Search Results (93)

Transmission error (TE) is a major excitation source in geared systems, but microgeometry deviations are usually evaluated through nominal amplitudes rather than their tooth-to-tooth spatial distribution. This study investigates how different tooth-level barreling deviation patterns influence TE modulation and spectral characteristics in a controlled single helical gear-pair model. The nominal barreling value was kept constant, while four deviation patterns were imposed on the 23-tooth pinion: harmonic, phase-shifted harmonic, clustered with an outlier, and random. The TE response was evaluated in the time domain and by Fast Fourier Transform (FFT)-based spectral analysis, with particular attention to the gear mesh frequency (GMF) and shaft-frequency-spaced sidebands. The results show that identical nominal barreling levels can produce different TE waveforms and spectral signatures. Harmonic distributions mainly preserve a regular response, whereas phase-shifted and clustered patterns increase waveform asymmetry and sideband activity. The clustered outlier case produced the most fault-like response. The findings indicate that tooth-level spatial distribution should be considered explicitly in simulation-based gear microgeometry and noise, vibration, and harshness (NVH) sensitivity studies. Full article

Triangular mesh is one of the most widely used representations for 3D surfaces. However, high-resolution mesh models often contain a large number of triangles, leading to significant burdens in storage, transmission, and real-time rendering. Mesh simplification aims to reduce model complexity while preserving geometric fidelity and structural features. Classical methods, such as quadric error metrics (QEM), rely solely on local geometric errors, making them difficult to distinguish between redundant regions and structurally important features, often resulting in feature loss and topological degradation. To address these limitations, this study proposes a structure-aware triangular mesh simplification framework based on graph neural networks (GNNs)-guided QEM. GNNs are employed as a structural importance estimator to predict geometric saliencies of mesh edges. The predicted importances are incorporated into the classical QEM edge collapse cost through a soft modulation mechanism. Furthermore, a geometry-saliency-driven dynamic cost modulation strategy is designed, enabling the simplification process to prioritize critical features in early stages and gradually transition to global error minimization in later stages, without compromising the geometric optimality of QEM. In terms of model design, hybrid structural representation GNNs are constructed by integrating spectral geometry and a dual-branch architecture. Laplacian positional encoding is introduced to capture global topological information, while 1-hop and 2-hop message passing branches enable multi-scale representation of complex geometric structures. In addition, a staged inference strategy is adopted to dynamically update graph structural features during simplification, effectively mitigating topological drift. Experimental results on the TOSCA dataset demonstrate that the proposed method achieves stable performance across various simplification ratios. It consistently outperforms FQMS and QEM in terms of geometric error ($P_{CD}$) and normal consistency ($P_{NE}$). For structural preservation ($P_{LE}$), the method shows advantages, with win-rates generally exceeding 90%. Moreover, it significantly improves the preservation of local geometric details at low to moderate simplification ratios. In summary, the proposed method effectively enhances local structural preservation while maintaining global geometric topology, providing an interpretable and practical solution for integrating learning-based structural awareness with classical geometric optimization in mesh simplification. Full article

This survey presents an overview of recent developments in the analysis and numerical treatment of spectral fractional diffusion equations. Particular attention is devoted to efficient strategies for solving spectral fractional diffusion problems, including approaches based on rational approximation that enable efficient numerical realization of fractional powers of elliptic operators. Building on these approximations, we discuss adaptive finite element discretization techniques for polygonal domains, where singularities and geometric irregularities require carefully designed mesh refinement strategies. The survey also highlights the role of fractional diffusion operators in the preconditioning of coupled and multiphysics problems, where they can significantly improve the robustness and convergence of iterative solvers. Furthermore, we review recent results on maximum principles and monotonicity preservation for spectral fractional diffusion–reaction equations, which are essential for ensuring physically meaningful numerical solutions. Finally, we discuss current efforts aimed at improving robustness and computational efficiency through reduced and multilevel iteration methods. These approaches provide scalable algorithms for large-scale problems while maintaining accuracy and stability. The survey concludes by outlining several open problems and promising directions for future research in the numerical analysis of fractional diffusion models. Full article

