Search Results (468)

Thermally induced frequency shift (FS) and energy dissipation are key factors limiting the quality factor (Q-factor) of resonators. This study combines nonlocal elasticity theory (NET) with the nonlocal dual-phase-lag (NDPL) heat-conduction model to establish a theoretical framework for evaluating thermoelastic damping (TED) in micro/nano cylindrical shells with size-dependent effects. The equation of motion of the cylindrical shell is simplified using the Donnell–Mushtari–Vlasov (DMV) approximation. The resonant frequency of the cylindrical shell with size-dependent effects is obtained by combining the compatibility equation with the equation of motion and applying the Galerkin method. Additionally, an analytical solution for the TED of cylindrical shells considering size effect under classical boundary conditions is derived using the complex frequency method. The proposed formulation is validated by comparing its predictions with available numerical results. Numerical results indicate that size effects have a significant impact on the TED of cylindrical shells, particularly as mechanical nonlocal effects increase TED, thereby reducing the Q factor of micro/nano cylindrical shells. Moreover, the impact of size effects on the FS and frequency attenuation (FA) is examined. This study lays crucial theoretical groundwork for the design of resonators utilizing micro/nano cylindrical shell materials. Full article

Intelligent mechanical fault diagnosis plays a key role in maintaining rotating machinery. Although data-driven unsupervised domain adaptation methods have achieved considerable progress, their industrial applications are often restricted by low-quality sensor data. Non-stationary vibration signals and background noise easily corrupt target pseudo-labels, while conventional methods focusing on global statistical matching usually neglect local structures, leading to confirmation bias under dynamic loads. To improve diagnostic reliability, we propose a Noise-Resilient Whitened Domain Adaptation (NRWDA) framework. To handle covariance fluctuations caused by changing working conditions, a Lipschitz-bounded Temporal Whitening (LTW) module is designed as a low-pass filter. An Entropy-guided Prototype Truncation (EPT) mechanism is adopted to discard ambiguous labels and better calibrate semantic centers. In addition, a Dispersion-Adaptive Contrastive Sharpening (DACS) strategy is introduced to dynamically adjust the contrastive temperature based on predictive dispersion, thus tightening decision boundaries. The proposed method is evaluated on CWRU, PU, and MFPT datasets. The PU dataset, featuring fluctuating loads and non-stationary signals, poses a strict test, yet our model maintains its stability even at a 0 dB SNR—a condition where standard approaches usually break down. During the $P0\to P3$ transfer task involving substantial radial force variations, NRWDA secures a 72.36% accuracy and surpasses established baselines. These findings confirm that our technique successfully isolates dependable diagnostic features from corrupted sensor measurements within actual industrial settings. Full article

In this study, 310S austenitic stainless-steel was welded using a laser with varying amounts of Nb to systematically investigate the effect of Nb on solidification cracking susceptibility, mechanical properties, and corrosion resistance of the weld. Under the present experimental conditions, the critical restraint width was higher for the 0.58 wt.% Nb and 1.45 wt.% Nb welds than for the Nb-free and 2.3 wt.% Nb welds, indicating that Nb addition affected the solidification cracking response of the weld. At low-to-moderate Nb contents, Nb can aggravate compositional segregation and increase the presence of low-melting-point liquid films, thereby increasing cracking susceptibility. At higher Nb contents, the reduced cracking susceptibility was accompanied by microstructural refinement and changes in the distribution of Nb-rich constituents during solidification. With increasing Nb content, the number of precipitated phases in the weld increases, mainly distributed at the austenite grain boundaries in granular, elongated, and chain-like forms. The introduction of Nb generally increases the microhardness and tensile strength of the welded joint, attributed to grain refinement strengthening and solid-solution strengthening. The reduction in area first increased and then decreased, suggesting that excessive Nb addition may reduce ductility because of the increased amount of grain-boundary precipitates and local strengthening heterogeneity. With increasing Nb content, the Ir/Ia ratio decreased from 67.6% to 52.2%, suggesting improved intergranular corrosion resistance. This improvement is likely related to the preferential reaction of Nb with carbon, which may suppress the formation of Cr-depleted zones at grain boundaries. Overall, Nb addition improved the corrosion resistance and increased the hardness and tensile strength of the weld; however, its effect on solidification cracking susceptibility was non-monotonic, indicating that careful control of Nb content is required to balance cracking susceptibility, mechanical properties, and corrosion resistance. Full article

