Search Results (817)

Ecosystem vulnerability assessment is paramount for local environmental stability and lasting economic progress. This study selects Hubei Province as the research area, applying multi-source spatiotemporal datasets spanning the period 2000–2023. A pressure–state–response (PSR) framework, incorporating 14 distinct indicators, was developed. The selection criteria for these indicators adhered to principles of scientific rigor, all-encompassing scope, statistical representativeness, and practical applicability. The chosen indicators effectively encompass natural, anthropogenic, and socio-economic drivers, aligning with the specific ecological attributes and key vulnerability factors pertinent to Hubei Province. The analytic network process (ANP) method and entropy weighting (EW) method were integrated to ascertain comprehensive weights, thereby computing the ecological vulnerability index (EVI). In the meantime, we analyzed temporal and spatial EVI shifts. Spatial autocorrelation analysis, the geodetic detector, the Theil–Sen median, the Mann–Kendall trend test, and the Grey–Markov model were employed to elucidate spatial distribution, driving factors, and future trends. Results indicate that Hubei Province exhibited mild ecological vulnerability from 2000 to 2023, but with a notable deteriorating trend: extreme vulnerability areas expanded from 0.34% to 0.94%, while moderate and severe vulnerability zones also increased. Eastern regions demonstrate elevated vulnerability, but they were lower in the west, correlating with human activity intensity. The global Moran’s I index ranged from 0.8579 to 0.8725, signifying a significant positive spatial correlation of ecological vulnerability, with the highly vulnerable areas concentrated in regions with intense human activities, while the less vulnerable areas are located in ecologically intact areas. Habitat quality index and carbon sinks emerged as key drivers, possibly stemming from the forest–wetland composite ecosystem’s high dependence on water conservation, biodiversity maintenance, and carbon storage functions. Future projections based on Grey–Markov models indicate that ecological fragility in Hubei Province will exhibit an upward trend, with ecological conservation pressures continuing to intensify. This research offers a preliminary reference basis of grounds for ecological zoning, as well as sustainable regional development in Hubei Province, while also providing a theoretical and practical framework for constructing an ecological security pattern within the Yangtze River Economic Belt (YREB) and facilitating ecological governance in analogous river basins globally, thereby contributing to regional sustainable development goals. Full article

As an important industrial organizational form in the era of the digital economy, digital industry agglomeration exerts a profound impact on urban ecological resilience. Using panel data of 281 prefecture-level cities in China from 2011 to 2021, this study measures the level of digital industry agglomeration by means of the location entropy method, and constructs an urban ecological resilience evaluation system based on the “Pressure-State-Response (PSR)” model. It systematically examines the impact effects and action mechanisms of digital industry agglomeration on urban ecological resilience. The results show that: (1) The spatio-temporal evolution of the two presents a gradient pattern of “eastern leadership and central-western catch-up”, and their spatial correlation deepens over time, with the synergy maturity in the eastern region being significantly higher than that in the central and western regions. (2) Digital industry agglomeration significantly promotes the improvement in urban ecological resilience, and this conclusion remains valid after endogeneity treatment and robustness tests. (3) The promotional effect is more prominent in central cities, coastal cities, and key environmental protection cities, whose advantages stem from digital infrastructure and innovation endowments, industrial synergy and an open environment, and the adaptability of green technologies under strict environmental regulations, respectively. (4) Digital industry agglomeration empowers ecological resilience by driving green innovation and improving the efficiency of land resource allocation, while the construction of digital infrastructure plays a positive regulatory role. Full article