In general, condition monitoring (CM) and noise, vibration and harshness (NVH) are often treated as separate disciplines, despite the fact that both rely on vibration measurements. CM relies on broadband statistical metrics such as RMS, kurtosis, and envelope analysis to detect faults. Meanwhile, NVH investigates tonal excitation mechanisms related to gear mesh frequency (GMF) and its modulation components. In this study, we investigate whether a numerical relationship can be established between classical CM indicators and physically based tonal excitation indicators derived from frequency–domain analysis. Using healthy and damaged benchmark gearbox recordings, Spearman correlation analysis was performed between broadband metrics and GMF-related tonal features, including GMF-band energy and absolute sideband energy. Results show moderate but statistically significant correlations between RMS, envelope peak amplitude, and tonal indicators, whereas kurtosis exhibits no meaningful association. Additionally, tonal response amplification in the damaged gearbox is shown to be non-uniformly distributed across sensor locations, indicating sensor-dependent structural sensitivity rather than uniform response growth. These findings demonstrate that broadband CM indicators partially encode changes in tonal excitation-related response, establishing a reproducible data-driven bridge between diagnostic condition monitoring and NVH excitation analysis. Full article

In this study, we investigate the upper- and lower-bound approximations of numerical eigenvalues derived by weak Galerkin spectral element methods on arbitrary convex quadrilateral meshes for the Laplace eigenvalue problem. Firstly, the Piola transformation is employed to construct the approximation space for weak gradients on each convex quadrilateral element, while a one-to-one mapping is used to establish the approximation space for weak functions. Subsequently, based on the weak Galerkin spectral element approximation space defined on convex quadrilateral meshes, a Galerkin approximation scheme is formulated, and its well-posedness is then analyzed. Furthermore, numerical experiments are performed on arbitrary convex quadrilateral meshes of the square and L-shaped domains to explore the upper- and lower-bound approximations of numerical eigenvalues. Numerical findings indicate that the presented method not only obtains optimal orders of convergence with respect to both the mesh size and the polynomial degree, but also provides upper- and lower-bound approximations for the reference eigenvalues by proper choices of polynomial degrees in approximation spaces and parameters of the approximation scheme in both h-version and p-version weak Galerkin spectral element methods. This study offers new perspectives and methodologies for the high-precision numerical solution of eigenvalue problems in elliptic equations. Full article

To address the challenges of strong electromechanical coupling, nonlinear friction, and poor disturbance rejection in semi-direct-drive scraper conveyor systems under complex coal mining conditions, this paper aims to propose a high-performance drive control strategy that balances dynamic response speed with steady-state operational smoothness. First, an integrated electromechanical coupling dynamic model incorporating Permanent Magnet Synchronous Motor (PMSM) vector control and the time-varying meshing stiffness of a two-stage planetary gear train is established. Subsequently, a Sliding Mode Control (SMC) strategy optimized with a saturation boundary layer is designed and compared with traditional Proportional-Integral (PI) control under multiple operating conditions. Time-frequency domain analysis indicates that SMC significantly enhances the dynamic stiffness of the drive system. Under sudden load change conditions, the speed recovery time is shortened by approximately 76%, and the steady-state error is reduced by 37% compared to PI control. Microscopic characteristic evaluation based on FFT and Total Variation (TV) metrics reveals that SMC achieves active disturbance rejection through spectral broadening of the electromagnetic torque. Crucially, the steady-state cumulative control effort of SMC is equivalent to that of PI, implying no additional mechanical stress burden, while the equivalent dynamic transmission force fluctuation in the mechanical chain is reduced by about 3%. The study confirms that the proposed strategy successfully achieves a synergistic optimization of “macroscopic rapid response” and “microscopic smooth operation,” providing a theoretical basis for the high-precision control of heavy-duty underground transmission equipment. Full article

