Search Results (352)

Land seismic data are always contaminated by surface waves, which demonstrate strong energy, low velocity, and long vibrations. Such noises often mask deep effective reflections, seriously reducing the data’s signal-to-noise ratio while limiting the imaging accuracy of complex deep structures and the efficiency of hydrocarbon reservoir identification. To address this critical technical bottleneck, this paper proposes a surface wave joint reconstruction method based on stationary phase analysis, combining the cross-correlation seismic interferometry method with the convolutional seismic interferometry method. This approach integrates cross-correlation and convolutional seismic interferometry techniques to achieve coordinated reconstruction of surface waves in both shot and receiver domains while introducing adaptive weight factors to optimize the reconstruction process and reduce interference from erroneous data. As a purely data-driven framework, this method does not rely on underground medium velocity models, achieving efficient noise reduction by adaptively removing reconstructed surface waves through multi-channel matched filtering. Application validation with field seismic data from the piedmont regions of western China demonstrates that this method effectively suppresses high-energy surface waves, significantly restores effective signals, improves the signal-to-noise ratio of seismic data, and greatly enhances the clarity of coherent events in stacked profiles. This study provides a reliable technical approach for noise reduction in seismic data under complex near-surface conditions, particularly suitable for hydrocarbon exploration in regions with developed scattering sources such as mountainous areas in western China. It holds significant practical application value and broad dissemination potential for advancing deep hydrocarbon resource exploration and improving the quality of complex structural imaging. Full article

Complex-valued convolutional neural networks (CVCNNs) have demonstrated strong capabilities for polarimetric synthetic aperture radar (PolSAR) image classification by effectively integrating both amplitude and phase information inherent in polarimetric data. However, their practical deployment faces significant challenges due to high computational costs and performance degradation caused by extremely limited labeled samples. To address these challenges, a lightweight CV Siamese network (LCVSNet) is proposed for few-shot PolSAR image classification. Considering the constraints of limited hardware resources in practical applications, simple one-dimensional (1D) CV convolutions along the scattering dimension are combined with two-dimensional (2D) lightweight CV convolutions. In this way, the inter-element dependencies of polarimetric coherency matrix and the spatial correlations between neighboring units can be captured effectively, while simultaneously reducing computational costs. Furthermore, LCVSNet incorporates a contrastive learning (CL) projection head to explicitly optimize the feature space. This optimization can effectively enhance the feature discriminability, leading to accurate classification with a limited number of labeled samples. Experiments on three real PolSAR datasets demonstrate the effectiveness and practical utility of LCVSNet for PolSAR image classification with a small number of labeled samples. Full article

Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) techniques have received rapid advance in recent years, and the Multi-temporal InSAR (MT-InSAR) has been widely applied in various earth observations. Distributed scatterer (DS) InSAR is one of the most advanced MT-InSAR methods, and has overcome the limitation of the lack of enough measurement points in the low coherent regions for traditional methods. While the Joint-Scatterer InSAR (JS-InSAR) is the extension of DS InSAR method, which exploited the overall information of Joint Scatterers to carry out DS identification and phase optimization. And it can avoid the inaccuracy caused by the offset errors between scatterers in complex terrain areas. However, the intensive computation and low efficiency have severely restricted the application of JS-InSAR, especially when dealing with massive and long historical SAR images. As the sequential estimator has proven to successfully improve the efficiency of MT-InAR and obtain near-time deformation time series, in this work, we proposed the sequential-based JS-InSAR (S-JSInSAR) method with flexible batches. This method has adaptively divided large single look complex (SLC) stack into different batches with flexible number and certain overlaps. Then, the JS-InSAR processing is performed on each batch, respectively, and these estimated results are integrated into the final deformation time series based on the connection mode. Thus, S-JSInSAR can efficiently process large InSAR dataset, and mitigate the decorrelation effect caused by long temporal baselines. To demonstrate the effectiveness of the S-JSInSAR, a multi-year of 145 Sentinel-1 ascending SAR images in Tangshan, China, were collected to estimate the long deformation time series. And the results compared with other methods have shown the processing time has substantially decreased without the loss of deformation accuracy, and obtain deformation spatial distribution with more details in local regions, which have well validated the efficiency and reliability of the proposed method. Full article