With rapid urbanization, issues such as blind planning, disorder, and inefficiency in urban construction and land use have become increasingly prominent. To address these challenges, this study proposes a comprehensive suitability evaluation framework for urban construction land, using Zhengzhou City as a case study. The evaluation system incorporates five dimensions: topography, transportation, location, current land use status, and soil clay content. A hybrid weighting method, combining the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM), was employed to determine indicator weights. The research indicates that the suitability of the construction land can be classified into four categories: highly suitable, moderately suitable, critically suitable, and unsuitable. Among them, the highly suitable area accounted for 6.907% (502.71 km²), the moderately suitable area accounted for 81.668% (5943.54 km²), the critically suitable area accounted for 11.422% (830.98 km²), and the unsuitable area only accounted for 0.003% (0.18 km²). The results show that most areas in Zhengzhou City are highly suitable or moderately suitable for construction land, while Gongyi and Dengfeng, due to their complex terrain and long distances from the city center, are mostly in the critically suitable or unsuitable construction land. This evaluation result is in good agreement with the actual situation and can offer valuable insights for sustainable urban development. Full article

According to existing procedures for defining the velocity distribution across cross profile sections of watercourses (e.g., Entropy theory and Power Law theory), surface velocity is a key input parameter, together with cross-sectional bathymetry. Field measurements to obtain velocity values and their distributions are often difficult due to limited equipment, unreliable data, missing data, or hazardous conditions such as flooding and inaccessible locations. This creates a strong need for alternative approaches to measuring surface velocities in rivers. The application of unmanned aerial vehicles (UAVs), mobile phones, and traditional field instruments such as the Propeller Current Meter (PCM) can significantly improve measurement efficiency, especially in situations where conventional methods are not feasible. This paper presents an algorithm for comparing these measurement approaches and quantifying their differences. The methodology is demonstrated using a real case study on the Bednja River in Croatia, which flows through alluvial deposits. The results show that video-based surface velocity estimation using UAV and mobile phone imagery is feasible under real river conditions. Still, its accuracy depends strongly on flow conditions and surface characteristics. While UAV recordings provide reliable results in fast and turbulent flows, mobile phone videos yield more stable performance in smoother flow conditions, where additional surface texture is available from natural tracers. Full article

Mobile edge-AI plant monitoring systems enable automated disease control in greenhouses and open fields, reducing dependence on manual inspection and the variability of visual diagnostics. This paper proposes a lightweight two-stage edge-AI system for strawberries, in which a YOLOv10n detector on board a mobile agricultural robot locates leaves affected by seven common diseases (including Leaf Spot) with real-time capability on an embedded platform. Patches are then automatically extracted for leaves classified as Leaf Spot and transmitted to the second module—a compact MobileViT-S-based classifier with ordinal output that assesses the severity of Leaf Spot on three levels (S1—mild, S2—moderate, S3—severe) on a specialised set of 373 manually labelled leaf patches. In a comparative experiment with lightweight architectures ResNet-18, EfficientNet-B0, MobileNetV3-Small and Swin-Tiny, the proposed Ordinal MobileViT-S demonstrated the highest accuracy in assessing the severity of Leaf Spot (accuracy ≈ 0.97 with 4.9 million parameters), surpassing both the baseline models and the standard MobileViT-S with a cross-entropy loss function. On the original image set, the YOLOv10n detector achieves an mAP@0.5 of 0.960, an F1 score of 0.93 and a recall of 0.917, ensuring reliable detection of affected leaves for subsequent Leaf Spot severity assessment. The results show that the “YOLOv10n + Ordinal MobileViT-S” cascade provides practical severity-aware Leaf Spot diagnosis on a mobile agricultural robot and can serve as the basis for real-time strawberry crop health monitoring systems. Full article

This work focuses on the development of a patient-specific transient CFD–FSI numerical model combined with the Time-Averaged Entropy Generation Rate (TAEGR) to predict hemodynamic parameters in the thoracic aorta, including the Oscillatory Shear Index (OSI) and the Time-Averaged Wall Shear Stress (TAWSS). While arterial blood flow can be modeled assuming either rigid or elastic arterial walls, the effect of wall compliance on these parameters, particularly on TAEGR, remains insufficiently characterized. Moreover, the interpretation of established indicators is not unique, as regions of vascular relevance may correspond to either high or low values of OSI and TAWSS. The proposed approach aims to identify symmetry and asymmetry in shear stress and entropy generation within the arterial wall, which are closely associated with the development of atherosclerotic plaque. Four aortas from clinical patients were analyzed using the proposed numerical framework to investigate blood flow behavior. The results revealed regions with high values of the hemodynamic parameters (OSI > 0.15, TAWSS ≥ 2 Pa, and TAEGR ≥ 20 $\mathrm{W}/{\mathrm{m}}^3\mathrm{K}$) predominantly located in the vicinity of the upper arterial branches. These regions, referred to as critical zones, are considered prone to the development of cardiovascular diseases, particularly atherosclerosis. The proposed numerical model provides a reliable qualitative framework for assessing symmetry and asymmetry in aortic blood flow patterns under different surgical conditions. Full article

