Search Results (271)

In this work, we explore the nonlinear wave phenomena of the (3+1)-dimensional Boussinesq (II) equation, a significantly higher-dimensional model that describes dispersive wave propagation in fluid dynamics, plasma systems, and nonlinear optics. Using exact analytic and qualitative dynamic approaches, we study a wide range of solutions and stability characteristics of the model. Initially, we use the Hirota bilinear method to obtain a number of exact solutions, such as breather waves, two-wave interaction solutions, and other types of localized nonlinear waves. These solutions display remarkable physical properties, including periodic energy trapping, oscillatory modulations, and nonlinear wave interactions in higher dimensions. In addition, the $(m+{\displaystyle \frac{1}{G^{\prime }}})$-expansion method is used to derive new soliton solutions, such as bright solitary waves and W-shaped solitons, which are found to be stable and undergo pulse-shaping dynamics under certain conditions. Three-dimensional, two-dimensional, and contour plots are displayed for some of the solutions to demonstrate the physical significance of the results. The visualizations reveal the presence of localized waves, wave interactions, periodical breathing, and stable soliton profiles. Furthermore, we conduct modulation instability analysis to describe the conditions under which small perturbations of continuous wave backgrounds are unstable. The dispersion relation and the instability gain spectrum are obtained, which explain the formation of breathers, soliton trains, and other coherent structures. Furthermore, a Galilean transformation converts the governing equation into a planar nonlinear dynamical system, enabling its qualitative study. The Hamiltonian structure is revealed, and the fixed points are identified as centers, saddles, and cusps through bifurcation analysis. To investigate more complex dynamics, a periodic forcing term is introduced into the system, resulting in chaos in the forced system. The chaotic behavior is confirmed via phase portraits, three-dimensional attractors, time series, Poincaré sections, return maps, fractal dimension, and positive Lyapunov exponents. We also perform a sensitivity test to show the effect of initial condition variations on the system’s long-term dynamics. The findings greatly expand the exact solution set and dynamics of the (3+1)-dimensional Boussinesq equation (II). The analytical approach presented in this paper can also be applied to other multidimensional nonlinear evolution equations of mathematical physics. Full article

Ion-acoustic waves in dense quantum plasmas are strongly influenced by Fermi degeneracy, Landau quantization, and finite-temperature effects, and in many relevant environments, they also experience memory and nonlocal transport processes that cannot be captured within the planar integer Korteweg-de Vries (KdV) paradigm. In the present work, we revisit this problem by considering a two-fluid, partially degenerate electron-ion plasma in which electron trapping in the presence of a quantizing field and finite temperature is taken into account. Starting from the normalized fluid-Poisson system appropriate for such magnetized quantum plasmas, the reductive perturbation technique is used to derive the planar integer KdV equation for weakly nonlinear ion-acoustic disturbances. Within this integer-order KdV framework, we recast the evolution equation as a planar dynamical system, construct the associated Hamiltonian and effective Sagdeev-like potential, and demonstrate the existence of compressive solitary waves and nonlinear periodic modes via homoclinic and periodic phase-space orbits. Exact multi-soliton solutions and interaction states are then obtained by combining Hirota’s direct bilinear method with generalized Wronskian representations, allowing us to describe not only standard one-, two-, and three-soliton profiles but also positon-negaton interactions relevant to magnetized, partially degenerate plasmas. To incorporate hereditary and history-dependent effects that arise from anomalous transport and nonlocal temporal response in dense environments, we extend the model by introducing a Caputo time-fractional derivative, thereby obtaining a time-fractional KdV (FKdV) equation that continuously connects the classical KdV limit to fractional dynamics. The FKdV equation is analyzed using the Tantawy technique. This semi-analytical iterative scheme yields rapidly convergent series approximations for the fractional ion-acoustic soliton and provides explicit control of the approximation error. The fractional solutions show that varying the order of the Caputo derivative modifies the amplitude, width, and temporal relaxation of the solitary structures and can even split the pulse into two distinct lobes, in contrast with the nearly rigid propagation predicted by the integer-order KdV equation. Taken together, these results clarify how Landau quantization, finite electron temperature, and fractional-order memory jointly shape the morphology, robustness, and interaction properties of ion-acoustic structures in strongly magnetized quantum plasmas of astrophysical and high-energy-density laboratory interest. Full article

