Search Results (20,195)

Maritime ship detection faces challenges due to complex object poses, variable target scales, and background interference. This paper introduces YOLO-PFA, a novel SAR ship detection model that integrates multi-scale feature fusion and dynamic alignment. By leveraging the Bidirectional Feature Pyramid Network (BiFPN), YOLO-PFA enhances cross-scale weighted feature fusion, improving detection of objects of varying sizes. The C2f-Partial Feature Aggregation (C2f-PFA) module aggregates raw and processed features, enhancing feature extraction efficiency. Furthermore, the Dynamic Alignment Detection Head (DADH) optimizes classification and regression feature interaction, enabling dynamic collaboration. Experimental results on the iVision-MRSSD dataset demonstrate YOLO-PFA’s superiority, achieving an mAP@0.5 of 95%, outperforming YOLOv11 by 1.2% and YOLOv12 by 2.8%. This paper contributes significantly to automated maritime target detection. Full article

Significant uncertainties persist across different Leaf Area Index (LAI) products due to multiple factors; therefore, the accuracy assessment of the global LAI products is an indispensable step before their application. In this study, comprehensive validation of multi-scale LAI products including Sentinel-2, Landsat-8/9, and MCD15A3H was implemented utilizing fine-resolution LAI maps which were based on UAV images and field-measured LAI data. The validation results demonstrated a consistent, systematic underestimation across all the LAI products within the study area, the RMSE of these products ranged from 0.56 to 1.63, and the coarse-resolution MCD15A3H LAI product demonstrated highest accuracy (RMSE = 0.56, R² = 0.69). The Sentinel-2 products exhibited intermediate accuracy among all those products (RMSE: 1.16–1.36). The Landsat-8/9 LAI product showed markedly lower accuracy relative to Sentinel-2; its RMSE (1.63) exceeded that of Sentinel-2 10 m LAI and 20 m LAI by 40.52% and 21.64%, respectively. In addition, all these LAI products showed consistent seasonal variation patterns with the reference LAI maps. Moreover, Sentinel-2 10 m LAI products showed serious underestimation for all vegetation types, with forests providing the highest RMSE = 0.89. This study serves as a valuable reference for the application of multi-scale LAI products in heterogeneous terrain and provides directions for the improvement of fine-resolution LAI retrieval algorithms. Full article

Smart contracts empower many blockchain applications but are exposed to code-level defects. Existing methods do not scale to the evolving code, do not represent complex control and data flows, and lack granular and calibrated evidence. To address the above concerns, we present an across-graph corresponding contract-graph method for vulnerability detection: abstract syntax, control flow, and data flow are fused into a typed, directed contract-graph whose nodes are enriched with pre-code embeddings (GraphCodeBERT or CodeT5+). A Graph Matching Network (GMN) with cross-graph attention compares contract-graphs, aligns homologous sub-graphs associated with vulnerabilities, and supports the interpretation of statements at the level of balance between a broad structural coverage and a discriminative pairwise alignment. The evaluation follows a deployment-oriented protocol with thresholds fixed for validation, multi-seed averaging, and a conservative estimate of sensitivity under low-false-positive budgets. On SmartBugs Wild, the method consistently and markedly exceeds strong rule-based and learning baselines and maintains a higher sensitivity to matching false-positive rates; ablations track the gains to multi-graph fusion, pre-trained encoders, and cross-graph matching, stable through seeds. Full article

Due to the diverse origins of shale reservoirs, the coal measure shales of the Wuxiang block, Qinshui Basin typically exhibit fractal pore structures, which significantly influence shale gas occurrence and migration. Clarifying the fractal nature of pore structures is significant for the efficient development and utilization of shale gas. In this study, mercury intrusion porosimetry and liquid nitrogen adsorption experiments were conducted to develop a method that integrates pore compressibility correction and nitrogen adsorption for pore structure characterization. On this basis, this study analyzed the fractal characteristics of coal measure shale pore structures across multiple scales. The results reveal that coal measure shale pores exhibit a three-stage fractal pattern, consisting of three regions with pore diameters >65 nm (seepage pores), 6–65 nm (transition pores), and <6 nm (micropores). Samples with fractal dimensions of seepage pores ($D_{\mathrm{a}}$) exceeding 2.9 and transition pores ($D_1$) exceeding 2.5 tend to have larger specific surface areas and more complex pore structures; this is indicated by the increased surface roughness of large-scale pores, which hinders gas seepage. Samples with lower fractal dimension of micropores ($D_2$)—in the range of 2.2–2.8—exhibit higher micropore development, larger specific surface area, and simpler pore structures, as demonstrated by a greater number of micropores and a more uniform pore distribution, which promotes gas adsorption. Full article

