Search Results (615)

This work introduces a comprehensive vision-based framework for autonomous space debris removal using robotic manipulators. A real-time debris detection module is built upon the YOLOv8 architecture, ensuring reliable target localization under varying illumination and occlusion conditions. Following detection, object motion states are estimated through a calibrated binocular vision system coupled with a physics-based collision model. Smooth interception trajectories are generated via a particle swarm optimization strategy integrated with a 5–5–5 polynomial interpolation scheme, enabling continuous and time-optimal end-effector motions. To anticipate future arm movements, a Transformer-based sequence predictor is enhanced by replacing conventional multilayer perceptrons with Kolmogorov–Arnold networks (KANs), improving both parameter efficiency and interpretability. In practice, the Transformer+KAN model compensates the manipulator’s trajectory planner to adapt to more complex scenarios. Each component is then evaluated separately in simulation, demonstrating stable tracking performance, precise trajectory execution, and robust motion prediction for intelligent on-orbit servicing. Full article

This paper introduces a novel hybrid shifted Gegenbauer integral–pseudospectral (HSG-IPS) method to solve the time-fractional Benjamin–Bona–Mahony–Burgers (FBBMB) equation with high accuracy. The approach transforms the equation into a form with only a first-order derivative, which is approximated using a stable shifted Gegenbauer differentiation matrix (SGDM), while other terms are computed with precise quadrature rules. By integrating advanced techniques such as the shifted Gegenbauer pseudospectral method (SGPS), fractional derivative and integral approximations, and barycentric integration matrices, the HSG-IPS method achieves spectral accuracy. Numerical results show it reduces average absolute errors (AAEs) by up to 99.99% compared to methods like Crank–Nicolson linearized difference scheme (CNLDS) and finite integration method using Chebyshev polynomial (FIM-CBS), with computational times as low as 0.04–0.05 s. The method’s stability is improved by avoiding ill-conditioned high-order derivative approximations, and its efficiency is boosted by precomputed matrices and Kronecker product structures. Robust across various fractional orders, the HSG-IPS method offers a powerful tool for modeling wave propagation and nonlinear phenomena in fractional calculus applications. Full article

High-precision satellite clock bias (SCB) prediction is essential for GNSS applications, including real-time precise point positioning (RT-PPP), Earth observation, planetary exploration, and spaceborne geodetic missions. However, during communication outages or when real-time SCB products are unavailable, RT-PPP may fail due to missing clock corrections. This underscores the necessity of reliable short-term SCB prediction in data-denied environments. To address this challenge, a hybrid model that integrates wavelet transform, a particle swarm optimization-enhanced gray model, and a first-order weighted local method is proposed for short-term SCB prediction. First, the novel model employs the db1 wavelet to perform three-level multi-resolution decomposition and single-branch reconstruction on preprocessed SCB, yielding one trend term and three detailed terms. Second, the particle swarm optimization algorithm is adopted to globally optimize the parameters of the traditional gray model to avoid falling into local optima, and the optimization-enhanced gray model is applied to predict the trend term. For the three detailed terms, the embedding dimension and time delay are calculated, and they are constructed in phase space to establish a first-order weighted local model for prediction. Third, the final SCB prediction is obtained by summing the predicted results of the trend term and the three detailed terms correspondingly. The BDS-3 SCB products from the GNSS Analysis Center of Wuhan University (WHU) are selected for experiments. Results indicate that the proposed model surpasses conventional linear polynomial (LP), quadratic polynomial (QP), gray model (GM), and Legendre (Leg.) polynomial models. The average precision and stability improvements reach (80.00, 79.16, 82.14, and 72.22) % and (36.36, 41.67, 41.67, and 61.11) % for 30 min prediction, (79.31, 78.57, 80.65, and 76.92) % and (44.44, 44.44, 47.37, and 74.36) % for 60 min prediction, and the average precision of the predicted SCB products is better than 0.20 ns and 0.21 ns for 30 min and 60 min, respectively. Furthermore, the proposed model exhibits strong robustness and is less affected by changes in clock types and the amount of modeling data. Therefore, in practical applications, the short-term SCB products predicted by the novel model are fully capable of satisfying the requirements of centimeter-level RT-PPP for clock bias precision. Full article

The Advanced Encryption Standard (AES) symmetric encryption algorithm plays a crucial role in data encryption. To address the limitations of the fixed Substitution-box (S-box) and static key expansion strategy in AES. This paper proposes an improved AES scheme that integrates a dynamic S-box structure with a key expansion mechanism based on dynamic perturbations. The dynamic S-box is generated by selecting affine transformation pairs and irreducible polynomials, and its cryptographic properties are tested in SageMath9.3 to obtain a set of S-boxes superior to the standard AES. In the key expansion process, the perturbation values generated by the hash function will be incorporated into the round key generation process to reduce the correlation between round keys. The improved AES algorithm, when applied to Over-the-Air (OTA) systems, not only achieves significant savings in storage resources of in-vehicle Electronic Control Units (ECUs) but also enhances the security of OTA communications. Furthermore, it consumes only a small amount of ECU computational resources, thereby effectively meeting the lightweight requirements of in-vehicle electronic control units. Full article

