Search Results (40)

While direct current (DC) ice-melting is currently adopted for some transmission lines, its application to 10 kV distribution transformers—often located in remote and rugged terrain—presents significant operational challenges. Disconnecting these transformers prior to ice-melting is a complex procedure that incurs substantial labor, material, and financial costs. Leaving transformers connected risks DC current flowing into idle windings, potentially causing damage. Furthermore, existing mobile DC ice-melting power supplies are bulky and impose stringent transportation requirements, rendering them unsuitable for use on mountain roads. To overcome these limitations, this paper proposes a compact, lightweight variable-frequency ice-melting device. The operating principle and output characteristics of the variable-frequency method are investigated in detail. Using Simulink, system modeling and simulation analyses are performed to obtain the voltage and current output characteristics, along with harmonic spectra. Simulation results demonstrate that the proposed device achieves significant miniaturization compared with conventional solutions: within the typical parameter range of conventional devices, the volume can be reduced by 44–58% and the weight by 43–52%. In addition, the selected LC filter parameters (L = 10.39 mH, C = 86.62 μF) represent an optimized compromise solution that effectively suppresses input harmonics while maintaining the output current total harmonic distortion (THD) within an acceptable limit of 3.6%. Experimental results further validate the feasibility of the variable-frequency ice-melting current. Based on a matrix converter topology, the proposed device enables flexible adjustment of the output melting voltage and frequency, exhibits excellent low-frequency performance and dynamic response, and maintains low output harmonic content—fully meeting the application requirements for variable-frequency ice-melting. The key novelty lies in a compact matrix-converter-based de-icing device with systematic low-frequency performance analysis, offering superior portability and adaptability over traditional DC solutions. Full article

Background/Objectives: Pterygoid implant placement represents a valuable alternative to conventional bone grafting procedures in the rehabilitation of the atrophic posterior maxilla; however, the procedure remains technically demanding because of limited visibility, difficult access, complex pterygomaxillary anatomy, and the need for precise angulation and distal bicortical anchorage. Although digital guidance has increasingly been applied in implant dentistry, a clearly described workflow integrating automatic segmentation, selective virtual trimming of the posterior maxillary anatomy, and direct three-dimensional planning for static-guided pterygoid implant placement remains insufficiently detailed in the literature. The aim of this report was to describe and illustrate such a workflow in a proof-of-concept clinical application. Methods: This work was designed as a methodological proof-of-concept with a single clinical illustration. A CBCT dataset was imported into BlueSkyPlan, where automatic segmentation was used to generate three-dimensional models of the maxilla, teeth, and pterygoid process. The segmented volumes were then selectively trimmed to expose the relevant pterygomaxillary anatomy and to support direct three-dimensional planning of the implant axis in the rendered model. A static surgical guide with combined tooth and mucosal support was subsequently designed, positioned on a printed jaw model derived from the intraoral scan, and assessed by CBCT-based internal verification. Results: In this proof-of-concept application, the workflow enabled three-dimensional visualization of the pterygomaxillary trajectory, supported implant axis planning in the rendered model, and facilitated guide design and radiographic verification of the planned trajectory. The verification step provided an internal methodological consistency check between the planned implant axis and the drill-guided direction visible on CBCT. Conclusions: The present report describes a segmentation-assisted three-dimensional planning workflow for static-guided pterygoid implant placement in a single proof-of-concept clinical application. The workflow should be interpreted as a methodological illustration rather than a quantitative validation study. Further investigations are required to evaluate accuracy, inter-operator reproducibility, and broader clinical applicability. Full article

Frequent large-scale construction projects have rendered subway box structures vulnerable to displacements. This study examined the adequacy of foundation reinforcement for a subway box structure exhibiting displacement behavior. A displacement function was derived from the optical leveling data, and a three-dimensional numerical analysis was performed by applying the computed subgrade elastic modulus as a boundary condition. The analysis produced estimates of uplift and subsidence at the nodes along both the transverse and longitudinal directions of the structure. To determine the required amount of reinforcement (grouting volume), the nodal reinforcement depth obtained from the analysis was applied to a grid-based volumetric calculation method. The nodal intervals were subdivided to the maximum feasible extent, and rectangular grids with sufficient resolution were established to ensure accurate reinforcement-volume calculation. The reinforcement volumes estimated through the numerical analysis were compared with actual field values to assess the adequacy of the foundation reinforcement. Some differences were observed, which were attributed to field constraints that prevented reinforcements at certain required locations. Based on these findings, additional reinforcements can be applied at the analytically identified locations to ensure the structural safety of the subway box structure. Full article