This study focuses on a bulb tubular pump to clarify the flow characteristics and energy loss laws of low-lift tubular pumps under variable speed regulation and addresses deviations from optimal operating conditions in complex scenarios. For two typical rotational speeds, a full-flow passage model was established; the SST k-ω turbulence model was used to solve 3D incompressible viscous flow, energy loss was analyzed via entropy production theory, and simulations were experimentally validated. The results showed the following: pump efficiency exhibited a “first rise then fall” trend, head decreased monotonically with flow rate, and the optimal operating point shifted to lower flow rates at slower speeds. Meanwhile, local entropy production rate effectively characterized loss location and intensity, with aggravated off-design loss concentrated near the hub and rim along the spanwise direction and within 30 mm of the near-wall region. This study clarifies core energy loss mechanisms, providing a quantitative basis for operation optimization and structural improvement to support the safe, economical operation of low-lift pump stations. Full article

Integrated urban–rural development is an inevitable requirement of regional development. Developing green industries based on rural ecological resources are important approaches to promoting urban–rural integration. The National Key Ecological Function Zones (NKEFZ) policy focuses on safeguarding national ecological security. However, whether the resulting ecological improvements can, through the realization of ecological value, provide momentum for urban–rural integration remains unclear in existing research. This study uses a sample of 284 prefecture-level cities in China from 2006 to 2023, treating the establishment of NKEFZ as a quasi-natural experiment. First, the study constructs a “Driving-constraining” bidirectional theoretical framework, and then uses the entropy weight method to measure the level of urban–rural integration, which is selected by 18 sub-indicators from the populational, spatial, and economic dimensions. Finally, a multi-period difference-in-differences (DID) model is constructed to test the impact of NKEFZ on urban–rural integration, and the transmission mechanisms and heterogeneity are explored. The results indicate the following: (1) Following the implementation of the NKEFZ policy, it shows an overall inhibitory trend on urban–rural integration, consequently slowing the progress of urban–rural integration. The inhibitory effects are particularly pronounced in spatial and economic integration dimensions, and these results are robust. (2) Constrained industrial upgrading and increased fiscal pressure on local governments are the main mechanisms behind the slowed urban–rural integration. (3) Due to differences in policy coverage and the heterogeneous characteristics of city locations, the negative effects of the policy are more pronounced in cities with a high proportion of key ecological function counties, as well as in prefecture-level cities in central and western regions. Based on these findings, it is suggested to promote high-quality urban–rural integration in eco-priority areas through pathways such as developing ecological industries, improving the ecological compensation system, and clarifying central–local collaborative governance. Full article

Mobile Global Positioning System (GPS) data offer a promising approach to inferring mental health status through behavioural analysis. Whilst previous research has explored location-based behavioural indicators including location clusters, entropy, and variance, persistent GPS measurement errors have compromised data reliability, limiting the practical deployment of smartphone-based digital phenotyping systems. This study develops and validates an algorithmic preprocessing method designed to mitigate inherent GPS measurement limitations in mobile health applications. We conducted comprehensive evaluation through controlled experimental protocols and naturalistic field assessments involving 38 participants over a seven-day period, capturing GPS data across diverse environmental contexts on both Android and iOS platforms. The proposed preprocessing algorithm demonstrated exceptional precision, consistently detecting major activity centres within an average 50-metre margin of error across both platforms. In naturalistic settings, the algorithm yielded robust location detection capabilities, producing spatial patterns that reflected plausible and behaviourally meaningful traits at the individual level. Cross-platform analysis revealed consistent performance regardless of operating system, with no significant differences in accuracy metrics between Android and iOS devices. These findings substantiate the potential of mobile GPS data as a reliable, objective source of behavioural information for mental health monitoring systems, contingent upon implementing sophisticated error-mitigation techniques. The validated algorithm addresses a critical technical barrier to the practical implementation of GPS-based digital phenotyping, enabling the more accurate assessment of mobility-related behavioural markers across diverse mental health conditions. This research contributes to the growing field of mobile health technology by providing a robust algorithmic framework for leveraging smartphone sensing capabilities in healthcare applications. Full article