The complex jet-ground interactions of Short Take-off and Vertical Landing (STOVL) aircraft are critical to flight safety and performance, yet studying them with traditional full-scale wind tunnel tests is prohibitively expensive and time-consuming, hindering design optimization. This study addresses this challenge by developing and numerically verifying a “pressure ratio–momentum–geometry” multi-dimensional similarity framework, enabling accurate and efficient scaled-model analysis. Systematic simulations of an F-35B-like configuration demonstrate the framework’s high fidelity. For a representative curved nozzle configuration (e.g., the F-35B three-bearing swivel duct nozzle, 3BSD), across scale factors ranging from 1:1 to 1:15, the plume deflection angle remains stable at 12° ± 1°. Concurrently, axial force (F) and mass flow rate (Q) strictly follow the square scaling relationship (F ∝ 1/n², Q ∝ 1/n²), with deviations from theory remaining below 0.15% and 0.58%, respectively, even at the 1:15 scale, confirming high-fidelity momentum similarity, particularly in the near-field flow direction. Second, a 1:13.25 scale aircraft model, constructed using Froude similarity principles, exhibits critical parameter agreement (intake total pressure and total temperature) of the prototype-including vertical axial force, lift fan mass flow, and intake total temperature—all less than 1.5%, while the critical intake total pressure error is only 2.2%. Fountain flow structures and ground temperature distributions show high consistency with the full-scale aircraft, validating the reliability of the proposed “pressure ratio–momentum–geometry” multi-dimensional similarity criterion. The framework developed herein has the potential to reduce wind tunnel testing costs and shorten development cycles, offering an efficient experimental strategy for STOVL aircraft research and development. Full article

Bone fragility represents a significant global health burden, characterized by the deterioration of bone strength, increased brittleness, and heightened fracture susceptibility. Osteoporosis substantially elevates the risk of fragility fractures, the principal clinical manifestation of the disease. Current diagnostic approaches, including biomedical imaging, bone strength assessment, and bone mineral density measurement, are closely linked to identifying bone fragility through various predictive models and tools. Although numerous studies have employed predictors to characterize fragility fractures, few have comprehensively examined the morpho-structural features of bone across multiple hierarchical scales, limiting the ability to fully elucidate the underlying mechanisms of bone fragility. This review summarizes recent advancements in predictive modeling and novel diagnostic tools, focusing on multiscale approaches for assessing bone fragility. We critically evaluate the translational potential of these tools for the early detection of fragility fractures and their clinical application in mitigating fracture risk. Moreover, this study discusses the integration of multiscale predictive methodologies, which promise to enhance early-stage bone fragility detection and potentially prevent severe fractures through timely intervention. Finally, the study reflects on current research limitations, addressing the challenges associated with multiscale predictive modeling of bone fragility, and proposes future directions to refine these tools to improve the accuracy and utility of fragility fracture prediction and prevention strategies. Full article

The deterioration of rocks’ mechanical properties during the late stage of water injection development significantly reduces the rock-breaking efficiency of PDC bits. In this study, X-ray diffraction mineral composition analysis and triaxial compression mechanics tests were used to systematically characterize the weakening mechanism of water injection on reservoir rocks. Based on an analysis of mechanical experimental characteristics, this study proposes a multi-scale collaborative optimization method: establish a single tooth–rock interaction model at the micro-scale through finite element simulation to optimize geometric cutting parameters; at the macro scale, adopt a differential bit design scheme. By comparing and analyzing the rock-breaking energy consumption characteristics of four-blade and five-blade bits, the most efficient rock-breaking configuration can be optimized. Based on Fluent simulation on the flow field scale, the nozzle configuration can be optimized to improve the bottom hole flow field. The research results provide important theoretical guidance and technical support for the personalized design of drill bits in the later stage of water injection development. Full article

In the interior decoration panel industry, automated production lines have become the standard configuration for large-scale enterprises. However, during the panel processing procedures such as sanding and painting, the loss of traditional identification markers like QR codes or barcodes is inevitable. This creates a critical technical bottleneck in the assembly stage of customized or multi-model parallel production lines, where identifying individual panels significantly limits production efficiency. To address this issue, this paper proposes a high-precision measurement method based on close-range photogrammetry for capturing panel dimensions and hole position features, enabling accurate extraction of identification markers. Building on this foundation, an identity discrimination method that integrates weighted dimension and hole position IDs has been developed, making it feasible to efficiently and automatically identify panels without physical identification markers. Experimental results demonstrate that the proposed method exhibits significant advantages in both recognition accuracy and production adaptability, providing an effective solution for intelligent manufacturing in the home decoration panel industry. Full article

