Search Results (311)

In some projects, a lack of data causes problems for presenting an accurate prediction model for surface settlement caused by shield tunneling. Existing models often rely on large volumes of data and struggle to maintain accuracy and reliability in shield tunneling. In particular, the spatial dependency between adjacent rings is overlooked. To address these limitations, this study presents a small-sample prediction framework for settlement induced by shield tunneling, using an adjacent-ring graph convolutional network (GCN-SSPM). Gaussian smoothing, empirical mode decomposition (EMD), and principal component analysis (PCA) are integrated into the model, which incorporates spatial topological priors by constructing a ring-based adjacency graph to extract essential features. A dynamic ensemble strategy is further employed to enhance robustness across layered geological conditions. Monitoring data from the Wuhan Metro project is used to demonstrate that GCN-SSPM yields accurate and stable predictions, particularly in zones facing abrupt settlement shifts. Compared to LSTM+GRU+Attention and XGBoost, the proposed model reduces RMSE by over 90% (LSTM) and 75% (XGBoost), respectively, while achieving an R² of about 0.71. Notably, the ensemble assigns over 70% of predictive weight to GCN-SSPM in disturbance-sensitive zones, emphasizing its effectiveness in capturing spatially coupled and nonlinear settlement behavior. The prediction error remains within ±1.2 mm, indicating strong potential for practical applications in intelligent construction and early risk mitigation in complex geological conditions. Full article

We address the inverse spectral problem for a PT-symmetric Dirac operator with discontinuity conditions imposed along the entire real axis—a configuration that has not been explicitly solved in prior literature. Our approach constructs fundamental solutions via convergent recursive series expansions and establishes their linear independence through a constant Wronskian. We derive explicit formulas for transmission and reflection coefficients, assemble them into a PT-symmetric scattering matrix, and demonstrate how both spectral and scattering data uniquely determine the underlying complex-valued, discontinuous potentials. Unlike classical treatments, which assume smoothness or limited discontinuities, our framework handles full-axis discontinuities within a non-Hermitian setting, proving uniqueness and providing a constructive recovery algorithm. This method not only generalizes existing inverse scattering theory to PT-symmetric discontinuous operators but also offers direct applicability to optical waveguides, metamaterials, and quantum field models where gain–loss mechanisms and zero-width resonances are critical. Full article

Endothelial dysfunction is closely linked to inflammation and oxidative stress and ultimately contributes to the development of cardiovascular diseases. Lipopolysaccharide (LPS), a major component of Gram-negative bacteria, induces vascular inflammation and oxidative damage in experimental models. Curcumin (Cur), a polyphenol from Curcuma longa, is well known for its anti-inflammatory and antioxidant properties. In this study, we examined the protective effects of CUD003, a novel synthetic Cur derivative, on the LPS-induced impairment of endothelium-dependent relaxation in the thoracic aorta of mice. Male ICR mice were pretreated with CUD003 or Cur (3 or 10 mg/kg, p.o.) 30 min prior to LPS injection (10 mg/kg, i.p.). Twenty-four hours after LPS injection, vascular reactivity was assessed in isolated aortic rings by evaluating vasorelaxation and vasoconstriction responses. LPS markedly impaired acetylcholine-induced vasorelaxation in the phenylephrine (PE)-precontracted aortic rings, while PE-induced contraction and sodium nitroprusside-induced relaxation were preserved, indicating that LPS impaired endothelium-dependent relaxation without affecting smooth muscle function. Immunohistochemical analysis revealed a reduction in eNOS expression and elevated levels of TNF-α, COX-2, O₂⁻, and malondialdehyde, indicating enhanced inflammation and oxidative stress in the aorta. Pretreatment with CUD003 (10 mg/kg) significantly ameliorated these changes and showed superior protective effects compared to the same dose of Cur. These findings suggest that CUD003 protects against LPS-induced vascular dysfunction and suppresses inflammation and oxidative stress, supporting its potential as a preventive candidate against vascular inflammation and dysfunction. Full article

Path tracking is a fundamental challenge in the development of automated driving systems, requiring precise control of vehicle motion while ensuring smooth and stable actuation signals. Advancements in this field often lead to increasingly complex control solutions that demand significant computational effort and are difficult to parameterize. A novel variable structure path-tracking control approach that is based on the geometrically optimal solution of a Dubins car offers a promising solution to this challenge. The controller generates an n-smooth and differentially bounded steering angle, and with n + 1 parameters, it can be tuned towards performance, robustness, or low magnitude of the steering angle derivatives. In prior work, this controller demonstrated its performance, robustness, and tunablity in various simulations. In this contribution, we address the challenges of implementing this controller in a real vehicle, including system dead time, low sampling rates, and discontinuous paths. Key adaptations are proposed to ensure robust performance under these conditions. The controller is integrated into a comprehensive automated driving system, incorporating planning and velocity control, and evaluated during an overtaking maneuver (double-lane change) in a real-world setting. Experimental results show that the implemented controller successfully handles system dead time and path discontinuities, achieving consistent tracking errors of less than $0.3$ m. Full article