China’s National Eco-Industrial Parks (NEIPs) represent a significant policy intervention designed to achieve the synergistic reduction in pollution and carbon emissions. While previous studies have examined the impacts of NEIPs on pollution and carbon emissions in isolation, research on their synergistic reduction is still limited. This study constructs a Carbon-Pollution Co-Reduction Index (CPCRI) with weights determined by the entropy weight method (EWM) to capture the joint performance of emission intensities. By applying a staggered difference-in-differences (SDID) model to city-level panel data from the Yangtze River Delta between 2003 and 2021, the study finds that NEIPs significantly improve the CPCRI of cities where NEIPs are located by 2.30 percentage points. This positive effect exhibits a time lag, becoming statistically significant three years after establishment and strengthening thereafter. Mechanism analyses indicate that the synergistic reductions are driven by technological innovation and reduced energy intensity, while heterogeneity analyses reveal that the policy effect is more pronounced in economically developed provinces and larger cities but has diminished in recent years. Then, a coupling coordination degree (CCD) is integrated to construct a new index to capture both joint performance and synergy between reductions. These findings provide robust empirical support for NEIPs as a practical policy tool to achieve sustainable industrial transformation in the Yangtze River Delta. Full article

Against the backdrop of China’s transition from the eradication of absolute poverty toward the pursuit of common prosperity, equitable access to energy has become an increasingly important policy concern. This study develops a multidimensional framework to assess energy poverty from three interrelated dimensions: energy use level, energy structure, and energy capability. Using panel data for 30 provincial-level regions from 2005 to 2020, a provincial energy poverty index (EPI) is constructed based on the entropy-weighting approach. The spatial and temporal dynamics of energy poverty are examined using Moran’s I, the Dagum Gini decomposition, kernel density estimation, and spatial Markov chain analysis. The results reveal several key patterns. (1) Although energy poverty has declined nationwide, it remains pronounced in parts of western, central, and northeastern China. (2) Energy poverty exhibits significant spatial clustering, with high-poverty clusters concentrated in resource-dependent regions such as Shanxi and Inner Mongolia, while low-poverty clusters are mainly located along the eastern coast. (3) Regional disparities follow an inverted U-shaped trajectory over time, with east–west differences constituting the primary source of overall inequality. (4) Moreover, the evolution of energy poverty displays strong path dependence and club convergence. These findings highlight the need to strengthen dynamic monitoring and governance mechanisms, promote region-specific clean energy development, and enhance cross-regional coordination to support energy security and green transformation under China’s “dual-carbon” objectives. Full article

Anomaly detection using multivariate time-series data remains a significant challenge for complex industrial systems, such as Cyber–Physical Systems (CPSs), Industrial Control Systems (ICSs), Intrusion Detection Systems (IDSs), the Internet of Things (IoT), and Remote Sensing Monitoring Platforms, including satellite Earth observation systems and Mars Rovers. In these systems, sensors are highly interconnected, and local anomalies frequently affect multiple components. Because these interconnections are often implicit and involve complex interactions, systematic characterization is required. To address this, our study employs graph neural networks with a structure-entropy-based attention mechanism, which models multi-element relationships and formally represents implicit relationships within complex industrial systems using a network-based structural model. Specifically, our method distinguishes the weights of different high-order neighbor nodes based on their locations, rather than treating all nodes equally. Through this formalization, we identify and represent key adjacent elements by analyzing system entropy. We validate our method on SMAT, MSL, SWaT, and WADI datasets, and experimental results demonstrate improved detection performance compared to baseline approaches. Full article

