Search Results (25)

Amid tightening ecological constraints, accelerating urbanization transition, and increasingly complex spatial governance, the coordinated evolution of Urban Green Space (UGS) and New-Type Urbanization (NTU) has become central to green transition and high-quality development in major river basins. Drawing on city-level panel data for the Yangtze River Economic Belt (YREB) and the Yellow River Basin (YRB) from 2006 to 2022, this study integrates a Coupling Coordination Degree (CCD) model, spatial statistical analysis, and interpretable machine learning to investigate UGS-NTU coordination, with emphasis on spatiotemporal evolution, spatial differentiation, and nonlinear contribution pathways. The findings indicate that: (1) UGS and NTU levels rise in both basins, but their spatial trajectories differ substantially. The YREB exhibits river-oriented expansion and gradient diffusion, whereas the YRB features nodal agglomeration and discontinuous expansion. (2) The CCD improves overall in both basins, with downstream areas leading, the middle reaches following, and the upper reaches lagging behind; UGS lag is widespread in the middle and upper reaches. (3) The YRB shows stronger spatial agglomeration, more pronounced regional differentiation, and more persistent low-value clustering, while the YREB is characterized by stable high-value clustering in the Yangtze River Delta. (4) The YREB is mainly associated with green space system optimization, whereas the YRB is more closely associated with improvements in the foundational capacities of NTU. Both associations exhibit clear nonlinear characteristics. This study provides empirical support for differentiated green transition and high-quality development strategies in major river basins. Full article

Quantifying the relative roles of climate change and human activities in vegetation change is essential for sustainable restoration planning, yet the impacts of extreme climate events and their time-lagged effects are often overlooked, biasing assessments of climatic controls. Here, we developed an integrated pattern–process–attribution framework to evaluate vegetation dynamics across China’s four major climatic zones using a long-term, high-resolution kernel normalized difference vegetation index (kNDVI) dataset for 2000–2024. Theil–Sen trend estimation and the coefficient of variation (CV) were used to characterize long-term changes and interannual stability. Partial correlation analysis was applied to isolate the independent associations between kNDVI and extreme climate indices while controlling for background mean temperature and precipitation, and lagged correlation analysis with 0–3-month lags was used to quantify delayed responses. A regression-based residual attribution was further used to decompose observed kNDVI changes into a climate-driven component and a human-activity-related component (approximated by the residual not explained by temperature and precipitation). Results show widespread greening with pronounced spatial heterogeneity, with the most extensive improvement in the Tropical and Subtropical Humid Region and the Temperate Humid and Semi-humid Region. Vegetation stability exhibits a southeast–northwest contrast, and the highest variability occurs in the Temperate Arid and Semi-arid Region and the western Qinghai–Tibet Plateau. Responses to climate extremes are region-dependent and generally short-lagged (mean 0.35–1.05 months), with drought constraints dominating in arid regions and thermal extremes (TXx) most relevant on the plateau. Nationally, human activities contribute 70.8% of vegetation change, exceeding the climate-driven contribution (29.2%). Full article

Amid China’s dual-carbon goals and widening regional disparities, land-use/cover change (LUCC)-induced volatility in carbon storage (CS) has emerged as a binding constraint on emission reduction and the low-carbon transition in resource-based regions. Yet integrated historical-scenario assessments and rigorous evidence on spatial-heterogeneity mechanisms remain limited, which hampers targeted spatial governance. Using Shanxi Province, a resource-based province, as the study area, this study develops a coupled PLUS-InVEST framework under SSP-RCP scenarios. It integrates spatial autocorrelation, geographically weighted regression (GWR), and structural equation modeling (SEM) to characterize spatiotemporal responses of CS to LUCC and to identify underlying drivers. The results indicate that: (1) Regional CS follows an inverted U-shaped trajectory, initially increasing due to ecological restoration projects and subsequently declining owing to industrial development and urban expansion; (2) By 2030, forestland expansion under SSP126 is projected to enhance CS, whereas accelerated urbanization under SSP585 is expected to intensify CS losses; (3) Significant spatial clustering of CS remains consistent from historical periods to future projections, underscoring its sensitivity to topography, vegetation patterns, and human activities; and (4) CS is jointly shaped by natural and anthropogenic drivers, with DEM and slope providing stable protection, while population density and transport-network configuration cause ongoing disturbances. The study provides an integrated historical-scenario assessment and reveals the underlying mechanisms for resource-based regions, offering quantitative evidence to support optimization of the Ecological Conservation Redline, managing urban growth boundaries, and implementing zoned ecological restoration. Full article

