Search

In the realm of neuroscience, mapping the three-dimensional (3D) neural circuitry and architecture of the brain is important for advancing our understanding of neural circuit organization and function. This study presents a novel pipeline that transforms mouse brain samples into detailed 3D brain models using a collaborative data analytics platform called Texera. The user-friendly Texera platform allows for effective interdisciplinary collaboration between team members in neuroscience, computer vision, and data processing. Our pipeline utilizes the tile images from a serial two-photon tomography/TissueCyte system, then stitches tile images into brain section images, and constructs 3D whole-brain image datasets. The resulting 3D data supports downstream analyses, including 3D whole-brain registration, atlas-based segmentation, cell counting, and high-resolution volumetric visualization. Using this platform, we implemented specialized optimization methods and obtained significant performance enhancement in workflow operations. We expect the neuroscience community can adopt our approach for large-scale image-based data processing and analysis.

Extended reality (XR) technologies are emerging as promising platforms for medical training and procedural guidance, particularly in complex cardiac interventions. This paper presents a high-fidelity methodology to perform real-time 3D catheter tracking and visualization during simulated cardiac interventions. A custom 3D-printed setup with mounted cameras enables biplane video capture of a catheter. A computer vision algorithm processes the biplane images in real-time to reconstruct the 3D catheter trajectory represented by any designated number of points along its length. This method accurately localizes the catheter tip within 1 mm and can reconstruct any arbitrary catheter configuration. The tracked catheter data is integrated into an interactive Unity-based scene rendered on the Meta Quest 3 headset. The visualization seamlessly combines a reconstructed 3D patient-specific heart model with the dynamically tracked catheter, creating an immersive extended reality training environment. Our experimental study, involving six participants, demonstrated that the 3D visualization provided by the proposed XR system significantly outperformed 2D visualization in terms of speed and user experience. This suggests that the XR system has the potential to enhance catheterization training by improving spatial comprehension and procedural skills. The proposed system demonstrates the potential of XR technologies to transform percutaneous cardiac interventions through improved visualization and interactivity. [ABSTRACT FROM AUTHOR]

Micro-computed tomography (CT) of ancient Chinese glass dragonfly eye beads has enabled detailed exploration of their internal structures, contributing to our understanding of their manufacture. Segmentation of these CT images is essential but challenging due to variation in grayscale values and the presence of bubbles. This study introduces a U-Net-based model called EBV-SegNet, which enables efficient and accurate segmentation and visualization of these beads. We developed, trained, and tested the model using a dataset comprising four typical Shampula dragonfly eye beads, and the results demonstrated high-precision segmentation and precise delineation of the beads' complex structures. These segmented data were further analyzed using the Visualization Toolkit for advanced volume rendering and reconstruction. Our application of EBV-SegNet to Shampula beads suggests the likelihood of two distinct manufacturing techniques, underscoring the potential of the model for enhancing the analysis of cultural artifacts using three-dimensional visualization and deep learning. [ABSTRACT FROM AUTHOR]

The rapid expansion of new technologies necessitates the adaptation of educational programs to meet the evolving demands of the labor market. This study focuses on students in technical fields, investigating their understanding of XR 3D visualization and proposing a unified training approach to improve both theoretical knowledge and practical skills. The research also evaluates how this training influences students' attitudes towards XR technologies in various applications. Key findings reveal significant improvements in technical competencies and a more positive perception of XR technologies after the training. These results suggest that targeted XR education can better prepare students for the technological challenges in their respective industries. [ABSTRACT FROM AUTHOR]

This article is devoted to the study of microwave-absorbing properties of Co-C coating applied by magnetron sputtering. This article presents the main results of the manufacturing of a Co-C coating by magnetron sputtering, and the evaluation of experimental and computational research data on the relationship between the structure and properties of the obtained films. The structure of Co-C systems has been modeled to control the resulting structures using X-ray diffraction analysis. The structural-phase state of the resulting thin-film Co-C systems and the microwave-absorbing properties of coatings were studied using the transmission line method, consisting of a vector network analyzer, an air coaxial line, a software program, and an external computer. It has been established that on the resulting Co-C film the reflection coefficient in the frequency range of 8.2–12.4 GHz varies in the range of 900 → 718 mU, with reflection losses of −0.86 → −2.57 dB, and a standing wave coefficient of 19 → 6.8. An analysis of the obtained data indicates the presence of losses of electromagnetic energy in the studied frequency range. [ABSTRACT FROM AUTHOR]