Adverse pressure gradients are intrinsic to compressor flow behavior and are further intensified by secondary effects associated with rotor tip clearance flow interactions. Tip clearance generates leakage flow, which leads to the formation of tip leakage vortices, a major contributor to aerodynamic losses in axial compressors. These vortices significantly influence both compressor performance and operational stability. Extensive prior research has demonstrated that passive casing treatments, particularly axial slots, can substantially improve the stall margin in axial compressors. In this work, the performance of a new casing treatment geometry is investigated using the concept of recirculating flow within semi-circular axial slots. The proposed casing treatment geometry builds upon recent experimental findings involving single-rotor configurations. It was applied to the first rotor row of a three-and-a-half-stage (3.5-stage) axial compressor comprising an inlet guide vane followed by three rotor–stator stages. The numerical model incorporates axial slots with a novel periodic interface approach implemented in a multistage compressor simulation. Three-dimensional steady-state RANS (Reynolds Average Navier-Stokes) simulations were performed to investigate the aerodynamic effects of the casing treatment across various rotational speeds. The results for the casing treatment configuration were compared with those of a baseline smooth casing. The introduction of the new casing treatment produced noticeable modifications to the internal flow structure, particularly in the tip region, resulting in improved overall compressor stability within the operating range of 85 to 100% of design speed. Full article

A mill relining manipulator is key maintenance equipment for liners exchanged and operated by workers inside a grinding mill. To improve the operation efficiency and safety, real-time path planning and end deformation compensation should be performed prior to actual execution. This paper proposes a five-dimensional digital twin framework to realize virtual–real interaction between a physical manipulator and virtual model. First, a real-time digital twin scene is established based on OpenGL. The involved technologies include scene rendering, a camera system, the light design, model importation, joint control, and data transmission. Next, different solving methods are introduced into the service space for relining tasks, including a kinematics model, collision detection, path planning, and end deformation compensation. Finally, a user application is developed to realize real-time condition monitoring and simulation analysis visualization. Through comparison experiments, the superiority of the proposed path planning algorithm is demonstrated. In the case of a long-distance relining task, the planning time and path length of the proposed algorithm are 1.7 s and 15,299 mm, respectively. For motion smoothness, the joint change curve exhibits no abrupt variation. In addition, the experimental results between original and modified end trajectories further verified the effectiveness and feasibility of the proposed end effector compensation method. This study can also be extended to other heavy-duty manipulators to realize intelligent automation. Full article

Optical remote sensing images are often partially obscured by clouds due to the inability of visible light to penetrate cloud cover, which significantly limits their subsequent applications. Most existing cloud removal methods formulate the problem using low-rank and sparse priors within a discrete representation framework. However, these approaches typically rely on manually designed regularization terms, which fail to accurately capture the complex geostructural patterns in remote sensing imagery. In response to this issue, we develop a continuous blind cloud removal model. Specifically, the cloud-free component is represented using a continuous tensor function that integrates implicit neural representations with low-rank tensor decomposition. This representation enables the model to capture both global correlations and local smoothness. Furthermore, a band-wise sparsity constraint is employed to represent the cloud component. To preserve the information in regions not covered by clouds during reconstruction, a box constraint is incorporated. In this constraint, cloud detection is performed using an adaptive thresholding strategy, and a morphological erosion function is employed to ensure accurate detection of cloud boundaries. To efficiently handle the developed model, we formulate an alternating minimization algorithm that decouples the optimization into three interpretable subproblems: cloud-free reconstruction, cloud component estimation, and cloud detection. Our extensive evaluations on both synthetic and real-world data reveal that the proposed method performs competitively against state-of-the-art cloud removal methods. Full article

Obstacle avoidance during high-speed, low-altitude flight remains a significant challenge for unmanned aerial vehicles (UAVs), particularly in unfamiliar environments where prior maps and heavy onboard sensors are unavailable. To address this, we present an entropy-aware deep reinforcement learning framework that enables fixed-wing UAVs to navigate safely using only monocular onboard cameras. Our system features a lightweight, single-frame depth estimation module optimized for real-time execution on edge computing platforms, followed by a reinforcement learning controller equipped with a novel reward function that balances goal-reaching performance with path smoothness under fixed-wing dynamic constraints. To enhance policy optimization, we incorporate high-quality experiences from the replay buffer into the gradient computation, introducing a soft imitation mechanism that encourages the agent to align its behavior with previously successful actions. To further balance exploration and exploitation, we integrate an adaptive entropy regularization mechanism into the Proximal Policy Optimization (PPO) algorithm. This module dynamically adjusts policy entropy during training, leading to improved stability, faster convergence, and better generalization to unseen scenarios. Extensive software-in-the-loop (SITL) and hardware-in-the-loop (HITL) experiments demonstrate that our approach outperforms baseline methods in obstacle avoidance success rate and path quality, while remaining lightweight and deployable on resource-constrained aerial platforms. Full article