In this paper, we develop a Legendre spectral operational matrix method for the numerical solution of two-dimensional Volterra–Fredholm integro-differential equations subject to mixed boundary conditions. The proposed approach transforms the physical domain onto a reference square and approximates the unknown solution using a tensor-product Legendre polynomial expansion. Exact operational matrices for differentiation and lower-limit integration are constructed, allowing the original integro-differential problem to be reduced systematically to a finite-dimensional algebraic system for the spectral coefficients. The formulation provides a unified treatment of differential, Volterra, and Fredholm operators within a single spectral framework and avoids complicated discretizations of multidimensional integral terms. For a specialized linear form of the problem, rigorous convergence estimates are established in both $L^2$ and $L^{\infty }$ norms under suitable regularity assumptions on the coefficients and kernels. The analysis shows that the dominant convergence behavior is governed by the differential operator, while the integral terms contribute only higher-order consistency effects. Several benchmark examples involving both linear and nonlinear two-dimensional integro-differential equations are presented to demonstrate the performance of the proposed method. Numerical results exhibit rapid spectral-type error decay as the polynomial degree increases, with the numerical errors approaching machine precision for moderate truncation orders. These results confirm the accuracy, efficiency, and reliability of the proposed Legendre spectral operational matrix framework for solving a broad class of multidimensional integro-differential equations with nonlocal operators. Full article

A remarkable feature of non-Hermitian topological systems with skin effects is that their spectra and eigenstates are strongly dependent on the choice of boundary conditions. Here, we investigate a system where the impurity couples to a nonreciprocal Su–Schrieffer–Heeger (SSH) chain at two points with nonreciprocal coupling. We first study the spectrum of the system and demonstrate that nonreciprocal couplings between the impurity and the chain alter its spectral structure. Particularly, this effect becomes particularly prominent in the limit of unidirectional coupling, inducing a shift in the parameter regime for the zero mode. Meanwhile, the impurity–chain couplings give rise to two effective boundary conditions and determine the spatial distribution of the zero mode. In addition, the localization of bulk states is significantly altered by tuning the nonreciprocity of the impurity–chain coupling. Notably, in the unidirectional coupling regime, two distinct types of bulk states coexist near the same boundary, one differing from the other in both spatial distribution and degree of localization. We also find that the bulk states undergo significant skin phase transitions as the coupling strength varies, characterized by a transition from conventional skin states to bipolar skin states. Our findings establish the feasibility of controlling non-Hermitian topological systems by coupling an impurity. Full article

Ground engaging tool (GET) wear monitoring is important for mining excavator maintenance, but progressive multi-tooth wear estimation remains insufficiently explored. This study presents a vibration-based framework for GET wear estimation during operations using modal analysis, finite element (FE) modelling, and machine learning as a supporting evaluation tool. A laboratory-scale mining bucket surrogate with detachable attached masses was used to represent progressive tooth wear through controlled mass-loss conditions. Experimental impact hammer tests under approximately free-free boundary conditions were conducted to validate the FE modal model through natural-frequency comparison and qualitative mode correspondence. The validated FE model was then used to generate a broader dataset of multi-tooth wear scenarios, from which the first ten natural frequencies were extracted as modal features. Linear Regression (LR) was adopted as a simple and interpretable baseline to evaluate both overall wear estimation and individual tooth wear estimation. High accuracy was obtained for overall wear estimation for both the non-symmetric and symmetry-augmented datasets, with R² values of 0.9983 and 0.9976, respectively. In contrast, individual tooth prediction was more challenging, and the symmetry-augmented results showed that mirrored tooth locations can produce non-unique frequency-based signatures. An additional asymmetric FE sensitivity study further confirmed that structural symmetry can limit local wear identifiability when only global natural frequencies are used. These findings demonstrate the potential of FE-derived modal frequency features for laboratory-scale GET wear assessment, while also highlighting the limitations of frequency-only features for unique local wear localisation in symmetric structures. This is a promising approach for wear estimation during mining operations. Full article