The maximum principle is a widely used qualitative property of linear (and not only) elliptic boundary value problems. A natural goal for developing numerical methods is for the approximate solution to have a similar property. In this case, we say that a discrete maximum principle holds. In many cases, such a requirement is critical to ensuring the reliability of computational models. Here, we consider multidimensional linear elliptic problems with diffusion and reaction terms. Such problems have been studied and analyzed for many decades. Since relatively recently, scientists have faced conceptually new challenges when considering anomalous (fractional) diffusion. In the present paper, we concentrate on the case of spectral fractional diffusion. Discretization was carried out using the finite difference method and the finite element method with a lumped mass matrix. In large-scale multidimensional problems, the computational complexity of dense matrix operations is critical. To overcome this problem, BURA (best uniform rational approximation) methods were applied to find the efficient numerical solutions of emerging dense linear systems. Thus, along with the need to satisfy the discrete maximum principle associated with the mesh method applied for discretization of the differential operator, the issue of the monotonicity of BURA numerical solution arises. The presented results are three-fold and include the following: (i) maximum principles for fractional diffusion–reaction problems; (ii) sufficient conditions for discrete maximum principles; and (iii) sufficient conditions for monotonicity of the investigated BURA- or BURA-like approximation methods. A novel, systematic theoretical analysis is developed for sub-diffusion with a fractional power $\alpha \in (1/2,1)$ and a constant reaction coefficient. The theoretical findings are further supported by numerical examples. Full article

High-speed silicon (Si) photonic transmitters operating in the O-band require higher on-chip optical power to support advanced modulation formats and ever-increasing line rates. A straightforward approach is to operate laser diodes at higher output power or employ more specialized sources, but this raises cost and exacerbates nonlinear effects such as self-phase modulation, two-photon absorption, and free-carrier generation in high-index-contrast Si waveguides. This paper proposes a low-cost 4 × 1 tree-cascade multimode interference (MMI) power combiner on a Si-on-insulator platform at 1310 nm wavelength that enables coherent power scaling while remaining fully compatible with standard commercial O-band lasers. The device employs adiabatic tapers and low-loss S-bends to ensure uniform field evolution, suppress local field enhancement, and mitigate nonlinear phase accumulation. The optimized layout occupies a compact footprint of 12 µm × 772 µm and achieves a simulated normalized power transmission of 0.975 with an insertion loss of 0.1 dB. Spectral analysis shows a 3 dB bandwidth of 15.8 nm around 1310 nm, across the O-band operating window. Thermal analysis shows that wavelength drift associated with ±50 °C temperature variation remains within the device bandwidth, ensuring stable operation under realistic laser self-heating and environmental changes. Owing to its broadband response, fabrication tolerance, and compatibility with off-the-shelf laser diodes, the proposed combiner is a promising building block for O-band transmitters and photonic neural-network architectures based on cascaded splitter and combiner meshes, while preserving linear transmission and enabling dense, large-scale photonic integration. Full article

Advances in robotic technology for hand rehabilitation, particularly soft robotic gloves, have significant potential to improve patient outcomes. While vision-based algorithms pave the way for fast and convenient hand pose estimation, most current models struggle to accurately track hand movements when soft robotic gloves are used, primarily due to severe occlusion. This limitation reduces the applicability of soft robotic gloves in digital and remote rehabilitation assessment. Furthermore, traditional clinical assessments like the Fugl-Meyer Assessment (FMA) rely on manual measurements and subjective scoring scales, lacking the efficiency and quantitative accuracy needed to monitor hand function recovery in data-driven personalised rehabilitation. Consequently, few integrated evaluation systems provide reliable quantitative assessments. In this work, we propose an RGB-based evaluation system for soft robotic glove applications, which is aimed at bridging these gaps in assessing hand function. By incorporating the Hand Mesh Reconstruction (HaMeR) model fine-tuned with motion capture data, our hand estimation framework overcomes occlusion and enables accurate continuous tracking of hand movements with reduced errors. The resulting functional metrics include conventional clinical benchmarks such as the mean per joint angle error (MPJAE) and range of motion (ROM), providing quantitative, consistent measures of rehabilitation progress and achieving tracking errors lower than 10°. In addition, we introduce adapted benchmarks such as the angle percentage of correct keypoints (APCK), mean per joint angular velocity error (MPJAVE) and angular spectral arc length (SPARC) error to characterise movement stability and smoothness. This extensible and adaptable solution demonstrates the potential of vision-based systems for future clinical and home-based rehabilitation assessment. Full article

With continued technological advancements, the sizes of fixed-bottom offshore wind turbines have increased, resulting in increased operational noise levels. In this study, we investigated the underwater radiated noise generated by wind turbine operation along the western coast of the Korean Peninsula using numerical simulations. Using the OpenFAST software, a load analysis of the National Renewable Energy Laboratory 5 MW reference turbine with a jacket substructure was conducted for the various wind speeds defined in Design Load Case 1.2. The load analysis results and gear mesh frequency components were applied as excitation forces in a finite-element-method-based structural–acoustic coupled analysis model to evaluate underwater radiated noise, incorporating the acoustic properties of the seabed along the western coast of the Korean Peninsula and dynamic state of the sea surface. The numerical results were subsequently compared with experimental measurements of the operational noise from wind turbines supported by jacket substructures. The results indicated that, excluding certain frequency bands, the spectral levels were similar across the frequency spectrum. Full article

