unchanged, indicating preferential modification of micropores with preservation of the main connected flow framework. Consistently, NMR responses reveal pronounced spatial heterogeneity along the flow direction. The NMR signal changes at the injection end were 11.2–18.4% and 7.7–21.7% during the early and intermediate stages, respectively, both exceeding those at the distal end (2.9–12.4% and 1.9–17.1%). These results indicate a downstream-attenuating structural modification gradient. The findings provide new insights into pore-structure evolution during prolonged water injection and offer a scientific basis for optimizing water-injection strategies in unconsolidated sandstone reservoirs. Full article

This study systematically analyzes the spatio-temporal evolution, trade-offs, synergies and driving mechanisms of five ecosystem services (ESs) in Fujian Province (carbon storage, CS; habitat quality, HQ; sediment delivery ratio, SDR; water yield, WY; food provision, FP) based on multi-source data from 2003, 2013 and 2023 by adopting the InVEST model, Spearman correlation analysis, geographically weighted regression (GWR), self-organizing maps (SOM) and geographic detectors. Results show that: (1) ESs present a spatial pattern of “high in northwest and low in southeast” in Fujian; CS, HQ and FP show an overall decline, while SDR and WY increase significantly. (2) ES trade-offs and synergies have obvious scale effects and spatial heterogeneity, with stronger relationship intensity at the county level than the grid level, and FP generally shows a trade-off relationship with other services. (3) Land use is the key driving factor for CS, FP and HQ; precipitation dominates the changes in WY and SDR; and dual-factor interactions generally enhance the explanatory power of ES changes. The findings enrich the theoretical system of multi-scale ES trade-off and synergy research under rapid urbanization and provide a scientific basis for sustainable territorial spatial planning and differentiated ecological governance in Fujian. Meanwhile, the research framework can serve as a reference for ES management in other coastal mountainous regions worldwide, contributing to the realization of regional sustainable development goals (SDGs). Full article

This paper proposes an innovative research algorithm “measurement—pattern—driving force—synergy” that determines the eco-efficiency of 83 Russian federal subjects (2000–2019) using the Slacks-Based Measure (SBM) model with non-desired outputs (incorporating comprehensive input indicators such as water resources and electricity input, and dual non-desired outputs of waste gas and wastewater). Combined with hot spot analysis, a gravity center model, and panel Tobit regression, we reveal the temporal-spatial evolution and driving mechanisms of eco-efficiency in resource-based economies. The research finds that the overall eco-efficiency of Russia is at a medium level and shows a dynamic correlation with the economic development stage. In the early stage of the period under review, there was a high degree of synergy, but the efficiency declined during the period of rapid economic growth. Later, it rebounded somewhat in tie with technological progress. Spatially, it presents a special pattern of low efficiency in the western European industrialized regions and high efficiency in the Arctic and Far East peripheral regions, reflecting the spatial heterogeneity of resource-dependent economies and the survival-constrained efficiency feature. The analysis of influencing factors indicates that per capita GDP has a significant positive driving effect on eco-efficiency, but the expansion of residents’ consumption, the improvement of education level and the dependence on foreign trade all have inhibitory effects, highlighting the path dependence of the current growth model on the structure of resource consumption. The research suggests that Russia should implement differentiated spatial governance in the future, promote the green transformation of consumption and trade structures, and strengthen the ecological orientation of the education and scientific research system to achieve a fundamental transformation of regional sustainable development from survival constraints to innovation-driven. Full article

Clarifying the spatiotemporal evolution and driving mechanisms of bike-sharing-to-metro feeder trips (BSMF) is key to optimizing urban public transport’s first-and-last-mile connectivity and advancing low-carbon development. Existing studies on BSMF mostly ignore spatiotemporal heterogeneity, lack in-depth exploration of multi-factor interaction effects, and have subjective stratification or model specification bias, which hinder the accurate depiction of BSMF’s complex evolutionary patterns. Taking Xi’an as a case with 126 metro stations as analysis units, this study integrates multi-source data including shared bike trip records, metro network and built environment attributes to address the above issues. A framework combining kernel density estimation, spatial autocorrelation analysis, Optimal Parameter Geographic Detector (OPGD) and Geographically and Temporally Weighted Regression (GTWR) models (OPGD-GTWR) is constructed to identify BSMF’s spatiotemporal patterns, screen key influencing factors and reveal their spatiotemporal heterogeneity and interactive mechanisms. Results show Xi’an’s BSMF trips feature a “double-peak and double-valley” temporal tidal pattern and core-periphery spatial agglomeration. The OPGD-GTWR model (R² = 0.853) outperforms traditional models in capturing spatiotemporal heterogeneity. These findings provide empirical evidence and refined references for shared mobility resource allocation, bike-metro integration improvement and transit-oriented urban planning. Full article

