Search Results (3,502)

Current internal dose assessments in nuclear emergencies rely on static, upright voxel phantoms, often neglecting realistic human postures and physiological factors—such as breathing rates specific to emergency scenarios—that influence radionuclide intake and biokinetics. We present a voxel deformation method based on an improved as-rigid-as-possible (ARAP) algorithm incorporating a novel smoothing term to generate anatomically consistent stooping and swivelling models. Coupled with Geant4 Monte Carlo simulations using the full decay spectra of radionuclides relevant to simulated nuclear accident scenarios (i.e., ${}^{131}\mathrm{I}$ and ${}^{137}\mathrm{Cs}$), and incorporating scenario-specific respiratory parameters into activity calculations, our results demonstrate that body posture significantly influences internal dose distributions: for ${}^{137}\mathrm{Cs}$, the specific absorbed fraction (SAF) of the liver increases by up to 24.9% in the stooping posture, while swivelling induces variations of up to 15.1%. In contrast, dose metrics for ${}^{131}\mathrm{I}$ show minimal sensitivity to posture (<5%). These findings highlight the importance of incorporating realistic postures and context-aware physiological parameters into emergency dosimetry. Our method enables behaviorally realistic internal dose reconstruction and provides a robust foundation for integrating human motion and respiratory data into rapid triage and risk assessment protocols. Full article

Wheel–rail coupled excitation introduces strong low-frequency modulation, random impact interference, and broadband background noise into the vibration system of locomotive gearboxes, causing early weak bearing fault features to become submerged and making traditional deconvolution methods insufficient for effective enhancement. To address this challenge, this study proposes an adaptive parameter optimization method for MCKD based on the weighted envelope spectrum factor (WESF). WESF integrates the Hoyer index, kurtosis, and envelope spectrum energy to jointly characterize sparsity, impulsiveness, and periodicity of signal components. By using WESF as the fitness function, the sparrow search algorithm (SSA) is employed to simultaneously optimize the key MCKD parameters L, T, and M, enabling optimal enhancement of weak periodic impacts. To further mitigate modal aliasing caused by wheel–rail excitation, the original signal is first adaptively decomposed using successive variational mode decomposition (SVMD), and modes with WESF values above the average are selected for signal reconstruction. The reconstructed signal is subsequently enhanced via SSA–MCKD, and fault characteristic frequencies are extracted using envelope spectrum analysis. Experimental validation using gearbox bearing data collected under 40, 50, and 60 Hz operating conditions shows that the proposed method achieves fault feature coefficient (FFC) values of 12.8%, 7.5%, and 7.2%, respectively—representing an average improvement of approximately 156% compared with traditional methods (average FFC of 3.6%). These results demonstrate that the proposed SVMD–WESF–SSA–MCKD approach can significantly enhance weak periodic impact features under strong background noise and wheel–rail excitation, exhibiting strong practical applicability for engineering implementation. Full article

To address the challenges of low accuracy and poor robustness in industrial crayfish weight estimation caused by variable postures, this paper proposes a lightweight method that integrates pose awareness. First, a multi-task perception model, Crayfish-YOLO, is developed based on the YOLOv8s-Seg framework. By reconstructing the backbone with MobileNetV3 and integrating Coordinate Attention (CA), CARAFE upsampling, and the Wise Intersection over Union (Wise-IoU) loss function, the model is significantly compressed while enhancing its ability to output high-fidelity pixel-level masks and pose categories. Second, a pose-adaptive weight estimation strategy is proposed, which leverages perceived pose information to dynamically invoke the optimal regression model from a pre-constructed heterogeneous model library. Using seven core geometric features extracted from the segmentation masks, the system achieves precise weight estimation. Experimental results on a self-built dataset show that Crayfish-YOLO reduces parameters by 75.2% compared to YOLOv8s-Seg, while core segmentation accuracy (mAP_50~95 (Seg)) improves by 1.1%. The integrated end-to-end system achieves a Mean Absolute Error (MAE) of 2.1 g and a mean coefficient of determination (R²) of 0.92, significantly outperforming comparative algorithms. This research provides an efficient visual perception and estimation solution for the automated grading of crayfish and similar non-rigid aquatic products. Full article