Objectives: Accurately describing and understanding the bridge construction process is of great significance to students' learning and field personnel's fine management. However, the bridge construction process involves a large number of objects, complex behaviors, and variable states, which leads to problems such as poor standardization and low cognitive efficiency in expression. Methods: This paper proposes a knowledge-guided three-dimensional visualization method of spatiotemporal narrative of bridge construction process. First, we construct a bridge construction knowledge map with "object-behavior-state" three-domain association by analyzing the bridge construction process, then break through the key technologies of narrative element analysis and matching, 3D scene mapping and instantiation, and 3D dynamic visualization of spatiotemporal narrative. Finally, we develop a prototype system and carries out case study experimental analysis. Results: The results show that the correct rate of answering questions in the experimental group reaches 76.5%, which is 13.2% higher than that of the control group; under the comprehensive perspective of clarity, richness and comprehension difficulty, 78.8% of the personnel in the experimental group have good cognitive experience, which is 45.5% higher than that of the control group. Conclusions: By accurately describing the bridge construction objects, features and interrelationships, the unified expression and association management of bridge construction knowledge map is realized, and based on this, the three-dimensional dynamic visualization expression of the bridge construction process is carried out by using spatiotemporal narratives, which effectively enhances the education level of bridge construction process cognition with the help of interactivity and immersion of three-dimensional scenes. [ABSTRACT FROM AUTHOR]

Creating digital twins of scenes has been widely studied in smart city applications. The development of 3D virtual games attracts many researchers. However, most products have high costs, neglect 3D recovery of the moving people, and lack structured analysis. In this paper, we present a mirror world based on PTZ cameras, which creates digital twins of the space and persons from video streamings, including real-time video registration for PTZ cameras and 3D recovery of the human pose and shape. Our goal is to quickly build a digital space to represent and visualize the physical world, leveraging the two tasks to improve the performance of structured scene understanding. We use the scene images from PTZ cameras to create the 3D scene model and propose an image edge alignment method to optimize the texture mismatching during real-time video registration. Then we propose a human analysis network for 3D recovery of the human pose and shape and add refinement to improve performance on two datasets. We place the 3D human bodies in the right position in the mirror world and utilize joint data from specific scenarios to drive the optimization of the system. Consequently, the mirror world provides 3D visualization and structured analysis of the scene with low cost, thereby enhancing users' spatial understanding. [ABSTRACT FROM AUTHOR]

Abstract Extended reality (XR) technologies are emerging as promising platforms for medical training and procedural guidance, particularly in complex cardiac interventions. This paper presents a high-fidelity methodology to perform real-time 3D catheter tracking and visualization during simulated cardiac interventions. A custom 3D-printed setup with mounted cameras enables biplane video capture of a catheter. A computer vision algorithm processes the biplane images in real-time to reconstruct the 3D catheter trajectory represented by any designated number of points along its length. This method accurately localizes the catheter tip within 1 mm and can reconstruct any arbitrary catheter configuration. The tracked catheter data is integrated into an interactive Unity-based scene rendered on the Meta Quest 3 headset. The visualization seamlessly combines a reconstructed 3D patient-specific heart model with the dynamically tracked catheter, creating an immersive extended reality training environment. Our experimental study, involving six participants, demonstrated that the 3D visualization provided by the proposed XR system significantly outperformed 2D visualization in terms of speed and user experience. This suggests that the XR system has the potential to enhance catheterization training by improving spatial comprehension and procedural skills. The proposed system demonstrates the potential of XR technologies to transform percutaneous cardiac interventions through improved visualization and interactivity.

Abstract Micro-computed tomography (CT) of ancient Chinese glass dragonfly eye beads has enabled detailed exploration of their internal structures, contributing to our understanding of their manufacture. Segmentation of these CT images is essential but challenging due to variation in grayscale values and the presence of bubbles. This study introduces a U-Net-based model called EBV-SegNet, which enables efficient and accurate segmentation and visualization of these beads. We developed, trained, and tested the model using a dataset comprising four typical Shampula dragonfly eye beads, and the results demonstrated high-precision segmentation and precise delineation of the beads’ complex structures. These segmented data were further analyzed using the Visualization Toolkit for advanced volume rendering and reconstruction. Our application of EBV-SegNet to Shampula beads suggests the likelihood of two distinct manufacturing techniques, underscoring the potential of the model for enhancing the analysis of cultural artifacts using three-dimensional visualization and deep learning.

Abstract The rapid expansion of new technologies necessitates the adaptation of educational programs to meet the evolving demands of the labor market. This study focuses on students in technical fields, investigating their understanding of XR 3D visualization and proposing a unified training approach to improve both theoretical knowledge and practical skills. The research also evaluates how this training influences students’ attitudes towards XR technologies in various applications. Key findings reveal significant improvements in technical competencies and a more positive perception of XR technologies after the training. These results suggest that targeted XR education can better prepare students for the technological challenges in their respective industries.