Traditional sliding mode control (SMC) lacks an active mechanism for redistributing energy among state channels during transient convergence, resulting in a rigid trade-off between response speed, overshoot suppression, and energy efficiency. This paper proposes a virtual state coupled SMC method that introduces a dynamic virtual state with bilinear product coupling $x_1{x}_2$ into the sliding surface. Unlike conventional virtual states that serve as static linear combinations or observer-based estimates, the proposed virtual state evolves dynamically and establishes an active energy exchange channel between the real and virtual state dynamics. Linearization and Lyapunov-based analyses prove local asymptotic stability of the closed-loop system. The coupling strength $\gamma$ is shown to be decoupled from the linearized local eigenvalues and thus governs the energy–performance trade-off independently, while the condition $c>\gamma /4$ guarantees a non-vanishing domain of attraction. Simulations demonstrate that the proposed method achieves up to 53.2% control energy reduction under disturbance-free conditions compared with conventional SMC. Under persistent high-frequency disturbances, increasing $\gamma$ reduces oscillations by 54.2% at a controllable energy cost of 45.7%. Systematic parameter selection guidelines are provided, and Monte Carlo simulations (500 trials, $\pm 30\%$ parameter perturbations) confirm 100% convergence. The proposed method offers an independently adjustable energy–performance trade-off mechanism suitable for sensor-based motion systems with stringent transient and energy requirements. Full article

To promote the industrial development of reinforced concrete engineering and enhance the construction quality of prefabricated buildings, an innovative partial prefabricated construction method is proposed in this paper, namely the prefabricated reinforced cage–cast in situ concrete (PRC-CISC) structure with an innovative steel bar connection technology. The connection techniques, including direct thread rolling of steel bars and hot-forged sleeves, are adopted. With the design axial compression ratio and the layout of couplers in the reinforcement cage as the main parameters, quasi-static tests are carried out to investigate the failure mode, seismic behavior, and mechanical mechanism of couplers of PRC-CISC columns. The results indicate that all specimens present typical compression–bending failure with plump hysteretic curves, gradual stiffness degradation, good ductility, and energy dissipation capacity. The new couplers can effectively satisfy the seismic performance requirements of PRC-CISC columns. With the increase in axial compression ratio, the bearing capacity rises while ductility decreases, and the stress of longitudinal bars increases. The layout of couplers exerts a controllable influence on the mechanical and deformation performance of specimens. The steel stress in the core stress region of PRC-CISC columns shows a bilinear distribution with stress concentration at both ends of the sleeves, which is related to the material difference in couplers. Finally, two “non-equal” calculation methods (plastic hinge model and fiber model) are established based on experimental results and finite element analysis, forming a systematic calculation theory for the new material–new technology–new structure system. The research provides important references for the engineering application of such structures. Full article

