Search Results (155)

Reliability assessment of conical shells in the chemical industry commonly relies on point cloud registration. Thus, accurate edge detection from 3D laser scan data is crucial for high-precision registration. However, existing edge detection methods often misclassify or omit gradual edge points on conical shell structures, significantly compromising registration accuracy and subsequent integrity assessment. This paper proposes an edge point detection method integrating Principal Component Analysis (PCA) and wavelet transform. First, characteristic curves are constructed by computing the ratio of PCA eigenvalues at all points to generate preliminary candidates for gradual edge points. Subsequently, distance vectors are calculated between the centroid of each characteristic curve and its sampled points. These vectors are then encoded via multi-level wavelet transform to produce mapping vectors that capture curvature variations. Finally, gradual edge points are discriminated effectively using these mapping vectors. Experimental results demonstrate that the proposed method achieves superior edge detection performance on complex conical shell surfaces and significantly enhances the accuracy of point cloud registration. Full article

To address the limitations of traditional non-local mean (NLM) denoising algorithms in terms of neighborhood similarity metrics, weight calculation, and computational efficiency, this paper proposed a structural-tensor-driven and dynamic window-based fast non-local mean denoising algorithm with dual kernel weighting. First, a Gaussian–Tukey dual-kernel weighting function was designed to optimize similarity metrics. Then, spatial neighborhood features were adopted. By measuring both grayscale similarity and spatial correlation, the weight distribution rationality was further enhanced. Second, structural tensor eigenvalues were used to quantify regional structural properties. A dynamic window allocation function was designed to adaptively match search window sizes to different image regions. Finally, an integral image acceleration mechanism was proposed, significantly improving algorithm execution efficiency. Experimental results demonstrated that the proposed algorithm achieved both excellent denoising performance and edge/texture preservation capabilities. In high-noise environments, its Peak Signal-to-Noise Ratio (PSNR) outperformed the Gauss kernel non-local mean algorithm by an average of 1.96 dB, while Structural Similarity (SSIM) improved by an average of 5.7%. Moreover, the algorithm’s execution efficiency increased by approximately 7–11 times, indicating strong potential for real-time application in digital image processing. Full article

Robust control over the coupling and propagation of surface plasmon polaritons (SPPs) is essential for advancing various plasmonic applications. Traditional planar structures, commonly used to design SPP directional couplers, face limitations such as low extinction ratios and design complexities. These issues frequently hinder the dense integration and miniaturisation of photonic systems. Recently, exceptional points (EPs)—unique degeneracies within the parameter space of non-Hermitian systems—have garnered significant attention for enabling a range of counterintuitive phenomena in non-conservative photonic systems, including the non-trivial control of light propagation. In this work, we develop a rigorous temporal coupled-mode theory (TCMT) description of a non-Hermitian metagrating composed of alternating silicon–germanium nanostrips and use it to explore the unidirectional excitation of SPPs at EPs in the visible spectrum. Within this framework, EPs, typically associated with the coalescence of eigenvalues and eigenstates, are leveraged to manipulate light propagation in nonconservative photonic systems, facilitating the refined control of SPPs. By spatially modulating the permittivity profile at a dielectric–metal interface, we induce a passive parity–time ($\mathcal{PT}$)-symmetry, which allows for refined tuning of the SPPs’ directional propagation by optimising the structure to operate at EPs. At these EPs, a unidirectional excitation of SPPs with a directional intensity extinction ratio as high as 40 dB between the left and right excited SPP modes can be reached, with potential applications in integrated optical circuits, visible communication technologies, and optical routing, where robust and flexible control of light at the nanoscale is crucial. Full article

The optimization of material design for subway interior structure is crucial for noise reduction and sustainability. Plant fiber-reinforced composites (PFRCs) used as interior structures offer both adequate load-bearing capacity and vibration reduction. In this study, a hybrid fiber technique was employed, integrating the Hashin failure criterion and complex eigenvalue method to investigate bending and damping performances of five distinct carbon/flax fiber-reinforced epoxy composite (CFFRC) stacking sequences (C80, C20F20C20, F15C20F15, F10C10F10C10F10, and F40) of an interior structure. The CFFRCs were fabricated via a hot press platen process with a consistent 60% overall fiber volume fraction. The experimental modal behaviors (damping ratios, frequencies, and mode shapes) were clarified by vibration tests using a non-contacting 3D Scanning Laser Doppler Vibrometer. The results revealed that hybrid composites can effectively balance the mechanical and damping properties. Hybrid composites with the flax fiber positioned in the outermost layer demonstrated superior damping performances. The optimal hybrid composite (F10C10F10C10F10) achieved a first-order modal damping ratio of 0.75% (numerically), which is significantly higher than the 0.30% observed for pure carbon fiber composites (C80). The numerical model’s validity was confirmed by a strong correlation with experimental results. It provides valuable parameters for designing safe and reliable subway interior structures, integrating load-bearing and damping capabilities. Full article

