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Volume visualization plays a crucial role in both academia and industry, as volumetric data is extensively utilized in fields such as medicine, geosciences, and engineering. Addressing the complexities of volume rendering, neural rendering has emerged as a potential solution, facilitating the production of high-quality volume rendered images. In this paper, we propose TwineNet, a neural network architecture specifically designed for volume rendering. TwineNet combines features extracted from volume data, transfer functions, and viewpoints by utilizing twining skip connections across multiple feature layers. Building upon the TwineNet architecture, we introduce two neural networks, VolTFNet and PosTFNet, which leverage convolutional encoder–decoders and multilayer perceptrons to synthesize volume rendered images with novel transfer functions and viewpoints. Our experimental findings demonstrate the superiority of our models compared to state-of-the-art approaches in generating high-quality volume rendered images with novel transfer functions and viewpoints. This research contributes to advancing the field of volume rendering and showcases the potential of neural rendering techniques in scientific visualization. [ABSTRACT FROM AUTHOR]

Recently, implicit neural representations (INRs) have demonstrated significant capabilities in handling 3D volume data, especially in the context of data compression. However, the majority of research has predominantly focused on structured grids, which are not commonly found in scientific domains, particularly in physics. To address this limitation, we propose an unstructured grid reduction method via implicit neural representation (UGINR). UGINR employs a divide-and-conquer approach; specifically, we segment the large-scale data into pieces based on values. Subsequently, we employ an INR network for each piece to learn its distinctive features. Finally, we integrate these individual networks to achieve the compression goal. To ensure compatibility with established research methods, we sample only the vertices of each cell in the unstructured grid. Through weight quantization, our model can achieve a high compression ratio. To illustrate the effectiveness of the proposed method, we conduct experiments on various datasets, demonstrating our approach's robustness in scientific visualization and large-scale data compression. [ABSTRACT FROM AUTHOR]

Russia's "Electronic Structure of Atoms" information and reference system for atomic spectroscopy is described. Its main characteristics and capabilities for supporting scientific research and training of specialists are explained. The data array contains more than 234 000 levels, with great attention given to experimental data and uniform filling across Z, including classified levels and transitions of all rare earth and transuranium elements and their ions. Original means of visualizing scientific data in the form of spectrograms, quantograms, and Grotrian diagrams are proposed. Presenting spectral data in the form of color interactive diagrams promotes understanding and analysis of the properties of an atomic system. In combination with its functionality, the use of spectral data from the "Electronic Structure of Atoms" information system is an effective way of solving different scientific problems and training specialists. [ABSTRACT FROM AUTHOR]

Insightful visualization of multidimensional scalar fields, in particular parameter spaces, is key to many computational science and engineering disciplines. We propose a principal component-based approach to visualize such fields that accurately reflects their sensitivity to their input parameters. The method performs dimensionality reduction on the space formed by all possible partial functions (i.e., those defined by fixing one or more input parameters to specific values), which are projected to low-dimensional parameterized manifolds such as 3D curves, surfaces, and ensembles thereof. Our mapping provides a direct geometrical and visual interpretation in terms of Sobol's celebrated method for variance-based sensitivity analysis. We furthermore contribute a practical realization of the proposed method by means of tensor decomposition, which enables accurate yet interactive integration and multilinear principal component analysis of high-dimensional models. [ABSTRACT FROM AUTHOR]

In this paper, we propose a correlation-aware probabilistic data summarization technique to efficiently analyze and visualize large-scale multi-block volume data generated by massively parallel scientific simulations. The core of our technique is correlation modeling of distribution representations of adjacent data blocks using copula functions and accurate data value estimation by combining numerical information, spatial location, and correlation distribution using Bayes' rule. This effectively preserves statistical properties without merging data blocks in different parallel computing nodes and repartitioning them, thus significantly reducing the computational cost. Furthermore, this enables reconstruction of the original data more accurately than existing methods. We demonstrate the effectiveness of our technique using six datasets, with the largest having one billion grid points. The experimental results show that our approach reduces the data storage cost by approximately one order of magnitude compared to state-of-the-art methods while providing a higher reconstruction accuracy at a lower computational cost. [ABSTRACT FROM AUTHOR]

Despite the increasing concern on workplace diversity, no research has been conducted to providing an objective visualization of the development of this fast-developing area and to complementing prior qualitative and meta-analytic reviews in the Asia-Pacific region. This research uses co-citation and co-occurrence analyses to trace how the landscape of diversity evolved from January 1993 to August 2021, using the scientific visualization tool CiteSpace. Based on a dataset of 636 diversity-related publications in the Asia-Pacific region collected from the Web of Science database, we conduct a bibliometric review to map the landscape, with the focus on identifying landmark researchers, articles, and journals, and the intellectual structure of, and changes in, diversity-related research themes. Further, a narrative review was adopted to summarize the frequently studied diversity themes, to capture the developing trends of diversity research, and to build an integrated model of workplace diversity in the Asia-Pacific context. Based on these findings, this review displays the evolution of diversity literature and uncovers future research directions to spur follow-up diversity studies. [ABSTRACT FROM AUTHOR]