We study the incompressible fractional viscous–resistive magnetohydrodynamic system on ${\mathbb{R}}^n$ with fractional diffusion ${(-\Delta )}^{\alpha }$, where $\alpha \in (1/2,1]$, and with positive viscosity and resistivity coefficients $\mu ,\nu >0$. The problem is treated at the scale-invariant regularity $s_c={\displaystyle \frac{n}{p}}+1-2\alpha .$ For small divergence-free initial data in the critical Triebel–Lizorkin–Lorentz space ${\dot{F}}_{p,r}^{s_c,q}$, we construct a unique global mild solution. The main contribution is the use of the single-norm time–frequency space $m_m^{\prime }{\dot{F}}_{p,r}^{s_c,q}$, built on Meyer wavelets and the parabolic gauge $t{2}^{2\alpha j}$. This space keeps the critical spatial size, the short-time behavior, and the high-frequency decay in one norm. By using a Gevrey-weighted Duhamel formulation, we prove boundedness of the corresponding fractional heat propagators and establish the bilinear paraproduct estimate required for the fixed-point argument. Consequently, $e^{{\left(t{(-\Delta )}^{\alpha }\right)}^{\gamma }}(u,b)\in {\left(m_m^{\prime }{\dot{F}}_{p,r}^{s_c,q}\right)}^{2n}$ for some $\gamma >0$ depending on the parameters. This gives a Gevrey-type spatial smoothing effect, which is stronger than ordinary analyticity in the adopted scale. The restriction $\alpha >{\displaystyle \frac{1}{2}}$ enters through the factor $2^{j(1-2\alpha )}$, which supplies the high-frequency gain needed to close the critical bilinear estimates; in this sense it is sharp for the present method. The classical viscous–resistive case is recovered when $\alpha =1$. Full article

Vision-based pantograph wear recognition plays a critical role in the safety and reliability of railway power supply systems. Although recent studies report promising deep learning-based results, these models solely depend on the integrity of the dataset. Data integrity is a critical yet often overlooked factor in research and production, and neglecting it may lead to inconsistencies and compromised operational safety. In the proposed approach, we demonstrate that a widely used pantograph wear dataset contains severe redundancy and label inconsistencies, including duplicate images appearing within classes and across different wear categories. These issues undermine supervised learning, reduce model generalisation, compromise predictive reliability, and may weaken the safety of rail infrastructure systems. This work (i) preprocesses the dataset by employing MD5-based cryptographic hashing and manual verification, where 626 redundant samples were identified from a dataset of 909 images; subsequently, a manual relabelling procedure is used to correct inherited annotation errors and consistent class definitions. (ii) It devises a Dual-Backbone Feature Fusion Ensemble Network (DBFF-Net) for small and challenging datasets by integrating frozen ShuffleNetV2 and DeiT-tiny as the best individual performing classifiers using various fusion strategies, including concat, weighted sum, Bilinear, Cross-Attention, and Gated. Amongst the different fusion approaches, we obtain the best results with the Gated approach. We reproduced the comparatively improved pantograph wear classification results and conducted extensive experiments to demonstrate that dataset sanitization improves the stability and reproducibility of the model. Moreover, it has been shown that DBFF-Net outperforms individually employed pretrained CNNs and transformer models and achieves an accuracy of 96.46% even with limited but sanitised data. Full article

Pneumatic artificial muscles exhibit pronounced hysteresis in the force-contraction domain, which complicates accurate force modeling under pressure-dependent operation. This work presents a discrete-time quasi-static hysteresis model for a compact pneumatic artificial muscle using a memristive system-based branch-memory formulation. The model combines separate loading and unloading force surfaces through a bounded internal state and is evaluated on experimental data acquired at a force-change rate of $4\mathrm{N}/\mathrm{s}$. Measurements were performed at 13 pressure levels from 0 to 0.6 MPa in 0.05 MPa increments, with 32 unloading points and 32 loading points per pressure level and five repetitions for each operating condition. Representative branch curves were obtained by median reduction in the repeated measurements, and the loading and unloading surfaces were identified with the five-parameter Sárosi–Fabulya exponential-bilinear function. The state update parameter was evaluated over a fixed grid, and the best loop reconstruction on the present dataset was obtained for the hard-switching case $\alpha =1$. Benchmark comparisons with Prandtl–Ishlinskii, discrete Preisach, Maxwell-slip, and sampled Bouc–Wen-type models show that Preisach and Bouc–Wen provide higher loop-reconstruction accuracy. The proposed memristive formulation should not be interpreted as a best-fit benchmark model, but as a low-order global branch-memory representation that preserves pressure dependence and branch asymmetry within a single analytical framework over the investigated quasi-static operating range. Full article

