Search Results (259)

The remote sensing monitoring of land use changes and future scenario simulation hold crucial significance for accurately characterizing urban expansion patterns, optimizing urban land use configurations, and thereby promoting coordinated regional development. Through the integration of multi-source data, this study systematically analyzes the spatiotemporal trajectories and driving forces of land use changes in the Pearl River Delta urban agglomeration (PRD) from 1990 to 2020 and further simulates the spatial patterns of urban land use under diverse development scenarios from 2025 to 2035. The results indicate the following: (1) During 1990–2020, urban expansion in the Pearl River Delta urban agglomeration exhibited a “stepwise growth” pattern, with an annual expansion rate of 3.7%. Regional land use remained dominated by forest (accounting for over 50%), while construction land surged from 6.5% to 21.8% of total land cover. The gravity center trajectory shifted southeastward. Concurrently, cropland fragmentation has intensified, accompanied by deteriorating connectivity of ecological lands. (2) Urban expansion in the PRD arises from synergistic interactions between natural and socioeconomic drivers. The Geographically and Temporally Weighted Regression (GTWR) model revealed that natural constraints—elevation (regression coefficients ranging −0.35 to −0.05) and river network density (−0.47 to −0.15)—exhibited significant spatial heterogeneity. Socioeconomic drivers dominated by year-end paved road area (0.26–0.28) and foreign direct investment (0.03–0.11) emerged as core expansion catalysts. Geographic detector analysis demonstrated pronounced interaction effects: all factor pairs exhibited either two-factor enhancement or nonlinear enhancement effects, with interaction explanatory power surpassing individual factors. (3) Validation of the Patch-generating Land Use Simulation (PLUS) model showed high reliability (Kappa coefficient = 0.9205, overall accuracy = 95.9%). Under the Natural Development Scenario, construction land would exceed the ecological security baseline, causing 408.60 km² of ecological space loss; Under the Ecological Protection Scenario, mandatory control boundaries could reduce cropland and forest loss by 3.04%, albeit with unused land development intensity rising to 24.09%; Under the Economic Development Scenario, cross-city contiguous development zones along the Pearl River Estuary would emerge, with land development intensity peaking in Guangzhou–Foshan and Shenzhen–Dongguan border areas. This study deciphers the spatiotemporal dynamics, driving mechanisms, and scenario outcomes of urban agglomeration expansion, providing critical insights for formulating regionally differentiated policies. Full article

As an important ecological barrier in Northwest China, the health of forest ecosystems in Shaanxi Province is crucial to regional ecological balance and sustainable development. However, forest degradation has become increasingly prominent in recent years due to both natural and anthropogenic pressures. This study aims to identify the spatio-temporal pattern of forest degradation in Shaanxi Province, construct an ecological network, and propose targeted restoration strategies. To this end, we first built a structural-functional forest degradation (SFD) assessment system and used the Landsat-based detection of trends in disturbance and recovery (LandTrendr) algorithm to identify degraded areas and types; subsequently, we used morphological spatial pattern analysis (MSPA) and the minimum cumulative resistance (MCR) model to construct a forest ecological network and identify key restoration nodes. Finally, we proposed a differentiated restoration strategy for near-natural forests based on the Miyawaki method as a conceptual framework to guide future ecological recovery efforts. The results showed that (1) in 1991–2020, the total area of forest degradation in Shaanxi Province was 1010.89 km², which was dominated by functional degradation (98%) and structural degradation (87.15%), with significant regional differences; (2) the constructed ecological network contained 189 ecological source sites, 189 ecological corridors, 89 key nodes, and 50 urgently restored; and (3) specific restoration measures were proposed for different degradation conditions (e.g., density regulation and forest window construction for functional light degradation and maintenance of the status quo or full reconstruction for structural heavy degradation). This study provides key data and systematic methods for the accurate monitoring of forest degradation, the optimization of ecological networks, and scientific restoration in Shaanxi Province, which holds great practical significance for establishing a robust ecological barrier in Northwest China. Full article

