Search Results (4,773)

Curved roofs constructed using hyperbolic paraboloid (HP) modules are gaining popularity in structural engineering due to their unique aesthetic and structural advantages. Consequently, these studies have investigated steel bar modules based on HP geometry, focusing on how variations in geometric configuration and bar topology affect internal force distribution and overall structural performance. Each module was designed on a 4 × 4 m square plan, incorporating external bars that formed the spatial frame and internal grid bars that filled the frame’s interior. Parametric modeling was conducted using Dynamo, while structural analysis and design were performed in Autodesk Robot Structural Analysis Professional (ARSAP). Key variables included the vertical displacement of frame corners (0–1.0 m at 0.25 m intervals), the orientation and spacing of internal bar divisions, and the overall mesh topology. A total of 126 structural models were analyzed, representing four distinct bar topology variants, including both planar and non-planar mesh configurations. The results demonstrate that structural efficiency is significantly influenced by the geometry and topology of the internal bar system, with notable differences observed across the various structural types. Computational analysis revealed that asymmetric configurations of non-planar quadrilateral subdivisions yielded the highest efficiency, while symmetric arrangements proved optimal for planar panel applications. These findings, along with observed design trends, offer valuable guidance for the development and optimization of steel bar structures based on HP geometry, applicable to both single-module and multi-module configurations. Full article

Purpose: The pulmonary function test holds significant clinical value in assessing the severity, prognosis, and treatment efficacy of respiratory diseases. However, the test is limited by patient compliance, thereby limiting its practical application. Moreover, it only reflects the current state of the patient and cannot directly indicate future health trends or prognosis. Computational fluid dynamics (CFD), combined with airway models built from medical image data, can assist in analyzing a patient’s ventilation function, thus addressing the aforementioned issues. However, current airway models have shortcomings in accurately representing the structural features of a patient’s airway. Additionally, these models exhibit geometric defects such as low smoothness, topological errors, and noise, which further reduce their usability. This study generates airway skeletons based on CT data and, in combination with convolutional surface technology, proposes an individualized airway modeling method to solve these deficiencies. This study also provides a method for predicting a patient’s lung function based on the constructed airway model and using CFD simulation technology. This study also explores the application of this method in preoperative prediction of the required extent of airway expansion for patients with large airway stenosis. Methods: Based on airway skeleton data extracted from patient CT images, a personalized airway model is constructed using convolutional surface technology. The airway model is simulated according to the patient’s clinical statistical values of pulmonary function to obtain airway simulation data. Finally, a regression equation is constructed between the patient’s measured pulmonary function values and the airway simulation data to predict the patient’s pulmonary function values based on the airway simulation data. Results: To preliminarily demonstrate the above method, this study used the prediction of FEV1 in patients with large airway stenosis as an example for a proof-of-concept study. A linear regression model was constructed between the outlet flow rate from the simulation of the stenosed airway and the patient’s measured FEV1 values. The linear regression model achieved a prediction result of RMSE = 0.0246 and R² = 0.9822 for the test set. Additionally, preoperative predictions were made for the degree of airway dilation needed for patients with large airway stenosis. According to the linear regression model, the proportion of airway radius expansion required at the stenotic position to achieve normal FEV1 was calculated as 72.86%. Conclusions: This study provides a method for predicting patient pulmonary function based on CFD simulation technology and convolutional surface technology. This approach addresses, to some extent, the limitations in pulmonary function testing and accuracy caused by patient compliance. Meanwhile, this study provides a method for preoperative evaluation of airway dilation therapy. Full article

