Search Results (108)

Phytoplankton are the primary producers in freshwater lake ecosystems and play a fundamental role in maintaining the structure and function of lacustrine food webs. Baiyangdian Lake, located at the core of Xiong’an New Area, is vital for regional aquatic ecological security. However, systematic data on phytoplankton community dynamics throughout the phytoplankton critical growing season are scarce. In this study, we conducted a monthly investigation of phytoplankton communities in Baiyangdian Lake from April to October 2025, analyzing community composition, abundance, and diversity patterns. A total of 152 phytoplankton taxa across 8 major algal groups were identified, with Chlorophyta, Bacillariophyta, and Cyanobacteria being the dominant groups. Phytoplankton abundance exhibited distinct seasonal variation, peaking in August and reaching its lowest in October. The Shannon–Wiener diversity index (H′) and Pielou evenness index (J′) were generally at favorable levels, indicating a relatively stable community structure. The mean phytoplankton density across all sampling sites during the growing season was 8.70 × 10⁶ cells/L, categorizing the lake as having “no obvious bloom” according to standard bloom severity classifications. The overall trophic state of Baiyangdian Lake during the study period was mesotrophic. These findings provide fundamental baseline data and scientific support for the management of algal bloom risks and the long-term conservation of the lake’s aquatic ecosystem. Full article

In situ stress field inversion is a fundamental challenge in geothermal resource development, oil and gas exploration, and mine safety assessment. To address the non-uniqueness and limited accuracy of traditional single-data-source inversion approaches, this study proposes a two-dimensional in situ stress field inversion method constrained by multi-source data, based on integrated well-seismic fault identification. By incorporating dynamic and static mechanical parameters from well logs and employing both a combined spring model and an anisotropic model, a fault-constrained stress field inversion framework is established. Deep learning and optimization algorithms are utilized to integrate the vertical constraints from well logging data with the lateral continuity characteristics of seismic data, enabling high-resolution reconstruction of the in situ stress field. Taking the complex fault-developed geothermal field in the Xiong’an New Area of the Jizhong Depression, Bohai Bay Basin, as a case study, the proposed method demonstrates a marked reduction in inversion error and a substantial improvement in both fault localization accuracy and stress characterization reliability. Full article

In urban environments characterized by narrow roads and dense networks with short intersection spacing and high connectivity, traffic flows exhibit strong spatiotemporal coupling and pose safety challenges. Conventional traffic signal control approaches are difficult to achieve effective regional coordination, while existing control models based on artificial intelligence (AI) lack consideration for trustworthiness and robustness. To address these challenges, an AI-driven traffic control method for digital city traffic signals is proposed. A unified and decodable latent action representation space is constructed, in which the dependency between phase selection and green time duration is captured using discrete action embedding tables and a conditional variational autoencoder (CVAE), ensuring the stability and interpretability of the AI-driven model. Building on this foundation, a globally shared latent representation is integrated with a local coordination mechanism, and the proximal policy optimization (PPO) algorithm is employed for policy training. A state residual prediction regularization loss is introduced to improve the model’s generalization capability and convergence efficiency. Experiments were conducted using a real-road network and traffic flow data from the Rongdong District of Xiongan New Area. Under spatially imbalanced peak hour traffic conditions, the model reduced average vehicle delay by 14.84% and average queue length by 9.2%; under temporally imbalanced peak hour traffic, it achieved reductions of 5.36% and 7.2% in delay and queue length, respectively. These results demonstrate that the proposed method significantly enhances both traffic efficiency and system robustness, offering scalable, reliable technical support for secure and intelligent transportation systems (ITSs). Full article