Understanding thrombosis in acute coronary syndromes (ACSs) has evolved through advances in biomarkers, intracoronary imaging, and emerging analytical tools, improving diagnostic accuracy and risk stratification in high-risk patients. This narrative review provides an integrative overview of contemporary evidence from clinical trials, meta-analyses, and international guidelines addressing circulating biomarkers, intracoronary imaging modalities—including optical coherence tomography (OCT), intravascular ultrasound (IVUS), and near-infrared spectroscopy (NIRS)—artificial intelligence–based analytical approaches, and emerging antithrombotic therapies. High-sensitivity cardiac troponins and natriuretic peptides remain the most robust and guideline-supported biomarkers for diagnosis and prognostic assessment in ACS, whereas inflammatory markers and multimarker strategies offer incremental prognostic information but lack definitive validation for routine therapeutic guidance. Intracoronary imaging with IVUS or OCT is supported by current guidelines to guide percutaneous coronary intervention in selected patients with ACS and complex coronary lesions, leading to improved procedural optimization and clinical outcomes compared with angiography-guided strategies. Beyond procedural guidance, OCT enables detailed plaque characterization and mechanistic insights into ACS, while NIRS provides complementary information on lipid-rich plaque burden, primarily for risk stratification based on observational evidence. Artificial intelligence represents a rapidly evolving tool for integrating clinical, laboratory, and imaging data, with promising results in retrospective and observational studies; however, its clinical application in thrombosis management remains investigational due to the lack of outcome-driven randomized trials. In the therapeutic domain, factor XI inhibitors have demonstrated favorable safety profiles with reduced bleeding and preserved antithrombotic efficacy in phase II and early phase III studies, but their definitive role in ACS management awaits confirmation in large, outcome-driven randomized trials. Overall, the integration of biomarkers, intracoronary imaging, and emerging analytical and pharmacological strategies highlights the potential for more individualized cardiovascular care. Nevertheless, careful interpretation of existing evidence, rigorous validation, and alignment with guideline-directed practice remain essential before widespread clinical adoption. Full article

Plasmonics and metasurfaces (MSs) have emerged as two of the most influential platforms for manipulating light at the nanoscale, each offering complementary strengths that challenge the limits of conventional optical design. Plasmonics enables extreme subwavelength field confinement, ultrafast light–matter interaction, and strong optical nonlinearities, while MSs provide versatile and compact control over phase, amplitude, polarization, and dispersion through planar, nanostructured interfaces. Recent advances in materials, nanofabrication, and device engineering are increasingly enabling these technologies to be combined within unified planar and hybrid optical platforms. This review surveys the physical principles, material strategies, and device architectures that underpin plasmonic, MS, and hybrid plasmonic–dielectric systems, with an emphasis on interface-mediated optical functionality rather than long-range guided-wave propagation. Key developments in modulators, detectors, nanolasers, metalenses, beam steering devices, and programmable optical surfaces are discussed, highlighting how hybrid designs can leverage strong field localization alongside low-loss wavefront control. System-level challenges including optical loss, thermal management, dispersion engineering, and large-area fabrication are critically examined. Looking forward, plasmonic and MS technologies are poised to define a new generation of flat, multifunctional, and programmable optical systems. Applications spanning imaging, sensing, communications, augmented and virtual reality, and optical information processing illustrate the transformative potential of these platforms. By consolidating recent progress and outlining future directions, this review provides a coherent perspective on how plasmonics and MSs are reshaping the design space of next-generation planar optical hardware. Full article

High-Performance Computing (HPC) has become essential for training medium- and large-scale Artificial Intelligence (AI) models, yet two bottlenecks remain under-exploited: the semantic coherence of training data and the interaction between distributed deep learning runtimes and heterogeneous HPC architectures. Existing work tends to optimise multi-node, multi-GPU training in isolation from data semantics or to apply semantic technologies to data curation without considering the constraints of large-scale training on modern clusters. This paper introduces SemanticHPC, an experimental framework that integrates ontology and Resource Description Framework (RDF)-based semantic preprocessing with distributed AI training (Horovod/PyTorch Distributed Data Parallel) and hardware-aware optimisations for Non-Uniform Memory Access (NUMA), multi-GPU and high-speed interconnects. The framework has been evaluated on 1–8 node configurations (4–32 GPUs) on a production-grade cluster. Experiments on a medium-size Open Images V7 workload show that semantic enrichment improves validation accuracy by 3.5–4.4 absolute percentage points while keeping the additional end-to-end overhead below 8% and preserving strong scaling efficiency above 79% on eight nodes. We argue that bringing semantic technologies into the training workflow—rather than treating them as an offline, detached phase—is a promising direction for large-scale AI on HPC systems. We detail an implementation based on standard Python libraries, RDF tooling and widely adopted deep learning runtimes, and we discuss the limitations and practical hurdles that need to be addressed for broader adoption. Full article