This study demonstrates the effectiveness of the differential transform method (DTM) in solving complex solid mechanics problems, focusing on static analysis of beams under various loads and boundary conditions. For cantilever beams (BSM1), DTM provided exact polynomial solutions for deflections and slopes: a cubic solution for concentrated end loads, a quadratic distribution for applied moments, and a fourth-degree polynomial for uniformly distributed loads, all matching established theoretical results. For simply supported beams (BSM2), DTM yielded solutions across two intervals for midspan concentrated forces, though required corrective terms for applied moments due to discontinuities. Under uniform loading, the method produced precise polynomial solutions with maximum deflection at midspan. Key advantages include DTM’s high-precision analytical solutions without additional approximations and its adaptability to diverse loading scenarios. However, for cases with pronounced discontinuities like concentrated moments, supplementary methods (e.g., Green’s functions) may be needed. The study highlights DTM’s potential for extension to nonlinear or dynamic problems, while software integration could broaden its engineering applications. This study demonstrates, for the first time, how DTM yields exact polynomial solutions for Euler–Bernoulli beams under discontinuous loads (e.g., concentrated moments), overcoming limitations of traditional numerical methods. The method’s analytical precision and avoidance of discretization errors are highlighted. Traditional methods like FEM require mesh refinement near discontinuities (e.g., concentrated moments), leading to computational inefficiencies. DTM overcomes this by providing exact polynomial solutions with corrective terms, achieving errors below 0.5% with only 4–5 series terms. Full article

As engines trend toward miniaturization, lightweight design, and higher power density, noise issues have become increasingly prominent, necessitating precise radiated noise prediction for effective noise control. This study develops a machine learning model based on surface vibration test data, which enhances the efficiency of engine noise prediction and has the potential to serve as an alternative to traditional high-cost engine noise test methods. Experiments were conducted on a four-cylinder, four-stroke diesel engine, collecting surface vibration and radiated noise data under full-load conditions (1600–3000 r/min). Five prediction models were developed using support vector regression (SVR, including linear, polynomial, and radial basis function kernels), random forest regression, and multilayer perceptron, suitable for non-anechoic environments. The models were trained on time-domain and frequency-domain vibration data, with performance evaluated using the maximum absolute error, mean absolute error, and median absolute error. The results show that polynomial kernel SVR performs best in time domain modelling, with an average relative error of 0.10 and a prediction accuracy of up to 90%, which is 16% higher than that of MLP; the model does not require Fourier transform and principal component analysis, and the computational overhead is low, but it needs to collect data from multiple measurement points. The linear kernel SVR works best in frequency domain modelling, with an average relative error of 0.18 and a prediction accuracy of about 82%, which is suitable for single-point measurement scenarios with moderate accuracy requirements. Analysis of measurement points indicates optimal performance using data from the engine top between cylinders 3 and 4. This approach reduces reliance on costly anechoic facilities, providing practical value for noise control and design optimization. Full article

The aim of this work was to construct explicit expressions for the summation of Dirichlet Beta functions with odd arguments and Zeta functions with even arguments. In the established literature, this is typically done using Fourier series expansions or Bernoulli numbers and polynomials. Here, instead, we achieve our goal by employing tools from probability: specifically, we introduce a generalisation of a technique based on multiple integrals and the algebra of random variables. This also allows us to increase the number of nested integrals and Cauchy random variables involved. Another key contribution is that, by generalising the exponent of Cauchy random variables, we obtain an original proof of the reflection formulae for the Digamma and Trigamma functions. These probabilistic proofs crucially utilise the Mellin transform to compute the integrals needed to determine probability density functions. It is noteworthy that, while understanding the presented topic requires knowledge of the rules for calculating multiple integrals (Fubini’s Theorem) and the algebra of continuous random variables, these are concepts commonly acquired by second-year university students in STEM disciplines. Our study thus offers new perspectives on how the mathematical functions considered relate and shows the significant role of probabilistic methods in promoting comprehension of this research area, in a way accessible to a broad and non-specialist audience. Full article