This paper investigates a new class of mixed impulsive fractional boundary value problems (BVPs) in which the mixing occurs both in the governing fractional differential equations—through the combined presence of $\psi$-Caputo and quantum (q-difference) fractional derivatives—and in the boundary conditions formulated via fractional integral constraints. By incorporating two distinct operators within the same dynamical framework, the proposed model is capable of capturing both memory effects and discrete-scale behaviors inherent in complex hybrid systems. Using the Banach contraction mapping principle and the Leray–Schauder nonlinear alternative, sufficient conditions ensuring the existence and uniqueness of solutions are established. The theoretical results unify and extend several known fractional models. Owing to its flexible structure, the proposed framework may serve as a useful mathematical tool for modeling impulsive phenomena in systems where non-local memory and scale-transition mechanisms coexist, such as in engineering, physics, and applied sciences. Finally, numerical examples are provided to illustrate the applicability and qualitative behavior of the solutions. Full article

The spread of fake news on online social networks is driven by imitation-based user behavior and network topology, often leading to persistent misinformation clusters and echo chambers. In this study, we develop a spatial evolutionary game-theoretic framework in which agents update their latent opinions through payoff-biased imitation, while external fact-checkers act as non-imitative intervention nodes. Building on this formulation, we propose an adaptive, boundary-aware intervention mechanism that dynamically regulates both the density and spatial allocation of fact-checkers according to real-time system conditions. Competing information clusters are identified through local neighborhood composition, enabling boundary nodes, i.e., interfaces between fake-news and non-fake-news regions, to be detected and targeted where strategic shifts are most likely to occur. Importantly, fact-checking is modeled as an external intervention that may induce a probabilistic lasting correction on agents’ latent opinions after removal, capturing more realistic post-intervention behavior. Unlike static strategies that assume fixed fact-checker distributions, the proposed approach continuously reallocates interventions toward structurally critical regions, while adaptively adjusting resource intensity based on misinformation prevalence. Extensive simulations on small-world, scale-free, and random networks show that the adaptive model consistently outperforms static baselines, reducing the final fake-news prevalence by over 90%, accelerating suppression, and improving overall system efficiency. Statistical tests confirm the significance of these improvements ($p<0.001$), while sensitivity analyses demonstrate robustness across parameter settings and intervention assumptions. Full article

Smooth window functions that restrict field actions to finite spacetime domains appear throughout quantum field theory, quantum optics, and open quantum systems, wherever interactions are switched on and off, detectors couple for finite durations, or systems decohere within bounded regions. When such a window function $◊\left(x\right)$ is introduced into the matter action of a covariant field theory, two structural consequences are unavoidable: the windowed Ward identities acquire boundary layer corrections confined to the window transition region, and the contracted Bianchi identity requires a compensating stress-energy contribution at the window boundary. Both consequences follow from the product rule of covariant differentiation and are independent of any specific physical motivation for the window. The present paper develops these consequences systematically for each sector of the Standard Model in curved spacetime. The windowed action prescription is applied to Dirac fermions, complex scalar fields, Maxwell theory, and the complete $SU{\left(3\right)}_c\times SU{\left(2\right)}_L\times U{\left(1\right)}_Y$ gauge Lagrangian. Each sector is shown to recover standard curved spacetime quantum field theory exactly within the localization window, with all deviations confined to a boundary layer whose thickness is set by the applicable operational localization scale—including decoherence, detector resolution, generalized uncertainty, or clock-precision bounds as appropriate. A Noether analysis yields windowed Ward identities of the form ${\nabla }_{\mu }(◊J^{\mu })=0$: gauge invariance and Lorentz symmetry are preserved exactly within the window, and apparent non-conservation is a kinematic boundary effect structurally identical to the open-system flux terms that arise when tracing over environmental degrees of freedom. The non-local boundary term $T_{\mu \nu }^{\mathrm{nl}}$ required by the Bianchi identity decomposes as $T_{\mu \nu }^{\mathrm{nl}}=T_{\mu \nu }^{\mathrm{comp}}+T_{\mu \nu }^{\mathrm{Rem}}$, where $T_{\mu \nu }^{\mathrm{comp}}$ is the boundary layer compensator and $T_{\mu \nu }^{\mathrm{Rem}}$ is its macroscopic coarse-grained remnant in the high-localization-density regime. A formal lemma establishes that, under stated regularity, phase-incoherence, finite-correlation-length, and variance-control assumptions, $T_{\mu \nu }^{\mathrm{comp}}$ vanishes upon coarse-graining for ordinary quantum fields, so standard field evolution leaves no macroscopic stress-energy remnant. The sharp-window limit recovers the Israel junction conditions exactly, and the smooth-window generalization is structurally identical to the Ashtekar–Krishnan dynamical horizon flux balance laws. The generalized uncertainty principle (GUP), extended uncertainty principle (EUP), relativistic GUP (RGUP), and Salecker–Wigner clock bounds constrain only the admissible operational thickness of the window boundary layer, $ϵ$, and do not alter the product rule origin of the windowed Ward identities or the Bianchi-required compensator. Full article