The interest in real-time volume graphics has grown rapidly in the last few years, driven by the increasing demands from both academia and industry. GPU-based volume rendering has been used in a wide variety of fields, including scientific visualization, visual effects, and video games. Similarly, real-time volume editing has been used to build terrain and create visual effects during game development; it has even become an integral part of gameplay in various video games (e.g., Minecraft). Nowadays, as the size of volume data increases, processing large volume data in real time is inevitable in many modern application scenarios. However, manipulation and editing of large volume data are associated with various challenges, such as dramatically increasing memory usage and computational burden. In this paper, we present a framework for interactive manipulation and editing of large volume data to address these challenges. A robust and efficient method for large signed distance function (SDF) volume generation is presented and incorporated into the framework. Also, a complete implementation with specialized GPU optimization is introduced to demonstrate its usefulness and effectiveness—it is included in the framework as well. The framework can be an easy-to-use middleware or a plugin that is able to integrate into game engines for the development of various types of applications (e.g., video games). It can also contribute to the research looking at large volume data from a user-centered perspective (e.g., for human–computer interaction researchers). [ABSTRACT FROM AUTHOR]

In this paper, based on height field surface example, an efficient technology of real-time modeling of complex procedural objects on the ray tracing pipeline (RT-pipeline) is proposed. The proposed technology doesn't overload the I-shader stage (intersection shader), but distributes the computational load between the I-shader and the AH-shader (any-hit shader). The key innovations of the technology are the early rejection at the I-shader stage of the bounding boxes (AABBs) extracted by the RT-pipeline hardware unit, and the "transparent AABB" concept which allows transferring costly computing of the "ray-procedural object" intersection to a later AH-shader stage. The paper also describes a number of modifications that reduce the amount of such calculations. The proposed technology was implemented in a software complex in C++, GLSL and using the Vulkan API. The performance of the developed solution was studied under various ray tracing conditions on the task of modeling the surface of a detailed Puget Sound height field. The obtained results confirmed efficiency of the developed technology and the possibility of its application in virtual environment systems, simulators, scientific visualization, etc. [ABSTRACT FROM AUTHOR]

Lava domes and lava flows are major manifestations of effusive volcanic eruptions. Less viscous lava tends to flow long distances depending on the volcanic slope topography, the eruption rate, and the viscosity of the erupted magma. When magma is highly viscous, its eruption to the surface leads to the formation of lava domes and their growth. The meshless smoothed particle hydrodynamics (SPH) method is used in this paper to simulate lava dynamics. We describe the SPH method and present a numerical algorithm to compute lava dynamics models. The numerical method is verified by solving a model of cylindrical dam-break fluid flow, and the modelled results are compared to the analytical solution of the axisymmetric thin-layer viscous current problem. The SPH method is applied to study three models of lava advancement along the volcanic slope, when the lava viscosity is constant, depends on time and on the volume fraction of crystals in the lava. Simulation results show characteristic features of lava flows, such as lava channel and tube formation, and lava domes, such as the formation of a highly viscous carapace versus a less viscous dome core. Finally, the simulation results and their dependence on a particle size in the SPH method are discussed. [ABSTRACT FROM AUTHOR]

The purpose of this study is to examine how trends in the use of images in modern life science journals have changed since the spread of computer-based visual and imaging technology. To this end, a new classification system was constructed to analyze how the graphics of a scientific journal have changed over the years. The focus was on one international peer-reviewed journal in life sciences, Cell, which was founded in 1974, whereby 1725 figures and 160 tables from the research articles in Cell were sampled. The unit of classification was defined as a graphic and the figures and tables were divided into 5952 graphics. These graphics were further classified into hierarchical categories, and the data in each category were aggregated every five years. The following categories were observed: (1) data graphics, (2) explanation graphics, and (3) hybrid graphics. Data graphics increased by more than sixfold between 1974 and 2014, and some types of data graphics including mechanical reproduction images and bar charts displayed notable changes. The representation of explanatory graphics changed from hand-painted illustrations to diagrams of Bezier-curves. It is suggested that in addition to the development of experimental technologies such as fluorescent microscopy and big data analysis, continuously evolving application software for image creation and researchers' motivation to convince reviewers and editors have influenced these changes. [ABSTRACT FROM AUTHOR]

This paper considers the task of real-time modeling of dynamic terrain shadows based on multilevel ray casting. A technology is proposed, where per-pixel detail controlling of synthesizing shadows as well as checking for intersections of sun rays not only with local maxima of terrain height, but also with local minima, are performed. The methods and algorithms implementing the proposed technology on the GPU are described. The proposed solution allows visualization rate for complex landforms, such as gorges and craters, as well as terrain visualization efficiency in viewports of size, less than source height map, to be increased. The developed technology, methods and algorithms were implemented in a software complex, which approbation confirmed the adequacy of the proposed solution to the task concerned. The results obtained can be applied in virtual environment systems, video simulators, scientific visualization, virtual globes, geo-applications, etc. [ABSTRACT FROM AUTHOR]

Graph databases capture richly linked domain knowledge by integrating heterogeneous data and metadata into a unified representation. Here, we present the use of bespoke, interactive data graphics (bar charts, scatter plots, etc.) for visual exploration of a knowledge graph. By modeling a chart as a set of metadata that describes semantic context (SPARQL query) separately from visual context (Vega-Lite specification), we leverage the high-level, declarative nature of the SPARQL and Vega-Lite grammars to concisely specify web-based, interactive data graphics synchronized to a knowledge graph. Resources with dereferenceable URIs (uniform resource identifiers) can employ the hyperlink encoding channel or image marks in Vega-Lite to amplify the information content of a given data graphic, and published charts populate a browsable gallery of the database. We discuss design considerations that arise in relation to portability, persistence, and performance. Altogether, this pairing of SPARQL and Vega-Lite—demonstrated here in the domain of polymer nanocomposite materials science—offers an extensible approach to FAIR (findable, accessible, interoperable, reusable) scientific data visualization within a knowledge graph framework. [ABSTRACT FROM AUTHOR]