This work presents a design technique for a type of recursive 2D filter, specifically anisotropic filters, with a frequency response depending on orientation. This design method is based on a 1D analog low-pass prototype filter of a specified approximation type (for instance, elliptical) and imposed order and selectivity. Next, a special frequency transformation is applied to this prototype, leading to a 2D oriented filter in the analog version. Next, applying the well-known bilinear transformation on the two frequency axes, we finally derive the frequency response of the desired 2D directional filter, with a given orientation angle in the frequency plane. The obtained 2D filter is of low complexity, its matrices being of size 5 × 5, and therefore can be efficiently implemented. Moreover, the filter is parametric (tunable), its selectivity and orientation angle being adjustable through independent parameters, which appear explicitly in the filter matrices. Several design examples using the proposed method are given for specified values of parameters (selectivity and orientation angle). The main application of this type of filter is enhancing and extracting straight lines or various oriented features and details from an image, as shown in the provided simulation results. A very efficient system-level implementation is also developed, using the block filtering approach, which ensures a higher degree of parallelism and a lower arithmetic complexity. Full article

The aim of the present work is to give a mathematical underpinning for the use of quasi-probabilities and pseudo-metrics in infinite-dimensional Banach manifolds. The notion of a continuous binary structure is introduced. It is a triple consisting of a continuous symmetric bilinear form together with a pair of closed linear subspaces of a Banach space. Such binary structures are abundant in Hilbert spaces. In order to confirm their existence in arbitrary Banach spaces, the auxiliary notion is introduced of subspaces that are positive with respect to a given symmetric bilinear form. It is shown that any subspace which is maximally positive with respect to the bilinear form induces a continuous binary structure on the Banach space. The Wigner function of a system of quantum mechanical particles is treated as an example. Full article

Conventional Image Signal Processors (ISPs) employ manually crafted designs with limited adaptability, resulting in suboptimal performance in dynamic environments for both visual quality and machine vision applications. While deep learning facilitates adaptive AI-ISPs, supervised approaches encounter domain shift limitations and substantial computational demands that impede edge deployment. This work introduces an adaptive zero-shot AI-ISP that dynamically optimizes processing pipelines without requiring paired training data. The proposed architecture implements dual specialized subnetworks for illumination estimation and denoising enhancement, operating collaboratively under Retinex theory principles to achieve boundary-aware illumination mapping and noise-resilient image restoration. Additionally, a physically constrained loss function is introduced to enhance color fidelity and noise suppression. For practical implementation, an FPGA-accelerated computing engine replaces transposed convolution with optimized bilinear interpolation, effectively eliminating artifacting while achieving superior memory efficiency through customized buffering architectures. A comprehensive evaluation demonstrates highly competitive performance, achieving a PSNR of 19.91/16.62 and an SSIM of 0.591/0.475 on LSRW-Huawei/Nikon datasets, alongside NIQE scores of 2.065/3.025 on DCIM and TM-DIED datasets. The hardware implementation attains 42.5 GOPS/W power efficiency, representing 35.4× and 7.3× improvements over conventional CPU and GPU platforms, establishing a comprehensive edge deployment solution for next-generation intelligent image processing systems. Full article