This study proposes a parallel model reduction method for two-dimensional discrete-time systems, utilizing Krawtchouk moments and equivalent transformation. This work makes two significant contributions. First, we introduce a projection subspace that is independent of the input as well as of the Krawtchouk parameters, thus ensuring robustness. Second, we propose an efficient parallel algorithm for computing the basis of the projection subspace. With the difference relation of Krawtchouk polynomials and the analytic identity theorem, we obtain the explicit formula for the Krawtchouk moments of the state, which is input-dependent and Krawtchouk-parameter-dependent. We derive a projection subspace that is independent of both input and Krawtchouk parameter, such that it is equivalent to the subspace spanned by the Krawtchouk moments. Further, we propose a parallel strategy based on the equivalent transformation of the block bi-diagonal Toeplitz matrices with bi-diagonal blocks to compute the basis of the projection subspace, facilitating acceleration of the model reduction process on high-performance computers. Moreover, we analyze the Krawtchouk moment invariants of the proposed parallel method. Finally, the effectiveness of the proposed method is illustrated by two numerical examples. Full article

The feature selection (FS) procedure is a critical preprocessing step in data mining and machine learning, aiming to enhance model performance by eliminating redundant features and reducing dimensionality. The Energy Valley Optimizer (EVO), inspired by particle physics concepts of stability and decay, offers a novel metaheuristic approach. This study introduces an enhanced binary version of EVO, termed Improved Binarization in the Energy Valley Optimizer with Fractional Chebyshev Transformation (IBEVO-FC), and specifically designed for feature selection challenges. IBEVO-FC incorporates several key advancements over the original EVO. Firstly, it employs a novel fractional Chebyshev transformation function to effectively map the continuous search space of EVO to the binary domain required for feature selection, leveraging the unique properties of fractional orthogonal polynomials for improved binarization. Secondly, the Laplace crossover method is integrated into the initialization phase to improve population diversity and local search capabilities. Thirdly, a random replacement strategy is applied to enhance exploitation and mitigate premature convergence. The efficacy of IBEVO-FC is rigorously evaluated on 26 benchmark datasets from the UCI Repository and compared against 7 contemporary wrapper-based feature selection algorithms. Statistical analysis confirms the competitive performance of the proposed IBEVO-FC method in terms of classification accuracy and feature subset size. Full article

This study investigates the dynamics of dust acoustic periodic waves in a three-component, unmagnetized dusty plasma system using generalized $(r^{\star },q^{\star })$ distributions. First, boundary conditions are applied to reduce the model to a second-order nonlinear ordinary differential equation. The Galilean transformation is subsequently applied to reformulate the second-order ordinary differential equation into an unperturbed dynamical system. Next, phase portraits of the system are examined under all possible conditions of the discriminant of the associated cubic polynomial, identifying regions of stability and instability. The Runge–Kutta method is employed to construct the phase portraits of the system. The Hamiltonian function of the unperturbed system is subsequently derived and used to analyze energy levels and verify the phase portraits. Under the influence of an external periodic perturbation, the quasi-periodic and chaotic dynamics of dust ion acoustic waves are explored. Chaos detection tools confirm the presence of quasi-periodic and chaotic patterns using Basin of attraction, Lyapunov exponents, Fractal Dimension, Bifurcation diagram, Poincaré map, Time analysis, Multi-stability analysis, Chaotic attractor, Return map, Power spectrum, and 3D and 2D phase portraits. In addition, the model’s response to different initial conditions was examined through sensitivity analysis. Full article

This paper presents a theoretical model of a thin, penny-shaped piezoelectric actuator bonded to an isotropic thermo-elastic substrate under coupled electrical-thermal-diffusive loading. The problem is assumed to be axisymmetric, and the peeling stress of the film is neglected in accordance with membrane theory, yielding a simplified equilibrium equation for the piezoelectric film. By employing potential theory and the Hankel transform technique, the surface strain of the substrate is analytically derived. Under the assumption of perfect bonding, a governing integral equation is established in terms of interfacial shear stress. The solution to this integral equation is obtained numerically using orthotropic Chebyshev polynomials. The derived results include the interfacial shear stress, stress intensity factors, as well as the radial and hoop stresses within the system. Finite element analysis is conducted to validate the theoretical predictions. Furthermore, parametric studies elucidate the influence of material mismatch and actuator geometry on the mechanical response. The findings demonstrate that, the performance of the piezoelectric actuator can be optimized through judicious control of the applied electrical-thermal-diffusive loads and careful selection of material and geometric parameters. This work provides valuable insights for the design and optimization of piezoelectric actuator structures in practical engineering applications. Full article