In the context of the “dual carbon” goals, this study examines the spatiotemporal patterns and evolution of urban low-carbon coordinated development (LCCD). Based on the integrated Economy–Energy–Environment–Society (3E1S) framework, this study constructs a multidimensional evaluation index system for urban LCCD and applies a composite system coordination degree model to quantitatively assess and analyze the spatiotemporal evolution of LCCD across 271 prefecture-level and above cities in China from 2005 to 2020. The results indicate that (1) from a temporal perspective, the level of urban LCCD in China exhibits an overall upward trend during the study period, with relatively rapid growth from 2005 to 2015, a subsequent slowdown after 2015, and a stage-wise decline observed in 2020, reflecting a transition from rapid improvement to gradual adjustment; (2) from a spatial perspective, urban LCCD demonstrates a certain degree of spatial autocorrelation and an overall spatial structure characterized by a southwest–northeast-oriented axis, with spatial agglomeration features gradually strengthening over time; (3) from a system structure perspective, the coordinated evolution of the 3E1S subsystems shows clear differentiation, with the energy and economic subsystems following an inverted U-shaped trajectory, the environmental subsystem exhibiting a fluctuating upward trend, and the social subsystem maintaining continuous improvement, highlighting the inherent imbalance in the multidimensional process of subsystem coordination. From a multisystem coordination perspective, this study systematically identifies the spatiotemporal evolutionary characteristics and subsystem coupling relationships of urban low-carbon coordinated development, providing empirical evidence for a deeper understanding of multidimensional low-carbon coordination processes in cities. Full article

Ground deformation is a major geohazard in many urban areas, requiring reliable monitoring and forecasting for hazard mitigation. Although Multi-Temporal InSAR enables high-resolution deformation monitoring, most prediction approaches rely on single-point modeling and fail to exploit spatial dependencies within deformation fields. This study proposes a spatiotemporally synchronous prediction framework for large-scale InSAR deformation fields, integrating sequence preprocessing, spatiotemporal modeling, and deformation pattern analysis. First-order differencing reduces sequence non-stationarity, while a patch-based encoder-decoder structure preserves spatial topology during dimensionality reduction. The core prediction model, built on PredRNNv2, captures the long-term spatiotemporal evolution of InSAR deformation sequences. In addition, independent component analysis (ICA) combined with K-means clustering identifies dominant deformation patterns and their geological associations. The framework is evaluated using synthetic datasets simulating multiple deformation mechanisms and Sentinel-1 InSAR time-series data over the Beijing Plain from 2015 to 2025. Results show that the model accurately captures deformation evolution and identifies transitions associated with groundwater regulation. These findings demonstrate the potential of deep spatiotemporal learning for large-scale InSAR deformation prediction and geohazard mechanism interpretation. Full article

Surface texture plays a key role in pavement safety and performance, yet its degradation is influenced by multiple interacting factors that vary across road networks. This study developed statistical models to characterize and predict surface texture evolution on Portuguese highways using linear mixed-effects modeling. Texture measurements collected on 7204 pavement sections, each 100 m in length, over three monitoring cycles were analyzed alongside traffic, climatic, pavement structural, geometric, and spatial variables. The hierarchical structure of the data, with repeated measurements nested within pavement sections, was explicitly accounted for via random intercepts and random slopes. At the same time, temporal correlation was modeled via an autoregressive error structure. Two model specifications were evaluated: a model including only traffic and climatic variables and an extended model incorporating pavement and geometric characteristics. Results indicate that texture evolution is statistically associated with cumulative traffic loading, temperature-related indicators, precipitation, surface course type, lane position, vertical alignment, and altitude. The extended model showed a significantly better fit and superior predictive performance, as confirmed by information criteria and cross-validation metrics. The findings highlight the importance of accounting for section-level heterogeneity and roadway characteristics when modeling texture degradation. The proposed modeling framework provides a statistically scalable and robust tool for texture prediction, accounting for regional-specificities and long-term pavement management decisions. Full article

Vegetation coverage is a critical indicator for assessing urban ecosystems and is essential for sustainable development. However, the evolution patterns and driving mechanisms of vegetation change at the urban agglomeration scale remain underexplored. This study used the Google Earth Engine (GEE) to compute the kernel Normalized Difference Vegetation Index (kNDVI) for the Beibu Gulf Urban Agglomeration (BGUA), an important emerging coastal urban cluster in southern China, from 2000 to 2022. Trend analysis was employed to examine spatiotemporal changes in kNDVI, and an interpretable machine learning framework was applied to quantify the nonlinear, spatially heterogeneous effects of environmental and anthropogenic drivers. The results show that (1) kNDVI showed a general increasing trend, with medium-to-high kNDVI predominating. Approximately 91.91% of the region maintained an improving trend, whereas vegetation degradation concentrated in the core urban areas. (2) The Categorical Boosting model demonstrated superior performance in predicting kNDVI compared to other machine learning models. (3) The SHAP analysis identified land cover, elevation, and nighttime lights as the primary determinants of kNDVI change. These factors exhibited significant spatial heterogeneity in their nonlinear effects. These findings provide theoretical insights and practical guidance for ecological planning and environmental management in urban agglomerations. Full article