Search Results (3,984)

This study investigates radio frequency (RF) wave propagation in sandstone and limestone cave environments, emphasizing the use of LiDAR-derived three-dimensional (3D) models to characterize cave geometry and support waveguide-based propagation analysis incorporating skin depth effects. RF transmission and reception measurements were conducted under line-of-sight (LOS) conditions across frequency bands from Low Frequency (LF) to Ultra-High Frequency (UHF). Comparative results reveal distinct attenuation behaviors governed by differences in cave geometry and electrical properties. The sandstone cave, with a more uniform geometry and relatively higher electrical conductivity, exhibits lower attenuation across most frequency bands, whereas the limestone cave shows higher attenuation due to its irregular structure. LiDAR-based 3D models are employed to extract key geometric parameters, including cavity dimensions, wall roughness, and wall inclination, which are incorporated into the proposed analytical framework. The model is further validated using experimental field measurements, demonstrating that the inclusion of LiDAR-derived geometry and skin depth effects enables a more realistic representation of underground RF propagation for communication system analysis. Full article

A magnetic vortex, characterized by curling in-plane magnetization, is generally unstable in two-dimensional (2D) ferromagnetic thin films. Here, we demonstrated that this vortex could be stable in three-dimensional (3D) pyramid-shaped Fe thin films and elucidated mechanistic origin of the stable vortex. Magnetization measurements reveal characteristic $M-H$ hysteresis loops with a pronounced bending and a gradual slope near zero magnetization, contrasting strongly with the abrupt switching seen in 2D films. By decomposing the magnetization processes on each facet in pyramid, we identify the sequence of vortex formation, stabilization, and annihilation. The key factor is the 3D geometry: non-coplanar facet junctions at the ridge lines act as structural singularities that naturally pin domain walls (DWs). These ridge lines restrict DW motion, confine local magnetic structures, and mediate inter-facet interactions, creating geometrical constraints enhancing vortex stability. Vortex formation is driven by magnetostatic energy minimization, as in 2D films. However, ridge-induced weakening of inter-facet exchange becomes the dominant factor in the 3D pyramidal structure. Overall, the interplay of shape anisotropy, magnetostatic, exchange, and Zeeman energies under 3D constraints provides a clear framework for vortex stability, offering the first mechanistic insight into stable vortices in 3D ferromagnetic films and supporting future 3D magnetic devices. Full article

Building information models (BIMs) serve as a foundational tool for digital management of existing structures. Traditional methods suffer from low automation and heavy reliance on manual intervention. This paper proposes an automated method for structural component dimension recognition and BIM modeling based on 3D point cloud data. The proposed methodology follows a three-step workflow. First, the raw point cloud is semantically segmented using the PointNet++ deep learning network, and individual structural components are effectively isolated using the Fast Euclidean Clustering (FEC) algorithm. Second, the principal axis of each component is determined through Principal Component Analysis, and the Random Sample Consensus (RANSAC) algorithm is applied to fit the boundary lines of the projected cross-sections, enabling the automated extraction of geometric dimensions. Finally, an automated script maps the extracted geometric parameters to standard IFC entities to generate the BIM model. The experimental results demonstrate that the average dimensional error for beams and columns is within 3 mm, with the exception of specific occluded components. This study realizes the efficient transformation from point cloud data to BIM models through an automated workflow, providing reliable technical support for the digital reconstruction of existing buildings. Full article

This work presents an analytical homogenization model developed to predict the tensile and bending behavior of double-wall corrugated cardboard. The proposed approach replaces the complex three-dimensional geometry, composed of five paper layers, with an equivalent two-dimensional homogenized plate. Based on lamination theory and enhanced by sandwich structure theory, the model accurately captures the orthotropic behavior of the material. To achieve this objective, three configurations of double-wall corrugated cardboard were investigated: KRAFT LINER (KL), DUOSAICA (DS), and AUSTRO LINER (AL). A comprehensive experimental characterization campaign was conducted, including physical analyses (density measurement, SEM imaging, and XRD analysis) and mechanical testing (tensile tests), to determine the input parameters required for the homogenization process. The proposed model significantly reduces geometric complexity and computational cost while maintaining excellent predictive accuracy. Validation was performed by comparing the results of a 3D finite element model (ANSYS-19.2) with those obtained from the homogenized H-2D model. The differences between both approaches remained systematically below 2%, confirming the ability of the H-2D model to accurately reproduce the axial and flexural stiffnesses of double-wall corrugated cardboard. The methodology provides a reliable and efficient framework specifically dedicated to the mechanical analysis and optimization of corrugated cardboard structures used in packaging applications. Full article