Background. Ovarian heterotopias are quite successfully diagnosed with ultrasound examinations. Considering the high risk of spread of the disease, as well as reproductive consequences (infertility, pelvic adhesive processes, chronic pelvic pain syndrome, dysmenorrhea), early differential diagnosis of endometrioid cysts and, accordingly, early initiation of treatment are of particular importance.Objective: to combine ultrasound criteria using 3D visualization and the content of biomarkers to verify stage 1–2 endometrioid ovarian cysts.Material and methods. In 59 infertile women with endometrioid ovarian cysts, in the dynamics of the menstrual cycle, ultrasound examinations were performed using Acuson S2000 (Siemens, Germany) and Voluson E8 (General Electric, USA) with the possibility of Doppler measurements and three-dimensional reconstruction of tissue images. Also, in laboratories “Invitro”, “Hemotest”, “Nauka” (Samara, Russia) the biomarkers were determined: interleukins IL-1β, IL-6, cancer antigen 125 (CA-125), human epididymis protein 4 (HE4) and the index by Risk of Ovarian Malignancy Algorithm (ROMA). The control group included 195 fertile women of reproductive age. In patients with endometrioid cysts, the diagnosis was subsequently confirmed morphologically.Results. Small unilateral and bilateral ovarian cysts corresponding to stage 1–2 endometriosis by revised American Fertility Society classification (rAFS) were clearly differentiated in 56 (94.9%) patients, in 3 (5.4%) of them the ultrasound result was questionable. In 48 (81.4%) women the lesion was unilateral and in 11 (18.6%) it was bilateral. The structure of cystic formations in all cases was hypoechoic, heterogeneous, in 36 (61.0%) cases it was finely cellular, without blood flow.Conclusion. Ovarian endometriomas in “gray scale” are round formations of reduced echogenicity and fine suspension, with an even, clear contour, not fused with the surrounding tissues, avascular, changing size depending on menstrual cycle phase. An ovarian mass may have a capsule with locus signals on Doppler. The sensitivity of ultrasound examination in “gray scale” in the presence of an endometrioid cyst is 94.6%.

In this article, a standalone executable software molVisual3D, for 3D visualization of molecules was developed. It uses the XYZ file of a molecule to generate its 3D graphics. Some parameters can be specified by the user. In the generated 3D graphics window, the user can right- or left-click mouse to zoom in or zoom out the 3D graphics, or mouse-drag the graphics to rotate the 3D graphics, or slide scrollbar to vertically or horizontally translate the 3D graphics. The 3D graphics can be saved into an image file (in BMP format). Both molVisual3D and demonstration data files were given.

Structural color originates from the interference of light with periodic structures that feature characteristic length scales on the order of the wavelength of visible light. Long‐range order in photonic structures usually causes iridescence, and increasing disorder renders colors angle‐independent. Random disorder distributes scattering intensity over all wavelengths, producing white in the absence of absorption. Various non‐iridescent, vivid color patterns are found in Sternotomini longhorn beetles. Herein, Sternotomis virescens is investigated, where elytral scales produce a green‐blue color pattern on otherwise black elytra. Combining focused ion beam scanning electron microscopy (FIB‐SEM) tomography, ultra‐small‐angle X‐ray scattering (USAXS), structural modeling, and full‐wave optical simulations, it is found that the color originates from amorphous photonic networks based on sub‐units resembling the I‐WP unit cell, a triply‐periodic minimal surface with body‐centered‐cubic symmetry. This work provides insights into how quasi‐order produces stable colors in S. virescens longhorn beetles, highlights the advantages of volumetric imaging using FIB‐SEM tomography of porous nanostructured materials, and raises interesting questions about the formation mechanisms of amorphous structures in vivo. [ABSTRACT FROM AUTHOR]

Backgrounds: Advanced ovarian cancer is frequently accompanied by extensive peritoneal metastasis, complicating surgical interventions. This study aims to explore the application of 3D reconstruction and 3D printing technology in the treatment of advanced ovarian cancer. Methods: We conducted a retrospective analysis of 60 patients with stage III ovarian cancer who underwent cytoreductive surgery at Hebei University Affiliated Hospital between 2020 and 2023. Patients were randomly assigned to three groups: a 3D visualization group, a 3D visualization plus 3D printing group, and a traditional 2D CT imaging evaluation group. High-precision medical imaging techniques (e.g., CT, MRI) were employed to create digital 3D models, which were then converted into physical entities using 3D printing for surgical planning and simulation. Results: Both the 3D visualization group and the 3D visualization plus 3D printing group demonstrated superior outcomes in terms of surgery duration and blood loss compared to the traditional 2D CT group, indicating the efficacy of 3D reconstruction and 3D printing in preoperative planning. Postoperative recovery indicators, such as hospital stay and time to first flatus, were also more favorable in the groups utilizing 3D technology. Although there were no significant differences in postoperative complications and recurrence rates among the three groups, the groups using 3D technology showed advantages in reducing certain complications. Conclusions: The results indicate that medical 3D technology has significant value in the surgical planning of advanced ovarian cancer, enhancing surgical precision and reducing intraoperative risks, which may aid in improving postoperative recovery. [ABSTRACT FROM AUTHOR]

