Search Results (14,635)

Building underground water-sealed oil storage (UWSOS) caverns on islands poses a potential risk of seawater intrusion. As UWSOS is mostly constructed within rock masses, research on seawater intrusion through rock fractures holds important engineering value. This study combines single-fracture model tests with numerical simulations to investigate patterns of seawater intrusion in fractured rocks. Results show that, due to the density difference between seawater and freshwater, a saltwater wedge forms in coastal zones. Under tidal action, an upper saltwater plume forms in the intertidal zone, with its scale positively correlated with tidal range. After cavern excavation, the saltwater–freshwater transition zone widens, and seawater gradually intrudes from the cavern bottom. The upper saltwater plume evolves into a “saltwater tongue” during intrusion, with a growth rate ranging from 921.89% to 5691.52%, while the lower saltwater wedge moves landward by 37.86% to 82.65%. The saltwater tongue scale increases with tidal amplitude, but the lower wedge scale shrinks. With the horizontal water curtain installed, the saltwater wedge area decreases by 45.42% to 57.33%; in contrast, installing a vertical water curtain can effectively block seawater intrusion. These results provide an important experimental foundation for seawater intrusion research in island UWSOS caverns. Full article

Green highway networks function as critical linear carbon sinks for sustainable transportation systems, yet the link between their network topological structure and sequestration efficiency remains poorly understood. This research establishes an integrated framework to explore the spatial synergy and mismatch between green highway network structure and carbon sequestration in Shandong Province. We constructed a spatially explicit “node-edge” network at a road corridor scale (250-m buffer) and quantified seasonal Net Primary Productivity (NPP) using the CASA model. Results demonstrate: (1) The green highway network exhibits a highly heterogeneous, heavy-tailed structure with low clustering coefficients (<0.01), characterized by high connectivity efficiency but limited structural redundancy; (2) The network’s NPP shows pronounced spatiotemporal dynamics, peaking in summer (mean: 364.7 gC · ${\mathrm{m}}^{-2}$· ${\mathrm{season}}^{-1}$) and reaching its nadir in winter (mean: 52.2 gC · ${\mathrm{m}}^{-2}$· ${\mathrm{season}}^{-1}$); (3) Statistically significant spatial synergies ($p<0.01,Z>4.00$) exist between green highway topology and NPP, with weighted closeness ($I=0.29$) and weighted degree ($I=0.21$) showing the highest effect sizes; (4) LISA analysis identified specific spatial mismatches, such as “High-Low” clusters (high structural importance but low carbon efficiency) in northern inland regions, which represent priority targets for ecological retrofitting. These outcomes quantify that network topology effectively reflects ecological performance, offering a “topology-guided” strategy to promote climate change mitigation and enhance the long-term sustainability of regional transportation infrastructure. Full article

Based on the large volume of observational data obtained from Argo and several satellites, an increasing number of datasets are being developed and applied to oceanographic research. However, there are still problems such as sparse subsurface observations, insufficient parameters, and weak pertinence. This study provides a basic framework for high-resolution data fusion that focuses on the multi-source observations in the Western North Pacific. Multi-source observations from satellites, Argo floats, and historical in situ profiles are fused using a statistical model and a gradient-dependent optimal interpolation method. A daily gridded dataset with a 0.25° horizontal resolution is developed, which includes temperature, salinity, and currents. The results show that the correlation coefficients between the observations and the inverted profiles of temperature and salinity are about 0.99 and 0.94, respectively, with mean root mean square errors of about 1.27 °C and 0.13, respectively. In the Northwest Pacific Ocean, the most suitable parameter settings are a search radius of 1.5° in longitude and latitude, correlation scale constant of 0.25°, and relative observation error of 2. Consequently, the average RMSEs of the fused temperature and salinity fields are 0.43°C and 0.056, respectively. Compared with other reanalysis datasets, the product constructed in this study retains more high-frequency ocean signals, and its temperature error relative to XBT observations is also the smallest. Furthermore, the dataset effectively depicts the characteristics of marine dynamic processes such as the Kuroshio paths and mesoscale eddies. Full article