Expansive soils present a significant geotechnical challenge due to their pronounced volume changes with moisture variations, leading to substantial infrastructure damage. This study investigates the efficacy of thermal stabilization in mitigating the swell potential and compressibility of a high-plasticity, kaolinite-rich clay from Al Ghat, Saudi Arabia. As well, the changes in basic properties including consistency limits, specific gravity, and compaction characteristics were studied and highlighted. Microstructural studies using X-ray diffraction (XRD), Scanning electron microscopy (SEM), and Energy-dispersive X-ray spectroscopic (EDX) were performed to trace the structural changes and interpret the achieved improvement. Soil specimens were subjected to heat treatment at levels of 200 °C, 400 °C, and 600 °C for two hours, after which their geotechnical and microstructural properties were comprehensively evaluated. The results demonstrate a direct correlation between increasing temperature and the reduction in expansive behavior. Treatment at 600 °C caused a substantial decrease in the plasticity index from 27.00 to 2.94. Correspondingly, oedometer tests showed that the free swell was reduced from 6% to nearly zero, and the swelling pressure was eliminated, dropping from 250 kPa to 0 kPa. XRD analysis confirmed kaolinite decomposition through dehydroxylation, producing metakaolin with diminished water absorption capacity. SEM further revealed significant particle aggregation and the formation of a coarser soil fabric. The findings confirm that heat treatment at temperatures of 400 °C and above is a highly effective method for permanently stabilizing kaolinitic expansive soils, rendering them suitable for construction applications. Full article

Natural gas hydrates (NGHs) are promising energy resources, although their marine exploitation is limited by low reservoir permeability and hydrate decomposition efficiency. Multi-cluster fracturing technology can enhance reservoir permeability, yet complex properties of hydrate sediments render the prediction of fracture behavior challenging. Therefore, we developed a three-dimensional (3D) fluid–solid coupling model for hydraulic fracturing in NGH reservoirs based on cohesive elements to analyze the effects of sediment plasticity, hydrate saturation, fracturing fluid viscosity, and injection rate, as well as the stress interference mechanisms in multi-cluster simultaneous fracturing under different cluster spacings. Results show that selecting low-plastic reservoirs with high hydrate saturation (S_H > 50%) and adopting an optimal combination of fracturing fluid viscosity and injection rate can achieve the co-optimization of stimulated reservoir volume (SRV) and cross-layer risk. In multi-cluster fracturing, inter-fracture stress interference promotes the propagation of fractures along the fracture plane while suppressing it in the normal direction of the fracture plane, and this effect diminishes significantly till 9 m cluster spacing. This study provides valuable insights for the selection of optimal multi-cluster fracturing parameters for marine NGH reservoirs. Full article

Physically based realistic direct volume rendering (DVR) is a critical area of research in scientific data visualization. The prevailing realistic DVR methods are primarily rooted in outdated theories of participating media rendering and often lack comprehensive analyses of their applicability to realistic DVR scenarios. As a result, the fidelity of material representation in the rendered output is frequently limited. To address these challenges, we present a novel multi-material radiative transfer model (MM-RTM) designed for realistic DVR, grounded in recent advancements in light transport theories. Additionally, we standardize various transfer function techniques and propose five distinct forms of transfer functions along with proxy volumes. This comprehensive approach enables our DVR framework to accommodate a wide range of complex transfer function techniques, which we illustrate through several visualizations. Furthermore, to enhance sampling efficiency, we develop a new multi-hierarchical volumetric acceleration method that supports multi-level searches and volume traversal. Our volumetric accelerator also facilitates real-time structural updates when applying complex transfer functions in DVR. Our MM-RTM, the unified representation of complex transfer functions, and the acceleration structure for real-time updates are complementary components that collectively establish a comprehensive framework for realistic multi-material DVR. Evaluation from a user study indicates that the rendering results produced by our method demonstrate the most realistic effects among various publicly available state-of-the-art techniques. Full article