With the continuous development of dredging engineering and dredging business, the requirements for monitoring the dredging construction process are also increasing. Aiming at the problems of poor display effect and low accuracy in 3D visualization monitoring during the dredging process of existing dredgers, a universal 3D visualization system for dredger construction operations was designed and developed based on the Unity3D engine. The system realizes the 3D visualization display of key dredging equipment driving, underwater terrain generation and rendering, and real-time terrain reconstruction. Through actual dredger application verification, the system can effectively improve construction efficiency and quality, enhance construction accuracy, reduce the occurrence of missed excavation and over excavation, and has important engineering application value. [ABSTRACT FROM AUTHOR]

In order to achieve visual management of geological hazard in Huangshui River Basin of Qinghai Province and improve the efficiency of disaster information processing, the design and development of a disaster 3D visualization system and a backend information management system were carried out through oblique photogrammetry and field investigation, combined with database, front-end and back-end technologies. The 3D visualization system was based on the Cesium engine, which has the functions of multidimensional display of the earth, fast positioning, and base map addition. It has expanded and developed three characteristic modules: data loading, spatial processing, and roaming display. It can load vector data, image data, and oblique photography models of the research area, meeting the needs of spatial measurement. It has achieved the dual control analysis of “ hidden danger points + risk areas” by drawing labels on the model and combining on-site investigation results. The backend information management system has achieved spatial query and analysis of basic disaster situations, development trends, precipitation, population, GDP and other information within the region, providing technical support for geological disaster risk assessment and protection. [ABSTRACT FROM AUTHOR]

In the realm of neuroscience, mapping the three-dimensional (3D) neural circuitry and architecture of the brain is important for advancing our understanding of neural circuit organization and function. This study presents a novel pipeline that transforms mouse brain samples into detailed 3D brain models using a collaborative data analytics platform called "Texera." The user-friendly Texera platform allows for effective interdisciplinary collaboration between team members in neuroscience, computer vision, and data processing. Our pipeline utilizes the tile images from a serial two-photon tomography/TissueCyte system, then stitches tile images into brain section images, and constructs 3D whole-brain image datasets. The resulting 3D data supports downstream analyses, including 3D whole-brain registration, atlas-based segmentation, cell counting, and high-resolution volumetric visualization. Using this platform, we implemented specialized optimization methods and obtained significant performance enhancement in workflow operations. We expect the neuroscience community can adopt our approach for large-scale image-based data processing and analysis. [ABSTRACT FROM AUTHOR]

Spiking Neural Networks have gained significant attention due to their potential for energy efficiency and biological plausibility. However, the reduced number of user-friendly tools for designing, training, and visualizing Spiking Neural Networks hinders widespread adoption. This paper presents the SNNtrainer3D v1.0.0, a novel software application that addresses these challenges. The application provides an intuitive interface for designing Spiking Neural Networks architectures, with features such as dynamic architecture editing, allowing users to add, remove, and edit hidden layers in real-time. A key innovation is the integration of Three.js for three-dimensional visualization of the network structure, enabling users to inspect connections and weights and facilitating a deeper understanding of the model's behavior. The application supports training on the Modified National Institute of Standards and Technology dataset and allows the downloading of trained weights for further use. Moreover, it lays the groundwork for future integration with physical memristor technology, positioning it as a crucial tool for advancing neuromorphic computing research. The advantages of the development process, technology stack, and visualization are discussed. The SNNtrainer3D represents a significant step in making Spiking Neural Networks more accessible, understandable, and easier for Artificial Intelligence researchers and practitioners. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The growing interest in building data management, especially the building information model (BIM), has significantly influenced urban management, materials supply chain analysis, documentation, and storage. However, the integration of BIM into 3D GIS tools is becoming more common, showing progress beyond the traditional problem. To address this, this study proposes data transformation methods involving mapping between three domains: industry foundation classes (IFC), city geometry markup language (CityGML), and web ontology framework (OWL)/resource description framework (RDF). Initially, IFC data are converted to CityGML format using the feature manipulation engine (FME) at CityGML standard's levels of detail 4 (LOD4) to enhance BIM data interoperability. Subsequently, CityGML is converted to the OWL/RDF diagram format to validate the proposed BIM conversion process. To ensure integration between BIM and GIS, geometric data and information are visualized through Cesium Ion web services and Unreal Engine. Additionally, an RDF graph is applied to analyze the association between the semantic mapping of the CityGML standard, with Neo4j (a graph database management system) utilized for visualization. The study's results demonstrate that the proposed data transformation methods significantly improve the interoperability and visualization of 3D city models, facilitating better urban management and planning. [ABSTRACT FROM AUTHOR]