Organoids are three-dimensional multicellular structures that mimic key aspects of native tissues consisting ideal tools to study organ development and pathophysiology when incorporated in customized bioscaffolds. In vivo, the extracellular matrix (ECM) maintains tissue integrity and regulates cell adhesion, migration, differentiation, and survival through biochemical and mechanical signals. Tissue-derived decellularized extracellular matrix (dECM) can preserve organ-specific biochemical signals and cell-adhesive motifs, creating a bioactive environment that supports physiologically relevant organoid growth. 3D bioprinting technology marks a transformative phase in organoid research by enhancing the structural and functional complexity of organoid models and expanding their application in pharmacology and regenerative medicine. These systems enhance tissue modeling and drug testing while adhering to the principles of animal replacement, reduction, and refining (3Rs) in research. Remaining challenges include donor variability, limited mechanical stability, and the lack of standardized decellularization protocols that can be addressed by adopting quality and safety metrics. The combination of dECM-based biomaterials and 3D bioprinting holds great potential for the development of human-relevant, customizable, and ethically sound in vitro models for regenerative medicine and personalized therapies. In this review, we discuss the latest (2021–2025) developments in applying extracellular matrix bioprinting techniques to organoid technology, presenting examples for the most commonly referenced organoid types. Full article

Alkali-activated cementitious materials are environmentally friendly alternatives to traditional cement. The structure of their core product, sodium aluminosilicate hydrate (N-A-S-H) gel, is regulated by the silicon-to-aluminum (Si/Al) ratio; however, the atomic-scale mechanism underlying this influence remains unclear. Integrating reactive force field molecular dynamics simulations and experiments, this study systematically reveals the regulation mechanism of the Si/Al ratio (1.0–2.0) on the microstructure and macroscopic properties of N-A-S-H gels. Starting from well-defined PS and PSS oligomers, the simulation results demonstrate that the Si/Al ratio governs the polymerization pathway, aluminum coordination environment (especially the content of pentacoordinate aluminum), and evolution of nanoporosity. When the Si/Al ratio is approximately 1.8, the system exhibits the highest silicate polymerization degree, lowest nanoporosity, and densest three-dimensional (3D) network structure; deviation from this ratio leads to structural degradation due to charge imbalance or excessive polymerization. These computational findings are validated by experiments on fly ash-based geopolymers: the material achieves the highest compressive strength at a Si/Al ratio of 1.8. The consistency between simulations and experiments collectively reveals a cross-scale action mechanism: the Si/Al ratio determines the macroscopic mechanical properties by regulating the nanoscale packing density and defect distribution of the gel. This study provides critical atomic-scale insights for the rational design of high-performance geopolymers. Full article

Direction of arrival (DOA) estimation is a fundamental problem in array signal processing, with extensive applications in radar, communications, sonar, and other fields. Traditional DOA estimation methods, such as MUSIC and ESPRIT, rely on eigenvalue decomposition or spectral peak search, which suffer from high computational complexity and performance degradation under conditions of low signal-to-noise ratio (SNR), coherent signals, and array imperfections. Cylindrical arrays offer unique advantages for omnidirectional sensing due to their circular structure and three-dimensional coverage capability; however, their nonlinear array manifold increases the difficulty of estimation. This paper proposes a circular-attention residual network (CA-ResNet) for DOA estimation using uniform cylindrical arrays. The proposed approach achieves high accuracy and robust angle estimation through phase difference feature extraction, a multi-scale residual network, an attention mechanism, and a joint output module. Simulation results demonstrate that the proposed CA-ResNet method delivers superior performance under challenging scenarios, including low SNR (−10 dB), a small number of snapshots (L = 5), and multiple sources (1 to 4 signal sources). The corresponding root mean square errors (RMSE) are 0.21°, 0.45°, and below 1.5°, respectively, significantly outperforming traditional methods like MUSIC and ESPRIT, as well as existing deep learning models (e.g., ResNet, CNN, MLP). Furthermore, the algorithm exhibits low computational complexity and a small parameter size, highlighting its strong potential for practical engineering applications and robustness. Full article

Magnetorheological fluids (MRFs) exhibit dynamic, field-responsive mechanical properties, as they form chain-like and networked microstructures under magnetic stimuli. This study numerically investigates the structural and mechanical behavior of three-dimensional (3D) microbead chain assemblies, focusing on cubic and hexagonal body-centered tetragonal (BCT) configurations formed under compressive and magnetic field-driven aggregation. A finite element-based model simulates magnetostatic and stress evolution in solidified structures composed of up to 20 particle chains. The analysis evaluates magnetic flux distribution, total magnetic force, and time-resolved stress profiles under vertical loading. Results show that increasing chain density significantly enhances magnetic coupling and reduces peak stress, especially in hexagonal lattices, where early stress equilibration and superior lateral load distribution are observed. The hexagonal BCT structure exhibits superior resilience, lower stress concentrations, and faster dissipation under dynamic loads. These findings offer insights into designing energy-absorbing MRF-based materials for impact mitigation, adaptive damping, and protective microfluidic structures. Full article