The growing volume of end-of-life photovoltaic (PV) panels, projected to reach 60–78 million tons by 2050, poses significant environmental challenges. With landfilling being the most cost-effective but unsustainable disposal method, developing eco-friendly processes to recover valuable materials is essential. One potential solution for recovering raw materials from PV panels is thermal treatment. Therefore, in this study, PV modules were heat-treated at a low heating rate, and their components were manually separated with an average efficiency of 90%. The recovered silicon wafers and tempered glass sheets were utilized to fabricate new PV panels using lamination technology. The applied heating parameters enabled the cells to be removed from the PV panels without structural damage. However, the results of electroluminescence tests showed that thermal treatment significantly damages the p-n junctions, rendering direct reuse in new panels unfeasible. The thermal separation methods outlined in this study offer valuable opportunities for industries employing various PV-panel-recycling technologies. These methods lay the groundwork for environmentally responsible management and recovery of materials from end-of-life solar panels, advancing sustainable recycling practices. Full article

Traditional tools, such as 3D Slicer, Fiji, and MATLAB^®, often encounter limitations in rendering performance and data management as the dataset sizes increase. This work presents a GPU-enabled volume renderer with a MATLAB^® interface that addresses these issues. The proposed renderer uses flexible memory management and leverages the GPU texture-mapping features of NVIDIA devices. It transfers data between the CPU and the GPU only in the case of a data change between renderings, and uses texture memory to make use of specific hardware benefits of the GPU and improve the quality. A case study using the ViBE-Z zebrafish larval dataset demonstrated the renderer’s ability to produce visualizations while managing extensive data effectively within the MATLAB^® environment. The renderer is available as open-source software. Full article

A method fusing spectral and image information with a one-dimensional convolutional neural network(1D-CNN) for the detection of moisture content in Orah mandarin (Citrus reticulata Blanco) was proposed. The 1D-CNN model integrated with three different attention modules (SEAM, ECAM, CBAM) and machine learning models were applied to individual spectrum and fused information by passing the traditional feature extraction stage. Additionally, the dimensionality reduction of hyperspectral images and extraction of one-dimensional color and textural features from the reduced images were performed, thus avoiding the large parameter volumes and efficiency decline inherent in the direct modeling of two-dimensional images. The results indicated that the 1D-CNN model with integrated attention modules exhibited clear advantages over machine learning models in handling multi-source information. The optimal machine learning model was determined to be the random forest (RF) model under the fusion information, with a correlation coefficient (R) of 0.8770 and a root mean square error (RMSE) of 0.0188 on the prediction set. The CBAM-1D-CNN model under the fusion information exhibited the best performance, with an R of 0.9172 and an RMSE of 0.0149 on the prediction set. The 1D-CNN models utilizing fusion information exhibited superior performance compared to single spectrum, and 1D-CNN with the fused information based on SEAM, ECAM, and CBAM respectively improved R_p by 4.54%, 0.18%, and 10.19% compared to the spectrum, with the RMSEP decreased by 11.70%, 14.06%, and 31.02%, respectively. The proposed approach of 1D-CNN integrated attention can obtain excellent regression results by only using one-dimensional data and without feature pre-extracting, reducing the complexity of the models, simplifying the calculation process, and rendering it a promising practical application. Full article

Magnetic resonance imaging and computed tomography produce three-dimensional volumetric medical images. While a scalar value represents each individual volume element, or voxel, volumetric data are characterized by features derived from groups of neighboring voxels and their inherent relationships, which may vary depending on the specific clinical application. Labeled samples are also required in most applications, which can be problematic for large datasets such as medical images. We propose a direct volume rendering (DVR) framework based on multi-scale dimensionality reduction neighbor embedding that generates two-dimensional transfer function (TF) domains. In this way, we present FSS.t-SNE, a fast semi-supervised version of the t-distributed stochastic neighbor embedding (t-SNE) method that works over hundreds of thousands of voxels without the problem of crowding and with better separation in a 2D histogram compared to traditional TF domains. Our FSS.t-SNE scatters voxels of the same sub-volume in a wider region through multi-scale neighbor embedding, better preserving both local and global data structures and allowing for its internal exploration based on the original features of the multi-dimensional space, taking advantage of the partially provided labels. Furthermore, FSS.t-SNE untangles sample paths among sub-volumes, allowing us to explore edges and transitions. In addition, our approach employs a Barnes–Hut approximation to reduce computational complexity from $\mathcal{O}\left(N^2\right)$ (t-SNE) to $\mathcal{O}\left(NlogN\right)$. Although we require the additional step of generating the 2D TF domain from multiple features, our experiments show promising performance in volume segmentation and visual inspection. Full article