Understanding wake interactions between multiple tidal stream turbines is essential for optimizing the performance and layout of tidal energy farms. This study investigates the hydrodynamic behavior of two horizontal-axis tidal turbines arranged in tandem under simplified inflow conditions, where the incoming flow was dominated by the streamwise velocity component without imposed external disturbances. Wake measurements were conducted in a large circulating water tunnel using a mobile, submerged particle image velocimetry (PIV) system capable of long-range, high-resolution measurements. Performance tests showed that the downstream turbine experienced a decrease of approximately 9% in maximum power coefficient compared to the upstream turbine due to reduced inflow velocity and increased turbulence generated by the upstream wake. Phase-averaged PIV results revealed the detailed evolution of velocity deficit, turbulence intensity, turbulent kinetic energy, and tip vortex structures. The tip vortices shed from the upstream turbine persisted over a long downstream distance, remaining coherent up to 10D and merging with those generated by the downstream turbine. These merged vortex structures produced elevated turbulence and complex flow patterns that significantly influenced the downstream turbine’s operating conditions. The results provide experimentally validated insight into turbine-to-turbine wake interactions and highlight the need for high-fidelity wake data to support array optimization and numerical model development for tidal stream turbine array. Full article

Bone regeneration relies on the coordinated interplay between mechanical and biological cues. Piezoelectric composites, capable of converting mechanical strain into electrical signals, offer a promising approach to stimulate osteogenesis. This study aimed to develop and characterize polycaprolactone (PCL) and barium titanate (BaTiO₃) composite scaffolds fabricated through thermally induced phase separation (TIPS), and to systematically evaluate the effects of polymer concentration and ceramic incorporation on scaffold morphology, porosity, mechanical properties, and cytocompatibility were systematically evaluated. The resulting scaffolds exhibited a highly porous, interconnected architecture, with 9% PCL formulation showing the most uniform morphology and consistent mechanical and biological behavior. Incorporation of BaTiO₃ did not alter pore structure or compromise cytocompatibility but slightly enhanced stiffness and surface uniformity. SEM-based image analysis confirmed homogeneous BaTiO₃ dispersion across all formulations. MTT assays and confocal microscopy demonstrated robust pre-osteoblast adhesion and spreading, particularly on denser composite scaffolds, confirming that the inclusion of BaTiO₃ supports a favorable environment for cell proliferation. Overall, optimizing polymer concentration and ceramic dispersion enables fabrication of structurally coherent, cytocompatible scaffolds. The findings establish structure–property–biology relationships that serve as a baseline for future investigations into the electromechanical behavior of PCL/BaTiO₃ scaffolds and their potential to promote osteogenic differentiation under physiological loading. Full article

Deep learning (DL), a hierarchical feature extraction method, has garnered increasing attention in the remote sensing community. Recently, self-supervised learning (SSL) methods in DL have gained wide recognition due to their ability to mitigate the dependence on both the quantity and quality of samples. This advantage is particularly significant when dealing with limited labeled samples in hyperspectral images (HSIs). However, conventional SSL methods face two main challenges. They struggle to construct self-supervised signals based on the unique characteristics of HSI. Moreover, they fail to design network optimization strategies that leverage the intrinsic manifold geometry within HSI. To tackle these issues, we propose a novel self-supervised learning method termed Manifold Geometry-Leveraged Self-supervised Learning (MSSL) for HSI classification. The approach employs a two-stage training strategy. In the initial pre-training stage, it develops self-supervised signals that exploit spatial homogeneity and spectral coherence properties of HSI. Furthermore, it introduces a manifold geometry-guided loss function that enhances feature discrimination by increasing intra-class compactness and inter-class separation. The second stage is a fine-tuning phase utilizing a small set of labeled samples. This stage optimizes the pre-trained model, enabling effective feature extraction from hyperspectral data for classification tasks. Experiments conducted on real-world HSI datasets demonstrate that MSSL achieves superior classification performance compared to several relevant state-of-the-art methods. Full article