The potential applications of microCT scanning in the field of archaeobotany are only just beginning to be explored. The imaging technique can extract new archaeobotanical information from existing archaeobotanical collections as well as create new archaeobotanical assemblages within ancient ceramics and other artefact types. The technique could aid in answering archaeobotanical questions about the early histories of some of the world's most important food crops from geographical regions with amongst the poorest rates of archaeobotanical preservation and where ancient plant exploitation remains poorly understood. This paper reviews current uses of microCT imaging in the investigation of archaeobotanical questions, as well as in cognate fields of geosciences, geoarchaeology, botany and palaeobotany. The technique has to date been used in a small number of novel methodological studies to extract internal anatomical morphologies and three-dimensional quantitative data from a range of food crops, which includes sexually-propagated cereals and legumes, and asexually-propagated underground storage organs (USOs). The large three-dimensional, digital datasets produced by microCT scanning have been shown to aid in taxonomic identification of archaeobotanical specimens, as well as robustly assess domestication status. In the future, as scanning technology, computer processing power and data storage capacities continue to improve, the possible applications of microCT scanning to archaeobotanical studies will only increase with the development of machine and deep learning networks enabling the automation of analyses of large archaeobotanical assemblages. [ABSTRACT FROM AUTHOR]

Copyright of Urban Geology is the property of Urban Geology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The experiments involving protein denaturation and refolding serve as the foundation for predicting the three-dimensional spatial structures of proteins based on their amino acid sequences. Despite significant progress in protein structure engineering, exemplified by AlphaFold2 and OmegaFold, there remains a gap in understanding the folding pathways of polypeptide chains leading to their final structures. We developed a lightweight tool for protein unfolding visualization called PUV whose graphics design is mainly implemented by OpenGL. PUV leverages principles from molecular biology and physics, and achieves rapid visual dynamics simulation of protein polypeptide chain unfolding through mechanical force and atom-level collision detection and elimination. After a series of experimental validations, we believe that this method can provide essential support for investigating protein folding mechanisms and pathways.

Abstract Background Cancers are spatially heterogenous, thus their clonal evolution, especially following anti-cancer treatments, depends on where the mutated cells are located within the tumor tissue. For example, cells exposed to different concentrations of drugs, such as cells located near the vessels in contrast to those residing far from the vasculature, can undergo a different evolutionary path. However, classical representations of cell lineage trees do not account for this spatial component of emerging cancer clones. Here, we propose routines to trace spatial and temporal clonal evolution in computer simulations of the tumor evolution models. Results The LinG3D (Lineage Graphs in 3D) is an open-source collection of routines (in MATLAB, Python, and R) that enables spatio-temporal visualization of clonal evolution in a two-dimensional tumor slice from computer simulations of the tumor evolution models. These routines draw traces of tumor clones in both time and space, and may include a projection of a selected microenvironmental factor, such as the drug or oxygen distribution within the tumor, if such a microenvironmental factor is used in the tumor evolution model. The utility of LinG3D has been demonstrated through examples of simulated tumors with different number of clones and, additionally, in experimental colony growth assay. Conclusions This routine package extends the classical lineage trees, that show cellular clone relationships in time, by adding the space component to show the locations of cellular clones within the 2D tumor tissue patch from computer simulations of tumor evolution models.

Abstract Background Ultrasound three-dimensional visualization, a cutting-edge technology in medical imaging, enhances diagnostic accuracy by providing a more comprehensive and readable portrayal of anatomical structures compared to traditional two-dimensional ultrasound. Crucial to this visualization is the segmentation of multiple targets. However, challenges like noise interference, inaccurate boundaries, and difficulties in segmenting small structures exist in the multi-target segmentation of ultrasound images. This study, using neck ultrasound images, concentrates on researching multi-target segmentation methods for the thyroid and surrounding tissues. Method We improved the Unet++ to propose PA-Unet++ to enhance the multi-target segmentation accuracy of the thyroid and its surrounding tissues by addressing ultrasound noise interference. This involves integrating multi-scale feature information using a pyramid pooling module to facilitate segmentation of structures of various sizes. Additionally, an attention gate mechanism is applied to each decoding layer to progressively highlight target tissues and suppress the impact of background pixels. Results Video data obtained from 2D ultrasound thyroid serial scans served as the dataset for this paper.4600 images containing 23,000 annotated regions were divided into training and test sets at a ratio of 9:1, the results showed that: compared with the results of U-net++, the Dice of our model increased from 78.78% to 81.88% (+ 3.10%), the mIOU increased from 73.44% to 80.35% (+ 6.91%), and the PA index increased from 92.95% to 94.79% (+ 1.84%). Conclusions Accurate segmentation is fundamental for various clinical applications, including disease diagnosis, treatment planning, and monitoring. This study will have a positive impact on the improvement of 3D visualization capabilities and clinical decision-making and research in the context of ultrasound image.