Amid accelerating global warming, surface albedo is a key indicator and regulator of how Earth’s surface reflects solar radiation, directly affecting the planetary radiation balance and climate. In this paper, we combined MODIS shortwave albedo (MCD43A3, 500 m), MODIS NDVI (MOD13A3, 1 km; NDVI = normalized difference vegetation index) and 1-km gridded meteorological data to analyze the spatiotemporal variations of surface albedo across China during 2001–2020 at a gridded scale. Temporal trends were quantified with the Theil–Sen slope and the Mann–Kendall test, and the seasonal contributions of NDVI, air temperature, and precipitation were assessed with a geographically and temporally weighted regression (GTWR) model. China’s mean annual shortwave albedo was 0.186 and showed a significant decline. Attribution indicates NDVI is the dominant driver (~48% of total change), followed by temperature (~27%) and precipitation (~25%). Seasonally, NDVI explains ~43.94–52.02% of the variation, ~26.81–28.07% of the temperature, and ~21.17–28.57% of the precipitation. Clear spatial patterns emerge. In high-latitude and high-elevation snow-dominated regions, albedo tends to decrease with warmer conditions and increase with greater precipitation. In much of eastern China, albedo is generally positively associated with temperature and negatively with precipitation. NDVI—reflecting vegetation greenness and canopy structure—captures the effects of vegetation greening, canopy densification, and land-cover change that reduce surface reflectivity by enhancing shortwave absorption. Temperature and precipitation affect albedo primarily by regulating vegetation growth. This study goes beyond correlation mapping by combining robust trend detection (Theil–Sen + MK) with GTWR to resolve seasonally varying, non-stationary controls on albedo at 1-km over 20 years. By explicitly separating snow-covered and snow-free conditions, we quantify how NDVI, temperature, and precipitation contributions shift across climate zones and seasons, providing a reproducible, national-scale attribution that can inform ecosystem restoration and land-surface radiative management. Full article

Rigid wingsails are increasingly adopted for wind-assisted ship propulsion, with Symmetrically Cambered (SC) profiles identified as highly efficient for thrust generation. This study investigates installation layouts for multiple SC wingsails, focusing on aerodynamic interference that limits their performance. A fast 2D potential-flow panel method is employed and benchmarked against wind tunnel and 3D IDDES data. Two representative layouts are analyzed: triple-in-line (TL) and quad-in-parallel (QP). Layout optimization is performed using a genetic algorithm with distances between sails as design variables, constrained by the total installation span, at apparent wind angles (AWAs) of $60°$, $90°$, and $120°$. Results show that thrust generation decreases progressively from upstream to downstream sails due to interference effects, with penalties of about 4–6% in the TL and up to 28% in the QP layout. The optimization improves performance only for the TL layout at $60°$, while the QP layout shows negligible gains. Analysis of pressure distributions confirms that downstream sails suffer from reduced suction on the leading edge caused by upstream wakes. Overall, the TL layout demonstrates significantly higher aerodynamic reliability than the QP layout. These findings provide new insights into multi-sail configurations and highlight the importance of layout optimization in maximizing thrust efficiency. Full article

While autonomous driving systems and intelligent transportation infrastructures become increasingly software-defined and network-connected, ensuring their cybersecurity has become a critical component of traffic safety. Large language models (LLMs) have recently shown promise in automating aspects of penetration testing, yet most existing approaches remain limited to simple, single-step exploits. They struggle to handle complex, multi-stage vulnerabilities that demand precise coordination, contextual reasoning, and knowledge reuse. This is particularly problematic in safety-critical domains, such as autonomous vehicles, where subtle software flaws can cascade across interdependent subsystems. In this work, we present CurriculumPT, a novel LLM-based penetration testing framework specifically designed for the security of intelligent systems. CurriculumPT combines curriculum learning and a multi-agent system to enable LLM agents to progressively acquire and apply exploitation skills across common vulnerabilities and exposures-based tasks. Through a structured progression from simple to complex vulnerabilities, agents build and refine an experience knowledge base that supports generalization to new attack surfaces without requiring model fine-tuning. We evaluate CurriculumPT on 15 real-world vulnerabilities scenarios and demonstrate that it outperforms three state-of-the-art baselines by up to 18 percentage points in exploit success rate, while achieving superior efficiency in execution time and resource usage. Our results confirm that CurriculumPT is capable of autonomous, scalable penetration testing and knowledge transfer, laying the groundwork for intelligent security auditing of modern autonomous driving systems and other cyberphysical transportation platforms. Full article