To reduce the computational burden of 3D finite element analysis for hybrid-flux high-speed switched reluctance motors (HFHSRMs), a quasi-3D parameterized equivalent magnetic network (EMN) is proposed. A parameterized radial–circumferential cross-grid is used to discretize the stator, air-gap, and rotor regions, and axial coupling branches are introduced to represent key 3D flux paths. Rotor rotation and rotor dislocation are implemented through a circumferential node-shift mapping, thereby avoiding topology reconstruction at different rotor positions. Core nonlinearity is incorporated using a piecewise fit of measured B–H data, and sparse-matrix assembly is adopted to improve solution efficiency. Based on the proposed EMN, key electromagnetic quantities are evaluated, including air-gap flux density, static characteristics, and dynamic characteristics. The results are validated against 3D finite element method (FEM) and prototype experiments. In the prototype experiments, the EMN prediction errors of key quantities are within 6%. In addition, computational efficiency is significantly improved compared with the 3D FEM, enabling rapid parameter iteration and early-stage design evaluation for HFHSRMs. Full article

Natural gas hydrates, as an abundant potential energy resource, are widely present in marine sediments. In this paper, a novel method using color recognition technology is proposed for reconstructing marine hydrate reservoirs. By identifying the red, green, and blue values of image colors within the study area’s grid, numerical values are assigned and translated into geological parameters. These parameters are then input into the Computer Modeling Group software to establish heterogeneous reservoirs, and numerical simulations are conducted. The results indicate that this method successfully establishes a correspondence between color features and geological parameters. The reconstructed model images exhibit a high degree of consistency with the original images, allowing for precise parameter readings. The method was applied to hydrate reservoirs in the second trial production area of the South China Sea, the Shenhu SH2 area, and the Nankai Trough. The cumulative gas production obtained through numerical simulation of the reconstructed models closely matched the known production data, with discrepancies of 3.5%, 0.9%, and 7.6%, respectively. These findings confirm the reliability of the model, providing valuable insights for future studies on heterogeneous hydrate reservoirs and extending its application prospects to heterogeneous oil and gas reservoir research. Full article

Accurate and stable acquisition of the spatial distribution of soybean planting areas is essential for supporting precision agricultural monitoring and ensuring food security. However, crop remote-sensing mapping for specific regions still faces critical data bottlenecks: high-precision, large-scale pixel-level annotation is costly, resulting in scarce available labeled samples that make it difficult to construct large-scale training datasets. Although parameter-intensive models such as FCN and SegNet can achieve sufficient end-to-end training on large-scale public remote sensing datasets like LoveDA, when directly applied to the data-limited dataset in this study area, the models are prone to overfitting, leading to a significant decline in generalization ability. To address these issues, this study proposes a lightweight U-shaped semantic segmentation model, SimSDRU-Net. The model utilizes a pre-trained VGG-16 backbone to extract shallow texture and deep semantic features. The pre-trained weights mitigate the impact of overfitting in data-limited settings. In the decoding stage, a parameter-free lightweight SimAM attention module enhances effective soybean features and suppresses soil background redundancy, while an embedded S-DRU unit fuses multi-scale features for deep complementary reconstruction to improve edge detail capture. A label dataset was constructed using Sentinel-2 images as the data source and Menard County (USA) as the study area. The USDA CDL was used as a foundation for the dataset, with Google high-resolution images serving as visual interpretation aids. In the context of the experiment, Deeplabv3+ and U-Net++ were compared with U-Net under identical conditions. The results demonstrated that SimSDRU-Net exhibited optimal performance, with MIoU of 89.03%, MPA of 93.81%, and OA of 95.96%. Specifically, SimSDRU-Net uses the SimAM attention module to generate spatial attention weights by analyzing feature statistical differences through an energy function, so as to adaptively enhance soybean texture features. Meanwhile, the S-DRU unit groups, dynamically weights, and cross-branch reconstructs multi-scale convolutional features to preserve fine boundary details and achieve accurate segmentation of soybean plots. The present study demonstrates that SimSDRU-Net integrates lightweight design and high precision in data-limited scenarios, thereby providing effective technical support for the rapid extraction of soybean planting areas in North America. Full article