This paper explores the application of the generalized beam theory (GBT) in analyzing the buckling behavior of isotropic and composite thin-walled beams with open cross-sections, both with and without branching. The composite beams are composed of orthotropic laminate layers arranged in arbitrary symmetrical orientations. By integrating GBT with the Ritz method and solving the associated generalized eigenvalue problem (GEP), an efficient and robust semi-analytical framework is developed to assess the stability of such isotropic and orthotropic members. The novelty of this work is not the GBT cross-sectional formulation itself, but its implementation at the beam level using a Ritz formulation leading to a generalized eigenvalue problem for the critical buckling loads and mode shapes that capture coupled global, local, and distortional modes in isotropic and orthotropic composite members. This makes the method suitable for early-stage design studies and parametric investigations, where many design variants (geometry, laminate lay-up, and aspect ratios) must be screened quickly without building large-scale high-fidelity finite element (FE) models for each case. The preliminary outcomes, when compared with those obtained using FE, confirm the approach’s effectiveness in evaluating buckling responses, particularly for open-section composite beams. Ultimately, the combined use of GBT and the Ritz method delivers both physical insight and computational efficiency, allowing engineers and researchers to address complex stability issues that were previously difficult to solve. In summary, the methodology can be correctly used for stability assessment of thin-walled composite members prone to interacting global–local–distortional buckling, especially when rapid, mechanistically transparent predictions are required rather than purely numerical FE output. Full article

This study investigates the communication network (MUAVN) of intelligent reflecting surface (IRS)-assisted high-speed multiple unmanned aerial vehicles, considering that highly dynamic UAVs may incur poor performance due to severe channel fading and rapid channel changes. Our objective is to design an adaptive dual-beam tracking scheme that mitigates beam misalignment, enhances the performance of the worst-case UAV, and sustains reliable communication links in the high-speed MUAVNs (HSMUAVNs). We first exploit an attention-based double-layer long short-term memory network to predict the spatial angle information of each UAV, which yields optimal beam coverage that matches to the UAV’s actual flight trajectory. Then, a worst-case UAV’s received beam components signal-to-interference plus noise ratio (SINR) maximization problem is formulated by jointly optimizing ground base station’s beam components and IRS’s phase shift matrix. To address this challenging problem, we decouple the optimization problem into two subproblems, which are then solved by leveraging semi-definite relaxation, the bisection method, and eigenvalue decomposition techniques. Finally, the adaptive dual beams are generated by linearly weighting the obtained beam components, each of which is well-matched to the corresponding moving UAV. Numerical results reveal that the proposed beam tracking scheme not only enhances the worst-case UAV’s performance but also guarantees a sufficient SINR demanded across the entire HSMUAVN. Full article

The linear stability of convection in a vertical two-layer porous structure representing a building external wall is studied. The wall is confined by two open vertical boundaries kept at different but uniform temperatures and is composed of two homogeneous porous layers, characterized by different values of permeability and thermal conductivity. The aim of this paper is investigating whether the wall can undergo the transition to thermal instability, namely, the onset of a multicellular convective pattern. The basic stationary state, given by the fully developed buoyant flow in the vertical direction, is perturbed by means of small-amplitude disturbances, and the resulting eigenvalue problem for neutrally stable modes is studied numerically. The solution of the perturbed governing equations shows that, for suitable values of the governing parameters, thermal instability can arise. The results highlight that the ratio of the permeabilities of the two layers as well as the ratio of their thermal conductivities, together with the aspect ratio between their thicknesses, are key parameters for the possible onset of instability. The temperature difference between the two open boundaries that can trigger instability is determined with reference to practical cases, namely, insulated walls that fulfill the Italian requirements in terms of overall thermal transmittance. Full article

Background: The PRO-CTCAE provides patient-reported data on symptomatic AEs. A summary metric—the ACS—reflecting total AE burden can be calculated by averaging AE-level composite scores at a given timepoint for each participant. This study investigated the psychometric properties and interpretability of this PRO-CTCAE ACS in patients with breast, lung, or head/neck cancers. Methods: We conducted a secondary analysis of a PRO-CTCAE validation dataset comprising 940 adults undergoing chemotherapy or radiation therapy (clinicaltrials.gov: NCT02158637). We focused on empirically recommended symptom terms for three cancer sites. Analyses included Spearman’s correlations, coefficient alpha, and eigenvalues from the correlation matrices, confirmatory factor analysis (CFA), and principal component analysis (PCA). Latent profile analysis (LPA) was used to assess ACS interpretability in the lung cohort. Results: Mean composite score inter-correlations were moderate (0.30–0.35), and coefficient alphas were high (0.81–0.91). Eigenvalue ratios and CFA supported retention of a single factor/component, with suitable model fit indices. ACS correlated highly with factor scores and the first principal component from the PCA. Reduced sets of terms produced reliable scores that closely approximated the full set scores and aligned with external criteria. LPA in the lung subgroup identified four latent classes; ACS differentiated high vs. low symptom burden groups but did not distinguish the two groups expressing distinct symptom profiles. Conclusion: The ACS demonstrated structural validity through adequately fitting linear factor models and effectively summarized symptomatic AE burden. However, similar ACS values may mask clinically distinct symptomatic AE profiles, underscoring the value of both summary metrics and profile-based approaches. Full article