Background: Whilst COVID-19 mainly affects the lungs, multiple other organs were also involved—patients with COVID-19 were reported to be at higher risk of acute coronary syndromes (ACS). Importantly, results show that the risk of ACS may extend well beyond the acute phase of COVID-19 infection. In our study, we sought to investigate optical coherence tomography (OCT)-derived vascular changes, including the prevalence of plaque erosion in patients who had recent COVID-19. Methods: Patients with ACS were divided into two groups: those after COVID-19 infection during the past 12 months (post-COVID group) and those without known prior COVID infection (non-COVID group). We enrolled 35 patients in the post-COVID group and 35 patients in the non-COVID group. Results: The mean time from COVID infection to the imaging in the post-COVID group was 10 ± 1 months. There were no major differences in baseline demographic, clinical, or laboratory characteristics between the two groups. Erosion was the underlying pathology in one-third (34.3%) of the non-COVID group and in one-half (48.6%) of the post-COVID group, although the difference did not reach statistical significance. No calcified nodules were observed. The lipid core tended to be longer in the post-COVID group (9.1 ± 3.6 vs. 12.0 ± 1.9 mm; p = 0.005), and the prevalence of macrophages was higher in patients who had prior COVID-19 infection (48.6 vs. 74.3%; p = 0.027). Conclusions: Our OCT study demonstrated that patients with a prior COVID-19 infection tended to have a higher prevalence of plaque erosion and more vulnerable plaque morphology at the culprit lesion compared to those without a history of prior COVID-19 infection. Full article

Accurate synthetic aperture radar (SAR) image co-registration is a crucial procedure for high-quality interferometry and its associated applications. Neglecting the effect of terrain elevation, conventional techniques employ simple polynomial models to achieve accurate co-registration between SAR image pairs during fine co-registration processing. However, these methods become inapplicable for tugged terrain, especially under longer spatial baseline conditions. On the basis of this, we introduced an elevation-dependent term into the conventional fine co-registration model to compensate for local offsets caused by variable topography. As a result, a new SAR image fine co-registration method was proposed. To validate the proposed method, experiments were conducted using data from China’s LuTan-1 satellite in two typical study areas (Madrid, Spain, and Shannan, China), across diverse land-cover types and terrain conditions. At the Madrid test site, the proposed co-registration algorithm can effectively improve the phase quality (average coherence improves from 0.57 to 0.77), and topography accuracy (quantified by root-mean-square-error, RMSE) improved from 3.67 m to 3.59 m in mountainous regions, and it shows similar performance in relatively flat areas to that of the conventional methods. At the Shannan test site, characterized by rugged terrain, the average coherence of the interferogram obtained by our method increased from 0.32 to 0.48 compared to the conventional co-registration approach. Against the reference topographic data, the InSAR DEM retrieved by our proposed method achieved an RMSE of 6.31 m, indicating an improvement of 23%. This study provides an effective method to enhance the quality of co-registration and interferometry in areas with complex terrain. Full article

Elevation Digital Beamforming (DBF) technology is key to achieving high-resolution wide-swath (HRWS) imaging in spaceborne Synthetic Aperture Radar (SAR) systems. However, multi-channel DBF-SAR systems face a prominent conflict between the need for real-time channel error calibration and the constraints of limited on-board hardware resources. To address this bottleneck, this paper proposes a real-time channel error calibration method based on Fast Fourier Transform (FFT) pulse compression and introduces a “calibration-operation” dual-mode control with a parameter-persistence architecture. This scheme decouples high-complexity computations by confining them to the system initialization phase, enabling on-board, real-time, closed-loop compensation for multi-channel signals with low resource overhead. Test results from a high-performance Field-Programmable Gate Array (FPGA) platform demonstrate that the system achieves high-precision compensation for inter-channel amplitude, phase, and time-delay errors. In the 4-channel system validation, the DBF synthesized signal-to-noise ratio (SNR) improved by 5.93 dB, reaching a final SNR of 44.26 dB. This performance approaches the theoretical ideal gain and significantly enhances the coherent integration gain of multi-channel signals. This research fully validates the feasibility of on-board, real-time calibration with low resource consumption, providing key technical support for the engineering robustness and efficient data processing of new-generation SAR systems. Full article