The acquisition of nighttime remote-sensing visible-light images is often accompanied by low-illumination effects and haze interference, resulting in significant image quality degradation and greatly affecting subsequent applications. Existing low-light enhancement and dehazing algorithms can handle each problem individually, but their simple cascade cannot effectively address unknown real-world degradations. Therefore, we design a joint processing framework, WFDiff, which fully exploits the advantages of Fourier–wavelet dual-domain features and innovatively integrates the inverse diffusion process through differentiable operators to construct a multi-scale degradation collaborative correction system. Specifically, in the reverse diffusion process, a dual-domain feature interaction module is designed, and the joint probability distribution of the generated image and real data is constrained through differentiable operators: on the one hand, a global frequency-domain prior is established by jointly constraining Fourier amplitude and phase, effectively maintaining the radiometric consistency of the image; on the other hand, wavelets are used to capture high-frequency details and edge structures in the spatial domain to improve the prediction process. On this basis, a cross-overlapping-block adaptive smoothing estimation algorithm is proposed, which achieves dynamic fusion of multi-scale features through a differentiable weighting strategy, effectively solving the problem of restoring images of different sizes and avoiding local inconsistencies. In view of the current lack of remote-sensing data for low-light haze scenarios, we constructed the Hazy-Dark dataset. Physical experiments and ablation experiments show that the proposed method outperforms existing single-task or simple cascade methods in terms of image fidelity, detail recovery capability, and visual naturalness, providing a new paradigm for remote-sensing image processing under coupled degradations. Full article

This paper presents a detailed numerical investigation of the effect of hub-mounted riblets on the thermal and aerodynamic performance of a radial turbine rotor. While prior studies have shown that riblets reduce wall shear stress and improve aerodynamic efficiency, their influence on heat transfer and thermal losses remains underexplored. Using numerical simulations, this study examines the heat transfer characteristics within the rotor passage, comparing ribbed and smooth hub configurations under the same operating conditions. Results reveal that although riblets reduce frictional drag, they also enhance convective heat transfer—leading to a 6% increase in the heat transfer coefficient at the hub and 2.8% at the blade surfaces. This intensification of heat transfer results in a 4.3% rise in overall thermal losses, counteracting some of the aerodynamic gains. The findings provide new insights into the thermofluidic implications of surface modifications in turbomachinery and emphasise the importance of considering surface finish not only for aerodynamic optimisation but also for thermal efficiency. These results can inform future turbine design and manufacturing practices aimed at controlling surface roughness to minimise heat loss. Full article

In order to solve the problems of modulation signals in low signal-to-noise ratio (SNR), such as poor feature extraction ability, strong dependence on single modal data, and insufficient recognition accuracy, this paper proposes a multi-dimensional feature MFCA-transformer recognition network that integrates phase, frequency and power information. The network uses Triple Dynamic Feature Fusion (TDFF) to fuse constellation, time-frequency, and power spectrum features through the adaptive dynamic mechanism to improve the quality of feature fusion. A Channel Prior Convolutional Attention (CPCA) module is introduced to solve the problem of insufficient information interaction between different channels in multi-dimensional feature recognition tasks, promote information transmission between various feature channels, and enhance the recognition ability of the model for complex features. The label smoothing technique is added to the loss function to reduce the overfitting of the model to the specific label and improve the generalization ability of the model by adjusting the distribution of the real label. Experiments show that the recognition accuracy of the proposed method is significantly improved on the public datasets, at high signal-to-noise ratios, the recognition accuracy can reach 93.2%, which is 3% to 14% higher than those of the existing deep learning recognition methods. Full article

The voltage source converters convert the DC to AC in order to interface distributed generation units with the utility grid, typically using an LCL filter to smooth the modulated wave. However, the LCL filter can introduce resonance, potentially cause instability, and necessitate the use of damping techniques, such as active damping, which utilizes feedback from the current control loop to suppress resonance. This paper presents a comprehensive performance assessment of four current-feedback-based active damping (AD) techniques—converter current feedback (CCF), CCF with capacitor current feedback (CCFAD), grid current feedback (GCF), and GCF with capacitor current feedback (GCFAD)—under a broad range of realistic grid disturbances and low switching frequency conditions. Unlike prior works that often analyze individual feedback strategies in isolation, this study highlights and compares their dynamic behavior, robustness, and total harmonic distortion (THD) in eight operational scenarios. The results reveal the severe instability of GCF in the absence of damping and the superior inherent damping property of CCF while demonstrating the comparable effectiveness of GCFAD. Moreover, a simplified yet robust design methodology for the LCL filter is proposed, enabling the filter to maintain stability and performance even under significant variations in grid impedance. Additionally, a sensitivity analysis of switching frequency variation is included. The findings offer valuable insights into selecting and implementing robust active damping methods for grid-connected converters operating at constrained switching frequencies. The effectiveness of the proposed methods has been validated through both MATLAB/Simulink simulations and hardware-in-the-loop (HIL) testing. Full article