Tensegrity structures, by definition composed of compressed members suspended in a network of tensile cables, are characterised by a high strength-to-weight ratio and the ability to undergo reversible deformations. Their application as cores of sandwich panels represents an innovative approach to lightweight design, enabling the regulation of mechanical properties while reducing material consumption. This study presents a finite element modelling procedure that combines analytical determination of prestress using singular value decomposition with implementation in the ABAQUS™ 2019 software. Geometry generation and prestress definitions were automated with Python 3 scripts, while algebraic analysis of individual modules was performed in Wolfram Mathematica. Two models were investigated: M1, composed of four identical modules, and M2, composed of four modules arranged in two mirrored pairs. Model M1 exhibited a linear elastic response with a constant global stiffness of 13.9 kN/mm, stable regardless of the prestress level. Model M2 showed nonlinear hardening behaviour with variable stiffness ranging from 0.135 to 1.1 kN/mm and required prestress to ensure static stability. Eigenvalue analysis confirmed the full stability of M1 and the increase in stability of M2 upon the introduction of prestress. The proposed method enables precise control of prestress distribution, which is crucial for the stability and stiffness of tensegrity structures. The M2 configuration, due to its sensitivity to prestress and variable stiffness, is particularly promising as an adaptive sandwich panel core in morphing structures, adaptive building systems, and deployable constructions. Full article

To rationally utilize Morchella germplasm resources, this study investigated 13 phenotypic traits in 231 Chinese Morchella germplasm accessions. Accessions were stratified by cap color and subjected to comparative analyses using four sampling methods, five sampling intensities, two genetic distance metrics, and four hierarchical clustering algorithms to determine the optimal strategy for core collection construction. The optimal sampling strategy for core collection construction was identified using six evaluation. Phenotypic traits of the core collection were evaluated using genetic diversity eigenvalues, t-tests, F-tests, and systematic clustering, with confirmation via principal component analysis. The results indicate that the logarithmic ratio method yielded the smallest differences in group proportions, making it the optimal sampling method. A 15% sample intensity proved optimal, with Euclidean distance outperforming Mahalanobis distance. The longest-distance method was determined to be the optimal clustering approach. Within the optimal sampling strategy combination, the CR value reached its maximum (97.77%). Ultimately, 34 Morchella germplasm resources were extracted, accounting for 14.72% of the total germplasm (original germplasm). The mean values, standard deviations, and genetic diversity of phenotypic traits were similar between the original germplasm and the core collection. However, the coefficient of variation for quantitative traits showed significant differences. In the t-test, only the maturity period showed a significant difference. In the F-test, only the cap length/width and maturity period showed significant differences. Cluster analysis grouped the germplasm resources of the core collection and the original germplasm into relatively consistent clusters. In principal component analysis, the eigenvalues and cumulative contribution rates of the first four principal components were higher for the core collection than for the original germplasm. This indicates that the core collection eliminated most genetic redundancy while preserving the genetic diversity of the original germplasm. The core collection selection is representative and can be effectively utilized as breeding material. This study provides a reference for the effective utilization and germplasm innovation of Morchella germplasm resources. Full article

Breeding improvement in alfalfa (Medicago sativa L.) is often constrained by the complexity of agronomic traits and trade-offs among yield-related characteristics. Conventional single-trait selection rarely captures the full range of phenotypic variation or the interactions among traits. To address this, we developed a principal component analysis (PCA)-based framework for multivariate selection in hybrid breeding. Six yield-related traits—plant height, branch number, fresh/hay yield ratio (FHR), leaf/stem ratio (LSR), multifoliolate leaf frequency, and dry weight per plant—were quantified in two parental lines and their F₁/F₂ generations. PCA identified three principal components (PC1–PC3) with eigenvalues >1, explaining 71.14% of the total phenotypic variance: PC1 (32.43% variance) was predominantly loaded with positive contributions from dry weight per single plant, height, and branches, biologically representing overall plant vigor and biomass accumulation; PC2 (21.77% variance) showed strong negative loadings for LSR, capturing architectural trade-offs between stem dominance and leaf production; PC3 (16.94% variance) had positive loadings on multifoliolate leaf rate and fresh/dry ratio, embodying quality and physiological resilience traits. Based on PCA scores, a composite selection index was constructed, and the top 31.1% of F1 hybrids were selected. Their F₂ progeny showed significant improvements in dry weight (+15.56%, p < 0.01), multifoliolate leaf frequency (+74.78%, p < 0.001), and reduced FHR (–8.2%, p < 0.05), accompanied by lower yield decline (−7.2% versus −14.1% in controls). These results show that PCA-based multivariate selection effectively balances trait trade-offs, enhances intergenerational stability, and improves selection efficiency. This framework offers a practical tool for alfalfa breeding. Full article