As China accelerates toward carbon neutrality, decrypting the causal drivers of renewable energy expansion is paramount for effective policy design. We develop a hybrid analytical framework bridging data-driven K2 structural learning with expert-informed Bayesian Networks to map the intricate interdependencies between policy instruments, resource endowments, and socio-economic variables. This causal mapping reveals a fundamental paradigm shift from resource-bound growth to institutional-steered expansion, particularly in the solar sector where the Renewable Portfolio Standard (RPS) has superseded natural radiation as the primary determinant for capacity scaling. Forward sensitivity and backward diagnostic analyses demonstrate that achieving high-growth milestones requires a synergistic convergence of technological cost reductions and mandatory consumption quotas; conversely, the absence of RPS leads to a 64% degradation in systemic causal connectivity. These findings underscore the necessity of transitioning from price-side stimuli to structural consumption-side mandates to ensure a resilient energy transition. Ultimately, this framework and the identified causal pathways provide a strategic blueprint for other emerging economies navigating the complex transition from subsidy-dependent to market-resilient renewable energy landscapes under stringent climate constraints. Full article

Hyperspectral imagery (HSI) offers rich spectral signatures and fine-grained spatial structures for remote sensing, but practical HSI classification is often constrained by scarce labels and complex geometric disturbances, including translation, rotation, scaling, and shear. Existing deep models are typically developed under Euclidean assumptions and rely on data-hungry training pipelines, which makes them brittle in the few-shot regime. To address this challenge, we propose EMNet, a Lie-group-based Equivariant Manifold Network for few-shot HSI classification that explicitly encodes geometric invariance and improves discriminative accuracy. EMNet couples an SE(2)-based Equivariance-Guided Module (EGM) to enforce equivariance to translations and rotations with an affine Lie-group-based Characteristic Filtering Convolution (CFC) that models scaling and shearing on the feature manifold while adaptively suppressing redundant responses. Extensive experiments on WHU-Hi-HongHu, Houston2013, and Indian Pines demonstrate state-of-the-art performance with competitive complexity, achieving OAs of 95.77% (50 samples/class), 97.37% (50 samples/class), and 96.09% (5% labeled samples), respectively, and yielding up to +3.34% OA, +6.01% AA, and +4.14% Kappa over the strong DGPF-RENet baseline. Under a stricter 25-samples-per-class protocol with 10 repeated random hold-out splits, EMNet consistently improves the mean accuracy while exhibiting lower variance, indicating better stability to sampling uncertainty. On the city-scale Xiongan New Area dataset with extreme long-tail imbalance (1580 × 3750 pixels, 256 bands, and 5.925 M labeled pixels), EMNet further boosts OA from 85.89% to 93.77% under the 1% labeled-sample protocol, highlighting robust generalization for large-area mapping. Beyond point estimates, we report mean ± SD/SE across repeated splits and provide rigorous statistical validation by computing Yule’s Q statistic for class-wise behavior similarity, performing the Friedman test with Nemenyi post hoc comparisons for multi-method ranking significance, and presenting 95% confidence intervals together with Cohen’s d effect sizes to quantify practical improvement. Full article

To meet the energy-saving requirements of user equipment (UE) operating in Radio Resource Control idle/inactive states (RRC_IDLE/RRC_INACTIVE) in the 3rd-Generation Partnership Project (3GPP) 5G-Advanced (5G-A) networks, the New Radio (NR) downlink paging procedure relies on periodic monitoring and frequent synchronization signal block (SSB) measurements, which wastes energy when no paging arrivals occur. Meanwhile, heterogeneous Quality of Service (QoS) constraints make it difficult for fixed-parameter Idle Discontinuous Reception and Paging Early Indication mechanisms (IDRX/PEI) to balance energy, delay, and reliability. This paper develops a UAV-assisted 5G-A paging control framework that maps services into multiple QoS classes and models QoS violation risk and system energy consumption under unified accounting, including UE monitoring/reception energy and unmanned aerial vehicle (UAV) forwarding energy. We then propose a QoS-aware risk-driven paging strategy: an offline Long Short-Term Memory (LSTM) predictor is trained to estimate the time-to-next-arrival (TTNA) of paging events and produce a bounded urgency/risk signal to initialize class-dependent thresholds, while online triggering and QoS-feedback-based threshold adaptation regulate the empirical violation rate toward target constraints under varying loads, enabling a controllable energy–delay trade-off. A simulation-based evaluation is conducted to compare the proposed method with representative baselines (Enhanced Paging Monitoring (EPM), Split Paging Occasion (SPOP), and Predicted Paging Early Indication (PPEI)) and to examine the impact of SSB overhead and UAV relaying on the energy–delay–reliability trade-offs. Full article

