Search Results (2,761)

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

A hybrid wave energy conversion (WEC) system, integrating a backward bent duct buoy (BBDB) with an oscillating buoy (OB) via a flexible mooring chain, is introduced in this study. Unlike existing hybrid WECs, the proposed system dispenses with rigid mechanical linkages and enables flexible offshore deployment. Flared BBDB and buoy models with spherical, cylindrical, and semi-capsule shapes are designed and tested experimentally in a wave flume using both regular and irregular wave conditions. The effects of nozzle ratio (NR), coupling distance, buoy draft, and buoy geometry are systematically examined to investigate the hydrodynamic performance and energy conversion characteristics. It is found that NR at 110 under unidirectional airflow produces an optimal balance between pressure response, free surface displacement, and energy conversion efficiency. Energy extraction is significantly influenced by the coupling distance, with the hybrid system achieving maximum performance at a specific normalized spacing. The semi-capsule buoy improves power extraction ability and expands effective bandwidth due to asymmetric shape and coupled motion. These findings provide valuable insights into the coupling mechanism and geometric optimization for hybrid WECs. Full article

Landscape quality and the productivity of $\mathit{Rhododendron\; simsii}$ are directly related to the maintenance of ecological functions in the alpine region. The specific relationship between the spatial pattern of colour patches and the visual quality of R. simsii landscape recreational forests has been insufficiently explored. In this study, we constructed a model of the relationship between landscape colour patches and the aesthetic value of such a forest, analysing the key factors driving changes in its landscape quality. A total of 1549 participants were asked to assess 16 groups of landscape photographs. The results showed that variations in perceived aesthetic quality were stimulated by colour patch dynamics and spatial heterogeneity. Utilising structural equation modelling (SEM), we identified key indicators synergistically influencing aesthetic quality, including the area percentage, shape, and distribution of colour patches, which demonstrated strong explanatory power ($R^2$ = 0.83). The SEM also revealed that the red patch area, mean perimeter area ratio, and separation index are critical latent variables with standardised coefficients of 0.54, 0.65, and 0.62, respectively. These findings provide actionable design strategies: (1) optimising chromatic contrast through high-saturation patches, (2) controlling geometric complexity, and (3) improving spatial coherence. These results advance the theoretical framework for landscape aesthetic evaluation and offer practical guidance for landscape recreational forest management. Full article

To improve collision risk prediction in high-traffic coastal waters and support real-time decision-making in maritime navigation, this study proposes a regional collision risk prediction system integrating the Computed Distance at Collision (CDC) method with an Adaptive Neuro-Fuzzy Inference System (ANFIS). Unlike Distance at Closest Point of Approach (DCPA), which depends on the position of Global Positioning System (GPS) antennas, Computed Distance at Collision (CDC) directly reflects the actual hull shape and potential collision point. This enables a more realistic assessment of collision risk by accounting for the hull geometry and boundary conditions specific to different ship types. The system was designed and validated using ship motion simulations involving bulk and container ships across varying speeds and crossing angles. The CDC method was used to define collision, almost-collision, and near-collision situations based on geometric and hydrodynamic criteria. Subsequently, the FIS–CDC model was constructed using the ANFIS by learning patterns in collision time and distance under each condition. A total of four input variables—ship speed, crossing angle, remaining time, and remaining distance—were used to infer the collision risk index (CRI), allowing for a more nuanced and vessel-specific assessment than traditional CPA-based indicators. Simulation results show that the time to collision decreases with higher speeds and increases with wider crossing angles. The bulk carrier exhibited a wider collision-prone angle range and a greater sensitivity to speed changes than the container ship, highlighting differences in maneuverability and risk response. The proposed system demonstrated real-time applicability and accurate risk differentiation across scenarios. This research contributes to enhancing situational awareness and proactive risk mitigation in Maritime Autonomous Surface Ship (MASS) and Vessel Traffic System (VTS) environments. Future work will focus on real-time CDC optimization and extending the model to accommodate diverse ship types and encounter geometries. Full article