Reflectometry-based sensing systems are widely used in industrial monitoring to assess the condition of distributed assets such as cables and transmission lines. In practical sensing environments, however, correlation-based interpretation can become unreliable because of bilinear interference, dispersive propagation, and excitation mismatch, often producing artifact-related responses that lead to unnecessary inspections and reduced decision reliability. This paper proposes a temporal-consistency-based reliability enhancement framework for correlation-driven time–frequency domain reflectometry (TFDR). Instead of replacing the conventional reflectometry pipeline, the proposed method introduces a reliability-estimation layer that evaluates the trustworthiness of correlation responses and suppresses temporally inconsistent artifacts. Multiple complementary descriptors extracted from the reflected signal are jointly analyzed to determine whether a correlation response is propagation-consistent or more likely to arise from non-physical artifacts. Temporal consistency is modeled using a bidirectional long short-term memory (BiLSTM) architecture that captures long-range dependencies along the propagation sequence. Experimental results obtained from cable reflectometry measurements under varying impedance conditions show that the proposed framework effectively suppresses artifact-related correlation responses while preserving physically meaningful reflections required for fault localization. Additional cross-excitation evaluation provides preliminary evidence that the learned temporal-consistency criterion is not tightly coupled to a single excitation waveform. Because the proposed framework operates as a post-processing reliability layer, it can be integrated into existing reflectometry-based monitoring systems without the modification of the sensing hardware or excitation scheme. Full article

This paper mainly considers the fractional parameter identification algorithms of the bilinear-in-parameter autoregressive (AR-BIP) system. The data filtering technique is introduced to improve the parameter estimation accuracy of the AR-BIP system, which involves using a filter to filter the data of the identification model. The filtering-based hierarchical fractional least mean square algorithm (F-HFLMS) and the filtering-based multi-innovation hierarchical fractional least mean square algorithm (F-MHFLMS) are proposed for effective and accurate parameter estimation of the AR-BIP system. Using the multi-innovation theory and expanding the scalar innovation into the innovation vector, the F-MHFLMS could take full advantage of the input and output data information of the system. The performance of the F-MHFLMS algorithm is compared with the F-HFLMS strategy for the AR-BIP system using the values of the mean square error (MSE) and the average predicted output error. The effectiveness and accuracy of F-HFLMS and F-MHFLMS algorithms are demonstrated under the numerical experimentation based on different noise variances, fractional orders and innovation lengths. Compared with the F-HFLMS algorithm, the F-MHFLMS algorithm can acquire more accurate and robust parameter estimation. Full article

Background/Objectives: Brain tumors are among the most severe neurological disorders, and their variability in size, morphology, and anatomical location complicates early and accurate diagnosis. Although magnetic resonance imaging (MRI) is the most reliable non-invasive modality for tumor detection, manual interpretation remains time-consuming, subjective, and susceptible to human error. This study aims to develop an optimization-driven hybrid machine learning framework for accurate and computationally efficient automatic brain tumor classification. Methods: The dataset includes 834 MRI images (583-training, 123-validation, 128-independent test). Because YOLOv11 detects tumor and non-tumor regions separately, the sample size doubled during region-based analysis, and all subsequent stages were conducted at the regions of interest (ROI) level. On the independent test set, YOLOv11 achieved 98.87% mAP@50, 98.54% precision, and 98.21% recall. The proposed framework combines automated tumor localization with image standardization using Gaussian noise reduction and bilinear interpolation. From the processed MR images, 39 entropy-based features were extracted. To enhance diagnostic performance and eliminate redundant information, the superb fairy-wren optimization algorithm (SFOA) was applied for feature selection and compared with particle swarm optimization (PSO), Harris hawk optimization (HHO), and puma optimization (PO). Final classification was primarily performed using k-nearest neighbors (kNN), while support vector machines (SVM) were used for comparative evaluation. Results: SFOA reduced the feature dimensionality from 39 to 5 features while achieving 99.20% classification accuracy on the independent test set. In comparison, PSO selected 10 features, HHO selected 6 features and PO selected 10 features, all achieving 98.45% accuracy. The best performance obtained with SVM was 98.45% accuracy (HHO-SVM), which remained lower than the 99.20% achieved by the proposed SFOA-kNN model. Conclusions: The results indicate that combining entropy-based feature extraction with SFOA-driven feature selection and kNN classification significantly enhances diagnostic accuracy while reducing computational complexity, highlighting the strong potential of the proposed framework for integration into computer-aided diagnosis systems to support clinical decision-making. Full article