Geothermal exploration and monitoring often require dense temperature observations in terrains where wired networks are impractical and battery replacement for in situ sensors is costly. This paper proposes an infrared-assisted, temperature-aware access scheme for a UAV-enabled backscatter IoT network tailored to geothermal hotspot sensing. A rotary-wing UAV equipped with a thermal infrared camera and an RF transceiver first surveys the area to construct a surface temperature map and identify candidate hotspots, and then hovers above a selected hotspot to perform periodic frames consisting of wireless energy transfer followed by backscatter uplink collection. Ground sensors harvest RF energy, measure their local temperature, and autonomously activate only when both the harvested energy exceeds a threshold and the measured temperature falls within a target interval broadcast by the UAV, thereby concentrating channel access on thermally relevant nodes. We develop a system model that couples a geothermal-like thermal field, RF energy harvesting, and framed slotted backscatter access, and introduce hotspot-oriented performance metrics including effective hotspot throughput, task completion time, and energy per hotspot report. The simulation results show that the proposed temperature–energy-gated access significantly increases the fraction of successfully decoded packets originating from hotspot regions and improves the energy efficiency of geothermal monitoring compared with full activation and purely energy-based activation. Full article

Earthquake emergency preparedness plays a vital role in strengthening disaster prevention and mitigation capacity, as well as societal resilience. This study focuses on the Xiong’an New Area, a rapidly developing national demonstration zone in China. An evaluation index system for earthquake emergency preparedness was established, and the entropy weight method was applied to objectively determine indicator weights. By integrating field questionnaire data with statistical analysis, preparedness was quantitatively assessed across three administrative levels: township, county, and city. The results reveal pronounced heterogeneity in earthquake emergency preparedness capacity, with township-level performance varying considerably, county-level performance being relatively higher yet still requiring improvement, and the New Area demonstrating strong overall capacity, particularly in emergency organization and coordination mechanisms. However, limited risk awareness and insufficient self-protection capability among grassroots residents remain key constraints on overall preparedness. This research enhances the understanding of earthquake preparedness and provides valuable insights for strengthening disaster prevention, emergency management, and public safety governance in rapidly urbanizing regions. Full article

During jacking construction of prefabricated utility tunnels, asynchronous jack output and interface friction may induce internal force redistribution and deformation amplification at the leading end. Taking a triple-cell prefabricated utility tunnel in Xiong’an New Area as a case study, a three-dimensional finite element model was established considering inter-segment contact, equivalent bolted connections, and bottom-slab-bedding friction. Jack asynchrony was idealized as a quasi-static thrust imbalance, and a synchronous case, asynchronous cases with thrust differences of 5–30%, and varying friction coefficients were analyzed. For the 30% thrust-difference condition, structural responses were examined at both the gasket-compression stage and the maximum jacking-force stage. The results show that jacking loads attenuate along the tunnel length in a staged manner, with the leading end acting as the primary load-transfer zone. Increasing thrust imbalance drives the response from axial compression toward eccentric compression-bending, accompanied by monotonic increases in principal stresses and vertical displacement. Higher friction further amplifies the leading-end response; nevertheless, for the investigated configuration, stresses and deformations under a 30% thrust imbalance remain within engineeringly acceptable limits. The findings provide a basis for identifying critical leading-end locations, arranging monitoring schemes, and supporting construction control under asynchronous jacking. Full article