The accurate extraction of damage information around airport runways is crucial for the rapid development of subsequent damage effect assessment work and the timely formulation of the ensuing operational plan. However, the presence of dark interference areas such as trees and shadows in the background, as well as the increased irregularity at the edge of the crater due to the proximity to the crater, pose challenges to the accurate extraction of the crater area in high entropy images. In this paper, we present a multi-feature fusion-based two-stage method for airport crater extraction from remote sensing images. In stage I, we designed an edge arc segment grouping and matching strategy based on the shape characteristics of craters for preliminary detection. In stage II, we established a crater model based on the regional distribution characteristics of craters and used the marked point processing method for crater detection. In addition, during the step of calculating the magnitude of the edge gradient, we proposed a near-region search strategy, which enhanced the ability of the proposed method to accurately extract craters with irregular shapes. In the test images, the proposed method accurately extracts craters located around and within the runways. Among them, the average recall R and precision P of the proposed method for extracting all craters around the airport runways reached 89% and 87%, respectively, and the average recall R and precision P of the proposed method for extracting craters inside the runways reached 94% and 92%, respectively. Meanwhile, the results of comparative tests showed that our method outperformed other representative algorithms in terms of both crater extraction recall and extraction precision. Full article

The Maximum Entropy (MaxEnt) model, integrated with ArcGIS (a geographic information system), was employed to project potential species distribution under current conditions and future climate scenarios (SSP1–2.6, SSP2–4.5, SSP5–8.5) for the 2050s, 2070s, and 2090s. Model optimization involved testing 1160 parameter combinations. The optimized model (FC = LQ, RM = 0.1) exhibited significantly improved predictive performance, with an average AUC of 0.967. Under current conditions, the estimated core suitable habitat spans 35.62 × 10⁴ km², primarily located in southern China. Future projections indicated a non-linear trajectory: an initial contraction of total suitable area by mid-century, followed by a substantial expansion by the 2090s, particularly under high-emission scenarios. Simultaneously, the distribution centroid shifted northwestward. The primary factors influencing distribution were the annual mean temperature (Bio1, 41.1%) and the precipitation of the coldest quarter (Bio19, 20.0%). These findings establish a critical scientific basis for developing climate-adaptive conservation strategies, including the identification of priority climate refugia in Fujian province, China, and planning for assisted migration to northwestern regions. Full article

Artificial underground compressed air energy storage (CAES) caverns have the advantages of large capacity and flexible location. However, the location selection of CAES in conditions of hard shallowly buried rock requires comprehensive consideration of multi-field coupling effects and engineering constraints, and the decision-making process involves multiple criteria and strong uncertainty. Aimed at addressing the problems of the evaluation index system not being detailed enough and the weight determination being biased to a single subjective or objective method in the existing research, this paper constructs a multi-criteria site selection evaluation method for an artificial underground CAES chamber in hard shallowly buried rock. Firstly, starting from the four criteria layers of ground environment, construction convenience, regional geological characteristics, and basic geological characteristics, combined with literature research and expert investigation, an evaluation index system containing 13 indicators was established. Secondly, the analytic hierarchy process (AHP) and entropy weight method (EWM) were introduced, the combination of subjective weight and objective weight realized through game theory, and the comprehensive weight of each index obtained. Then, the VIKOR method was used to rank the four candidate sites—A, B, C, and D—and the results were compared with those of the weighted TOPSIS method and the weighted gray relational analysis method. The engineering example shows that site B has advantages in group utility value, individual regret value, and compromise index. It is judged the optimal scheme by the three methods, and the ranking is stable under different decision-making mechanism coefficients, which verifies the robustness and applicability of the AHP–EWM–VIKOR model. The results show that the proposed method can distinguish different site selection schemes more clearly, effectively and comprehensively reflect suitability under complex geological and engineering conditions, and provide quantitative decision support for engineering site selection of artificial underground CAES caverns. Full article