The suction pile well construction technique is increasingly adopted in deepwater drilling projects. The soil–structure interaction mechanism during the penetration and installation of the wellhead suction pile in clay is complex. Given the critical demand for precise installation outcomes in engineering practice, the influence of penetration velocity on installation performance requires significant consideration. Through scale-model experimental methods, various penetration velocities were configured primarily by adjusting suction pump flow rates. The influences of these velocities on penetration resistance, penetration depth, and related metrics were systematically assessed. A case study was conducted based on the engineering parameters of a wellsite in the South China Sea. A theoretical algorithm for WSP penetration resistance was developed and subsequently refined through experimental data. Coefficient optimization was established via theoretical assessment of strain-rate dependency and experimental data calibration. The optimized algorithm demonstrated strong agreement with field measurements, achieving a coefficient of determination ($R^2$) exceeding 0.9. Compared to conventional theoretical approaches, it incorporated explicit consideration of penetration velocity. The analysis indicates that in soft clay, the penetration resistance of wellhead suction piles exhibits significant sensitivity to penetration rate, increasing with higher velocities. The influence of penetration rate on penetration depth is relatively weak. This computational approach offers design guidance for installation procedures and enables the implementation of the suction pile well construction mode in the South China Sea. Full article

Open-set recognition of wild plants in natural complex scenes is an important task for plant conservation, ecological monitoring, and precision agriculture. Traditional closed-set learning methods struggle to handle unseen species not covered by the training set and complex environmental interferences, while existing open-vocabulary methods lack knowledge-driven reasoning capabilities and cannot provide interpretable recognition for unknown categories. This research proposes the Reasoning-Aware Perceptual Framework that integrates open-vocabulary vision-language models, foundation mask-generation tools, and domain knowledge reasoning to achieve known/unknown category recognition, online perception, and interpretable reasoning of unknown wild plant species. Centered on a five-stage closed loop of Perception-Retrieval-Reasoning-Decision-Iteration, the framework captures open concepts through vision-language feature alignment, completes evidence-based reasoning and confidence evaluation in combination with a botanical domain knowledge base, and finally outputs species classification decisions, interpretable reasoning reports with family/genus-level taxonomic affinity, and uncertainty-calibrated confidence scores. The unknown category estimation with family/genus-level taxonomic affinity in this framework refers to a general unknown label combined with taxonomic affinity at the family/genus level, which can clearly reflect the evolutionary relationship between unknown species and known species. Experiments on the self-constructed WildPlantOpenSet-10K dataset and public benchmark datasets report an F1-score of 84.7% for unknown species recognition, AUROC of 0.93 for known/unknown discriminability, and mean F1 of 87.0% across all categories. This framework focuses on open-set wild plant recognition and interpretable reasoning, using off-the-shelf instance extraction to acquire visual features for downstream reasoning. It maintains stable robustness in complex scenarios such as occlusion, strong light, and multi-species coexistence, and can adapt to the open-world environment without relying on large-scale pixel annotations, providing a research prototype for interpretable open-set recognition in complex natural environments. Full article

For high-speed pneumatic seeders, accurate real-time seed detection underpins downstream quality assessments including seed counting, seeding-rate estimation, and uniformity evaluation. Under high-speed operating conditions, seeds exhibit rapid motion, dense distribution, frequent occlusion, and severe motion-blur-induced edge degradation, posing substantial challenges for vision-based detection. This study proposes HSSD-YOLO, an improved detection algorithm built upon YOLOv11, incorporating three modules: a Motion Blur Enhanced Stem module (MBE-Stem) employing learnable Sobel gradient operators for edge feature extraction under motion blur; an Attention-enhanced Deformable Convolutional Network (ADCN) with a Residual Spatial-Channel Attention (RSCA) mechanism for adaptive sampling of irregularly shaped seeds; and an Edge-Guided Adaptive Recalibration Feature Pyramid Network (EGAR-FPN) injecting edge prior information into multi-scale feature fusion. On a self-constructed dataset of indica rice, japonica rice, and wheat seeds, HSSD-YOLO achieves 96.6% mAP@0.5 and 77.4% mAP@0.5–0.95, surpassing YOLOv11n by 2.5 and 5.4 percentage points, respectively, with only 5.2 M parameters. Ablation studies confirm synergistic gains exceeding linear superposition. Under the conditions evaluated, HSSD-YOLO outperformed all compared algorithms, providing the per-frame detection foundation for downstream seeding-quality tasks; empirical validation of those tasks on continuous video and embedded hardware remains outside the present scope. Full article