The safe utilization of underground spaces constitutes a critical challenge for densely populated cities, making geosafety risk prevention in underground development a focal point for both academic research and governmental governance. As the pivotal link and ultimate objective in geological safety management, risk prevention facilitates the transition from theoretical research to administrative practice. This study establishes a management-oriented technical framework for geological risk preventive zoning in underground space utilization, addressing the current research gap where zoning methodologies inadequately integrate with governmental decision-making processes due to insufficient consideration of multidimensional attributes from both researcher and administrator perspectives. Taking Xiong’an New Area in China as a case study, the framework employs a tri-level analytical structure, restrictive tier, limiting tier, and influencing tier, with phased weighting methodologies, CRITIC-EWM for objective weighting vs. AHP-FAHP for subjective weighting. The scientifically validated results demonstrate the framework’s feasibility and scalability. Limitations and future research directions are identified to guide subsequent studies in this field. Full article

Spatial equity of healthcare services is a critical concern in social equity and spatial justice research. Despite the availability of various methods to measure this equity, few studies have integrated the supply–demand coupling perspective with the analysis of impacts of residents’ travel behaviors’ on equity. This study develops and applies a Travel Behavior-based Coupling Coordination Degree (TB-CCD) method to assess the spatial equity of healthcare services in the Xi’an region. The results show the following: (1) Traditional single-mode models may fail to accurately assess this equity, whereas the TB-CCD model provides a more realistic evaluation. (2) Public transportation and driving provide a more equitable distribution of healthcare services compared to walking and cycling modes. The spatial equity of healthcare services exhibits a distinct core–periphery pattern, where accessibility and equity levels are significantly higher in city centers than in suburban areas. (3) The distribution of inequity ‘deserts’ and ‘oases’ in healthcare services is found to be travel-mode dependent, with the walking and public transportation modes exhibiting the highest incidence of these classifications. These findings provide valuable insights for urban planners and policymakers to formulate strategies and spatial plans aimed at enhancing equity in healthcare services. Full article

High-rise building areas (HRBs), a key urban land-cover type defined by distinct morphological and functional characteristics, play a critical role in urban development. Their spatial distribution and temporal dynamics serve as essential indicators for quantifying urbanization and analyzing the evolution of urban spatial structure. This study addresses the dynamic monitoring needs of HRBs by developing a temporal change detection model, TCA-Unet (Temporal Change-Aware U-Net), based on a temporal change-aware attention module. The model adopts a dual-path design, combining a temporal attention encoder and a change-aware encoder. By explicitly modeling temporal difference features, it captures change information in temporal remote sensing images. It incorporates a multi-level weight generation mechanism that dynamically balances temporal features and change-aware features through an adaptive fusion strategy. This mechanism effectively integrates temporal context and enhances the model’s ability to capture long-term temporal dependencies. Using the Xiong’an New Area and its surrounding regions as the study area, experiments were conducted using Sentinel-2 time-series imagery from 2017 to 2024. The results demonstrate that the proposed model outperforms existing approaches, achieving an overall accuracy (OA) of 90.98%, an F1 score of 82.63%, and a mean intersection over union (mIoU) of 72.22%. Overall, this study provides an effective tool for extracting HRBs for dynamic monitoring and offers valuable guidance for urban development and regulation. Full article

Geothermal fluids are the main carrier of hydrothermal geothermal resources. Identifying the differences in geothermal fluids in different types of reservoirs is a prerequisite and fundamental for the efficient development of geothermal resources and is of great significance for scientific research on geothermal resources. The North China Plain contains a typical carbonate thermal reservoir, and in this paper, the hydrochemical, isotopic, and redox characteristics of the geothermal fluids in sandstone and carbonate reservoirs are studied to obtain the differences in the geothermal fluids in the Rongcheng geothermal field in Xiong’an New Area. The results indicate that the geothermal fluids in the sandstone and carbonate reservoirs are mainly supplied by atmospheric rainfall, and the hydrochemical type is mainly Cl-Na type. By comparing and analyzing the stable isotope (O, H, C, S, and Sr) characteristics of the two types of geothermal fluids, it is found that the variation range of δ13C values for two types of sandstone thermal storage geothermal fluids was found to be −10.6‰~−12.8‰, while the variation range of δ¹³C values for carbonate thermal storage geothermal fluids was −3.3‰~−7.5‰. The ⁸⁷Sr/⁸⁶Sr ratio of sandstone thermal storage geothermal fluids was distributed between 0.708–0.718, and the ⁸⁷Sr/⁸⁶Sr ratio of carbonate thermal storage geothermal fluids was distributed between 0.708–0.713. The range of δ³⁴S values for sandstone thermal storage geothermal fluids was +9.46‰~+10.5‰, and the range of δ³⁴S values for carbonate thermal storage geothermal fluids was +24.84‰~+34.49‰. The two types of geothermal fluids have been subjected to varying degrees of oxidation-reduction, and their cycling and mixing characteristics are different. This has resulted in the formation of relatively oxidized geothermal fluids in the sandstone geothermal reservoir and relatively reduced geothermal fluids in the carbonate geothermal reservoir. In future development and utilization of geothermal resources, paying attention to the basic characteristics of the geothermal fluids in different reservoirs and identifying the differences in different geothermal fluids can further improve the efficiency of geothermal resource development and utilization. Full article