Computational fluid dynamics (CFD) simulations can provide meaningful technical content in engineering education, broad engineering and business. However, computationally demanding data production and complex data processing environments of CFD simulations turn them into esoteric tools for potential non-expert users. This consequently limits applications and communications of CFD simulations and results. Augmented and virtual reality (AR/VR) technologies are opening new gates for visualization and interaction techniques. Despite the many recent attempts, the literature lacks an inclusive system development procedure for CFD simulations with AR/VR. The present study proposes a component-oriented system architecture to generate dedicated workflows for any kind of AR/VR environment supported by CFD simulations. The study further explores the potential of data processing options throughout the preparation of the simulation dataset with AR/VR. An automated data coupling strategy is additionally introduced to ease multiplatform integration. We provide an integration strategy with simple, easy-to-implement, end-to-end, automated and free-to-use utilities that the practitioners can readily pursue. [ABSTRACT FROM AUTHOR]

A semiconductor chip usually has thousands test parameters in order to guaranteed its quality. Hence, a batch of chips' test data set include thousands of float data. The primary goal of dealing with this test data is to obtain the fault parameter distribution and judge the chip's quality. It is a challenge due to the large scale and complex relationship of the test data set. This paper presents a novel method to analyze the test data set by meshing the quality theory and scientific data visualization. First, transfer the test data set to a quality classifier matrix Q: a series of quality region is defined based on quality theory, which is the baseline to classify the test data set into different group and mark them with various number. Second, form a quality-spectrum: define a color rule based on the RGB color model and color the quality classifier matrix Q. Hence chip's quality distribution could be observed through the quality-spectrum. Furthermore, by analyzing the quality-spectrum, the chip's quality could be quantitative and fault diagnose has a data basic. One case is included to illustrate appropriateness of the proposed method. [ABSTRACT FROM AUTHOR]

NEANIAS is a research and innovation action project funded by the European Union under the Horizon 2020 program. The project addresses the challenge of prototyping novel solutions for the underwater, atmospheric and space research communities, creating a collaborative research ecosystem, and contributing to the effective materialization of the European Open Science Cloud (EOSC). NEANIAS drives the co-design, implementation, delivery, and integration into EOSC of innovative thematic and core services, derived from state-of-the-art assets and practices in the target scientific communities. We present the overall NEANIAS ecosystem architecture, with an emphasis on its core visualization services, detailing their specifications and software development plan, and focusing on the underpinning service-oriented architecture for their delivery. We report on the underlying ideas and guiding principles for designing such visualization services, outlining their current release status and future development roadmaps towards Technological Readiness Level (TRL) 8 maturity and EOSC integration. [ABSTRACT FROM AUTHOR]

Data visualization blends art and science to convey stories from data via graphical representations. Considering different problems, applications, requirements, and design goals, it is challenging to combine these two components at their full force. While the art component involves creating visually appealing and easily interpreted graphics for users, the science component requires accurate representations of a large amount of input data. With a lack of the science component, visualization cannot serve its role of creating correct representations of the actual data, thus leading to wrong perception, interpretation, and decision. It might be even worse if incorrect visual representations were intentionally produced to deceive the viewers. To address common pitfalls in graphical representations, this paper focuses on identifying and understanding the root causes of misinformation in graphical representations. We reviewed the misleading data visualization examples in the scientific publications collected from indexing databases and then projected them onto the fundamental units of visual communication such as color, shape, size, and spatial orientation. Moreover, a text mining technique was applied to extract practical insights from common visualization pitfalls. Cochran's Q test and McNemar's test were conducted to examine if there is any difference in the proportions of common errors among color, shape, size, and spatial orientation. The findings showed that the pie chart is the most misused graphical representation, and size is the most critical issue. It was also observed that there were statistically significant differences in the proportion of errors among color, shape, size, and spatial orientation. [ABSTRACT FROM AUTHOR]

The visualization of computer simulations is currently undergoing a transition from post-hoc to in-situ visualization in which visualization processes are applied, while the simulation is running. The selection of an appropriate method or tool is essential to efficiently perform in-situ visualization in parallelized large-scale computer simulations that run on supercomputers. Although some generic in-situ visualization libraries are available, they are overengineered for certain geophysical simulations. In this study, we focus on spherical simulations using the Yin-Yang grid. Computer simulations that use the Yin-Yang grid are gaining popularity in geophysics. We propose an in-situ visualization method dedicated to the Yin-Yang grid simulations and demonstrate its effectiveness through sample simulations. [ABSTRACT FROM AUTHOR]

This paper proposes new technology and methods to implement panoramic 360-degree video based on virtual environment projection onto a regular dodecahedron. The idea is to construct a virtual panorama observed by the viewer by means of rectangular snapshots of a virtual environment that simulate the inner surface of the dodecahedron. Based on the geometry of golden rectangles, a method to compute the projection and orientation parameters for 12 identical virtual dodecahedron cameras is developed; based on an original pentagon packing scheme, a method and algorithms to synthesize frames of 360-degree video are proposed; and a method and algorithm for rectangular snapshot visualization that enable the synthesis of a continuous 360-panorama are developed. The proposed solutions are implemented in a software complex and tested by visualizing an ISS orbital flight around the Earth. The research results can be applied in virtual environment systems, simulators, scientific visualization, virtual laboratories, educational applications, video guides, etc. [ABSTRACT FROM AUTHOR]