Robotic timber joinery demands integrated, adaptive methods to compensate for the inherent dimensional variability of wood. We introduce a seamless robotic workflow to enhance the measurement accuracy of the Workpiece Coordinate System (WCS). The approach leverages a Zivid 3D camera mounted in an eye-in-hand configuration on a KUKA industrial robot. The proposed algorithm applies a geometric method that strategically crops the point cloud and fits planes to the workpiece surfaces to define a reference frame, calculate the corresponding transformation between coordinate systems, and measure the cross-section of the workpiece. This enables reliable toolpath generation by dynamically updating WCS and effectively accommodating real-world geometric deviations in timber components. The workflow includes camera-to-robot calibration, point cloud acquisition, robust detection of workpiece features, and precise alignment of the WCS. Experimental validation confirms that the proposed method is efficient and improves milling accuracy. By dynamically identifying the workpiece geometry, the system successfully addresses challenges posed by irregular timber shapes, resulting in higher accuracy for timber joints. This method contributes to advanced manufacturing strategies in robotic timber construction and supports the processing of diverse workpiece geometries, with potential applications in civil engineering for building construction through the precise fabrication of structural timber components. Full article

Image super-resolution (SR) is essential for enhancing image quality in critical applications, such as medical imaging and satellite remote sensing. However, existing methods were often limited in their ability to effectively process and integrate multi-scales information from fine textures to global structures. To address these limitations, this paper proposes DSCNN, a dynamic snake convolution neural network for enhanced image super-resolution. DSCNN optimizes feature extraction and network architecture to enhance both performance and efficiency: To improve feature extraction, the core innovation is a feature extraction and enhancement module with dynamic snake convolution that dynamically adjusts the convolution kernel’s shape and position to better fit the image’s geometric structures, significantly improving feature extraction. To optimize the network’s structure, DSCNN employs an enhanced residual network framework. This framework utilizes parallel convolutional layers and a global feature fusion mechanism to further strengthen feature extraction capability and gradient flow efficiency. Additionally, the network incorporates a SwishReLU-based activation function and a multi-scale convolutional concatenation structure. This multi-scale design effectively captures both local details and global image structure, enhancing SR reconstruction. In summary, the proposed DSCNN outperforms existing methods in both objective metrics and visual perception (e.g., our method achieved optimal PSNR and SSIM results on the Set5 $\times 4$ dataset). Full article

Three-dimensional (3D) radar imaging is essential for target detection and measurement of scattering characteristics. Cylindrical scanning, a prevalent spatial sampling technique, provides benefits in engineering applications and has been extensively utilized for assessing the radar stealth capabilities of large aircraft. Traditional cylindrical scanning generally utilizes highly sampled full-coverage techniques, leading to an excessive quantity of sampling points and diminished image efficiency, constraining its use for quick detection applications. This work presents an efficient 3D sampling strategy that integrates vertical linear scanning with horizontal rotating motion to overcome these restrictions. A joint angle–space sampling model is developed, and geometric constraints are implemented to enhance the scanning trajectory. The experimental results demonstrate that, compared to conventional techniques, the proposed method achieves a 94% reduction in the scanning duration while maintaining a peak sidelobe level ratio (PSLR) of 12 dB. Furthermore, this study demonstrates that 3D imaging may be accomplished solely by a “V”-shaped trajectory, efficiently determining the minimal possible sampling aperture. This approach offers novel insights and theoretical backing for the advancement of high-efficiency, low-redundancy 3D radar imaging systems. Full article

The morphological diversity of Moroccan honey bees (Apis mellifera) was investigated using geometric morphometrics to assess wing venation patterns among three populations representing three climatic zones: desert, semiarid, and Mediterranean. A total of 193 honey bee samples were analyzed and compared to historical reference samples from the Morphometric Bee Data Bank in Oberursel, representing the three subspecies: A. m. intermissa, A. m. sahariensis, and A. m. major. Principal component analysis and linear discriminant analysis revealed significant, yet overlapping morphological differences among the climatic groups. Spatial modeling showed a significant southwest–northeast clinal gradient in wing morphology. Almost all samples were assigned to the African evolutionary lineage, except one, suggesting a dominant African genetic background. Interestingly, all three populations showed greater morphological affinity to A. m. intermissa than to A. m. sahariensis, which could indicate introgression or limitations in the current reference dataset. These discrepancies highlight the necessity of revising subspecies boundaries using updated morphometric and genomic approaches. These findings improve our understanding of honey bee biodiversity in Morocco and provide valuable information for conservation and breeding programs. Full article