The open-loop geothermal system with a horizontal well is expected to be a favorable approach to develop geothermal energy from reservoirs through an extended horizontal section. The wellbore design is essential for enhancing the performance of a geothermal system. Nevertheless, research on wellbore parameters for open-loop geothermal systems remains scarce. Hence, a coupled wellbore–fractured reservoir mathematical model is developed and numerically solved using COMSOL Multiphysics 5.6. The target geothermal reservoir is located in Xiongan New Area, China. The temperature distribution in the wellbore is analyzed based on the numerical model. Furthermore, the impacts of wellbore parameters—including the thermal conductivity, thickness, and diameter of the insulated pipe, as well as the injection–production spacing—on production performance are systematically evaluated. Grey relational analysis is further employed to quantify the sensitivity of the wellbore parameters. The results show that the wellbore, particularly the insulated pipe, determines the heat loss of the production fluid, while the fractured reservoir dominates the heat extraction capacity of the injection fluid. Reducing the thermal conductivity of the insulation layer has the most significant effect on decreasing the production temperature and thermal power. The injection–production pressure demonstrates a notable decline as the insulation thickness decreases. Increasing the pipe diameter leads to a non-monotonic trend in the injection–production pressure, which first decreases and then increases. There are critical design thresholds for the thermal conductivity, thickness, and diameter of the insulated pipe. Additionally, expanding the injection–production spacing can effectively delay thermal breakthrough and enhance overall system performance. These findings provide valuable guidance for wellbore design in field applications. Full article

Understanding interactions among ecosystem services (ESs) is vital for ecological conservation and governance. As a newly established national-level New Area in China, Xiong’an New Area holds significant ecological importance. This study first explores its long-term spatiotemporal changes in ESs using an “assessment-attribution-correlation-zoning” framework. Results show that net primary productivity (NPP) remained stable from 1990 to 2023; soil conservation (SC) and habitat quality (HQ) improved from 2018 to 2023; carbon storage (CS) declined significantly from 2010 to 2015; and water yield (WY) decreased continuously from 1990 to 2023. Rainfall was the key natural driver, while GDP and road network density were critical anthropogenic factors. Correlations among the five ESs weakened: synergies between soil conservation–water yield, soil conservation–carbon storage, soil conservation–habitat quality, water yield–carbon storage, and habitat quality–carbon storage diminished, and the water yield–habitat quality synergy turned into a trade-off. Spatial autocorrelation analysis revealed significant spatial heterogeneity in ESs. Carbon storage–habitat quality, carbon storage–soil conservation, habitat quality–soil conservation, net primary productivity–habitat quality, water yield–soil conservation, and net primary productivity–water yield showed low-low clustering; net primary productivity–carbon storage, net primary productivity–soil conservation, and water yield–habitat quality exhibited low-high clustering; and water yield–carbon storage showed high-high clustering. Finally, ESs were classified into six bundles via self-organizing maps, with the carbon–ecology maintenance bundle being the largest. These findings provide a basis for scientific ecosystem management and sustainable development in Xiong’an. Full article