The implementation of Australia’s 2024 waste export ban has increased pressure on domestic recycling systems, resulting in an additional 650,000 tonnes of waste annually. This emphasises the urgent need for high volume landfill waste material recovery, especially in sustainable construction materials such as geopolymer concrete (GPC). Geopolymer concrete is recognised as a sustainable construction material; however, the scientific understanding of the compatibility between landfill waste and the geopolymer matrix, particularly under harsh environments, remains unknown. This paper presents an experimental investigation on five types of geopolymer concrete (GPC) mixes. The study included a control mix with natural stone chips and four additional mixes in which stone chips were 100% replaced with waste materials including shredded plastic, cardboard, crushed glass, and granular crumb rubber as fine aggregates. The mechanical performance, durability behaviour and stress-strain characteristics of these mixes were evaluated. Concrete samples were exposed to normal air, a saline environment with 10% salinity, and a hygrothermal environment at 60 °C and 98% humidity for four months to assess durability performance. The results demonstrate that GPC is compatible with landfill waste aggregates and enables the production of a workable mixture. As a result of saline environments, waste aggregate-based geopolymer concrete reduces compressive strength by 15%, while natural stone chips-based geopolymer concrete decreases strength by 45% during the same period, indicating that waste aggregates are more appropriate than natural aggregates in marine environments. Although the inclusion of waste aggregates reduces the strength and stiffness of the GPC, the materials continue to meet the mechanical property requirements for non-structural applications. A theoretical model considering the elastic modulus, ultimate strength and corresponding strain has been developed to predict compressive stress–strain behaviour of waste-based GPC. High modulus aggregates, typically ranging from approximately 10.0 GPa to 85.0 GPa such as stone chips and glass sand demonstrate parabolic stress–strain behaviour. In contrast low modulus aggregates, generally ranging from 1.0 GPa to 5.0 GPa including plastic, cardboard, and crumb rubber, exhibit a bilinear stress–strain response. Full article

This study presents a systematic investigation into the seismic performance of precast concrete shear walls using cold-extruded sleeve connections for reinforcement splicing. Quasi-static cyclic loading tests were conducted on a full-scale precast wall specimen and a cast-in-place reference wall to evaluate the influence of construction joint detailing on structural behavior. The experimental results show that the precast wall exhibited progressive crack propagation, stable energy dissipation, and slightly higher ultimate lateral load and deformation capacity compared to the cast-in-place counterpart. In contrast, the cast-in-place wall experienced abrupt failure due to concrete spalling and out-of-plane splitting, highlighting the critical importance of reinforcement continuity and joint configuration. To further investigate key design parameters, high-fidelity finite element models were developed in ABAQUS. Concrete was modeled using the Concrete Damaged Plasticity model, while steel rebars and sleeves were simulated with a bilinear constitutive law. The numerical simulations, validated against experimental data, achieved good agreement in terms of load-drift response, crack patterns, and stress distributions. A parametric study was conducted by varying the wall aspect ratio, axial compression ratio, and longitudinal reinforcement ratio in the boundary elements. The results indicate that both the aspect ratio and axial compression ratio have significant effects on lateral load capacity and drift capacity, whereas the reinforcement ratio in the boundary elements exerts a relatively minor influence. For walls with low shear-span-to-depth ratios and high axial compression, increasing both longitudinal and horizontal reinforcement leads to noticeable improvements in load-carrying capacity and ductility. These findings confirm the reliability of the cold-extruded sleeve connection system in precast shear wall applications. The study establishes a validated numerical framework for seismic performance prediction and provides practical guidance for optimizing the design of prefabricated walls. This contributes to enhancing structural safety and improving seismic ductility, thereby supporting the broader adoption of precast systems in sustainable construction. Full article