As climate change intensifies, resulting in more severe rainfall events, coastal cities globally are witnessing significant life and property losses. A growingly crucial component for flood prevention and relief are urban storm flood simulations, which aid in informed decision-making for emergency management. The vastness of data and the intricacies of 3D computations can make visualizing the urban flood effects on infrastructure daunting. This study offers a 3D visualization of the repercussions of hurricane storm surge flooding on Galveston, TX residences, illustrating the impact on each structure and road across varied storm conditions. We employ target detection to pinpoint house door locations, using door inundation as a metric to gauge potential flood damage. Within a GIS-based framework, we model the damage scope for residences exposed to varying storm intensities. Our research achieves three core goals: 1) Estimating the storm inundation levels on homes across different storm conditions; 2) Assessing first-floor elevations to categorize housing damages into three distinct groups; and 3) Through visualization, showcasing the efficacy of a proposed dike designed to shield Galveston Island from future storm surge and flood events. [ABSTRACT FROM AUTHOR]

The development of new approaches to digital art is based on improving a variety of technologies. The main goal of this paper is to identify the gamification of digital art through the promotion of speculative design and interactive experiences. To achieve this goal, theoretical and practical methods were used in the research. A survey determined approaches the designers used to improve their skills. Most respondents (28%) prefer reading books and watching video blogs on their own while 22% of designers took professional courses. Therefore, mechanisms that promote respondents' involvement in selecting professional courses for designers have been described. Cognitive assessment of the skills acquired after completing the course showed significant advantages compared to those observed at the start of the experiment. This was confirmed by the Cohen coefficient. The hierarchy analysis method also contributed to the identification of program elements that are most important for the development of professional skills while not relying exclusively on the data from respondents. The value of the presented work lies in the possibility to improve the skills of gamification in the field of design with the help of the developed mechanisms, the effectiveness of which has been confirmed in practice. [ABSTRACT FROM AUTHOR]

Desktop-based virtual colonoscopy is a proven and accurate process for identifying colon abnormalities. However, it is time-consuming. Faster, immersive interfaces for virtual colonoscopy are still incipient and need to be better understood. This article introduces a novel design that leverages VR paradigm components to enhance the efficiency and effectiveness of immersive analysis. Our approach contributes a novel tool highlighting unseen areas within the colon via eye-tracking, a flexible navigation approach, and a distinct interface for displaying scans blended with the reconstructed colon surface. The path to evaluating and validating such a tool for clinical settings is arduous. This article contributes a formative evaluation using think-aloud sessions with radiology experts and students. Questions related to colon coverage, diagnostic accuracy, and time to complete are analyzed with different user profiles. Although not aimed at quantitatively measuring performance, the experiment provides lessons learned to guide other researchers in the field. [ABSTRACT FROM AUTHOR]

Despite the recent advances in Virtual Reality technology and its use in education, the review of the literature shows several gaps in research on how immersive virtual environments impact the learning process. In particular, the lack of curriculum-specific experiments along with investigations of the effects of different content, activity, and interaction types in the current VR studies has been identified as a significant shortcoming. This has been more significant in STEM fields, where VR has the potential to offer engaging experiential learning opportunities. The study reported here was designed to address this gap by assessing the effect of authentic visualization and interaction types on learning a particular scientific concept. A use case scenario of "orbital hybridization" in chemistry education was selected to create this experiment and to collect data for analysis. We collected data on learning outcomes, task-completion efficiency, accuracy, and subjective usability. A combination of learning content and tasks designed based on the relevant educational theories was presented to three groups: 2D, VR interaction type 1 (hand gestures), and VR interaction type 2 (ray casting). The results showed that VR could improve learning and that interaction type could influence efficiency and accuracy depending on the task. [ABSTRACT FROM AUTHOR]