This paper investigates a physics-informed surrogate modeling framework for multi-phase flow in porous media based on the Fourier Neural Operator. Traditional numerical simulators, though accurate, suffer from severe computational bottlenecks due to fine-grid discretizations and the iterative solution of highly nonlinear partial differential equations. By parameterizing the kernel integral directly in Fourier space, the operator provides a discretization-invariant mapping between function spaces, enabling efficient spectral convolutions. We introduce a Dual-Branch Adaptive Fourier Neural Operator with a shared Fourier encoder and two decoders: a saturation branch that uses an inverse Fourier transform followed by a multilayer perceptron and a pressure branch that uses a convolutional decoder. Temporal information is injected via Time2Vec embeddings and a causal temporal transformer, conditioning each forward pass on step index and time step to maintain consistent dynamics across horizons. Physics-informed losses couple data fidelity with residuals from mass conservation and Darcy pressure, enforcing the governing constraints in Fourier space; truncated spectral kernels promote generalization across meshes without retraining. On SPE10-style heterogeneities, the model shifts the infinity-norm error mass into the ${10}^{-2}$ to ${10}^{-1}$ band during early transients and sustains lower errors during pseudo-steady state. In zero-shot three-dimensional coarse-to-fine upscaling from $30\times 110\times 5$ to $60\times 220\times 5$, it attains $R^2=0.90$, RMSE = $4.4\times {10}^{-2}$, and MAE = $3.2\times {10}^{-2}$, with more than 90% of voxels below five percent absolute error across five unseen layers, while the end-to-end pipeline runs about three times faster than a full-order fine-grid solve and preserves water-flood fronts and channel connectivity. Benchmarking against established baselines indicates a scalable, high-fidelity alternative for high-resolution multi-phase flow simulation in porous media. Full article

Strain localization is a critical phenomenon in geotechnical materials, serving as a precursor to the failure of engineering structures such as slopes, foundations, and tunnels. This paper presents a comprehensive review of the theoretical, experimental, and numerical advances in the study of strain localization. Theoretically, the review spans from classical empirical criteria for shear band inclination to the more rigorous bifurcation theory, which mathematically defines the onset of localization as a loss of uniqueness in the governing equations. Experimentally, various laboratory techniques including direct shear, triaxial, plane strain, and true triaxial tests are discussed, highlighting how they have revealed the influences of microstructure, stress path, and boundary conditions on shear band development. The core of the review focuses on numerical simulations, critically analyzing the limitations of the classical Finite Element Method (FEM) due to mesh dependency. It then elaborates on advanced regularization strategies, encompassing weak discontinuity methods (e.g., Cosserat continuum theory) that introduce an internal length scale to model finite-width shear bands, and strong discontinuity methods (e.g., the Strong Discontinuity Approach, SDA) for simulating discrete cracks. Significant emphasis is placed on innovative coupled approaches, particularly the Cos-SDA model, which integrates the advantages of both weak and strong discontinuity methods to seamlessly simulate the entire progressive failure process from diffuse localization to discrete slip. Furthermore, the application of spectral analysis for evaluating the regularization performance of these numerical methods is examined. Finally, the review concludes by identifying persistent challenges and outlining promising future research directions, including 3D modeling, multi-field coupling, and the integration of data-driven techniques. This synthesis aims to provide a valuable reference for advancing the prediction and management of failure in geotechnical structures. Full article