In this paper, we introduce a novel kernel-based estimator for the regression operator of a scalar response variable $\mathcal{R}$ given a functional covariate $\mathcal{F}$ taking values in a semi-metric space. The estimator is constructed through the minimization of the least absolute relative error (LARE) criterion, which provides an invariant scale and more balanced measure of predictive performance than conventional squared error methods. By focusing on relative deviations, the LARE approach effectively reduces the influence of extreme response values and enhances robustness in the presence of heteroscedasticity. From a theoretical point of view, we investigate the asymptotic behavior of the proposed estimator under strong mixing conditions for functional time series data. We show that, despite the temporal dependence structure, the estimator remains consistent and achieves convergence rates comparable to those obtained under independence. In the computational part, we show that the proposed method is computationally efficient and straightforward to implement. Its empirical performance is evaluated through simulation studies conducted under different dependence scenarios. In addition, the applicability of the method is illustrated through the analysis of a real data set. Full article

Real-time kinematic (RTK) positioning in open-pit mining environments is critically compromised by non-line-of-sight (NLOS) signals and anisotropic multipath effects induced by pit walls, haul roads, and industrial infrastructure. Conventional elevation-dependent stochastic models fail to discriminate between geometrically favorable low-elevation satellites and those subject to directional obstruction, resulting in degraded ambiguity resolution and decimeter-level positioning errors that undermine safety-critical deformation monitoring. This paper presents an adaptive RTK positioning framework utilizing azimuth-integer-based elevation masking to explicitly model site-specific obstruction geometry. The proposed method discretizes the horizontal plane into 360 integer-degree sectors, extracts minimum elevation angles per sector from 24 h line-of-sight (LOS) data, and constructs a smoothed 360°mask profile via moving-window filtering. A virtual elevation-angle transformation is introduced to normalize satellite geometry relative to the local mask, enabling adaptive down-weighting of diffraction-susceptible observations within the stochastic model without requiring multi-day satellite repeat arcs or hardware modifications. The approach was validated using 54 h of BDS data collected at eight monitoring stations within the Wangjialing open-pit mine, China. Implementation of the mask model engendered a selective 8.1% reduction in satellite participation (15.66 to 14.39 satellites) while significantly enhancing observation quality. The ambiguity validation ratio improved by 19.5% (from 9.43 to 11.27 in the experimental project), and the fix success rate increased from 92.4% to 97.2% (exceeding the 95% reliability threshold at all stations). The RMS errors in the east, north, and up directions improved by 34.8% to 65.2%, 28.7% to 77.0%, and 44.8% to 70.8%, respectively, with the most dramatic gains observed at stations subject to severe azimuthal obstruction (e.g., ZDH6 vertical RMS: from 50.7 mm to 14.8 mm). By explicitly modeling anisotropic obstruction geometry through discrete angular sampling, the proposed method achieves sub-centimeter positioning accuracy and robust ambiguity resolution in challenging mining environments without additional hardware or empirical threshold tuning, offering a cost-effective solution for large-scale, real-time deformation monitoring systems. Full article

Liquid distributors are critical internals in packed columns, whose distribution uniformity directly governs the column’s hydrodynamic performance, mass transfer efficiency, and operational stability. To address the poor liquid distribution uniformity of trough distributors under high liquid loads, this study proposes a novel trough distributor integrated with a resistance–guidance synergistic composite unit. Combining numerical simulations and experimental validation, the core synergistic mechanism of the unit was systematically investigated. The horizontal baffle serves as a secondary throttling point, which converts axial kinetic energy into static pressure energy to supplement the driving force for transverse energy redistribution and physically suppresses the generation and development of large-scale vortices. Meanwhile, vertical guide vanes guide liquid flow, constrain the expansion of harmful secondary flows, and construct a controllable transverse pressure gradient. The resistance–guidance unit collaboratively realizes two-stage energy conversion and redistribution, reconstructs the liquid momentum transfer path, and restores the static pressure gradient-dominated transverse energy transport mechanism. This study clarifies the intrinsic mechanism of resistance–diversion synergy for liquid distribution control, laying a theoretical foundation for the structural optimization of trough liquid distributors under high-liquid-load conditions. Full article