Side-scan sonar (SSS) serves as the primary perceptual instrument for Autonomous Underwater Vehicles (AUVs) in large-scale marine search and rescue (SAR) operations. However, the detection of critical targets is frequently hindered by severe hydro-acoustic noise, the spatial discontinuity of wreckage, and the weak visual signatures of small targets. To surmount these challenges, this paper presents WPG-DetNet. First, we introduce a Wavelet-Embedded Residual Backbone (WERB) to reconstruct the conventional downsampling paradigm. By substituting standard pooling with the Discrete Wavelet Transform (DWT), this architecture explicitly disentangles high-frequency noise from structural information in the frequency domain, thereby achieving the adaptive preservation of edge fidelity for large human-made targets while filtering out speckle interference. Then, addressing the distinct challenge of discontinuous aircraft wreckage, the framework further incorporates a Debris Graph Reasoning Module (D-GRM). This module models scattered fragments as nodes in a topological graph to capture long-range semantic dependencies, transforming isolated instance recognition into context-aware scene understanding. Finally, to bridge the gap between AI and underwater physics, we design a Shadow-Aided Decoupling Head (SADH) equipped with a physics-informed geometric loss. By enforcing mathematical consistency between target height and acoustic shadow length, this mechanism establishes a rigorous discriminative criterion capable of distinguishing weak-echo human bodies from seabed rocks based on shadow geometry. Experiments on the SCTD dataset demonstrate that WPG-DetNet achieves a mean Average Precision ($mA{P}_{50}$) of 97.5% and a Recall of 96.9%. Quantitative analysis reveals that our framework outperforms the classic Faster R-CNN by a margin of 12.8% in $mA{P}_{50}$ and surpasses the Transformer-based RT-DETR-R18 by 5.6% in high-precision localization metrics ($mA{P}_{50:95}$). Simultaneously, WPG-DetNet maintains superior efficiency with an inference speed of 62.5 FPS and a lightweight parameter count of 16.8 M, striking an optimal balance between robust perception and the real-time constraints of AUV operations. Full article

Cardiovascular imaging is essential in the diagnosis, phenotyping and prognostic assessment of cardiovascular disease. However, longstanding limitations constrain the accuracy, throughput, and scalability of cardiovascular imaging techniques. Artificial intelligence (AI) has demonstrated a diverse range of potential benefits across modalities, including echocardiography, computerised tomography, nuclear imaging, and magnetic resonance imaging. These benefits include automated quantification of key heart parameters, ability to improve traditional disease detection and phenotyping, and image reconstruction. While the use of AI in clinical workflows is still largely emerging, its significance is becoming established through numerous promising studies. The evidence reviewed indicates that AI can meaningfully enhance disease management, clinical operations and patient experience when used alongside physician expertise. However, several challenges restrict the widespread clinical implementation of AI, including a lack of robust prospective evidence, regulatory hurdles, bias in training datasets, and ethical drawbacks such as data privacy and accountability. Future developments should prioritise large-scale prospective and multicentre validation and address practical and ethical barriers to ensure AI can be utilised safely and effectively in clinical settings. This narrative review comprehensively analyses advances in AI-driven cardiovascular imaging with a focus on clinical implementation. Full article

Accurate prediction of thermo-mechanical behavior in Fused Deposition Modeling (FDM) is often limited by mismatches between idealized Computer-Aided Design (CAD) geometry and path-dependent material deposition. This paper presents a G-code-driven, filament-level modeling and process-simulation workflow for complex geometries and infill strategies, especially toolpaths with in-plane inclinations. Extrusion segments are parsed from slicing G-code to obtain endpoints and process parameters, and each filament is reconstructed as a path-aligned rectangular bead using a dedicated local coordinate system. Progressive deposition is simulated in ANSYS Parametric Design Language (APDL) via an element birth–death method, enhanced by a centroid-based element selection strategy that reduces dependence on strictly aligned hexahedral partitions and improves robustness for complex meshes. A nonlinear transient thermal analysis is performed, and temperatures are mapped to the structural model through an indirect thermo-mechanical coupling scheme to predict warpage and residual stresses. Case studies on square plates with triangular and hexagonal infills (with/without sidewalls and a bottom base) show that the high-temperature zone follows newly deposited paths with peak temperatures near 220 °C, while displacement and von Mises stress accumulate and are strongly affected by infill topology and boundary conditions. Full article