This paper proposes an improved wave function asperity elastic–plastic model. A cosine function that could better fit the geometric morphology was selected to construct the asperity, the elastic phase was controlled by the Hertz contact theory, the elastoplastic transition phase was corrected by the hyperbolic tangent function, and the fully plastic phase was improved by the projected area theory. The model broke through the limitations of the spherical assumption and was able to capture the stress concentration and plastic flow phenomena. The results show that the contact pressure in the elastic phase was 22% higher than that of the spherical shape, the plastic strain in the elastoplastic phase was 52% lower than that of the spherical shape, and the fully plastic phase reduced the contact area error by 20%. The improved hyperbolic tangent function eliminated the unphysical oscillation phenomenon in the elastoplastic phase and ensured the continuity and monotonicity of the contact variables, with an error of <5% from the finite element analysis. Meanwhile, extending the proposed model, we developed a rough surface contact model, and it was verified that the wavy asperity could better match the mechanical properties of the real rough surface and exhibited progressive stiffness reduction during the plastic flow process. The model in this paper can provide a theoretical basis for predicting stress distribution, plastic evolution, and multi-scale mechanical behavior in the connection interface. Full article

This paper introduces a hierarchical object-centric descriptor framework called TOSD (Triplet Object-Centric Semantic Descriptor). The goal of this method is to overcome the limitations of existing pixel-based and global feature embedding approaches. To this end, the framework adopts a hierarchical representation that is explicitly designed for multi-level reasoning. TOSD combines shape, color, and topological information without depending on predefined class labels. The shape descriptor captures the geometric configuration of each object. The color descriptor focuses on internal appearance by extracting normalized color features. The topology descriptor models the spatial and semantic relationships between objects in a scene. These components are integrated at both object and scene levels to produce compact and consistent embeddings. The resulting representation covers three levels of abstraction: low-level pixel details, mid-level object features, and high-level semantic structure. This hierarchical organization makes it possible to represent both local cues and global context in a unified form. We evaluate the proposed method on multiple vision tasks. The results show that TOSD performs competitively compared to baseline methods, while maintaining robustness in challenging cases such as occlusion and viewpoint changes. The framework is applicable to visual odometry, SLAM, object tracking, global localization, scene clustering, and image retrieval. In addition, this work extends our previous research on the Semantic Modeling Framework, which represents environments using layered structures of places, objects, and their ontological relations. Full article

Ensuring the structural integrity of reinforced concrete (RC) components in nuclear facilities exposed to extreme conditions is essential for safe decommissioning. This study investigates the impact of high-temperature exposure on RC pedestal structures supported by steel ring-shaped shear connectors—critical elements for maintaining vertical and lateral load paths in containment systems. Scaled-down cyclic loading tests were performed on pedestal specimens with and without prior thermal exposure, simulating post-accident conditions observed at a damaged nuclear power plant. Experimental results show that thermal degradation significantly reduces lateral stiffness, with failure mechanisms concentrating at the interface between the concrete and the embedded steel skirt. Complementary finite element analyses, incorporating temperature-dependent material degradation, highlight the crucial role of load redistribution to steel components when concrete strength is compromised. Parametric studies reveal that while geometric variations in the inner skirt have limited influence, thermal history is the dominant factor affecting vertical capacity. Notably, even with substantial section loss in the concrete, the steel inner skirt maintained considerable load-bearing capacity. This study establishes a validated analytical framework for assessing structural performance under extreme conditions, offering critical insights for risk evaluation and retrofit strategies in the context of nuclear facility decommissioning. Full article

Creating high-fidelity digital twins (DTs) for Industry 4.0 applications, it is fundamentally reliant on the accurate 3D modeling of physical assets, a task complicated by the inherent imperfections of real-world point cloud data. This paper addresses the challenge of reconstructing accurate, watertight, and topologically sound 3D meshes from sparse, noisy, and incomplete point clouds acquired in complex industrial environments. We introduce a robust two-stage completion-to-reconstruction framework, C2R3D-Net, that systematically tackles this problem. The methodology first employs a pretrained, self-supervised point cloud completion network to infer a dense and structurally coherent geometric representation from degraded inputs. Subsequently, a novel adaptive surface reconstruction network generates the final high-fidelity mesh. This network features a hybrid encoder (FKAConv-LSA-DC), which integrates fixed-kernel and deformable convolutions with local self-attention to robustly capture both coarse geometry and fine details, and a boundary-aware multi-head interpolation decoder, which explicitly models sharp edges and thin structures to preserve geometric fidelity. Comprehensive experiments on the large-scale synthetic ShapeNet benchmark demonstrate state-of-the-art performance across all standard metrics. Crucially, we validate the framework’s strong zero-shot generalization capability by deploying the model—trained exclusively on synthetic data—to reconstruct complex assets from a custom-collected industrial dataset without any additional fine-tuning. The results confirm the method’s suitability as a robust and scalable approach for 3D asset modeling, a critical enabling step for creating high-fidelity DTs in demanding, unseen industrial settings. Full article