Intersectionality, especially with a race–class–gender focus, has been used to study many facets of women’s experiences. However, this framework has been underutilized in the study of breastfeeding prevalence. Our study is the first of its kind to use intersectionality to illuminate breastfeeding network prevalence disparities with empirical data. We use insights from this theory to examine breastfeeding patterns reported by women living on the Mississippi Gulf Coast. Mississippi makes an excellent site for such an examination, given its history of racial discrimination, entrenched poverty, and strikingly low rates of breastfeeding, particularly for African American women. We identify a series of factors that influence racial disparities in lactation network prevalence, that is, breastfeeding among friends and family of the women we surveyed. Our investigation relies on survey data drawn from a random sample of adult women who are representative of the Mississippi Gulf Coast population supplemented by a non-random oversample of African American women in this predominantly rural tri-county area. Results from the first wave of the CDC-funded 2019 Mississippi REACH Social Climate Survey reveal that Black-White differentials in breastfeeding network prevalence are significantly reduced for African American women who report (1) higher income levels and (2) more robust community support for breastfeeding. We conclude that breastfeeding is subject to two key structural factors: economic standing and community context. An appreciation of these intersecting influences on breastfeeding and long-term efforts to alter them could bring about greater breastfeeding parity among African American and White women in Mississippi and perhaps elsewhere. We end by identifying the practical implications of our findings and promising directions for future research. Full article

This paper presents a wideband circularly polarized (CP) substrate-integrated waveguide (SIW) cavity-backed slot antenna array arranged in a 2 × 2 configuration with differential feeding structures. The design features arc-shaped microstrips within the SIW cavity to excite the ${\mathrm{TE}}_{011}^x$/${\mathrm{TE}}_{101}^y$ and ${\mathrm{TE}}_{211}^y$/${\mathrm{TE}}_{121}^x$ modes. By overlapping the center frequencies of the two modes, wideband CP radiation is achieved. The introduction of four modified ring couplers composes a simple but efficient differential feeding network, eliminating the need for balanced resistors like baluns, making it more suitable for millimeter wave or even higher frequency applications. Experimental results show that the antenna array achieves a −10 dB impedance bandwidth of 32.6% (from 17.28 to 24.00 GHz), a 3 dB axial ratio (AR) bandwidth of 13.8% (from 17.05 to 19.57 GHz), a 3 dB gain bandwidth of 41.8% (from 15.39 to 23.51 GHz) and a peak gain of 10.6 dBi, with results closely matching simulation data. This study enhances the development of differential CP SIW cavity-backed slot antenna arrays, offering a potential solution for creating compact integrated front-end circuits in the millimeter wave or Terahertz frequency range. Full article

Wind turbine wake modeling is critical for the design and optimization of wind farms. Traditional methods often struggle with the trade-off between accuracy and computational cost. Recently, data-driven neural networks have emerged as a promising solution, offering both high fidelity and fast inference speeds. To advance this field, a novel machine learning model has been developed to predict wind farm mean flow fields through an adaptive multi-fidelity framework. This model extends transfer-learning-based high-dimensional multi-fidelity modeling to scenarios where varying fidelity levels correspond to distinct physical models, rather than merely differing grid resolutions. Built upon a U-Net architecture and incorporating a wind farm parameter encoder, our framework integrates high-fidelity large-eddy simulation (LES) data with a low-fidelity engineering wake model. By directly predicting time-averaged velocity fields from wind farm parameters, our approach eliminates the need for computationally expensive simulations during inference, achieving real-time performance ($1.32\times {10}^{-5}$ GPU hours per instance with negligible CPU workload). Comparisons against field-measured data demonstrate that the model accurately approximates high-fidelity LES predictions, even when trained with limited high-fidelity data. Furthermore, its end-to-end extensible design allows full differentiability and seamless integration of multiple fidelity levels, providing a versatile and scalable solution for various downstream tasks, including wind farm control co-design. Full article

Dragon fruit comprises a wide variety of species that are rich in nutritional value and have great economic potential; however, numerous studies have focused on their nutritional and commercial quality. In contrast, few studies have addressed their flavor quality, particularly with respect to the regulatory networks responsible for their flavor-related substance contents. To this end, we sequenced the transcriptomes and metabolomes of red-skin/white-fleshed and red-skin/red-fleshed dragon fruit at different timepoints during fruit development. RNA-seq and metabolome data were used to divide the seven developmental stages of the dragon fruit into four categories (young fruit, expansion, maturity, and senescence). In all, 16,827 differentially expressed genes (DEGs), including 958 transcription factors, were identified and grouped into 10 clusters, and the pathways in each cluster were annotated. Additionally, 318 differentially accumulated metabolites (DAMs) were identified, including 88 common metabolites. The main flavor-related substances and the key genes regulating them were determined via joint analysis via RNA-seq and metabolomics. Furthermore, 10 volatile active components related to green flavors and aromas were screened according to the relative odor activity value (ROAV), and 15 candidate genes related to key flavor compounds were screened via WGCNA, 3 of which encoded transcription factors. In conclusion, our results provide a theoretical basis for an in-depth understanding of the volatile flavor compounds in dragon fruit and provide new genetic resources for the subsequent study of fruit flavor compounds. Full article