The fractal extension of the time-dependent Ginzburg–Landau (TDGL) equation, formulated within the framework of Scale Relativity, generalizes superconducting dynamics to non-differentiable space–time. Although analytically well established, its numerical solution remains difficult because of the strong coupling between amplitude and phase curvature. Here we develop two complementary deep learning solvers for the fractal TDGL (FTDGL) system. The Fractal Physics-Informed Neural Network (F-PINN) embeds the Scale-Relativity covariant derivative through automatic differentiation on continuous fields, whereas the Fractal Graph Neural Network (F-GNN) represents the same dynamics on a sparse spatial graph and learns local gauge-covariant interactions via message passing. Both models are trained against finite-difference reference data, and a parametric study over the dimensionless fractality parameter $\mathcal{D}$ quantifies its influence on the coherence length, penetration depth, and peak magnetic field. Across multivortex benchmarks, the F-GNN reduces the relative L₂ error on ${\left|\psi \right|}^2$ from 0.190 to 0.046 and on B_z from approximately 0.62 to 0.36 (averaged over three seeds). This ≈4× improvement in condensate-density accuracy corresponds to a substantial enhancement in vortex-core localization—from tens of pixels of uncertainty to sub-pixel precision—and yields a cleaner reconstruction of the 2π phase winding around each vortex, improving the extraction of experimentally relevant observables such as ξ_eff, λ_eff, and local B_z peaks. The model also preserves flux quantization and remains robust under 2–5% Gaussian noise, demonstrating stable learning under experimentally realistic perturbations. The $\mathcal{D}$—scan reveals broader vortex cores, a non-monotonic variation in the penetration depth, and moderate modulation of the peak magnetic field, while preserving topological structure. These results show that graph-based learning provides a superior inductive bias for modeling non-differentiable, gauge-coupled systems. The proposed F-PINN and F-GNN architectures therefore offer accurate, data-efficient solvers for fractal superconductivity and open pathways toward data-driven inference of fractal parameters from magneto-optical or Hall-probe imaging experiments. Full article

Radar coincidence imaging (RCI) is widely used in military reconnaissance, hovering unmanned aerial vehicles (UAVs), and non-local Earth observation due to its superior super-resolution imaging performance. However, in portable radar exploration or UAV remote sensing scenarios, the imaging resolution may be limited by the size constraints of the radar’s aperture. Moreover, although the resolution of RCI depends on the randomness of the signal, an excessively random signal setup may be difficult to implement in engineering applications due to rapid frequency jumps and related issues. Therefore, it is essential to achieve super-resolution imaging while maintaining a small aperture and an effectively random signal. In this paper, an amplitude-random linear frequency modulation (AR-LFM) waveform is employed in RCI using a dual-frequency, dual-phase-center, and dual-polarized antenna (DDPA). A multi-channel structure is introduced, and different frequencies and polarization modes are combined using the proposed method, which provides more independent signal information while maintaining a small aperture and effectively reducing signal coherence. This approach increases the singularity between grid points in the target area, thereby enhancing the effective rank of the reference matrix. The simulation results show that the angular resolution of the proposed imaging method is 15 times higher than that of conventional radar imaging. Furthermore, the proposed structure can improve the resolution improvement factor (RIF) by more than two times compared with the traditional RCI method using a conventional antenna and random signals. Full article

The use of imaging during percutaneous coronary intervention (PCI) can improve the outcomes by giving key information in every phase of the procedure. It can improve the knowledge of plaque composition thus helping the subsequent technical strategy; it can precisely define the measure of the stent to implant; it can assess in detail the correct positioning of the stent (apposition, expansion, and full coverage of the atherosclerotic plaque); it helps in recognizing the complications that may occur after stenting (e.g., edge dissection or tissue/thrombus protrusion in the stent area). Further, it could help evaluation for both diagnostic and therapeutic purposes of angiographic unknown or questionable findings [e.g., spontaneous coronary artery dissection (SCAD), characterization of mycotic aneurysm and pseudoaneurysm]. In the follow up phase, the use of intracoronary imaging may significantly improve the understanding of the mechanisms leading to the procedural failure. What this review adds is to describe the similarities and differences between intravascular ultrasound (IVUS) and optical coherence tomography (OCT) technologies, to highlight the evidence supporting their utility to improve PCI outcomes, to give practical advice and tools on daily interventional routine, to show a point of view on future perspectives and integration with artificial intelligence (AI). Full article