Non-traumatic thoracic aorta emergencies are acute conditions associated with substantial morbidity and mortality. In the emergency setting, timely detection of aortic injury through radiological imaging is crucial for prompt treatment planning and favorable patient outcomes. 3D cinematic rendering (CR), a novel rendering algorithm for computed tomography (CT) image processing, allows for life-like visualization of spatial details and contours of highly complex anatomic structures such as the thoracic aorta and its vessels, generating a photorealistic view that not just adds to diagnostic confidence, but is especially useful for non-radiologists, including surgeons and emergency medicine physicians. In this pictorial review, we demonstrate the utility of CR in the setting of non-traumatic thoracic aorta emergencies through 10 cases that were processed at a standalone 3D CR station at the time of presentation, including its role in diagnosis and management. [ABSTRACT FROM AUTHOR]

As it is well known, mobile phones have become a basic gadget for any individual that usually stores sensitive information. This mainly motivates the increase in the number of attacks aimed at jeopardizing smartphones, being an extreme concern above all on Android OS, which is the most popular platform in the market. Consequently, a strong effort has been devoted for mitigating mentioned incidents in recent years, even though few researchers have addressed the application of visualization techniques for the analysis of malware. Within this field, the present work proposes the extension of a new technique called Hybrid Unsupervised Exploratory Plots to visualize Android malware datasets. More precisely, the novel Beta-Hebbian Learning (BHL) method is applied for the first time and validated under the frame of Hybrid Unsupervised Exploratory Plots, in conjunction with clustering methods. The informative visualization achieved provides a picture of the structure of the malware families, allowing subsequent analysis of their organization. To validate the Hybrid Unsupervised Exploratory Plot extension and its tuning, the popular Android Malware Genome dataset has been used in the experimental setting. Promising results have been obtained, suggesting that BHL applied in combination with clustering techniques in Hybrid Unsupervised Exploratory Plots are a viable resource for the visualization of malware families. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Ultrasound three-dimensional visualization, a cutting-edge technology in medical imaging, enhances diagnostic accuracy by providing a more comprehensive and readable portrayal of anatomical structures compared to traditional two-dimensional ultrasound. Crucial to this visualization is the segmentation of multiple targets. However, challenges like noise interference, inaccurate boundaries, and difficulties in segmenting small structures exist in the multi-target segmentation of ultrasound images. This study, using neck ultrasound images, concentrates on researching multi-target segmentation methods for the thyroid and surrounding tissues. Method: We improved the Unet++ to propose PA-Unet++ to enhance the multi-target segmentation accuracy of the thyroid and its surrounding tissues by addressing ultrasound noise interference. This involves integrating multi-scale feature information using a pyramid pooling module to facilitate segmentation of structures of various sizes. Additionally, an attention gate mechanism is applied to each decoding layer to progressively highlight target tissues and suppress the impact of background pixels. Results: Video data obtained from 2D ultrasound thyroid serial scans served as the dataset for this paper.4600 images containing 23,000 annotated regions were divided into training and test sets at a ratio of 9:1, the results showed that: compared with the results of U-net++, the Dice of our model increased from 78.78% to 81.88% (+ 3.10%), the mIOU increased from 73.44% to 80.35% (+ 6.91%), and the PA index increased from 92.95% to 94.79% (+ 1.84%). Conclusions: Accurate segmentation is fundamental for various clinical applications, including disease diagnosis, treatment planning, and monitoring. This study will have a positive impact on the improvement of 3D visualization capabilities and clinical decision-making and research in the context of ultrasound image. [ABSTRACT FROM AUTHOR]

Three-dimensional visualization technologies such as stereoscopic 3D, virtual reality, and augmented reality have already emerged in training and education; however, light field displays are yet to be introduced in such contexts. In this paper, we characterize light field visualization as a potential candidate for the future of training and education, and compare it to other state-of-the-art 3D technologies. We separately address preschool and elementary school education, middle and high school education, higher education, and specialized training, and assess the suitability of light field displays for these utilization contexts via key performance indicators. This paper exhibits various examples for education, and highlights the differences in terms of display requirements and characteristics. Additionally, our contribution analyzes the scientific-literature-related trends of the past 20 years for 3D technologies, and the past 5 years for the level of education. While the acquired data indicates that light field is still lacking in the context of education, general research on the visualization technology is steadily rising. Finally, we specify a number of future research directions that shall contribute to the emergence of light field visualization for training and education. [ABSTRACT FROM AUTHOR]