Air pockets in water distribution networks can cause various operational issues, as their expansion during drainage operations leads to sub-atmospheric conditions that may result in pipeline collapse depending on soil conditions and pipe stiffness. This study presents an analytical solution for calculating air pocket pressure, water column length, and water velocity during drainage operations in a pipeline with an entrapped air pocket and a closed upstream end. The existing system of three differential equations is reduced to two first-order nonlinear differential equations, enabling a rigorous analysis of the existence and uniqueness of solutions. The system is then further reduced to a single secondorder nonlinear ordinary differential equation (ODE), providing an intuitive framework for examining the physical behaviour of the hydraulic and thermodynamic variables. Furthermore, through a change of variables, the second-order ODE is transformed into a first-order linear ODE, facilitating the derivation of an analytical solution. The analytical solution is validated by comparing it with a numerical solution. Additionally, a practical application demonstrates the effectiveness of the developed tool in predicting the extreme pressure values in the air pocket during the water drainage process in a pipe, within a controlled environment. Full article

The limited field of view, cumulative inter-frame jitter, and dynamic parallax interference in handheld video stitching often lead to misalignment and distortion. In this paper, we propose an end-to-end, unsupervised deep-learning framework that jointly performs real-time video stabilization and stitching. First, collaborative optimization architecture allows the stabilization and stitching modules to share parameters and propagate errors through a fully differentiable network, ensuring consistent image alignment. Second, a Markov trajectory smoothing strategy in relative coordinates models inter-frame motion as incremental relationships, effectively reducing cumulative errors. Third, a dynamic attention mask generates spatiotemporal weight maps based on foreground motion prediction, suppressing misalignment caused by dynamic objects. Experimental evaluation on diverse handheld sequences shows that our method achieves higher stitching quality, lower geometric distortion rates, and improved video stability compared to state-of-the-art baselines, while maintaining real-time processing capabilities. Ablation studies validate that relative trajectory modeling substantially mitigates long-term jitter and that the dynamic attention mask enhances stitching accuracy in dynamic scenes. These results demonstrate that the proposed framework provides a robust solution for high-quality, real-time handheld video stitching. Full article

The steel strip is an important and ideal material for the automotive and aerospace industries due to its superior machinability, cost efficiency, and flexibility. However, surface defects such as inclusions, spots, and scratches can significantly impact product performance and durability. Accurately identifying these defects remains challenging due to the complex texture structures and subtle variations in the material. In order to tackle this challenge, we propose a Differentiable Edge-guided Pyramid Aggregation Network (DEPANet) to utilize edge information for improving segmentation performance. DEPANet adopts an end-to-end encoder-decoder framework, where the encoder consisting of three key components: a backbone network, a Differentiable Edge Feature Pyramid network (DEFP), and Edge-aware Feature Aggregation Modules (EFAMs). The backbone network is designed to extract overall features from the strip steel surface, while the proposed DEFP utilizes learnable Laplacian operators to extract multiscale edge information of defects across scales. In addition, the proposed EFAMs aggregate the overall features generating from the backbone and the edge information obtained from DEFP using the Convolutional Block Attention Module (CBAM), which combines channel attention and spatial attention mechanisms, to enhance feature expression. Finally, through the decoder, implemented as a Feature Pyramid Network (FPN), the multiscale edge-enhanced features are progressively upsampled and fused to reconstruct high-resolution segmentation maps, enabling precise defect localization and robust handling of defects across various sizes and shapes. DEPANet demonstrates superior segmentation accuracy, edge preservation, and feature representation on the SD-saliency-900 dataset, outperforming other state-of-the-art methods and delivering more precise and reliable defect segmentation. Full article