Background/Objectives: Children with myelomeningocele (MMC) often experience lower extremity muscular contractures, which can impact their functional mobility. While standing programs have demonstrated benefits for children with other neuromuscular conditions, there is limited evidence on their use in ambulatory children with MMC who have joint deformities. This single-subject design study examined the impact of a home-based standing program on two ambulatory children with MMC, focusing on lower extremity muscle flexibility, functional movement quality, gait velocity, and participation in daily activities. Methods: Two children participated in a multi-phase single-subject (ABABA) withdrawal design beginning with the baseline phase and then alternating between the intervention and withdrawal phases. The intervention consisted of 60-minute standing sessions, five days a week, using a sit-to-stand stander (STSS) with support and supervision from a physical therapist (PT) and the parent. Primary outcomes included goniometric passive range of motion (PROM) and 10-Meter Walk Test (10 MWT). Secondary outcomes included the Pediatric Neuromuscular Recovery Scale (Peds NRS) and the Pediatric Evaluation of Disability Inventory Computer Adaptive Test (PEDI-CAT). Results: Improvements in hip and knee muscle flexibility were observed during the intervention phases, with some loss during the withdrawal phase. Functional movement quality improved in both children. Gait velocity and participation in daily activity scores remained stable during intervention phases. Parental feedback reflected increased independence and high engagement with the home program. One child discontinued due to a heel injury, highlighting the need for individualized support. Conclusions: Personalized standing programs may improve muscle flexibility and functional movement quality in ambulatory children with MMC. Further research is warranted to determine the optimal dosing regimen, ensure safety, and assess long-term functional outcomes. Full article

The hydrogen economy stands at the forefront of the global energy transition, and additive manufacturing (AM) is increasingly recognized as a critical enabler of this transformation. AM offers unique capabilities for improving the performance and durability of hydrogen energy components through rapid prototyping, topology optimization, functional integration of cooling channels, and the fabrication of intricate, hierarchical, structured pores with precisely controlled connectivity. These features facilitate efficient heat and mass transfer, thereby improving hydrogen production, storage, and utilization efficiency. Furthermore, AM’s multi-material and functionally graded printing capability holds promise for producing components with tailored properties to mitigate hydrogen embrittlement, significantly extending operational lifespan. Collectively, these advances suggest that AM could lower manufacturing costs for hydrogen-related systems while improving performance and reliability. However, the current literature provides limited evidence on the integrated techno-economic advantages of AM in hydrogen applications, posing a significant barrier to large-scale industrial adoption. At present, the technological readiness level (TRL) of AM-based hydrogen components is estimated to be 4–5, reflecting laboratory-scale progress but underscoring the need for further development, validation and industrial-scale demonstration before commercialization can be realized. Full article

Urban park green space (UPGS) is a key component of urban green infrastructure, yet it faces multiple contradictions, such as insufficient quantity and uneven distribution. Taking Zhengzhou City as a case study, this research explored the impacts of temporal thresholds and the modifiable areal unit problem (MAUP) on UPGS accessibility and equity. An improved multi-modal Gaussian two-step floating catchment area (G2SFCA) method was employed to measure UPGS accessibility, while the Gini coefficient and Lorenz curve were used to analyze its equity. The results show that (1) UPGS presents a dual-core agglomeration feature, with accessibility blind spots surrounding the edge of the study area and relatively low equity in the western and southern regions; (2) changes in temporal thresholds and spatial scales have a significant impact on UPGS accessibility (p < 0.001), whereas their impact on equity is minor; and (3) UPGS distribution suffers from spatial imbalance, with a huge disparity in resource allocation. This study overcomes the limitations of traditional evaluation methods that rely on a single mode or ignore scale effects and provides a more scientific analytical framework for accurately identifying the spatial heterogeneity of UPGS accessibility and the imbalance between supply and demand. Full article