This paper is devoted to automating the development of standalone scientific visualization modules based on systems on chips with custom tangible user interfaces. Modules of this type can be used as interactive exhibits in the so-called smart museum. The basic idea of the automation lies in software generation leveraged by the SciVi ontology-driven platform. By extending the underlying ontologies of SciVi, we enable this platform to generate code for the Raspberry Pi and Orange Pi systems on chips. The algorithms for the generated software are described in the SciVi platform by means of a high-level visual programming language based on data flow diagrams. Scientific visualization support in the generated software is based on hardware graphics acceleration implemented via OpenGL ES API. Tangible user interface support is implemented by linking special libraries and utilizing operating system APIs to interconnect the system on a chip with its peripheral devices. The effectiveness of the proposed approach is confirmed in practice by developing several cyber-physical museum items for the "Transmutations" exhibition in the Kidsmuseum, a branch of the Perm Regional Museum (Perm city). [ABSTRACT FROM AUTHOR]

This paper considers the construction of a generalized computational experiment for solving verification problems. The problem of comparative accuracy assessment of numerical methods is currently acquiring special relevance due to the introduction of published standards and widespread use of software packages that include a large number of different solvers. A generalized computational experiment makes it possible to obtain a numerical solution for a class of problems determined by variation ranges of their governing parameters. Analysis of results represented as multidimensional arrays, where the number of measurements depends on the dimension of the space of governing parameters, requires the use of scientific visualization and visual analytics tools. Some approaches to the application of generalized computational experiments with and without a reference solution are discussed. An example of constructing error surfaces when comparing some solvers from the OpenFOAM software package is considered. The classical problem of an inviscid oblique shock wave is used as a basic problem. Certain variations of its main parameters—Mach number and angle of attack—are analyzed. In addition, we consider an example of the cone flow problem with variable Mach number, cone angle, and angle of attack. The concept of an error index is introduced as an integral characteristic of deviations from the exact solution for each solver in the class of problems under consideration. [ABSTRACT FROM AUTHOR]

The scholarly output of the new coronavirus research has been proliferating. During five months, an amount of 14,588 scientific publications about nCoV-2 and COVID-19 has been generated intensively (as indexed in Scopus on 31 May 2020). Such a knowledge outburst has created ample interest in understanding the research landscape of this newly configured area. This paper demonstrates on scientometric dimensions of the novel coronavirus (2019-nCov) research using quantifiable characteristics of the publication dataset. Findings reveal that the rate of publication growth (1600%) is very significant to a synergic response of the researchers to combat with the most extended sequence of an RNA virus. Indeed their response has geared up to an average of 100 articles per day. Many scholarly publishers have disclosed their preprint servers to make the publications available immediately, even by enabling Open Access. The scientific contents have published in more than 500 journals from 240 academic publishers. While the top-ten publishers occupied almost 70% of the articles, then about 25% of the studies were sponsored by 300 funding agencies. Among the notable journals Lancet, Nature, BMJ, JAMA, JMV, and NEJM are prominent. Findings also reveal that majority of the contributions have occurred in Medical Science, focusing on virology, immunology, epidemiology, pharmacology, public health, critical care, and emergency medicine. However, the closely associated terms are virus transmission, infection control, asymptomatic, quarantine, pneumonia, human, disease severity, clinical trials, viral pathogenesis, pandemic, risk, and mortality. The study suggests that academic hubs are located mostly in the USA, China, Italy, and the UK. Among the productive institutions; Huazhong Univ (China), Tongji Med. College (China), Harvard Med. School (USA), Univ of Milan (Italy), INSERM (France), UCL (UK) are outstanding. The G7 countries together produced 50% of the global research output on nCov-2. It also noted an encouraging trend of collaborative research across many countries and disciplines, where the values of CI (6.46), DC (0.79), and CC (0.59) are very significant. It examines the geographical diversity of the collaborating authors, thereby visualized their linkages via co-authorship occurrences. Finally, it analyzed the publications' impact to showcase the most influential contributions of the new coronavirus research. [ABSTRACT FROM AUTHOR]

Contemporary virtual reality systems enable academics to more efficiently explore and analyze complex three-dimensional (3D) content, but their utility is limited by visual short-term memory. Janus, a geometry agnostic shader script, circumvents this cognitive limitation by automatically rendering complex object meshes to fit entirely within the field-of-view of consumer head-mounted displays. The resulting 360° vision experience represents an advantage over existing scientific data visualization tools, which have sought to replicate real-world viewing experiences but have inadvertently replicated associated limitations as well. By presenting data in such a way so as to effectively circumvent cognitive loads associated with body (or object) movement, academics can use the Janus shader to more readily engage in the exploratory analysis of complex 3D data sets, thereby facilitating scientific insight. This paper explores the motivations and design of the Janus shader and describes preliminary results from user testing conducted under controlled conditions. For the 24 study participants (N = 24), statistically significant time-to-completion decreases were observed for spatial analysis tasks taking place in intervention (Janus-enabled) VR scenes of low-to-moderate complexity. [ABSTRACT FROM AUTHOR]