With the rapid expansion of high-speed railway (HSR) networks, the vibration impact on adjacent energy infrastructure has become a critical safety concern. However, existing research lacks a comprehensive evaluation of buried sour gas pipelines specifically in tunnel-undercrossing scenarios. This research investigates the dynamic response characteristics of a sour natural gas pipeline under train-induced vibration loads using a case study in Chongqing. A three-dimensional dynamic coupling model of the track lining soil pipeline system was established based on FLAC-3D. The study innovatively quantifies the vibration superposition effect during bidirectional train encounters and assesses safety using fatigue life and velocity thresholds. Results indicate that pipeline vibration is predominantly vertical. As train speed increases from 250 km/h to 350 km/h, the response exhibits a non-linear rapid growth within the 300–350 km/h range. Under bidirectional encounters, the peak displacement reaches 2.00 times that of unilateral passage, representing the most critical load condition. The maximum peak vibration velocity is 0.1 mm/s, far below the 2 mm/s safety threshold, ensuring structural integrity under current operational standards. Full article

Elastic wavefield decomposition into quasi-compressional (qP) and quasi-shear-vertical (qSV) modes is essential for elastic imaging and inversion in VTI media, but becomes computationally expensive when polarization vectors vary strongly in space. I propose a tensor-train (TT) representation of mixed-domain decomposition projectors, constructed via TT-cross with a single user-specified tolerance and applied efficiently using FFT-based operations. A residual-orthogonal strategy extracts qSV from the residual wavefield after qP removal to suppress mode leakage. The method is implemented in Python/PyTorch with GPU acceleration. Numerical experiments on three 2D VTI models (a two-layer benchmark, a BP 2007 benchmark subset, and an Overthrust-based structurally complex model) demonstrate reconstruction errors of 0.094–0.89% for TT, compared to 1.67–6.44% for a conventional CUR low-rank approach (4–46× improvement), with consistently lower cross-talk and near-unity energy ratios. Time-domain receiver traces further confirm that TT yields smaller reconstruction residual spikes and reduced cross-mode leakage than CUR. Runtime tests show that CUR can be faster on smaller grids, whereas TT with GPU acceleration becomes competitive and can outperform CUR for larger models. The TT representation scales linearly with tensor $\mathcal{O}\left(d N_s r^2\right)$—enabling practical extension to higher-dimensional projector tensors where conven-tional methods become impractical. Full article

Building height is an important indicator for describing the three-dimensional structure of cities. However, monitoring its changes is still difficult due to high labor costs, low efficiency, and the limited resolution and viewing angles of remote sensing images. This study proposes an automatic framework for estimating building height changes using multi-temporal street view images. First, buildings are detected by the YOLO-v5 model, and their contours are extracted through edge detection and hole filling. To reduce false detections, greenness and depth information are combined to filter out pseudo changes. Then, a neighboring region resampling strategy is used to select visually similar images for better alignment, which helps to reduce the influence of sampling errors. In addition, the framework applies cylindrical projection correction and introduces a triangulation-based method (HCAOT) for building height estimation. Experimental results show that the proposed framework achieves an accuracy of 85.11% in detecting real changes and 91.23% in identifying unchanged areas. For height estimation, the HCAOT method reaches an RMSE of 0.65 m and an NRMSE of 0.04, which performs better than several comparison methods. Overall, the proposed framework provides an efficient and reliable approach for dynamically updating 3D urban information and supporting spatial monitoring in smart cities. Full article

We describe a patient with recurrent, brief episodes of chest discomfort caused by a highly mobile papillary fibroelastoma originating from the aortic wall and intermittently encroaching on the right coronary artery ostium. Initial 2D and 3D transthoracic and 2D transesophageal echocardiography identified a highly mobile mass in the ascending aorta above the aortic valve; the exact site of attachment and its relationship to the coronary ostia could not be clearly defined. Three-dimensional transesophageal echocardiography enabled precise anatomical reconstruction of the lesion and surrounding structures, clearly demonstrating its pedicle and proximity to the right coronary ostium. This imaging modality clarified the pathophysiological mechanism of symptoms and facilitated optimal surgical planning without the need for additional complex imaging techniques. Full article