Biological hydrogels are widely available in terms of raw material sources and can be processed and molded using relatively simple techniques. Hydrogels can offer abundant three-dimensional, water-containing channels that facilitate the reaction between gases and dye, making them the preferred choice for the solid support layer in colorimetric indicators. However, biomass hydrogels exhibit inferior mechanical properties, making them unsuitable for large-scale manufacturing processes. In this study, four dual-network composite hydrogels Agar/Gelatin, Sodium Alginate/Agar, Sodium Alginate/Poly (vinyl alcohol), Sodium Alginate/Gelatin (AG/Gel, SA/AG, SA/PVA and SA/Gel) prepared through hybrid strategies. Furthermore, the influence of the dual-network structure on the mechanical properties and ammonia response was systematically investigated, using microscopy and Fourier transform infrared spectroscopy (FTIR) characterization method. The experimental results demonstrate that the incorporation of SA into original hydrogel matrices can significantly enhance both the mechanical and ammonia response performance due to the secondary topological network structure. The interpenetrating double network structure was effectively regulated through the calcium ion cross-linking process. The color difference threshold of SA/PVA’s response to ammonia gas is 10, it holds promise for rapid detection applications. The SA/Gel composite hydrogel exhibits excellent mechanical robustness and toughness. The tensile strength of the SA/Gel sample is 11 times that of a single gel, and the toughness is 80 times greater, suggesting its suitability for large-scale manufacturing of colorimetric indicator. Full article

In this paper, we investigate the existence and properties of Kuratowski partitions (K-partitions), i.e., partitions of Baire spaces such that all subfamilies of such partition sum to a set with the Baire property. We focus on the inherent symmetries in their structure and prove their connections to existence of measurable cardinals and precipitous ideals. Our results reveal that the existence of a K-partition in any Baire or compact space is symmetrically reflected in metrizable and completely metrizable spaces, respectively, and we explore how these symmetries extend to the realm of set-theoretic ideals and large cardinals. We also outline possible connections with real-measurable cardinals, extensions of Lebesgue measure on the closed interval, and density topologies. Full article

Structural topology optimization is a crucial approach for achieving lightweight design. An effective topology optimization algorithm must strike a balance between the objective functions, constraints, and design variables, which essentially reflects the symmetry and tradeoff between the objective and constraints. In this study, a topology optimization method grounded in load path theory is proposed. Element bearing capacity is quantified using the element birth and death method, with an explicit formulation derived via finite element theory. The effectiveness in evaluating structural performance is assessed through comparisons with stress distributions and topology optimization density maps. In addition, a novel evaluation index for element bearing capacity is proposed as the objective function in the topology optimization model, which is validated through thin plate optimization. Subsequently, sensitivity redistribution mitigates checkerboard patterns, while mesh filtering suppresses multi-branch structures and prevents local optima. The method is applied for the lightweight design of a triangular arm, with results benchmarked against the variable density method, demonstrating the feasibility and effectiveness of the proposed method. The element bearing capacity seeks to homogenize the load distribution of each element; the technique in this study can be extended to the optimization of symmetric structures. Full article

Ultrasonic guided wave interdigital transducers realized with piezoelectric materials are of interest for structural health monitoring systems because of their capability of performing Lamb wave mode selection with respect to single-element transducers. Besides this advantage, the coverage of large areas with a minimum number of elements is an important challenge and the problem of efficient excitation with integrated electronics must be solved. This work proposes an electrical matching network topology made of L and C passive components that can be designed for the trade-off between electrical to mechanical conversion efficiency and bandwidth. The network circuit is analyzed considering the equivalent transducer impedance and the output impedance of the driving electronics. The design rules derived by the transfer function analysis are described and a case study for a piezopolymer IDT is presented. Finally, with the implementation of the integrated matching network with the connector of the IDT, the effect of cable capacitance is minimized. In conclusion this article is a contribution to the study of using IDT efficiently and in a versatile mode for different electronic front-ends that usually operate at low power supply voltage. Full article

This paper introduces a compact analog configuration that concurrently realizes a mixed-mode biquadratic filter and a dual-mode quadrature oscillator (QO) by employing a single differential differencing gain amplifier (DDGA) and all-grounded passive components. The proposed design supports four fundamental operation modes—voltage-mode (VM), current-mode (CM), trans-impedance-mode (TIM), and trans-admittance-mode (TAM)—utilizing the same circuit topology without structural modifications. In filter operation, it offers low-pass, high-pass, band-pass, band-stop, and all-pass responses with orthogonal and electronic pole frequency and quality factor. In oscillator operation, it delivers simultaneous voltage and current quadrature outputs with independent tuning of oscillator frequency and condition. The grounded-component configuration simplifies layout and enhances its suitability for monolithic integration. Numerical simulations in a 0.18-μm CMOS process with ±0.9 V supply confirm theoretical predictions, demonstrating precise gain-phase characteristics, low total harmonic distortion (<7%), modest sensitivity to 5% component variations, and stable operation from −40 °C to 120 °C. These results, combined with the circuit’s low component count and integration suitability, suggest strong potential for future development in low-power IoT devices, adaptive communication front-ends, and integrated biomedical systems. Full article