The proliferation of Internet of Things (IoT) devices continues to expand the network attack surface while introducing stringent privacy requirements that challenge effective intrusion detection. Federated learning enables collaborative model training without centralizing raw network telemetry. However, existing federated intrusion detection approaches often degrade under statistical heterogeneity and remain vulnerable to zero-day attacks when they rely on labeled data or reconstruction-based objectives. This work proposes Fed-DTCN (Federated Dual Temporal Contrastive Network), an unsupervised federated framework for zero-day anomaly detection in IoT environments. Fed-DTCN learns robust representations of benign IoT traffic using contrastive learning with semantic-preserving augmentations. A dual-encoder architecture disentangles globally shared features from client-specific patterns, improving generalization under heterogeneous federated deployments. Personalization and privacy are preserved by selectively aggregating only the shared encoder parameters. The framework employs a compact temporal convolutional backbone together with a soft-weighted contrastive objective to constrain benign representations, thereby enabling reliable detection of out-of-distribution threats. Extensive experiments on the TON_IoT and CSE-CIC-IDS2018 benchmarks show that Fed-DTCN matches or surpasses a state-of-the-art supervised baseline on standard attacks, achieving an F1-score of 99.99% on TON_IoT. In a zero-day evaluation where the Botnet class is withheld during training, Fed-DTCN attains an F1-score of 96%, compared to 0.52% for the supervised baseline. Ablation studies validate the effectiveness of the proposed augmentations, while evaluations under heterogeneous client partitions demonstrate reduced inter-client variance and consistent per-client improvements, indicating suitability for realistic IoT deployments. Full article

Dynamic visual cryptography (DVC) can be formulated as a discrete-time reconstruction problem for time-averaged moiré fringes generated by oscillatory transformations of periodic gratings. When implemented on digital display hardware, the continuous oscillatory motion must be realized through discrete frames, which may prevent correct reconstruction of higher-order time-averaged fringes due to refresh-rate limitations. In this work, mathematical criteria are derived to ensure the reliable reconstruction of higher-order time-averaged moiré fringes under finite refresh rate constraints. Harmonic, stochastic, and rectangular temporal waveforms are examined within a unified framework based on the number of frames per oscillation period and the discrete structure of the resulting time-averaged intensity distribution. Stochastic waveforms are shown to not guaranty reproducible fringe formation. For harmonic modulation with a 240 Hz display refresh rate and a 50 Hz oscillation frequency, only four full frames per period are obtained, which is insufficient to reconstruct the third time-averaged moiré fringe requiring at least sixteen frames per period. Rectangular waveforms satisfy the derived reconstruction conditions when the pitch of the grating, the oscillation amplitude, and the resolution of the rendered grating meet explicit constraints. These results establish quantitative parameter bounds for a mathematically consistent software-based DVC implementation on digital displays. Full article

Accurate correction of radial lens distortion is a fundamental requirement in computer vision and photogrammetry, as geometric inaccuracies directly affect 3D reconstruction, mapping, and geospatial measurements, particularly in high-precision imaging systems. In this study, we propose a fully analytical, non-iterative method for truncated inverse modeling of radial lens distortion, applicable to general radial distortion polynomials that contain constant terms. Unlike classical truncated Lagrange series reversion, which relies on recursive expansion and combinatorial series construction, the proposed formulation determines inverse distortion coefficients directly through a system of constrained algebraic inverse polynomials. This enables deterministic computation of inverse parameters without iterative refinement, numerical root finding, or combinatorial complexity. The method was evaluated using ultra-wide-angle smartphone camera imagery exhibiting severe barrel distortion modeled by an eighth-degree depressed radial distortion polynomial. Its performance was compared with a commonly used iterative inverse modeling approach. The analytical formulation demonstrated improved numerical stability and substantially reduced reprojection errors when correcting highly nonlinear distortion profiles, achieving sub-pixel accuracy in image rectification. In contrast, the iterative approach exhibited instability and significantly larger reprojection errors under identical conditions. These results demonstrate that the proposed framework provides a general, robust, and repeatable solution for inverse radial distortion modeling, particularly for high-order polynomial models. The method offers clear practical advantages for camera calibration pipelines in photogrammetry, remote sensing, robotics, and other applications requiring high-fidelity imaging. Full article