Arid and semi-arid areas serve a critical regulatory function within the global carbon cycle. Understanding the response mechanisms of vegetation productivity to variations in moisture availability represents a fundamental scientific challenge in elucidating terrestrial carbon dynamics. This study systematically disentangled the respective influences of summer surface soil moisture (RSM) and precipitation (PRE) on gross primary productivity (GPP) across arid and semi-arid regions of China from 2000 to 2022. Utilizing GPP datasets alongside correlation analysis, ridge regression, and data binning techniques, the investigation yielded several key findings: (1) Both GPP and RSM exhibited significant upward trends within the study area, whereas precipitation showed no statistically significant trend; notably, GPP demonstrated the highest rate of increase at 0.455 Cg m⁻² a⁻¹. (2) Decoupling analysis indicated a coupled relationship between RSM and PRE; however, their individual effects on GPP were not merely a consequence of this coupling. Controlling for evapotranspiration and root-zone soil moisture interference, the analysis revealed that under conditions of elevated RSM, the average increase in summer–autumn GPP (SA_GPP) was 0.249, significantly surpassing the increase observed under high-PRE conditions (−0.088). Areas dominated by RSM accounted for 62.13% of the total study region. Furthermore, examination of the aridity gradient demonstrated that the predominance of RSM intensified with increasing aridity, reaching its peak influence in extremely arid zones. This research provides a quantitative assessment of the differential impacts of RSM and PRE on vegetation productivity in China’s arid and semi-arid areas, thereby offering a vital theoretical foundation for improving predictions of terrestrial carbon sink dynamics under future climate change scenarios. Full article

Ultrahigh resolution fine particulate matter (PM_2.5) mass concentration remote sensing products are crucial for atmospheric environmental monitoring, pollution source verification, health exposure risk assessment, and other fine-scale applications in urban environments. This study developed an ultrahigh resolution retrieval algorithm to estimate 30 m resolution PM_2.5 mass concentrations over urban areas from Landsat-8 and Sentinel-2A/B satellite measurements. The algorithm utilized aerosol optical depth (AOD) products retrieved from the Landsat-8 OLI and Sentinel-2 MSI measurements from 2017 to 2020, combined with multi-source auxiliary data to establish a PM_2.5-AOD relationship model across China. The results showed an overall high coefficient of determination (R²) of 0.82 and 0.76 for the model training accuracy based on samples and stations, respectively. The model prediction accuracy in Beijing and Wuhan reached R² values of 0.86 and 0.85. Applications in both cities demonstrated that ultrahigh resolution PM_2.5 has significant advantages in resolving fine-scale spatial patterns of urban air pollution and pinpointing pollution hotspots. Furthermore, an analysis of point source pollution at a typical heavy pollution emission enterprise confirmed that ultrahigh spatial resolution PM_2.5 can accurately identify the diffusion trend of point source pollution, providing fundamental data support for refined monitoring of urban air pollution and air pollution prevention and control. Full article