The shallow water equations (SWEs) model fluid flow in rivers, coasts, and tsunamis. Their nonlinearity challenges analytical solutions. We present a numerical algorithm combining the finite integration method with Chebyshev polynomial expansion (FIM-CPE) to solve one- and two-dimensional SWEs. The method transforms partial differential equations into integral equations, approximates spatial terms via Chebyshev polynomials, and uses forward differences for time discretization. Validated on stationary lakes, dam breaks, and Gaussian pulses, the scheme achieved errors below ${10}^{-12}$ for water height and velocity, while conserving mass with volume deviations under ${10}^{-5}$. Comparisons showed superior shock-capturing versus finite difference methods. For two-dimensional cases, it accurately resolved wave interactions over complex topographies. Though limited to wet beds and small-scale two-dimensional problems, the method provides a robust simulation tool. Full article

In this study, a novel direct laser beam turning (DLBT) approach is proposed for the precision machining of AISI 308L austenitic stainless steel, which eliminates the need for cutting tools and thereby eradicates tool wear and vibration-induced surface irregularities. A nanosecond-pulsed Nd:YAG fiber laser (λ = 1064 nm, spot size = 0.05 mm) was used, and Ø1.6 mm × 20 mm cylindrical rods were processed under ambient conditions without auxiliary cooling. The experimental framework systematically evaluated the influence of scanning speed, pulse frequency, and the number of laser passes on dimensional accuracy and material removal efficiency. The results indicate that a maximum diameter reduction of 0.271 mm was achieved at a scanning speed of 3200 mm/s and 50 kHz, whereas 0.195 mm was attained at 6400 mm/s and 200 kHz. A robust second-order polynomial correlation (R² = 0.99) was established between diameter reduction and the number of passes, revealing the high predictability of the process. Crucially, when the scanning speed was doubled, the effective fluence was halved, considerably influencing the ablation characteristics. Despite the low fluence, evidence of material evaporation at elevated frequencies due to the incubation effect underscores the complex photothermal dynamics governing the process. This work constitutes the first comprehensive quantification of pass-dependent diameter modulation in DLBT and introduces a transformative, noncontact micromachining strategy for hard-to-machine alloys. The demonstrated precision, repeatability, and thermal control position DLBT as a promising candidate for next-generation manufacturing of high-performance miniaturized components. Full article

Global policy narratives on livestock development increasingly emphasize environmental concerns, often overlooking the social dimensions of the sector. In the case of dairy, the world’s most valuable agricultural commodity, its role in social and economic development remains poorly quantified. Our study contributes to a more balanced vision of the UN SDGs thanks to the inclusion of a socio-economic dimension. Here we present a novel empirical approach to assess the socio-economic impacts of dairy development using a new global dataset and non-parametric modelling techniques (local polynomial regressions), with yield as a proxy for sectoral performance. We find that as dairy systems intensify, the number of farm households engaged in production declines, yet household incomes rise. On-farm labour productivity also increases, accompanied by a reduction in employment but higher wages. In dairy processing, employment initially grows, peaks, and then contracts, again with rising wages. The most substantial impact is observed among consumers: an increased milk supply leads to lower prices and improved affordability, expanding the access to dairy products. Additionally, dairy development is associated with greater agricultural value added, an expanding tax base, and the increased formalization of the economy. These findings suggest that dairy development, beyond its environmental footprint, plays a significant and largely positive role in social transformation, yet is having to adapt sustainably while tackling labour force relocation, and that dairy development’s social impacts mimic the general agricultural sector. These results might be of interest for the assessment of policies regarding dairy development. Full article

Timely and accurate diagnosis of gastrointestinal diseases (GIDs) remains a critical bottleneck in clinical endoscopy, particularly due to the limited contrast and sensitivity of conventional white light imaging (WLI) in detecting early-stage mucosal abnormalities. To overcome this, this research presents Spectrum Aided Vision Enhancer (SAVE), an innovative, software-driven framework that transforms standard WLI into high-fidelity hyperspectral imaging (HSI) and simulated narrow-band imaging (NBI) without any hardware modification. SAVE leverages advanced spectral reconstruction techniques, including Macbeth Color Checker-based calibration, principal component analysis (PCA), and multivariate polynomial regression, achieving a root mean square error (RMSE) of 0.056 and structural similarity index (SSIM) exceeding 90%. Trained and validated on the Kvasir v2 dataset (n = 6490) using deep learning models like ResNet-50, ResNet-101, EfficientNet-B2, both EfficientNet-B5 and EfficientNetV2-B0 were used to assess diagnostic performance across six key GI conditions. Results demonstrated that SAVE enhanced imagery and consistently outperformed raw WLI across precision, recall, and F1-score metrics, with EfficientNet-B2 and EfficientNetV2-B0 achieving the highest classification accuracy. Notably, this performance gain was achieved without the need for specialized imaging hardware. These findings highlight SAVE as a transformative solution for augmenting GI diagnostics, with the potential to significantly improve early detection, streamline clinical workflows, and broaden access to advanced imaging especially in resource constrained settings. Full article