The increasing integration of high-frequency power electronic converters in renewable energy-grid systems has escalated reliability concerns, necessitating FPGA-accelerated large-scale real-time electromagnetic transient (EMT) computation to prevent failures. However, most existing studies prioritize computational performance and struggle to achieve large-scale EMT computation. To enhance the computational scale, we propose a scalable hardware architecture comprising domain-specific components and data-centric processing element (PE) arrays. This architecture is further enhanced by a graph-based matrix mapping methodology and matrix-aware fixed-point quantization for hardware-efficient computation. We demonstrate our principles with FPGA implementations of large-scale multi-converter systems. The experimental results show that we set a new record of supporting 1200 switches with a computation latency of 373 ns and an accuracy of 99.83% on FPGA implementations. Compared to the state-of-the-art large-scale EMT computation on FPGAs, our design on U55C FPGA achieves an up-to 200.00× increase in the switch scale, without I/O resource limitations, and demonstrates up-to 71.70% reduction in computation error and 51.43% reduction in DSP consumption, respectively. Full article

Few-shot image classification seeks to recognize novel categories from only a handful of labeled examples, but conventional metric-based methods that rely mainly on global image features often produce unstable prototypes under extreme data scarcity, while local-descriptor approaches can lose context and suffer from inter-class local-pattern overlap. To address these limitations, we propose a Global–Local Feature Fusion network that combines a frozen, pretrained global feature branch with a self-attention based multi-local feature fusion branch. Multiple random crops are encoded by a shared backbone (ResNet-12), projected to Query/Key/Value embeddings, and fused via scaled dot-product self-attention to suppress background noise and highlight discriminative local cues. The fused local representation is concatenated with the global feature to form robust class prototypes used in a prototypical-network style classifier. On four benchmarks, our method achieves strong improvements: Mini-ImageNet 70.31% ± 0.20 (1-shot)/85.91% ± 0.13 (5-shot), Tiered-ImageNet 73.37% ± 0.22/87.62% ± 0.14, FC-100 47.01% ± 0.20/64.13% ± 0.19, and CUB-200-2011 82.80% ± 0.18/93.19% ± 0.09, demonstrating consistent gains over competitive baselines. Ablation studies show that (1) naive local averaging improves over global-only baselines, (2) self-attention fusion yields a large additional gain (e.g., +4.50% in 1-shot on Mini-ImageNet), and (3) concatenating global and fused local features gives the best overall performance. These results indicate that explicitly modeling inter-patch relations and fusing multi-granularity cues produces markedly more discriminative prototypes in few-shot regimes. Full article

Fractional heat conduction models extend classical formulations by incorporating fractional differential operators that capture multiscale relaxation effects. In this work, we introduce an electrical analogy that represents the action of these operators via generalized longitudinal impedance and admittance elements, thereby clarifying their physical role in energy transfer: fractional derivatives account for the redistribution of heat accumulation and dissipation within micro-scale heterogeneous structures. This analogy unifies different classes of fractional models—diffusive, wave-like, and mixed—as well as distinct fractional operator types, including the Caputo and Atangana–Baleanu forms. It also provides a general computational methodology for solving heat conduction problems through the concept of thermal impedance, defined as the ratio of surface temperature variations (relative to ambient equilibrium) to the applied heat flux. The approach is illustrated for a semi-infinite sample, where different models and operators are shown to generate characteristic spectral patterns in thermal impedance. By linking these spectral signatures of microstructural relaxation to experimentally measurable quantities, the framework not only establishes a unified theoretical foundation but also offers a practical computational tool for identifying relaxation mechanisms through impedance analysis in microscale thermal transport. Full article

Global climate change and rapid urbanization have intensified urban heat risks, particularly in cities such as Jinan that face pronounced heat-related environmental challenges. This study takes Jinan’s main urban area as a case example, integrating the Local Climate Zone (LCZ) framework with the Hazard–Exposure–Vulnerability–Adaptability (HEVA) model to develop multi-temporal heat risk maps. The results indicate the following: (1) High-risk zones are primarily concentrated in the densely built urban core, whereas low-risk areas are mostly located in peripheral green spaces, water bodies, and forested regions. (2) Heat risk shows clear diurnal patterns, peaking between noon and early afternoon and expanding outward from the city center. (3) LCZ6 (open low-rise), despite its theoretical advantage for ventilation, exhibits unexpectedly high levels of heat hazard, exposure, and vulnerability. (4) SHAP-based analysis identifies land surface temperature (LST), floor area ratio (FAR), impervious surface area ratio (ISA), housing value, building coverage ratio (BCR), and the distribution of cooling facilities as the most influential drivers of heat risk. These findings offer a scientific foundation for developing multi-scale, climate-resilient urban planning strategies in Jinan and hold significant practical value for improving urban resilience to extreme heat events. Full article