The transition to low-carbon hydrogen is recognized as a priority decarbonization pathway, yet the risk profiles of hydrogen projects remain poorly characterized for non-Western, resource-rich, and geopolitically constrained economies. This study develops and applies a structured expert-based risk mapping framework for low-carbon hydrogen production in Russia. The framework integrates three procedural steps: (1) identification and classification of 21 risk factors across seven thematic groups based on systematic literature analysis; (2) construction of a directed interdependency matrix (7 × 7, ordinal scale 0–2) via structured expert elicitation (n = 10, February 2026); and (3) probability–impact prioritization using the P × S scoring heuristic (both axes on a 1–5 scale, per ISO 31000:2018). Results reveal three critical risk factors (P × S Score ≥ 20): high cost of capital and restricted access to external financing (Score = 24, P = 5, S = 5), dependence on imported electrolyzer components (Score = 20, P = 4, S = 4), and insufficient export infrastructure (Score = 20, P = 5, S = 4). The interdependency matrix identifies economic and financial risks as the primary “accumulator” of systemic influence, receiving maximum incoming impact from all other six groups. Regulatory risks occupy a medium position but exert disproportionate cascading effects on technology choice and project economics. The framework is explicitly designed for transferability to other resource-abundant, capital-constrained economies (Kazakhstan, Iran, Algeria), with structural adaptation conditions specified. Findings are relevant for policymakers, investors, and multilateral stakeholders shaping hydrogen value chains in non-Western contexts. Full article

Accurate identification of weak nodes is a prerequisite for online security assessment, preventive control, and resilience enhancement in modern power systems. However, conventional single-layer graph-learning models mainly emphasize local neighborhood aggregation and are insufficient for characterizing vulnerability propagation from equipment-level disturbance to regional congestion and system-level transfer constraints. This paper proposes a mechanism-aware hierarchical graph-learning framework for weak-node identification in complex interconnected power grids. We emphasize that attention, fusion, and gating operations are standard neural-network mechanisms and are not claimed as new generic deep-learning blocks. The contribution of this paper is the power-system-specific formulation: constructing an electrically meaningful local-supernode hierarchy, defining reproducible mechanism-based node and branch-vulnerability proxies, and interpreting weak-node rankings through node–line–corridor coupling evidence. In the validated implementation, a local graph convolutional encoder and a supernode/global graph convolutional encoder generate 32-dimensional local embeddings and 16-dimensional global embeddings, which are concatenated and decoded by a 48 → 24 → 1 multilayer perceptron to obtain node vulnerability scores. Experiments are conducted on reproducible IEEE benchmark data generated from pandapower standard systems, with representative comparisons on the IEEE 57-bus, 145-bus, and 300-bus systems and a detailed structural interpretation on the IEEE 145-bus case. The present results validate the ability of the implemented local–global hierarchical model to reproduce the proposed mechanism-based vulnerability proxy on representative small- and medium-scale benchmarks. Full article

Medical resources are primary public goods, but the nature of their distribution across different-sized cities is unclear. Here, we examined the nonlinear scaling relationship between urban populations and medical resources in China, moving beyond the limitations of traditional linear evaluation metrics. Taking 296 Chinese cities as samples, we constructed scaling law models between population size and three medical resource indicators: the numbers of hospital beds, doctors, and hospitals. The results show that the number of doctors maintained a linear scaling relationship on the whole (scaling exponent $\beta$: 0.98–1.06), while the numbers of hospitals ($\beta$: 0.79–0.91) and hospital beds ($\beta$: 0.91–0.99) both exhibited sublinear scaling (2000–2022), confirming the existence of economies of scale in basic medical facilities. The Scale-Adjusted Metropolitan Indicator (SAMI) further reveals spatial agglomeration characteristics: the northern and southwestern regions of China perform notably better than expected in hospital availability, while provincial cites show advantages in terms of the numbers of beds and doctors. This study quantifies the nonlinear allocation of medical resources across Chinese cities and advocates for a reasonable allocation mechanism to promote medical equity. Full article