Recent advances in "developmental" approach (combining experimental study with computational modeling) of neural networks produce increasingly large data sets, in both complexity and size. This poses a significant challenge in analyzing, visualizing and understanding not only the spatial structure but also the behavior of such networks. This paper describes a virtual reality application for visualization of two biologically accurate computational models that model the anatomical structure of a neural network comprised of 1500 neurons and over 80,000 connections. The visualization enables a user to observe the complex spatiotemporal interplay between seven unique types of neurons culminating in an observable swimming pattern. We present a detailed description of the design approach for the virtual environment, based on a set of initial requirements, followed up by the implementation and optimization steps. Lastly, the results of a pilot usability study are being presented on how confident participants are in their ability to understand how the alternating firing pattern between the two sides of the tadpole's body generates swimming motion. [ABSTRACT FROM AUTHOR]

Vortex identification methods have been extensively studied in recent years due to their importance in understanding the potential physical mechanism of the flow field. Although demonstrating great success in various scenarios, these methods cannot achieve a compromise between computational speed and accuracy, which restricts their usage in large-scale applications. In specific, local methods provide results rapidly with pool accuracies. By contrast, global methods can obtain reliable results by consuming much more time. To take the advantages of both local and global methods, several methods based on convolutional neural networks are proposed. These methods use local patches around each point and the labels obtained by global methods to train the network. They convert the vortex identification tasks into binary classification problems. In this manner, these methods detect vortices rapidly and robustly. By revisiting these methods, we observe two drawbacks that limit their performance: (i) the large number of parameters and (ii) high computational complexity. To address these issues, we provide a rapid vortex identification method by using a fully convolutional segmentation network in this work. Specifically, we discard the fully connected layers to decrease the number of parameters and design a segmentation network to reduce computational complexity. Intensive experimental results show that the accuracy and recall performance of our method are comparable with those of the global methods. Moreover, the time consumption of our method is less than that of all other methods. [ABSTRACT FROM AUTHOR]

This study quantitatively investigated the scientific progress of walkability research landscape and its future prospects using bibliometric indicators to highlight the research hotspots. The results accentuated multifaceted nature of walkability research landscape with a strong association towards public health disciplines. Keyword co-occurrence analysis emphasized that majority of the walkability studies centred on the interactions between walking and other three main factors such as built environment attributes, transportation and obesity. Based on the identified research hotspots, a brief state-of-the-art review of walkability studies was presented. Future prospects based on the unexplored research gaps within the hotspots were also discussed. High correlation (r = 0.99, p < 0.05) between annual publications and citation counts demonstrated the significance of walkability studies to the contemporary scientific community. Being one of the comprehensive studies to evaluate the historic trajectory of walkability research landscape, the findings were expected to accelerate a comprehensive understanding of the walkability research domain that will assist future research direction. [ABSTRACT FROM AUTHOR]

While the relation between visualization and scientific understanding has been a topic of long-standing discussion, recent developments in physics have pushed the boundaries of this debate to new and still unexplored realms. For it is claimed that, in certain theories of quantum gravity, spacetime 'disappears': and this suggests that one may have sensible physical theories in which spacetime is completely absent. This makes the philosophical question whether such theories are intelligible, even more pressing. And if such theories are intelligible, the question then is how they manage to do so. In this paper, we adapt the contextual theory of scientific understanding, developed by one of us, to fit the novel challenges posed by physical theories without spacetime. We construe understanding as a matter of skill rather than just knowledge. The appeal is thus to understanding, rather than explanation, because we will be concerned with the tools that scientists have at their disposal for understanding these theories. Our central thesis is that such physical theories can provide scientific understanding, and that such understanding does not require spacetimes of any sort. Our argument consists of four consecutive steps: (a) We argue, from the general theory of scientific understanding, that although visualization is an oft-used tool for understanding, it is not a necessary condition for it; (b) we criticise certain metaphysical preconceptions which can stand in the way of recognising how intelligibility without spacetime can be had; (c) we catalogue tools for rendering theories without a spacetime intelligible; and (d) we give examples of cases in which understanding is attained without a spacetime, and explain what kind of understanding these examples provide. [ABSTRACT FROM AUTHOR]

Background: Many patients with brain tumours exhibit mild to severe (neuro)cognitive impairments at some point during the course of the disease. Social cognition, as an instance of higher-order cognitive functioning, specifically enables initiation and maintenance of appropriate social interactions. For individuals being confronted with the diagnosis of a brain tumour, impairment of social function represents an additional burden, since those patients deeply depend on support and empathy provided by family, friends and caregivers.Methods: The present study explores the scientific landscape on (socio)cognitive functioning in brain tumour patients by conducting a comprehensive bibliometric analysis using VOSviewer. The Web of Science Core Collection database was examined to identify relevant documents published between 1945 and 2019.Results: A total of 664 English titles on (socio)cognitive functions in patients with brain tumours was retrieved. Automated textual analysis revealed that the data available so far focus on three major topics in brain tumour patients: cognitive functions in general and in paediatric cases, as well as psychological factors and their influence on quality of life. The focus of research has gradually moved from clinical studies with cognitive functions as one of the outcome measures to investigations of interactions between cognitive functions and psychological constructs such as anxiety, depression or fatigue. Medical, neurological and neuropsychological journals, in particular neuro-oncological journals published most of the relevant articles authored by a relatively small network of well interconnected researchers in the field.Conclusion: The bibliometric analysis highlights the necessity of more research on social cognition in brain tumour patients. [ABSTRACT FROM AUTHOR]