This paper presents a multi-constraint topology optimization strategy for rudder structures, integrating additive manufacturing (AM)-related overhang angle and flutter-performance considerations. To the best of our knowledge, this is the first study to couple AM overhang control with mass center (flutter) steering in a single density-based formulation for flight control rudder structures. The approach incorporates constraints on structural volume fraction, overhang angle for AM, and mass center positioning to address multi-function design objectives—structural lightweighting, stiffness, aerodynamic stability, and manufacturability. A build-direction-aware projection filter and a smooth Heaviside mass center constraint are introduced to enforce these requirements during every optimization iteration. The resulting layout converges to a sandwich-type rudder with balanced mechanical performance and AM feasibility. Simulation results show that enforcing overhang constraints reduces support material usage by 46.9% and residual deformation by 14.2%, significantly enhancing AM feasibility. Additionally, introducing center-of-mass constraints improves flutter velocity from 3327 m s⁻¹ to 3759 m s⁻¹, indicating a 6.84% increase over conventional optimization and demonstrating improved dynamic stability. These simultaneous gains in manufacturability and aeroelastic safety, achieved without post-processing, underline the novelty and practical value of the proposed constraint set. The strategy thus offers a practical and efficient design method for high-performance, AM-friendly rudder structures with superior mechanical and aerodynamic characteristics, and it can be readily extended to other mission-critical AM components. Full article

Extracting road network information from satellite remote sensing images is an effective method of dealing with dynamic changes in road networks. At present, the use of deep learning methods to automatically segment road networks from remote sensing images has become mainstream. However, existing methods often produce fragmented extraction results. This is usually caused by insufficient feature extraction and occlusion. In order to solve these problems, we propose an enhanced feature extraction and multi-branch occlusion discrimination network (EFMOD-Net) based on an encoder–decoder architecture. Firstly, a multi-directional feature extraction (MFE) module was proposed as the input for the network, which utilizes multi-directional strip convolution for feature extraction to better capture the linear features of the road. Subsequently, an enhanced feature extraction (EFE) module was designed to enhance the performance of the model in the feature extraction stage by using a dual-branch structure. The proposed multi-branch occlusion discrimination (MOD) module combines the attention mechanism and strip convolution to learn the topological relationship between pixels, enhance the network’s detection of occlusion and complex backgrounds, and reduce the generation of road debris. On the public dataset, the proposed method is compared with other SOTA methods. The experimental results show that the network designed in this paper achieves an IoU of 64.73 and 63.58 on the DeepGlobe and CHN6-CUG datasets, respectively, which is 1.66% and 1.84% higher than the IoU of performance-based methods. The proposed method combines multi-directional bar convolution and a multi-branch structure for road extraction, which provides a new idea for linear object segmentation in complex backgrounds that could be applied directly to urban renewal, disaster assessment, and other application scenarios. Full article