Bridge structural health monitoring (SHM) systems often suffer from large-scale data missing due to sensor faults, communication interruptions and other reasons during long-term operation, which seriously restricts the reliability of structural state assessment and maintenance decision-making. Compared with conventional single-channel independent modeling strategies commonly used for data imputation, their inherent neglect of spatial correlations and cross-modal causal associations among multi-source heterogeneous monitoring data such as displacement, wind speed, and temperature constrain the imputation capability, particularly when the target channel suffers from long-term continuous data loss. To address the above problems, this paper proposes a collaborative imputation framework integrating a graph attention network (GAT), a modal-aware cross-attention (MACA) mechanism and temporal encoder–decoder architecture (ITimeGAN). Firstly, the sensor feature topological graph is constructed based on the Pearson correlation coefficient, and the spatial dependency among multi-source features is adaptively learned through GAT. Then, the MACA module is introduced, which takes the target displacement as Query and environmental loads as Key/Value, and dynamically aggregates cross-modal driving information through multi-head attention. Finally, a bidirectional LSTM encoder and a unidirectional LSTM decoder are adopted to capture long-range temporal dependencies, so as to realize the accurate reconstruction of missing displacement data. Validated on the 9-dimensional real-world monitoring data from the GeoSHM system of the Forth Road Bridge (UK) under both random missing (10–50%) and continuous long-term missing (1–10 days) scenarios, ITimeGAN achieves an R² of 0.9950 (MAE = 4.25 mm) for longitudinal displacement and 0.9759 (MAE = 6.70 mm) for vertical displacement even under 10 consecutive days of complete data absence. Ablation analysis further reveals that the incorporation of graph attention and cross-modal attention modules reduces the longitudinal displacement MAE by 57% over the baseline, with the imputation performance ranking across three displacement directions being fully consistent with the underlying physical correlation strengths, thereby confirming the effectiveness of the proposed cross-modal collaborative strategy. Full article

Large language models (LLMs) have demonstrated strong potential in medical knowledge applications; however, their reliability in knowledge-intensive medical reasoning—remains limited due to hallucination, inadequate domain grounding, and unstable inference behavior. These limitations are particularly pronounced in tasks of professional medical reasoning that require strict logical consistency and authoritative knowledge support. This study proposes a generative AI architecture that integrates RAG (Retrieval-Augmented Generation) with parameter-efficient supervised fine-tuning based on Low-Rank Adaptation (LoRA) to improve reasoning stability and diagnostic accuracy in complex medical domains. The architecture combines internalized domain reasoning learned through LoRA-based fine-tuning with external knowledge grounding enabled by a dynamic RAG mechanism, allowing the model to selectively retrieve domain-specific knowledge only when it is semantically relevant and evidence supported. To validate the proposed architecture, a large-scale real-world dataset comprising 11,476 multiple-choice questions from Taiwan’s national Traditional Chinese Medicine (TCM) licensing examinations (2005–2025) is constructed as a representative case study of knowledge-intensive medical reasoning. The experimental results show that the baseline LLM achieves an accuracy of 61.0%. Incorporating RAG improves accuracy to 89.0%, while combined LoRA-based fine-tuning and RAG architecture further increases accuracy to 90.1%, with reduced variance across repeated evaluations. Statistical analysis using McNemar’s test confirms that the performance improvements introduced by the retrieval mechanism are highly significant. The results demonstrate that integrating parameter-efficient fine-tuning with dynamically controlled retrieval is critical to balancing reasoning stability and knowledge enhancement in generative AI systems. Beyond the specific medical case study examined in this work, the proposed architecture offers a reproducible and extensible framework for developing reliable generative AI systems in other knowledge-intensive professional reasoning and educational domains. Full article

Under the combined impacts of climate change and urbanization, flood disasters have exhibited increasing non-stationarity, low-frequency but high-impact characteristics, and enhanced spatial dependence. Traditional indemnity-based flood insurance has certain limitations in claim efficiency and loss assessment. In contrast, index-based flood insurance, characterized by objective triggering mechanisms, rapid claim settlement, and low operational costs, has gradually become an important tool for flood catastrophe risk management. Based on a literature review approach, this study systematically reviews the index system, pricing mechanisms, and basis risk of index-based flood insurance, and provides a comprehensive analysis from the perspectives of index construction, threshold determination, and payout design. The results indicate that index systems have evolved from single hazard indicators to coupled indices integrating hazard characteristics and loss information, and multiple pricing approaches have been developed, including fixed, linear, piecewise payout, and probabilistic payout schemes (payouts determined by loss probabilities rather than fixed thresholds). Among the reviewed approaches, inundation-area-based indices generally show stronger consistency with actual losses at urban scales, whereas precipitation-based indices are more suitable for large-scale regional applications due to their rapid triggering capability. However, basis risk remains a critical issue, mainly arising from index errors, spatial scale mismatches, and inappropriate threshold settings. Therefore, to address the identified limitations of basis risk, threshold uncertainty, and spatial mismatches, future research should focus on multi-dimensional risk indices, dynamic threshold setting, and optimized spatial risk zoning, as well as the integration of remote sensing and machine learning methods to improve the consistency between indices and actual losses. The findings provide practical guidance for insurers in product design, for policymakers in regional flood risk financing, and for disaster managers in improving climate adaptation strategies. Full article