Fixed-wing unmanned aerial vehicles, with their advantages of long endurance and substantial payload capacity, are poised to be a key platform for the future low-altitude economy. However, the challenge of achieving precise attitude tracking control under unknown time-varying disturbances persists. To tackle this difficulty, this article introduces a soft-sign function-based active disturbance rejection control (SSADRC) method, and develops a hybrid grey wolf optimizer (HGWO) with balanced exploration–exploitation mechanisms for intelligent parameter tuning. Specifically, SSADRC utilizes a novel smooth nonlinear function with saturation constraints to reconstruct the nonlinear feedback controller and the extended state observer, ensuring smooth and stable control output. Subsequently, HGWO integrates the good point set-based initialization strategy, the fitness-based dynamic-weight strategy, the diversity-based adaptive-mutation strategy, and the logistic chaotic map-based survival-of-the-fittest strategy, addressing the tuning of multiple coupled parameters in SSADRC. Additionally, the SSADRC-based pitch attitude controller is designed for a fixed-wing unmanned aerial vehicle, and an HGWO and seven other swarm optimization algorithms are employed to tune the parameters. The results demonstrate that the HGWO exhibits the best convergence accuracy in the SSADRC parameter optimization task, and SSADRC illustrates better command tracking performance and state estimation accuracy than typical ADRC. Full article

Volatile organic compounds (VOCs) from historical industrial activities can persist for decades, contaminating groundwater and the unsaturated zone, yet their transport through thick, heterogeneous vadose zones is poorly understood. This study reconstructs long-term migration of tetrachloroethylene (PCE) from a former industrial site in the Jerusalem Mountains, where leakage likely began ten years after plant commissioning and systematic monitoring started decades later. A three-dimensional numerical model of flow and transport was applied, incorporating calibrated hydraulic parameters, karstic conduits, and multiphase VOC processes including advection, dispersion, phase partitioning, volatilization, and first-order degradation kinetics. Multiple model runs explored plausible leakage scenarios under sparse historical data. Simulated PCE concentrations reproduce measurements in the vadose zone (R² = 0.89) and deep regional aquifer (~20% normalized relative error). Results reveal pronounced preferential flows horizontally through perched aquifers and vertically along discrete faults, amplified by karstic networks. The upper vadose zone remains a persistent source, sustaining gas-phase emissions toward nearby residential areas unless targeted remediation is applied. Integrated modeling, even with limited monitoring, quantitatively reconstructs complex contaminant dynamics across saturated and unsaturated compartments, providing critical guidance for remediation. Protecting groundwater and human health requires addressing both vadose and saturated zones to prevent prolonged environmental and exposure risks. Full article

Against the backdrop of sustained growth in energy demand and energy transformation in China, accurately predicting future energy consumption trends is essential to developing science-based energy strategies and ensuring energy security. Traditional grey models suffer from limited prediction accuracy due to irrational background value settings. To address this issue, we introduced a structural optimization by adjusting the parameter count within the background value and employed the Simpson formula to reconstruct it. We proposed a novel three-parameter background value grey model, designated as TPBSVGM(1,1). It utilized the annual consumption data of petroleum, natural gas, and primary electricity and other energy consumption from 2014 to 2023 to construct TPBSVGM(1,1) for energy consumption analysis. To assess the predictive accuracy of TPBSVGM(1,1), this study compared its performance with GM(1,1) and FGM(1,1) in two dimensions: the trends between predicted values and actual values, and error metrics. The results indicate that TPBSVGM(1,1) outperforms the comparative models in energy consumption forecasting. We further used the model to predict annual consumption of the three energy sources from 2024 to 2030, finding that total consumption continues to grow while growth rates decline to varying degrees. It provides reliable data support for China’s energy consumption regulation and energy structure optimization. Full article