Prefabricated construction elements are widely used in both large- and small-scale projects, serving structural and infrastructural purposes. One notable application is in power transmission poles, which ensure the safe and efficient delivery of electricity. Despite their importance, limited research exists on the structural and modal behavior of reinforced concrete power poles. This study presents a comprehensive modal analysis of such poles, focusing on how factors like modulus of elasticity, height, and lower/upper inner and outer diameters influence dynamic performance. A total of 3240 finite element models were created, with reinforced concrete poles partially embedded in the ground. Modal analyses were performed to evaluate natural frequencies, mode shapes, and modal mass participation ratios. Results showed that increasing the modulus of elasticity raised frequency values, while greater pole height decreased them. Enlarging the lower inner and upper outer radii also led to higher frequencies. Regression analysis yielded high accuracy, with R² values exceeding 90% and an average error rate of about 6%. The study provides empirical formulas that allow for quick frequency estimations without the need for detailed finite element modeling, as long as the material and geometric properties remain consistent. The approach can be extended to other prefabricated structural elements. Full article

Among marine organisms, baleen whale species like the humpback whale (Megaptera novaeangliae) are a case for which individual-based models are necessary to study population changes because individual trait variabilities predominate over average demographic rates to govern population dynamics. These models require quantification of individual organisms’ dynamics with respect to local conditions, which implies optimal strategy frameworks cannot be used. Instead, to quantify how individuals perform according to the environmental conditions they encounter, we formulated a model linking individual mechanical characteristics of swimming and diving with their aerobic metabolism and behavior. The model simulates the dynamics of swimming and diving for the reported range of whale sizes (1000 to 50,000 kg). Additional processes simulate foraging events including rapid accelerations and water engulfment, which modifies whale shape, weight and drag. Simulations show how the energy cost of swimming at equilibrium increases geometrically with velocity and linearly with mass. The duration and distance covered under apnea vary monotonically with mass but not with velocity; hence, there is a positive mass-dependent optimal velocity that maximizes the distance and duration of apnea. The dive limit was explored with a combination of the physiological state, mechanical force produced and distance to return to surface. This combination is imposed as an inequality constraint on the whale individual. The inequality constraint, transformed as a multi-layer perceptron, which continuously processes information about oxygen, depth and relative velocity, provides the whale individual with autonomous decision-making about whether or not to continue the dive. The results also highlight where missing metabolic information is needed to simulate the dynamics of a population of autonomous individuals at the scale of the Global Ocean. Full article

Geometric morphometrics is performed on different species in different contexts. Here, the aim was to investigate morphological differences in the head of the Sanmartinero Creole bovine to examine head shape variations between sexes using geometric morphometric methods. A sample of cranial pictures of 43 animals (13 males and 30 females) was obtained, and form (size + shape) was studied by means of geometric morphometric techniques using a set of 14 landmarks. This approach eliminated potential dietary effects, ensuring that the observed shape variations were primarily due to intrinsic morphological differences. Sexual dimorphism was found in form (for both size and shape) of the head of the Sanmartinero Creole bovine breed. Males had significantly larger heads based on centroid size (U = 714, p = 0.0004), confirming true sexual size differences, and Principal Component Analysis revealed overlapping head shapes with sexual dimorphism concentrated at midline sagittal landmarks (between the most rostral and caudal orbit points) and paired lateral points, indicating that males have broader and longer heads. The two evaluated characters (head size and shape) are of special interest for the conservation of the breed, especially in those cases whose objectives are to maintain the uniqueness, distinctiveness, and uniformity of the populations. This study analyzed animals subjected to the same feeding program, ensuring the elimination of additional variables. Full article