The growing importance of social networks has led to increased research into trust estimation and interpretation among network entities. It is important to predict the trust score between users in order to minimize the risks in user interactions. This article enables the identification of the most reliable and least reliable entities in a network by expressing trust scores numerically. In this paper, the social network is modeled as a graph, and trust scores are calculated by taking the powers of the ratio matrix between entities and summing them. Taking the power of the proportion matrix based on the number of entities in the network requires a lot of arithmetic load. After taking the powers of the eigenvalues of the ratio matrix, these are multiplied by the eigenvector matrix to obtain the power of the ratio matrix. In this way, the arithmetic cost required for calculating trust between entities is reduced. This paper calculates the trust score between entities using linear algebra techniques to reduce the arithmetic load. Trust detection algorithms use shortest paths and similar methods to eliminate paths that are deemed unimportant, which makes the result questionable because of the loss of data. The novelty of this method is that it calculates the trust score without the need for explicit path numbering and without any data loss. Full article

Composite materials have gained prominence in aerospace engineering due to their high strength-to-weight and stiffness-to-weight ratios. However, their susceptibility to interlaminar damage, particularly delamination, remains a significant concern, especially under compressive loads. This study presents a detailed numerical investigation into the buckling behavior and delamination propagation in flat and curved composite panels with centrally located circular delaminations. Four configurations were analyzed, differing by geometry (flat vs. curved) and delamination interface. The critical buckling load was first estimated through linear eigenvalue analysis, while post-buckling behavior and damage progression were studied using a nonlinear static analysis enhanced by the Smart-time XB (SMXB) tool. Numerical results, including out-of-plane displacements and delamination length evolution, were validated against experimental data from the literature. The findings confirm the accuracy of the adopted FEM approach and highlight the beneficial role of curvature in increasing buckling resistance and improving damage tolerance, offering valuable insights for the design of aerospace composite structures. Full article

The physics of systems of quantum emitters in waveguide quantum electrodynamics is significantly influenced by the relation between their spatial separation and the wavelength of the emitted photons. If the distance that separates a pair of emitters meets specific resonance conditions, the photon amplitudes produced from decay may destructively interfere. In an infinite-waveguide setting, this effect gives rise to bound states in the continuum, where a photon remains confined between the emitters. In the case of a finite-length waveguide with periodic boundary conditions, there exist two such relevant distances for a given arrangement of the quantum emitters, leading to states in which a photon is confined to either the shorter or the longer path that connects the emitters. If the ratio of the shorter and the longer path is a rational number, these two kinds of resonant eigenstates are allowed to co-exist for the same Hamiltonian. In this paper, we investigate the existence of quasi-degenerate resonant doublets of a pair of identical emitters coupled to a linear waveguide mode. The states that form the doublet are searched among the ones in which a single excitation tends to remain bound to the emitters. We investigate the spectrum in a finite range around degeneracy points to check whether the doublet remains well separated from the closest eigenvalues in the spectrum. The identification of quasi-degenerate doublets opens the possibility to manipulate the emitters-waveguide system as an effectively two-level system in specific energy ranges, providing an innovative tool for quantum technology tasks. Full article

This paper addresses stability issues in modern power grids arising from extensive integration of power electronic converters, which introduce complex multi-time-scale interactions. A symbolic simplification method is proposed to accurately model grid-connected converter dynamics, significantly reducing computational complexity through transfer function approximations and yielding efficient reduced-order models. An impedance-based approach utilizing impedance ratio (IR) is developed for stability assessment under active-reactive (PQ) and active power-AC voltage (PV) control strategies. The impacts of Phase-Locked Loop (PLL) and proportional-integral (PI) controllers on system stability are analysed, with a particular focus on quantifying remote converter interactions and delineating stability boundaries across varying network strengths and configurations. Furthermore, time-scale separation effectively simplifies Multi-Voltage Source Converter (MVSC) systems by minimizing inner-loop dynamics. Validation is conducted through frequency response evaluations, IR characterizations, and eigenvalue analyses, demonstrating enhanced accuracy, particularly with the application of lead–lag compensators within the critical 50–250 Hz frequency band. Time-domain simulations further illustrate the adaptability of the proposed models and reduction methodology, providing an effective and computationally efficient tool for stability assessment in converter-dominated power networks. Full article