Background: There are approximately 5.4 M basal cell (BCC) and squamous cell (SCC) carcinomas diagnosed each year, and the number is increasing. Currently, the gold standard for skin cancer diagnosis is histopathology, which requires the surgical excision of the tumor followed by pathological evaluation of a tissue biopsy. The three-dimensional (3D) nature of human tissue suggests that two-dimensional (2D) cross sections may be insufficient in some cases to represent the complex structure due to sampling bias. There is a need for new techniques that can be used to classify skin lesion types and margins noninvasively. Methods: We use optical coherence tomography volume scan images and AI to noninvasively create 3D images of basal cell and squamous cell carcinomas. Results: Three-dimensional optical coherence tomography images can be broken down into a series of cross sections that can be classified as benign or cancerous using convolutional neural network models developed in this study. These models can identify cancerous regions as well as clear edges. Cancerous regions can also be verified based on visual review of the color-coded images and the loss of the green and blue subchannel pixel intensities. Conclusions: Three-dimensional optical coherence tomography cross sections of cancerous lesions can be collected noninvasively, and AI can be used to classify skin lesions and detect clear lesion edges. These images may provide a means to speed up treatment and promote better patient screening, especially in older patients who will likely develop several lesions as they age. Full article

Saline water intrusion poses a major threat to groundwater security in arid and semi-arid regions, reducing freshwater availability and challenging sustainable water resource management. Accurate delineation of the fresh-saline water interface is therefore essential; however, conventional hydrochemical and laboratory-based assessments remain costly, invasive, and spatially limited. Resistivity methods have long been used to infer subsurface salinity, as low resistivity typically reflects clay-rich saline water and higher resistivity reflects freshwater-bearing sand or gravel. Yet, resistivity values for similar lithologies frequently overlap, causing ambiguity in distinguishing fresh and saline aquifers. To overcome this limitation, Dar–Zarrouk (D–Z) parameters are often applied to enhance hydrogeophysical discrimination, but previous studies have relied exclusively on one-dimensional (1D) D–Z derivations using vertical electrical sounding (VES), which cannot resolve the lateral complexity of alluvial aquifers. This study presents the first application of electrical resistivity tomography (ERT) to derive two- and three-dimensional D–Z parameters for detailed mapping of the fresh-saline water interface in the alluvial aquifers of Punjab, Pakistan. ERT provides non-invasive, continuous, and high-resolution subsurface imaging, enabling volumetric assessment of aquifer electrical properties and salinity structure. The resulting 2D/3D models reveal the geometry, depth, and spatial continuity of salinity transitions with far greater clarity than VES-based or purely hydrochemical methods. Physicochemical analyses from boreholes along the ERT profiles independently verify the geophysical interpretations. The findings demonstrate that ERT-derived 2D/3D D–Z modeling offers a cost-effective, scalable, and significantly more accurate framework for assessing fresh-saline water boundaries. This approach provides a transformative pathway for sustainable groundwater monitoring, improved well siting, and long-term aquifer protection in salinity-stressed alluvial regions. Full article

This work presents the development of an Artificial Intelligence (AI)-based pipeline for patient-specific three-dimensional (3D) reconstruction from oncological magnetic resonance imaging (MRI), leveraging image-derived information to enhance the analysis process. These developments were carried out within the framework of Cella Medical Solutions, forming part of a broader initiative to improve and optimize the company’s medical-image processing pipeline. The system integrates automatic MRI sequence classification using a ResNet-based architecture and segmentation of anatomical structures with a modular nnU-Net v2 framework. The classification stage achieved over 90% accuracy and showed improved segmentation performance over prior state-of-the-art pipelines, particularly for contrast-sensitive anatomies such as the hepatic vasculature and pancreas, where dedicated vascular networks showed Dice score differences of approximately 20–22%, and for musculoskeletal structures, where the model outperformed specialized networks in several elements. In terms of computational efficiency, the complete processing of a full MRI case, including sequence classification and segmentation, required approximately four minutes on the target hardware. The integration of sequence-aware information allows for a more comprehensive understanding of MRI signals, leading to more accurate delineations than approaches without such differentiation. From a clinical perspective, the proposed method has the potential to be integrated into surgical planning workflows. The segmentation outputs were converted into a patient-specific 3D model, which was subsequently integrated into Cella’s surgical planner as a proof of concept. This process illustrates the transition from voxel-wise anatomical labels to a fully navigable 3D reconstruction, representing a step toward more robust and personalized AI-driven medical-image analysis workflows that leverage sequence-aware information for enhanced clinical utility. Full article