Irrigation dams and irrigation suitability analysis is important for optimal water management, crop selection and productivity, water conservation, environmental sustainability, and economic viability in agriculture arena. Thus, the main objectives of this study were to identify a suitable dam site and irrigation area in the Gedeb River, Ethiopia, using Multi-Criteria Decision-Making analysis and 3D Visualization techniques. To identify a suitable dam site, various parametrs such as rainfall, runoff, stream flow, mineral site, faulting areas, landslide site, rock types, elevation points, relief features, soil types were used while to identify a suitable irrigation area, different parametrs such as altitude, slope, soil, geological structure, distance, and land use land cover datasets were used. The necessary dataset which were used to identify a suitable dam site and irrigation area collected from Ethiopian Mapping Authority (EMA), Ethiopian irrigation and energy ministry freely. In addition, for the final irrigation dam site selection and suitable irrigation area in the Gedeb watershed, multi-criteria decision-making method with expert judgment were applied respectively. Based on the study's findings, a suitable irrigation water reservoir dam covering an area of 1886 ha, with a potential water holding capacity of 2,961,145,697 cubic meters was identified. The results also revealed a highly suitable area of 18,362.05 ha, a moderately suitable area of 19,204.05 ha, a marginally suitable area of 2095.25 ha, and a not suitable area of 2.89 ha for the aforementioned purpose. The methodological approach and research findings presented in this study can greatly assist government and non-governmental organization planners and decision-makers in the development of irrigation projects.

3D visualization and segmentation are increasingly widely used in physical, biological and medical science, facilitating advanced investigative methodologies. However, the integration and reproduction of segmented volumes or results across the spectrum of mainstream 3D visualization platforms remain hindered by compatibility constraints. These barriers not only challenge the replication of findings but also obstruct the process of cross-validating the accuracy of 3D visualization outputs. To address this gap, we developed an innovative revisualization method implemented within the open-source framework of Drishti, a 3D visualization software. Leveraging four animal samples alongside three mainstream 3D visualization platforms as case studies, our method demonstrates the seamless transferability of segmented results into Drishti. This capability effectively fosters a new avenue for authentication and enhanced scrutiny of segmented data. By facilitating this interoperability, our approach underscores the potential for significant advancements in accuracy validation and collaborative research efforts across diverse scientific domains.

Background: Intelligent garments, a burgeoning class of wearable devices, have extensive applications in domains such as sports training and medical rehabilitation. Nonetheless, existing research in the smart wearables domain predominantly emphasizes sensor functionality and quantity, often skipping crucial aspects related to user experience and interaction. Methods: To address this gap, this study introduces a novel real-time 3D interactive system based on intelligent garments. The system utilizes lightweight sensor modules to collect human motion data and introduces a dual-stream fusion network based on pulsed neural units to classify and recognize human movements, thereby achieving real-time interaction between users and sensors. Additionally, the system in- corporates 3D human visualization functionality, which visualizes sensor data and recognizes human actions as 3D models in realtime, providing accurate and comprehensive visual feedback to help users better understand and analyze the details and features of human motion. This system has significant potential for applications in motion detection, medical monitoring, virtual reality, and other fields. The accurate classification of human actions con- tributes to the development of personalized training plans and injury prevention strategies. Conclusions: This study has substantial implications in the domains of intelligent garments, human motion monitoring, and digital twin visualization. The advancement of this system is expected to propel the progress of wearable technology and foster a deeper comprehension of human motion.

Rock-encased-backfill (RB) structures are common in underground mining, for example in the cut-and-fill and stoping methods. To understand the effects of cyclic excavation and blasting activities on the damage of these RB structures, a series of triaxial stepwise-increasing-amplitude cyclic loading experiments was conducted with cylindrical RB specimens (rock on outside, backfill on inside) with different volume fractions of rock (VF = 0.48, 0.61, 0.73, and 0.84), confining pressures (0, 6, 9, and 12 MPa), and cyclic loading rates (200, 300, 400, and 500 N/s). The damage evolution and meso-crack formation during the cyclic tests were analyzed with results from stress-strain hysteresis loops, acoustic emission events, and post-failure X-ray 3D fracture morphology. The results showed significant differences between cyclic and monotonic loadings of RB specimens, particularly with regard to the generation of shear microcracks, the development of stress memory and strain hardening, and the contact forces and associated friction that develops along the rock-backfill interface. One important finding is that as a function of the number of cycles, the elastic strain increases linearly and the dissipated energy increases exponentially. Also, compared with monotonic loading, the cyclic strain hardening characteristics are more sensitive to rising confining pressures during the initial compaction stage. Another finding is that compared with monotonic loading, more shear microcracks are generated during every reloading stage, but these microcracks tend to be dispersed and lessen the likelihood of large shear fracture formation. The transition from elastic to plastic behavior varies depending on the parameters of each test (confinement, volume fraction, and cyclic rate), and an interesting finding was that the transformation to plastic behavior is significantly lower under the conditions of 0.73 rock volume fraction, 400 N/s cyclic loading rate, and 9 MPa confinement. All the findings have important practical implications on the ability of backfill to support underground excavations.