Pedestrian detection systems are widely used in safety-critical domains such as autonomous driving, where deep neural networks accurately perceive individuals and distinguish them from other objects. However, their vulnerability to backdoor attacks remains understudied. Existing backdoor attacks, relying on unnatural digital perturbations or explicit patches, are difficult to deploy stealthily in the physical world. In this paper, we propose a novel backdoor attack method that leverages real-world occlusions (e.g., backpacks) as natural triggers for the first time. We design a dynamically optimized heuristic-based strategy to adaptively adjust the trigger’s position and size for diverse occlusion scenarios, and develop three model-independent trigger embedding mechanisms for attack implementation. We conduct extensive experiments on two different pedestrian detection models using publicly available datasets. The results demonstrate that while maintaining baseline performance, the backdoored models achieve average attack success rates of 75.1% on KITTI and 97.1% on CityPersons datasets, respectively. Physical tests verify that pedestrians wearing backpack triggers could successfully evade detection under varying shooting distances of iPhone cameras, though the attack failed when pedestrians rotated by 90°, confirming the practical feasibility of our method. Through ablation studies, we further investigate the impact of key parameters such as trigger patterns and poisoning rates on attack effectiveness. Finally, we evaluate the defense resistance capability of our proposed method. This study reveals that common occlusion phenomena can serve as backdoor carriers, providing critical insights for designing physically robust pedestrian detection systems. Full article

Air pollution is influenced by a combination of pollutant emissions and meteorological conditions. Anthropogenic emissions and meteorological conditions are the two main causes of atmospheric pollution, and the contribution of meteorology and emissions to the reduction of PM_2.5 concentrations across the country has not yet been comprehensively examined. This study used the Kolmogorov–Zurbenko (KZ) filter and random forest (RF) model to decompose and reconstruct PM_2.5 time series in five major urban agglomerations in China, analyzing the impact of meteorological factors on PM_2.5 concentrations. From 2015 to 2021, PM_2.5 concentrations significantly decreased in all urban agglomerations, with annual averages dropping by approximately 50% in Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD), Central Plain (CP), and Chengdu–Chongqing (CC). This reduction was due to both favorable meteorological conditions and emission reductions. The KZ filter effectively separated the PM_2.5 time series, and the RF model achieved high squared correlation coefficient (R²) values between predicted and observed values, ranging from 0.94 to 0.98. Initially, meteorological factors had a positive contribution to PM_2.5 reduction, indicating unfavorable conditions, but this gradually turned negative, indicating favorable conditions. By 2021, the rates of meteorological contribution to PM_2.5 reduction in BTH, YRD, PRD, CP, and CC changed from 14.3%, 16.9%, 7.2%, 12.2%, and 11.5% to −36.5%, −31.5%, −26.9%, −30.3%, and −23.5%, respectively. Temperature and atmospheric pressure had the most significant effects on PM_2.5 concentrations. The significant decline in PM_2.5 concentrations in BTH and CP after 2017 indicated that emission control measures were gradually taking effect. This study confirms that effective pollution control measures combined with favorable meteorological conditions jointly contributed to the improvement in air quality. Full article

The advancements in deep learning have significantly enhanced the accuracy and robustness of pedestrian detection. However, recent studies reveal that adversarial attacks can exploit the vulnerabilities of deep learning models to mislead detection systems. These attacks are effective not only in digital environments but also pose significant threats to the reliability of pedestrian detection systems in the physical world. Existing adversarial attacks targeting pedestrian detection primarily focus on daytime scenarios and are easily noticeable by road observers. In this paper, we propose a novel adversarial attack method against vehicle–pedestrian detection systems at night. Our approach utilizes reflective optical materials that can effectively reflect light back to its source. We optimize the placement of these reflective patches using the particle swarm optimization (PSO) algorithm and deploy patches that blend with the color of pedestrian clothing in real-world scenarios. These patches remain inconspicuous during the day or under low-light conditions, but at night, the reflected light from vehicle headlights effectively disrupts the vehicle’s pedestrian detection systems. Considering that real-world detection models are often black-box systems, we propose a “symmetry” strategy, which involves using the behavior of an alternative model to simulate the response of the target model to adversarial patches. We generate adversarial examples using YOLOv5 and apply our attack to various types of pedestrian detection models. Experiments demonstrate that our approach is both effective and broadly applicable. Full article