Electric vehicles’ batteries, referred to as Battery Packs (BPs), are composed of interconnected battery cells and modules. The utilisation of different materials, configurations, and welding processes forms a plethora of different applications. This level of diversity along with the low maturity of welding designs and the lack of standardisation result in great variations in the mechanical and electrical quality of the joints. Moreover, the high-volume production requirements, meaning the high number of joints per module/BP, increase the absolute number of defects. The first part of this study focuses on associating the challenges of welding application in battery assembly with the key performance indicators of the joints. The second part reviews the existing methods for quality assurance which concerns the joining of battery cells and busbars. Additionally, the second part of this paper identifies the general trends and the research gaps for the most widely adopted welding methods in this domain, while it renders the future directions. Full article

Hydrogels, with their distinctive three-dimensional networks of hydrophilic polymers, drive innovations across various biomedical applications. The ability of hydrogels to absorb and retain significant volumes of water, coupled with their structural integrity and responsiveness to environmental stimuli, renders them ideal for drug delivery, tissue engineering, and wound healing. This review delves into the classification of hydrogels based on cross-linking methods, providing insights into their synthesis, properties, and applications. We further discuss the recent advancements in hydrogel-based drug delivery systems, including oral, injectable, topical, and ocular approaches, highlighting their significance in enhancing therapeutic outcomes. Additionally, we address the challenges faced in the clinical translation of hydrogels and propose future directions for leveraging their potential in personalized medicine and regenerative healthcare solutions. Full article

In this case study on volume determination in waste sorting facilities, we evaluate the effectiveness of ultrasonic sensors and address waste-material-specific challenges. Although ultrasonic sensors offer a cost-effective automation solution, their accuracy is affected by irregular waste shapes, varied compositions, and environmental factors. Notable inconsistencies in volume measurements between storage bunkers and conveyor belts underscore the need for a comprehensive approach to standardize bale production. With prediction reliability being constrained by limited datasets, undocumented modifications to machine settings, and sensor failures, this task renders a challenging application area for machine learning. We explore related research and present dataset analyses from three distinct waste sorting facilities in Europe, addressing issues such as sensor usability, data quality, and material specifics. Our analysis suggests promising strategies and future directions for enhancing waste volume measurement accuracy, ultimately aiming to advance sustainable waste management. Full article

In urban ecological development, the effective planning and design of living spaces are crucial. Traditional color plan rendering methods, mainly using generative adversarial networks (GANs), rely heavily on edge extraction. This often leads to the loss of important details from hand-drawn drafts, significantly affecting the portrayal of the designer’s key concepts. This issue is especially critical in complex park planning. To address this, our study introduces a system based on conditional GANs. This system rapidly converts black-and-white park sketches into comprehensive color designs. We also employ a data augmentation strategy to enhance the quality of the output. The research reveals: (1) Our model efficiently produces designs suitable for industrial applications. (2) The GAN-based data augmentation improves the data volume, leading to enhanced rendering effects. (3) Our unique approach of direct rendering from sketches offers a novel method in urban planning and design. This study aims to enhance the rendering aspect of an intelligent workflow for landscape design. More efficient rendering techniques will reduce the iteration time of early design solutions and promote the iterative speed of designers’ thinking, thus improving the speed and efficiency of the whole design process. Full article

Approaches aimed at achieving improved energy efficiency for determination of descriptors—used in volumetric data analysis and one common mode of scientific visualisation—in one x86-class setting are described and evaluated. These approaches are evaluated against standard approaches for the computational setting. In all, six approaches for improved efficiency are considered. Four of them are computation-based. The other two are memory-based. The descriptors are classic gradient and curvature descriptors. In addition to their use in volume analyses, they are used in the classic ray-casting-based direct volume rendering (DVR), which is a particular application area of interest here. An ideal combination of the described approaches applied to gradient descriptor determination allowed them to to be computed with only 80% of the energy of a standard approach in the computational setting; energy efficiency was improved by a factor of 1.2. For curvature descriptor determination, the ideal combination of described approaches achieved a factor-of-two improvement in energy efficiency. Full article