The line structure is an efficient form of representation and modeling for LiDAR point clouds, while the Line Structure Construction (LSC) method aims to extract complete and coherent line structures from complex 3D point clouds, thereby providing a foundation for geometric modeling, scene understanding, and downstream applications. However, traditional LSC methods often fall short in preserving both the geometric integrity and topological connectivity of line structures derived from such datasets. To address this issue, we propose the Geometry and Topology Preservable Line Structure Construction (GTP-LSC) method, based on the Encoding and Extracting Framework (EEF). First, in the encoding phase, point cloud features related to line structures are mapped into a high-dimensional feature space. A 3D U-Net is then employed to compute Subsets with Structure feature of Line (SSL) from the dense, unstructured, and noisy indoor LiDAR point cloud data. Next, in the extraction phase, the SSL is transformed into a 3D field enriched with line features. Initially extracted line structures are then constructed based on Morse theory, effectively preserving the topological relationships. In the final step, these line structures are optimized using RANdom SAmple Consensus (RANSAC) and Constructive Solid Geometry (CSG) to ensure geometric completeness. This step also facilitates the generation of complex entities, enabling an accurate and comprehensive representation of both geometric and topological aspects of the line structures. Experiments were conducted using the Indoor Laser Scanning Dataset, focusing on the parking garage (D₁), the corridor (D₂), and the multi-room structure (D₃). The results demonstrated that the proposed GTP-LSC method outperformed existing approaches in terms of both geometric integrity and topological connectivity. To evaluate the performance of different LSC methods, the IoU Buffer Ratio (IBR) was used to measure the overlap between the actual and constructed line structures. The proposed method achieved IBR scores of 92.5% (D₁), 94.2% (D₂), and 90.8% (D₃) for these scenes. Additionally, Precision, Recall, and F-Score were calculated to further assess the LSC results. The F-Score of the proposed method was 0.89 (D₁), 0.92 (D₂), and 0.89 (D₃), demonstrating superior performance in both visual analysis and quantitative results compared to other methods. Full article

Accurate genome binning is essential for resolving microbial community structure and functional potential from metagenomic data. However, existing approaches—primarily reliant on tetranucleotide frequency (TNF) and abundance profiles—often perform sub-optimally in the face of complex community compositions, low-abundance taxa, and long-read sequencing datasets. To address these limitations, we present MBGCCA, a novel metagenomic binning framework that synergistically integrates graph neural networks (GNNs), contrastive learning, and information-theoretic regularization to enhance binning accuracy, robustness, and biological coherence. MBGCCA operates in two stages: (1) multimodal information integration, where TNF and abundance profiles are fused via a deep neural network trained using a multi-view contrastive loss, and (2) self-supervised graph representation learning, which leverages assembly graph topology to refine contig embeddings. The contrastive learning objective follows the InfoMax principle by maximizing mutual information across augmented views and modalities, encouraging the model to extract globally consistent and high-information representations. By aligning perturbed graph views while preserving topological structure, MBGCCA effectively captures both global genomic characteristics and local contig relationships. Comprehensive evaluations using both synthetic and real-world datasets—including wastewater and soil microbiomes—demonstrate that MBGCCA consistently outperforms state-of-the-art binning methods, particularly in challenging scenarios marked by sparse data and high community complexity. These results highlight the value of entropy-aware, topology-preserving learning for advancing metagenomic genome reconstruction. Full article

The aromaticity of small boron clusters remains under scrutiny due to persistent inconsistencies between magnetic and electronic descriptors. Here, we reexamine B₃⁻, B₃⁺, B₄, B₄²⁺, and B₄²⁻ using a multidimensional approach that integrates Adaptive Natural Density Partitioning, Electron Density of Delocalized Bonds, magnetically induced current density, and the z-component of the induced magnetic field. We introduce a model in which σ-aromaticity arises from two distinct delocalization topologies: a radial 2e⁻ σ-pathway and a tangential multicenter circuit formed by alternating filled and vacant sp² orbitals. This framework accounts for the evolution of aromaticity upon oxidation or reduction, preserving coherence between electronic structure and magnetic response. B₃⁻ features cooperative radial and tangential σ-delocalization, together with a delocalized 2e⁻ π-bond, yielding robust double aromaticity. B₃⁺ retains σ- and π-aromaticity, but only via a tangential 6e⁻ σ-framework, leading to a more compact delocalization and slightly attenuated ring currents. In B₄, the presence of a radial 2e⁻ σ-bond and a 4c–2e π-bond confers partial aromatic character, while the tangential 8e⁻ σ-framework satisfies the 4n rule and induces a paratropic current. In contrast, B₄²⁺ lacks the radial σ-component but retains a tangential 8e⁻ σ-circuit and a 2e⁻ 4c–2e π-bond, leading to a σ-antiaromatic and π-aromatic configuration. Finally, B₄²⁻, exhibits delocalized π- and σ-circuits, yielding consistent diatropic ring currents, which confirms its fully doubly aromatic nature. Altogether, this analysis underscores the importance of resolving σ-framework topology and demonstrates that, when radial and tangential contributions are correctly distinguished, Hückel’s rule remains a powerful tool for interpreting aromaticity in small boron rings. Full article