Quantitative measurement of urban green spaces (UGSs) plays a fundamental role in enhancing their ecological functions and services. Current studies have not adequately analyzed the multifunctional characteristics and the diverse benefits of urban green spaces at the national scale. This study developed a multidimensional indicator system in terms of scale, pattern, and services to measure green spaces in 285 prefecture-level cities in China. The influences of different geographic zones and urban development on UGS characteristics were also investigated. The results showed that per capita area of urban green spaces were significantly different between the two sides of the “Botai Line”, and the UGS structure was mainly dominated by woodlands. Urban green spaces in the central and east had higher fragmentation, lower landscape diversity, and weaker connectivity. The spatial accessibility of green spaces exhibited a “low–high–medium” pattern from north to south in China, with central-eastern China experiencing the worst equity in green space supply. Overall, cities with higher integrated benefits of green spaces were mainly located in Northeast and North China. Type I large cities had higher ratings for UGS characteristics compared with the other types of cities. These findings can serve as solid guidance for cities seeking to build green space systems with highly integrated socio–ecological benefits. Full article

Thanks to the line-scanning camera, the measurement method based on line-scanning stereo vision has high optical accuracy, data transmission efficiency, and a wide field of vision. It is more suitable for continuous operation and high-speed transmission of industrial product detection sites. However, the one-dimensional imaging characteristics of the line-scanning camera cause motion distortion during image data acquisition, which directly affects the accuracy of detection. Effectively reducing the influence of motion distortion is the primary problem to ensure detection accuracy. To obtain the two-dimensional color image and three-dimensional contour data of the heavy rail surface at the same time, a binocular color line-scanning stereo vision system is designed to collect the heavy rail surface data combined with the bright field illumination of the symmetrical linear light source. Aiming at the image motion distortion caused by system installation error and collaborative acquisition frame rate mismatch, this paper uses the checkerboard target and two-step cubature Kalman filter algorithm to solve the nonlinear parameters in the motion distortion model, estimate the real motion, and correct the image information. The experiments show that the accuracy of the data contained in the image is improved by 57.3% after correction. Full article

We study noise generation at the blade tips of propellers designed for future electric aircraft propulsion and, furthermore, analyze the interrelationship between noise mitigation and aerodynamics improvement in terms of propeller geometric designs. Classical propellers with three or six blades and a conceptual propeller with three joined dual-blades are compared to understand the effects of blade tip vortices on the noise generation and aerodynamics. The dual blade of the conceptual propeller is constructed by joining the tips of two sub-blades. These propellers are designed to operate under the same freestream flow conditions and similar electric power consumption. The Improved Delayed Detached Eddy Simulation (IDDES) is adopted for the flow simulation to identify high-resolution time-dependent noise sources around the blade tips. The acoustic computations use a time-domain method based on the convective Ffowcs Williams–Hawkings (FW-H) equation. The thrust of the 3-blade conceptual propeller is $4\%$ larger than the 3-blade classical propeller and $8\%$ more than the 6-blade one, given that they have similar efficiencies. Blade tip vortices are found emitting broadband noise. Since the classical and conceptual 3-blade propellers have different geometries, especially at the blade tips, they introduce deviations in the vortex development. However, the differences are small regarding the broadband noise generation. As compared to the 6-blade classical propeller, both 3-blade propellers produce much larger noise. The reason is that the increased number of blades leads to the reduced strength of tip vortices. The findings indicate that the noise mitigation through the modification of the blade design and number can be traded-off by the changed aerodynamic performance. Full article

Panel methods have been applied to many fields of fluid owing to their computational efficiency. However, their applications are limited in simulating highly turbulent flow with separations due to the inviscid flow assumptions, such as those associated with train aerodynamics. Some researchers employed the wake models to simulate large vortices in the wake of trains with predetermined separation locations according to experimental results. In this paper, a modified 2-D constant source/vortex panel method for modelling the separated flow around 2-D bluff bodies is presented. The proposed separated-flow model includes the prediction of separation locations based on the integral boundary-layer method and the shear layer, and large vortices in the wake of the bluff bodies are modelled by the wake model. The proposed method is validated by comparing the calculated pressure distribution on a 2-D circular cylinder with the experimental results. The method is then applied to simulate the flow around a 2-D generic train and calculate the pressure distribution on the train. Since trains run very close to the ground, the effect of the ground configuration on the pressure distribution of the 2-D train is also investigated in this paper using the proposed method. The main contribution of the work is to present a 2-D separated-flow model with wake modelling and separation prediction. The proposed model can be used in the rapid evaluation of bluff-body aerodynamics. Full article