High-strength and high-modulus epoxy resins are key elements for preparing carbon-fiber-reinforced polymer composites, which play an irreplaceable role in aerospace. In this study, five optimal epoxy systems were developed utilizing the reverse design strategy. The reverse design strategy was based on the ideal resin and curing agent structures offered by the AI polymer platform, and the rules were summarized to create an optimum resin formulation. The formulations used m-phenylenediamine (MPD) as the principal curing agent, which was modified with 10 wt% diethyltetramethylenediamine (DETDA), 10 wt% 4,4′-diaminodiphenylmethane (DDM), or 10 wt% triethylenetetramine (TETA) to establish multiple crosslinking networks. Systematic characterization using differential scanning calorimetry (DSC) and rheological analysis revealed that the optimized activation energy was 55.95–63.42 kJ/mol, and the processing viscosity was ≤500 mPa·s at 80 °C. A stepwise curing protocol (3 h@80 °C, 2 h@120 °C, and 3 h@180 °C) was established to achieve a complete crosslinking network. The results showed that the system with 10% DDM had a tensile strength of 132.6 MPa, a modulus of 5.0 GPa, and a glass transition temperature of 253.1 °C. This work advances the rational design of epoxy resins by bridging molecular architecture with macroscopic performance, offering a paradigm for developing a next-generation matrix tailored to accommodate extreme operational demands in high-end engineering sectors. Full article

This study explores the fabrication, structural characteristics, and performance of an innovative porous geopolymer membrane made from waste natural zeolite powder for Pb(II) removal, with potential applications in wastewater treatment. A hybrid geopolymer membrane incorporating polyvinyl acetate (PVAc) (10, 20, and 30 wt.%) was synthesized and thermally treated at 300 °C to achieve a controlled porous architecture. Characterization techniques, including Fourier-transform infrared spectroscopy (FTIR), revealed the disappearance of characteristic C=O and C-H stretching bands (~1730 cm⁻¹ and ~2900 cm⁻¹, respectively), confirming the full degradation of PVAc. Thermogravimetric analysis (TG) and differential scanning calorimetry (DSC) indicated a total mass loss of approximately 14.5% for the sample with PVAc 20 wt.%, corresponding to PVAc decomposition and water loss. Energy-dispersive spectroscopy (EDS) elemental mapping showed the absence of carbon residues post-annealing, further validating complete PVAc removal. X-ray diffraction (XRD) provided insight into the crystalline phases of the raw zeolite and geopolymer structure. Once PVAc removal was confirmed, the second phase of characterization assessed the membrane’s mechanical properties and filtration performance. The thermally treated membrane, with a thickness of 2.27 mm, exhibited enhanced mechanical properties, measured with a nano-indenter, showing a hardness of 1.8 GPa and an elastic modulus of 46.7 GPa, indicating improved structural integrity. Scanning electron microscopy (SEM) revealed a well-defined porous network. Filtration performance was evaluated using a laboratory-scale dead-end setup for Pb(II) removal. The optimal PVAc concentration was determined to be 20 wt.%, resulting in a permeation rate of 78.5 L/(m²·h) and an 87% rejection rate at an initial Pb(II) concentration of 50 ppm. With increasing Pb(II) concentrations, the flux rates declined across all membranes, while maximum rejection was achieved at 200 ppm. FTIR and EDS analyses confirmed Pb(II) adsorption onto the zeolite-based geopolymer matrix, with elemental mapping showing a uniform Pb(II) distribution across the membrane surface. The next step is to evaluate the membrane’s performance in a multi-cation water treatment environment, assessing the sorption kinetics and its selectivity and efficiency in removing various heavy metal contaminants from complex wastewater systems. Full article

Life cycle carbon emissions from the construction industry (CE) have a profound impact on China’s “dual carbon” goals, with significant emissions posing severe challenges to the environment. In this paper, four prediction models were trained and compared, and the optimal model, the Genetic Algorithm Optimized BP Neural Network (GA-BP), was finally selected for multi-scenario prediction of CE. Firstly, this study performs a comprehensive accounting and indicator analysis of CE over its entire life cycle. In addition, this paper further conducts a spatial differentiation analysis of CE. Subsequently, parameter analysis was conducted using an improved STIRPAT model, followed by LMDI factor decomposition based on this model. Finally, the model performance was verified using three evaluation metrics: the coefficient of determination (R²), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results indicate that (1) in the carbon emission impact assessment, CE reached a peak of 42.52 t per capita annually and 8.90 t CO₂/m² per unit area; (2) the year-end resident population has the greatest influence on CE, with other related variables also contributing positively; and (3) the GA-BP model outperforms other models, with R² increasing from 0.0435 to 0.0981, MAE reducing from 63% to 76%, and MAPE decreasing from 23% to 68%. Full article