Ocean data have been improved with the enhancement of observed values and the evolution of computational technologies. It has also been verified based on the reproducibility of various ocean phenomena. Mode water is one of the indicators for assessing ocean data because of its special properties. However, its definition differs for each ocean data. Besides, its observation is primarily performed by 2D analysis using the cutting plane of the ocean space. Therefore, reproducibility of the ocean space may have not been fully examined. Here, this paper presents a visual analysis tool for the feature of ocean data based on the 3D shape comparison of the mode water regions among three ocean datasets. Our comparison is based on similarity measure from shape appearances of the mode water regions extracted as isosurfaces. Users can interact with shape similarity data and a pair of isosurfaces. Our visualization tool supports to easily explore the relationship of different variable thresholds that are used for conditions of the mode water region and observe the specified parts of the pair in the 3D space. We demonstrate the availability and potential benefit of this approach through three examples that searched for the best conditions and expert feedback. [ABSTRACT FROM AUTHOR]

In time-varying scientific datasets from simulations or experimental observations, scientists always need to understand when and where interesting events occur. An event is a complex spatial and temporal pattern that happens over a course of timesteps and includes the involved features and interactions. Event detection allows scientists to query a time-varying dataset from a much smaller set of possible choices. However, with many events detected from a dataset, each spanning different time intervals, querying and visualizing these events pose a challenge. In this work, we propose a framework for the visualization of events in time-varying scientific datasets. Our method extracts features from a data, tracks features over time, and saves the evolution process of features in an event database where a set of database operations are provided to model an event by defining the stages or individual steps that make up an event. Using the feature metadata and the event database, three types of event visualizations can be created to give a unique insight into the dynamics of data from temporal, spatial, and physical perspectives and to summarize multiple events or even the whole dataset. Three case studies are used to demonstrate the usability and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Scientific visualizations offer domain experts the ability to explore data visually and interactively to gain insights from data. Most visualization systems focus on functionality and scalability. However, we believe that with the advent of faster rendering techniques and higher-speed networks, accessibility for every device should also be a goal for scientific visualizations. In this paper, we propose a novel scientific visualization system, Voxer, which provides ubiquitous visualizations by decoupling user interfaces from system space. In Voxer, we encapsulate the data processing and rendering functionality as a web service and design a module-based user interface for domain experts to create and customize different visualization pipelines in response to their specific requirements. These configured visualizations can be shared with the public through embedding visualizations on the web and interactively rendering on the server. Use cases and benchmarks are used to demonstrate how our system can help domain experts easily create and customize visualizations and improve visualizations accessibility. [ABSTRACT FROM AUTHOR]

The ATLAS experiment at the LHC processes, analyses and stores vast amounts of data, which is either recorded by the detector or simulated worldwide using Monte Carlo methods. ATLAS Computing metadata is generated at very high rates and volumes. The necessity to analyze this metadata is constantly increasing, since the heterogeneous, distributed and dynamically changing computing infrastructure requires sophisticated optimization decisions, made by human or/and by machines. Visual analytics is one of the methods facilitating the analysis of massive amounts of data (structured, semi-structured, and unstructured) which leverages human judgement by means of interactive visual representations. Given the huge number of ATLAS computing jobs that need to be visualized simultaneously for error investigations or other optimization processes, resources of the client application responsible for such visualization may reach its limits. Data objects that share similar feature values can be represented and visualized as a single group, thus initial large data sample would be represented at different levels of detail. This approach will also avoid client overload. In this paper we evaluate implementations of k-means-based Level-of-Detail generator method applied to the metadata of ATLAS jobs. This method is used in the visual analytics application InVEx (Interactive Visual Explorer) that is under development, and which is based on 3-dimensional interactive visualization of multidimensional data. [ABSTRACT FROM AUTHOR]

Viewpoint is vital in guiding the user to understand the volume data. However, a model that can recommend viewpoints conforming to user preference is hard to be represented explicitly. In this work, we propose an implicit model for the best viewpoint recommendation of volume visualization with CNN-based models to learn the traditional scoring method and user preference. Residual structures are applied for reducing overfitting in simple scalar regression and solving the problem of accuracy getting lower as the network getting deeper. Multi-level-based structures are applied to imitate the coarse and fine level in human perception. The detailed experiments of comparison between our model and traditional methods confirm the efficiency of our work. A case of application verifies that our model can flexibly realize a user preference-based best viewpoint selection in volume visualization. [ABSTRACT FROM AUTHOR]

This paper is devoted to the development of tools that allow museum workers to create interactive exhibits by using the Internet of Things (IoT) technology. We propose a unified software and hardware solution that enables the users who do not have deep knowledge in electronics and programming to assemble and interconnect various devices by the principle of a construction set. This, in turn, makes it possible to implement the concept of Smart Museum without the involvement of third-party IT specialists and large investments. The hardware part of the proposed solution is based on the ESP8266 programmable microcontroller with a built-in WiFi module. Peripheral devices can be connected to this microcontroller via expansion boards, the so-called shields. The software part of the solution is based on the SciVi adaptive multiplatform scientific visualization system. This system is fully controlled by the ontological knowledge base and provides the user with a high-level graphical interface that allows him or her to describe visualization algorithms by using data flow diagrams (DFDs). In this work, the SciVi system is supplemented with a mechanism that automatically generates firmware for IoT devices. It is based on an electronic component ontology that describes these devices and their programming methods. The firmware code generator controlled by this ontology automates the creation of lightweight embedded SciVi copies, which are installed on IoT devices and act as servers for data collection, processing, and visualization. To test the proposed solution, we created an interactive exhibit of Dimetrodon grandis, an extinct species of synapsids that lived during the Early Permian. [ABSTRACT FROM AUTHOR]