Duct fan motors must provide high torque within limited space to maintain airflow while requiring low vibration characteristics to minimize fluid resistance caused by fan oscillation. Axial Flux Permanent Magnet Motor (AFPM) offers higher torque performance than Radial Flux Permanent Magnet Motor (RFPM) due to their large radial and short axial dimensions. In particular, the coreless AFPM structure enables superior low-vibration performance. Conventional AFPM typically employs a core-type stator, which presents manufacturing difficulties. In core-type AFPM, applying a multi-stator configuration linearly increases winding takt time in proportion to the number of stators. Conversely, a Printed Circuit Board (PCB) stator AFPM significantly reduces stator production time, making it favorable for implementing multi-stator topologies. The use of multi-stator structures enables various topological configurations depending on (1) stator placement, (2) magnetization pattern of permanent magnets, and (3) rotor arrangement—each offering specific advantages. This study evaluates and analyzes the performance of different topologies based on efficient arrangements of magnets and stators, aiming to identify the optimal structure for duct fan applications. The validity of the proposed approach and design was verified through three-dimensional finite element analysis (FEA). Full article

Background: Traditional morphology-based classification of the Oriental Plover (Anarhynchus veredus) is inconsistent with molecular evidence, underscoring the necessity of incorporating molecular data to elucidate its evolutionary relationships within Charadriidae. Methods: Here, we present the first complete mitochondrial genome of A. veredus by Illumina NovaSeq Sequencing and explore its evolutionary implications within Charadriidae. Results: The mitogenome spans 16,886 bp and exhibits conserved structural features typical of Charadriidae, including gene order, overlapping coding regions, and intergenic spacers. Nucleotide composition analysis revealed a GC content of 44.3%, aligning with other Charadriidae species (44.5–45.8%), and hierarchical GC distribution across rRNA, tRNA, and protein-coding genes (PCGs) reflects structural and functional optimization. Evolutionary rate heterogeneity was observed among PCGs, with ATP8 and ND6 showing accelerated substitution rates (Ka/Ks = 0.1748 and 0.1352) and COX2 under strong purifying selection (Ka/Ks = 0.0678). Notably, a conserved translational frameshift in ND3 (position 174) was identified. Phylogenetic analyses (ML/NJ) of 88 Charadriiformes species recovered robust topologies, confirming that the division of Charadriidae into four monophyletic clades (Pluvialis, Vanellus, Charadrius, and Anarhynchus) and supporting the reclassification of A. veredus under Anarhynchus. Conclusions: This study resolves the systematic position of A. veredus and highlights the interplay between conserved mitochondrial architecture and lineage-specific adaptations in shaping shorebird evolution. Full article

This work presents a novel Graph Neural Network (GNN) based framework for structural damage detection and localization in composite aerospace structures. The sensor network is modeled as a graph whose nodes correspond to the strain measurement points placed on the system, while the edges capture spatial and structural relationships among sensors. Strain mode shapes, extracted via Automated Operational Modal Analysis (AOMA), are used as input features for the GNN. Two architectures are developed: one for binary damage detection and another for damage localization, the latter outputting a spatial probability distribution of damage over the structure. Both networks are trained and validated on synthetic datasets generated from high-fidelity finite element transient simulations performed on a composite wing equipped with 40 strain sensors. The obtained results show strong effectiveness in both detection and localization tasks, thus highlighting the potential of leveraging GNNs for topology-aware Structural Health Monitoring applications. In particular, the proposed framework achieves an AUC of 0.97 for damage detection and a mean localization error of approximately 3% of the wingspan on the synthetic dataset. The performance of the GNN is also compared with a fully connected and a convolutional neural network, demonstrating significant improvements in the localization accuracy. Full article