Background: Early identification of visual disorders in children is essential to prevent long-term visual impairment and support academic development. Despite the recognized importance of visual screenings, no universal consensus exists on which visual parameters or threshold values should be used, particularly for measuring visual acuity (VA) in pediatric populations. Objectives: This study aimed to develop age-related percentile norms for VA using LEA symbol charts. Methods: A sample of Italian schoolchildren aged 5 to 15 years (n = 1510) participated in the study. Data were collected retrospectively from school-based vision screenings conducted across 12 schools in the Lombardy and Piedmont regions from 2010 to 2019. Monocular and binocular VA were measured at 3 m using a standardized LEA symbol chart, and values were scored letter-by-letter on a LogMAR scale. Smoothed percentile curves were derived using Box–Cox, Cole, and Green distribution modeling and regression analysis. Results: The results showed a non-linear improvement in VA with age. Compared to prior studies, LEA symbols yielded slightly lower VA scores, reinforcing the need for chart-specific norms. The 50th percentile VA improved from approximately +0.07 LogMAR at age 6 to about −0.09 LogMAR at age 15. Conclusions: These findings highlight the importance of age-specific, chart-specific, and statistically robust reference data for VA screening in children. The derived percentile tables offer a more sensitive tool than fixed cut-offs for identifying visual anomalies and tailoring clinical interventions. This work contributes to standardizing pediatric VA screening practices and improving early detection of visual deficits. Full article

Population aging poses significant challenges to the sustainability of pension systems. This study presents an integrated methodological approach that uniquely combines actuarial life-cycle modeling with agent-based simulation to assess the potential of Reverse Mortgage Loans (RMLs) as a dual lever for enhancing retiree welfare and supporting pension system resilience under demographic and financial uncertainty. We explore Reverse Mortgage Loans (RMLs) as a potential financial instrument to support retirees while alleviating pressure on public pensions. Unlike prior research that treats individual decisions or policy outcomes in isolation, our hybrid model explicitly captures feedback loops between household-level behavior and system-wide financial stability. To test our hypothesis that RMLs can improve individual consumption outcomes and bolster systemic solvency, we develop a hybrid model combining actuarial techniques and agent-based simulations, incorporating stochastic housing prices, longevity risk, regulatory capital requirements, and demographic shifts. This dual-framework enables a structured investigation of how micro-level financial decisions propagate through market dynamics, influencing solvency, pricing, and adoption trends. Our central hypothesis is that reverse mortgages, when actuarially calibrated and macroprudentially regulated, enhance individual financial well-being while preserving long-run solvency at the system level. Simulation results indicate that RMLs can improve consumption smoothing, raise expected utility for retirees, and contribute to long-term fiscal sustainability. Moreover, we introduce a dynamic regulatory mechanism that adjusts capital buffers based on evolving market and demographic conditions, enhancing system resilience. Our simulation design supports multi-scenario testing of financial robustness and policy outcomes, providing a transparent tool for stress-testing RML adoption at scale. These findings suggest that, when well-regulated, RMLs can serve as a viable supplement to traditional retirement financing. Rather than offering prescriptive guidance, this framework provides insights to policymakers, financial institutions, and regulators seeking to integrate RMLs into broader pension strategies. Full article

This paper investigates the problem of tracking targets with unknown but fixed 3D star-convex shapes using point cloud measurements. While existing methods typically model shape parameters as random variables evolving according to predefined prior models, this evolution process is often unknown in practice. We propose a particular approach within the Expectation Conditional Maximization (ECM) framework that circumvents this limitation by treating shape-defining quantities as parameters estimated directly via optimization. The objective is the joint estimation of target kinematics, extent, and orientation in 3D space. Specifically, the 3D shape is modeled using a radial function estimated via double Fourier series (DFS) expansion, and orientation is represented using the compact, singularity-free axis-angle method. The ECM algorithm facilitates this joint estimation: an Unscented Kalman Smoother infers kinematics in the E-step, while the M-step estimates DFS shape parameters and rotation angles by minimizing regularized cost functions, promoting robustness and smoothness. The effectiveness of the proposed algorithm is substantiated through two experimental evaluations. Full article