Recent advances in parallel architectures for numerical simulation of natural black oil reservoirs have allowed the use of very discretized domains. As a consequence, these simulations produce an unprecedented volume of data, which must be visualized in 3D environments for careful analysis and inspection. Conventional scientific visualization techniques are not viable on such large models, creating a demand for the development of scalable visualization solutions. In this paper, we propose a hierarchical multiresolution technique to render massively large black oil reservoir meshes. A new simplification algorithm specialized for such models is presented, which accurately represents boundary surfaces, while keeping the hexahedral mesh with good quality. Original model properties, wireframe and surface normals are mapped onto the simplified meshes through texture mapping. This allows the system to reuse the structure for different simulations that use the same geometry model. The viewer application is designed to guarantee a minimum refresh rate, allocating geometric detail where it is most needed, given the available hardware. Experimental results, considering up to 1.2 billion cell models, demonstrate the effectiveness of the proposed solution. [ABSTRACT FROM AUTHOR]

Computational systems biologists create and manipulate computational models of biological systems, but they do not always have straightforward epistemic access to the content and behavioural profile of such models because of their length, coding idiosyncrasies, and formal complexity. This creates difficulties both for modellers in their research groups and for their bioscience collaborators who rely on these models. In this paper we introduce a new kind of visualization (observed in a qualitative study of a systems biology laboratory) that was developed to address just this sort of epistemic opacity. The visualization is unusual in that it depicts the dynamics and structure of a computer model instead of that model's target system, and because it is generated algorithmically. Using considerations from epistemology and aesthetics, we explore how this new kind of visualization increases scientific understanding of the content and function of computer models in systems biology to reduce epistemic opacity. [ABSTRACT FROM AUTHOR]

The paper is devoted to the analysis of multichannel biomedical signals acquired in the sleep laboratory. The data analyzed represent polysomnographic records of (i) 33 healthy individuals, (ii) 25 individuals with sleep apnea, and (iii) 18 individuals with sleep apnea and restless leg syndrome. The initial statistical analysis of the sleep segments points to an increase in the number of Wake stages and the decrease in REM stages with increase in age. The goal of the study is visualization of features associated with sleep stages as specified by an experienced neurologist and in their adaptive classification. The results of the support vector machine classifier are compared with those obtained by the k-nearest neighbors method, decision tree and neural network classification using sigmoidal and Bayesian transfer functions. The achieved accuracy for the classification into two classes (to separate the Wake stage from one of NonREM and REM stages) is between 85.6 and 97.5% for the given set of patients with sleep apnea. The proposed models allow adaptive modification of the model coefficients during the learning process to increase the diagnostic efficiency of sleep disorder analysis, in both the clinical and home environments. [ABSTRACT FROM AUTHOR]

Abstract: This paper is devoted to a unified approach to monitoring data generated by various electronic devices that are based on programmable microcontrollers. We suggest that communication between visualization systems and target devices be automatically tuned by retrieving the description of the input/output data structure from the firmware of the devices. For this purpose, we propose an ontology-based generator of firmware parsers. In our approach, the ontology that describes the syntax of input/output statements of different programming languages and the generator of firmware parsers become an essential part of the visualization system. Next, we propose to enrich the visualization pipeline with a data filtering stage. To make the filtering and rendering stages highly configurable, we use data flow diagrams (DFDs) that define data transformation. To enable the user to compose these diagrams, we develop a special high-level graphical editor. The description of DFD nodes is stored in the ontological knowledge base of the visualization system. To specify the nodes in ontological notation, we use ontologies of semantic filters, visual objects, and graphical scenes. We implement the proposed approach in the SciVi multiplatform client-server scientific visualization system and test its new capabilities by monitoring the orientation and light direction sensors. [ABSTRACT FROM AUTHOR]

Abstract: Integration-based geometric method is widely used in vector field visualization. To improve the efficiency of integration advection-based visualization, we propose a uniform integrated advection (UIA) algorithm on steady and unsteady vector field according to common piecewise linear field data set analysis. UIA employs cell gradient-based interpolation along spatial and temporal direction, and transforms multi-step advection into single-step advection in association with fourth-order Runge-Kutta advection process. UIA can significantly reduce computational load, and is applicable on arbitrary grid type with cell-center/cell-vertex data structure. The experiments are performed on steady/unsteady vector fields with two-dimensional cell-center unstructured grids and three-dimensional cell-vertex grids, and also on unsteady field from fluid dynamics numerical simulation. The result shows that the proposed algorithm can significantly improve advection efficiency and reduce visualization computational time compared with fourth-order Runge-Kutta.Graphical abstract: [ABSTRACT FROM AUTHOR]

By 2050, 75 % of the world's population will live in cities and the occurrence of heat wave events might have doubled. Mapping the climate and land use change impact for urban heat events should set the agenda for adaptation planning at the local scale. Literature on urban heat mapping does not reveal a clear indicator to visualise the urban heat impacts that includes consequences of land use and climate changes for planning purposes. This paper introduces a stepwise approach to develop a single complex indicator to map the urban heat impact for local climate adaptation planning processes. Information on climatic drivers and land use characteristics are combined and projected for future land use and climate change impacts. Next, several visualisation techniques are developed to investigate which techniques are most effective to visualise complex information with multiple variables in one visualisation. A usability test is performed to investigate how indicator and map meet the information and communication needs of policy makers. Our findings reveal that it is important to add information on future impacts to set the agenda for adaptation planning at the local scale. Applying cartographic techniques in a map series presentation has proven to be effective to map complex information in a single image and fulfil most of the identified information needs. Based on our finding, we introduce the information enrichment chain as a promising approach to support local adaptation planning. [ABSTRACT FROM AUTHOR]