Background: Meat adulteration poses serious public health, economic, and religious concerns, particularly in the Middle East and North Africa (MENA) and Gulf Cooperation Council (GCC) regions where halal authenticity is essential. While isolated studies have reported undeclared species in meat products, a comprehensive regional synthesis of prevalence, detection technologies, and regulatory responses has been lacking. Methods: This scoping review followed PRISMA-ScR guidelines. A systematic search of PubMed, Scopus, and Web of Science from database inception to 15 September 2025 was conducted using controlled vocabulary (MeSH) and free-text terms. Eligible studies included laboratory-based investigations of meat adulteration in MENA and GCC countries. Data were charted on study characteristics, adulteration types, detection methods, and regulatory context. Results: Out of 50 records screened, 35 studies were included, covering 27 MENA/GCC countries. Prevalence of adulteration varied widely, from 5% in UAE surveillance studies to 66.7% in Egyptian native sausages. Undeclared species most frequently detected were poultry, donkey, equine, pig, and dog. Molecular methods, particularly PCR and qPCR, were most widely applied, followed by ELISA and spectroscopy. Recent studies introduced biosensors, AI-assisted spectroscopy, and blockchain traceability, but adoption in regulatory practice remains limited. Conclusions: Meat adulteration in the MENA and GCC regions is localized and product-specific rather than uniformly widespread. Detection technologies are advancing, yet regulatory enforcement and halal-sensitive verification remain fragmented. Strengthening laboratory capacity, harmonizing regional standards, and investing in portable biosensors, AI-enhanced spectral tools, and blockchain-based traceability are critical for consumer trust, halal integrity, and food safety. Full article

Advancements in flexible, low-cost, and recyclable alternatives to transparent conductive oxides (TCOs) are critical challenges in the sustainability of third-generation solar cells. This work introduces a copper mesh-based transparent electrode for dye-sensitized solar cells, replacing conventional fluorine doped-tin oxide (FTO)-coated glass to simultaneously reduce spectral reflection losses, enhance mechanical flexibility, and enable material recyclability. Titanium dioxide (TiO₂) photoanodes were synthesized and directly deposited onto the mesh via a single-step, low-energy ball milling process, which eliminates TiO₂ paste preparation and high-temperature annealing while reducing fabrication time from over three hours to 30 min. Structural and surface analyses confirmed the deposition of high-purity anatase-phase TiO₂ with strong adhesion to the mesh branches, enabling improved dye loading and electron injection pathways. Optical studies revealed higher visible light absorption for the copper mesh compared to FTO in the visible range, further enhanced upon TiO₂ and Ru-based dye deposition. Electrochemical measurements showed that TiO₂/Cu mesh electrodes exhibited significantly higher photocurrent densities and faster photo response rates than bare Cu mesh, with dye-sensitized Cu mesh achieving the lowest charge transfer resistance in impedance analysis. Techno–economic and sustainability assessments revealed a decrease of 7.8% in cost and 82% in CO₂ emissions associated with the fabrication of electrodes as compared to conventional TCO electrodes. The synergy between high conductivity, transparency, mechanical durability, and a scalable, recyclable fabrication route positions this architecture as a strong candidate for next-generation dye-sensitized solar modules that are both flexible and sustainable. Full article

This study presents the Fourier–Gegenbauer integral Galerkin (FGIG) method, a new numerical framework that uniquely integrates Fourier series and Gegenbauer polynomials to solve the one-dimensional advection–diffusion (AD) equation with spatially symmetric periodic boundary conditions, achieving exponential convergence and reduced computational cost compared to traditional methods. The FGIG method uniquely combines Fourier series for spatial periodicity and Gegenbauer polynomials for temporal integration within a Galerkin framework, resulting in highly accurate numerical and semi-analytical solutions. Unlike traditional approaches, this method eliminates the need for time-stepping procedures by reformulating the problem as a system of integral equations, reducing error accumulation over long-time simulations and improving computational efficiency. Key contributions include exponential convergence rates for smooth solutions, robustness under oscillatory conditions, and an inherently parallelizable structure, enabling scalable computation for large-scale problems. Additionally, the method introduces a barycentric formulation of the shifted Gegenbauer–Gauss (SGG) quadrature to ensure high accuracy and stability for relatively low Péclet numbers. This approach simplifies calculations of integrals, making the method faster and more reliable for diverse problems. Numerical experiments presented validate the method’s superior performance over traditional techniques, such as finite difference, finite element, and spline-based methods, achieving near-machine precision with significantly fewer mesh points. These results demonstrate its potential for extending to higher-dimensional problems and diverse applications in computational mathematics and engineering. The method’s fusion of spectral precision and integral reformulation marks a significant advancement in numerical PDE solvers, offering a scalable, high-fidelity alternative to conventional time-stepping techniques. Full article