Fog computing introduces a new dimension to the network edge by pooling diverse resources (e.g., processing power, memory, and bandwidth). However, allocating resources from heterogeneous fog nodes often faces limited capacity. To overcome these limitations, integrating fog nodes with cloud resources is crucial, ensuring that Service Providers (SPs) have adequate resources to deliver their services efficiently. In this paper, we propose a game-theoretic model to describe the competition among non-cooperative SPs as they bid for resources from both fog and cloud environments, managed by an Infrastructure Provider (InP), to offer paid services to their end-users. In our game model, each SP bids for the resources it requires, determining its willingness to pay based on its specific service demands and quality requirements. Resource allocation prioritizes the fog environment, which offers local access with lower latency but limited capacity. When fog resources are insufficient, the remaining demand is fulfilled by cloud resources, which provide virtually unlimited capacity. However, this approach has a weakness in that some SPs may struggle to fully utilize the resources allocated in the Nash equilibrium-balanced cloud solution. Specifically, under a nondiscriminatory pricing scheme, the Nash equilibrium may enable certain SPs to acquire more resources, granting them a significant advantage in utilizing fog resources. This leads to unfairness among SPs competing for fog resources. To address this issue, we propose a price differentiation mechanism among SPs to ensure a fair allocation of resources at the Nash equilibrium in the fog environment. We establish the existence and uniqueness of the Nash equilibrium and analyze its key properties. The effectiveness of the proposed model is validated through simulations using Amazon EC2 instances, where we investigate the impact of various parameters on market equilibrium. The results show that SPs may experience profit reductions as they invest to attract end-users and enhance their quality of service QoS. Furthermore, unequal access to resources can lead to an imbalance in competition, negatively affecting the fairness of resource distribution. The results demonstrate that the proposed model is coherent and that it offers valuable information on the allocation of resources, pricing strategies, and QoS management in cloud- and fog-based environments. Full article

Large-volume parenterals (LVPs), as essential medical products, are widely used in healthcare settings, making their safety inspection crucial. Current methods for detecting foreign particles in LVP solutions through image analysis primarily rely on single-frame detection or simple temporal smoothing strategies, which fail to effectively utilize spatiotemporal correlations across multiple frames. Factors such as occlusion, motion blur, and refractive distortion can significantly impact detection accuracy. To address these challenges, this paper proposes a multi-frame object detection framework based on spatiotemporal collaborative learning, incorporating three key innovations: a YOLO network optimized with deformable convolution, a differentiable cross-frame association module, and an uncertainty-aware feature fusion and re-identification module. Experimental results demonstrate that our method achieves a 97% detection rate for contaminated LVP solutions on the LVPD dataset. Furthermore, the proposed method enables end-to-end training and processes five bottles per second, meeting the requirements for real-time pipeline applications. Full article

Regional planning agencies increasingly rely on Spatial Economic Models (SEMs) to evaluate the impact of various policies. However, traditional SEMs often employ homogeneous technical coefficients (TCs) to represent technology patterns used by activities located in very different areas of a region, leading to misrepresentations of production and consumption behaviors, and consequently, inaccurate modeling results. To this end, we propose a Differential Spatial Economic Modeling (DSEM) framework that incorporates region-specific TCs for activities within Independent Planning Units (IPUs), such as provinces or cities, each characterized by unique economic, demographic, and technological features. The DSEM framework comprises three core components: (1) a regional economy model that forecasts activity totals for each IPU using economic and demographic forecasting model, supplemented by statistical analyses like the Gini index and K-means clustering to group activities from different IPUs into homogeneous ‘technology’ clusters based on their TCs; (2) a land use model that allocates IPU activity totals to corresponding traffic analysis zones (e.g., counties or districts) using the Differential Spatial Activity Allocation (DSAA) method. This determines the spatial distribution of commodities (such as goods, services, floor space, and labor) across exchange zones, balancing supply and demand to achieve spatial equilibrium in both quantity and price; and (3) a transport model that performs modal split and network assignment, distributing commodity trip origin–destination matrices across a multimodal transportation supernetwork (highways, railways, and waterways) using a probit-based stochastic user equilibrium assignment model. The proposed method is applied to a case study of the Yangtze River Economic Belt, China. The results demonstrate that the proposed DSEM yields better goodness-of-fit (R²) values between observed and estimated flows compared to the traditional aggregate SEM. This indicates a more precise and objective representation of spatial economic activities and technological patterns, thus resulting in improved estimates of freight flows for individual transportation modes and specific links. Full article