This study investigates peri-urban land management in Uttar Pradesh through a comparative analysis of Varanasi, Kanpur, and Prayagraj, focusing on the gap between planned frameworks and actual urban growth. As rapidly expanding Tier-II cities, they represent critical sites where formal planning intersects with complex peri-urban transformations. The study employs a mixed-method approach, combining GIS-based master plan conformance analysis using Effective Boundary Control (EBC) with semi-structured expert interviews. This integration enables both spatial measurement of urban expansion and interpretive understanding of underlying governance and institutional dynamics. The results reveal significant divergence between planned and observed development, particularly in peripheral areas, with clear variation across cities. Kanpur exhibits the highest level of non-conformance (EBC: 2.23), indicating weak boundary control and pronounced peri-urban sprawl. Varanasi also demonstrates substantial deviation (EBC: 2.06), reflecting persistent gaps between planning intent and implementation. In contrast, Prayagraj shows relatively stronger conformance (EBC: 1.04), though underlying challenges remain. These differences are shaped by local conditions, including land acquisition conflicts, fragmented governance structures, infrastructure deficits, and limited financial mechanisms. Importantly, the findings underscore that even where spatial conformity appears stronger, it does not necessarily translate into effective planning outcomes. The study concludes that peri-urban growth is not simply unplanned but is shaped by negotiated and context-specific processes. It highlights the need for adaptive, implementation-focused planning, stronger institutional capacity, and integrated financial strategies. By bridging spatial and qualitative analysis, the research provides a more comprehensive framework for understanding and managing peri-urban development in rapidly urbanizing regions. Full article

Bolt loosening is a critical precursor to structural failure in major industrial and transportation equipment. Although acoustic non-destructive testing (NDT) offers a cost-effective diagnostic solution, its practical deployment is often hindered by low signal-to-noise ratios (SNRs) and the limited ability of conventional models to isolate fine-grained transient acoustic signatures from complex background interference. To address these challenges, this paper proposes PCT-Net, a multi-scenario noise-adaptive fusion network for bolt-state recognition. First, an Adaptive Spectral Masking mechanism is introduced as a data augmentation strategy. Instead of rigid zero-padding, it dynamically blends local spectral energies to encourage the learning of more robust and noise-invariant representations. Furthermore, rather than simply concatenating multiple modules, PCT-Net adopts a synergistic feature extraction framework to decouple complex acoustic signatures. A perceptual frontend is used to establish acoustically meaningful representation priors. To handle the highly dispersed characteristics of loosening signals, cascaded convolutional modules progressively suppress redundant environmental interference while capturing high-frequency local transient impacts. Meanwhile, to overcome the limited receptive field of convolutional operations, an embedded Transformer mechanism is introduced to model long-range temporal dependencies and low-frequency structural variations throughout the tapping cycle. By integrating local fine-grained transient modeling with global structural dependency modeling, the proposed network can better distinguish subtle decision boundaries among different loosening states. Extensive experiments show that PCT-Net achieves a classification accuracy of 97.12% under standard conditions and maintains stable performance under severe noise scenarios. These results demonstrate the effectiveness of the proposed method and highlight its potential for intelligent industrial safety monitoring. Full article

This study investigates the role of longitudinal aquifer slope in controlling stream–aquifer interaction within bounded floodplain aquifer systems. A series of numerical simulations were conducted to analyze groundwater flow patterns, hyporheic exchange fluxes, and contaminant transport behavior under varying slope conditions. The results showed that increasing slope does not simply enhance hydraulic gradients but fundamentally reorganizes subsurface flow structure. As the slope increases, groundwater flow becomes progressively aligned with the stream, reducing lateral connectivity and confining exchange to a narrow corridor adjacent to the stream. This reorganization leads to the expansion of hydraulically inactive zones and a non-linear response in hyporheic exchange. Exchange flow rates initially increase at low to moderate slopes but decline beyond a threshold at higher slopes, despite higher local gradients. The transition begins at around a 2% slope and becomes pronounced within the range of approximately 3–7%, indicating a shift in flow regime rather than a continuous scaling of interaction intensity. Particle tracking analyses further reveal that slope controls the spatial distribution of contaminant vulnerability. While the overall extent of active transport zones decreases with increasing slope, localized transport potential intensifies near the stream boundary due to higher velocities and reduced residence times. These findings demonstrate that hydraulic gradient magnitude alone is insufficient to characterize stream–aquifer interaction and highlight the importance of flow geometry and connectivity. The results provide a process-based framework for understanding slope-controlled hyporheic exchange and offer insights for improving groundwater vulnerability assessment and management in alluvial systems. Full article

Ground penetrating radar (GPR), as an efficient non-destructive testing technique, plays a crucial role in the structural condition assessment and defect identification of railway ballast. Typical defects such as mud pumping generally exhibit characteristics in B-scan images including weak reflections, blurred boundaries, and irregular structures, which pose significant challenges for stable detection and precise localization using existing methods that rely primarily on spatial feature modeling. Most current deep learning approaches focus on modeling spatial or temporal information, while lacking effective utilization of frequency-domain features, thereby limiting their discriminative capability under complex electromagnetic environments. To address these issues, this paper proposes a single-stage object detection framework, termed YOLO-DGW, based on time-frequency collaborative modeling. Built upon YOLOv8, the proposed method introduces a structure-aware spatial enhancement module to improve the representation of continuous GPR echo structures. Meanwhile, frequency-domain information is incorporated as a modulation prior to guide spatial feature learning, enhancing the model’s sensitivity to weak reflections and complex-shaped targets. In addition, A-CIoU loss function is designed to improve localization accuracy and stability for defect regions of varying scales. Experimental results demonstrate that YOLO-DGW achieves an F1-score of 63.06% and an AP@0.50 of 62.07%, representing improvements of approximately 7.41% and 2.8%, respectively, over the strongest baseline method. Compared with several mainstream object detection models, the proposed approach exhibits superior performance in both detection accuracy and cross-region generalization capability. These findings indicate that integrating frequency-domain information into spatial feature learning through a modulation mechanism can effectively enhance the model’s ability to discriminate weak-reflection anomalies, providing a novel time-frequency collaborative modeling paradigm for railway GPR defect detection. Full article

This paper investigates the existence, uniqueness, and stability of solutions for a new class of coupled systems of sequential fractional differential equations involving the Hilfer fractional derivative. Generalizing previous works based on Caputo derivatives, we employ the Hilfer operator, which interpolates between Riemann–Liouville and Caputo derivatives. The nonlinear terms are fully coupled, and the boundary conditions are nonlocal and coupled. The main results are established using the Banach Contraction Principle and Schaefer’s Fixed Point Theorem, with rigorous, detailed proofs for each step, addressing specific methodological requirements regarding operator invariance and space completeness. Furthermore, we provide a comprehensive analysis of the Ulam–Hyers stability of the proposed system, with explicitly tracked stability constants. An illustrative example with numerical verification is provided to validate the theoretical findings. Full article

This study examines the bending of cross-ply laminated composite nanoplates coupled to a piezoelectric fiber-reinforced composite layer via the nonlocal strain gradient theory. The aim is to accurately capture size-dependent impacts and electromechanical interaction in nanoscale composite structures. The mechanical response is modeled utilizing a refined four-variable shear deformation theory, with the governing equilibrium equations developed using the virtual work assumption. The nanoplate is examined under simply supported boundary conditions exposed to both mechanical loading and applied electric voltage. A detailed parametric investigation is done to assess the contribution of non-local and strain gradient factors, imposed voltage, and geometric ratios on the bending behavior. The results show that the nonlocal parameter generates a softening result, increasing deflection, whereas the strain gradient parameter raises stiffness and minimizes deformation. Moreover, the applied voltage successfully controls the bending response by electromechanical actuation, underlining the potential of PFRC-integrated nanoplates in smart nanoscale systems. Full article