Free Disposal Hull (FDH) model was proposed in the end of 20-th century for performance measurement and management of complex units. Production possibility set of FDH model is a nonconvex set. For this reason, nonlinear integer programming methods and enumeration methods have been used for computations of various characteristics of units' performance. However, as Cesaroni, Kerstens and Van de Woestyne noted, there are no methods in the world scientific literature for frontier visualization in nonconvex FDH models. In this paper, an approach is proposed for frontier visualization with the use of methods of purposeful enumeration. Computational experiments, conducted with the use of real-life data sets, confirm reliability and effectiveness of proposed algorithms. [ABSTRACT FROM AUTHOR]

As vortex is of great significance to structure analysis and mechanism research in flow field, vortex feature extraction has always been a hot research topic in flow field visualization. We investigate the data precision and numerical algorithm accuracy impact on vortex feature area extraction of Lagrangian Averaged Vorticity Deviation (LAVD). Then, an LAVD-based high-order accurate vortex extraction algorithm is proposed, which incorporates vorticity computation of cell-vertexed data by Weighted Essentially Non-Oscillatory (WENO) scheme, vorticity and velocity computation of off-grid point by candidate stencil weight-based high-order polynomial interpolation method, flow map computation by 4th-order Runge-Kutta (RK) method and integration by compound Simpson rule. We perform vortex feature extraction and visualization on both analytical flow field and high-order unsteady flow field. The experiment results demonstrate that the proposed algorithm can practically reflect the vortex feature of high-order flow field and accurately describe small scale vortex structure, which is unavailable by low-order method. Graphical Abstract: [ABSTRACT FROM AUTHOR]

The concepts and methods of the visual representation of fluid dynamics computations of vortical flows are studied. Approaches to the visualization of vortical flows based on the use of various definitions of a vortex and various tests for its identification are discussed. Examples of the visual representation of solutions to some fluid dynamics problems related to the computation of vortical flows in jets, channels, and cavities and of the computation of separated flows occurring in flows around bodies of various shapes are discussed. [ABSTRACT FROM AUTHOR]

The article presents historical studies on mathematical modeling and visualization in the mathematical and physical sciences, particularly on geometric models and biology and particle physics. Topics include the role of models in the history of geometry in connection with modern axiomatics, physical representations of idealized objects that challenge the imagination of special mathematical constructs, and Nicolas Rashevsky's 1934 paper on cell division.

The article looks at models for visualization of four-dimensional mathematical objects. Topics include the study of solid geometry in both the analytic and synthetic form in 1870s, work of model-makers Washington Irving Stringham, Victor Schlegel, and Howard Hinton, and geometry of four-dimensional Euclidean space. Also tackled is the production of models of four-dimensional polytopes.

We present a real-time volume rendering component for the Web, which provides a set of illustrative and non-photorealistic styles. Volume data is used in many scientific disciplines, requiring the visualization of the inner data, features for enhancing extracted characteristics or even coloring the volume. The Medical Working Group of X3D published a volume rendering specification. The next step is to build a component that realizes the functionalities defined by the specification. We have designed and built a volume rendering component integrated in the X3DOM framework. This component allows content developers to use the X3D specification. It combines and applies multiple rendering styles to several volume data types, offering a suitable tool for declarative volume rendering on the Web. As we show in the result section, the proposed component can be used in many fields that requires the visualization of multi-dimensional data, such as in medical and scientific fields. Our approach is based on WebGL and X3DOM, providing content developers with an easy and flexible declarative way of sharing and visualizing volumetric content over the Web. [ABSTRACT FROM AUTHOR]

We present HNC-Plot, an Excel Visual Basic Application (Excel VBA) that can easily be used to create graphical plots of fluids associated with volcanic gases or geothermal manifestations, based on their helium, nitrogen and CO isotope data. HNC-Plot has been designed in a simple way; all the user has to do is to input data in an Excel spreadsheet. The spreadsheet also contains buttons which have links to Excel VBA codes that generate plots. Once, the respective button is clicked, and all required variables are defined, Excel VBA generates a plot with labels and axes. Based upon the user preferences, additional tasks such as construction of mixing lines between mantle and crustal gases or calculation of end-member proportions can also be performed. We sincerely believe geoscientists can quickly and easily create these plots, free of charge, with this application. This study also presents previously published data from related literature to show the foundations of the application and to present sample plots created with it. [ABSTRACT FROM AUTHOR]

Tracing the trajectory of scientific fields has been recognized by informaticians, nonetheless, little effort has been dedicated to understanding the evolution of the fast-moving research field of transport, quantitatively and qualitatively. This paper identifies intellectual turning points and emerging trends in the area of transport. Using bibliometric methods, co-keyword networks, journal co-citation networks, highly cited categories, and country and institute networks are detected, visualized and discussed. To conduct this analysis, all publications (35,712) in 23 top journals in the field of transport are extracted from the Institute for Scientific Information (Web of Science). The output of this article could be a valuable source for academics and practitioners working in the field of transport planning and those who work in the areas having a strong relationship with transport issues including mathematicians, economics, operation research, management and geography. [ABSTRACT FROM AUTHOR]

Analyzing and processing various data types in a privacy-preserving perspective has been researched in many disciplines; however, such an issue draws very limited attention in the research field of scientific visualization. We wondered if it is possible to delegate the rendering of a volume data set to a remote server(s) while still being able to preserve its privacy to certain extent. This paper presents a block-based volume data transformation algorithm that obfuscates a volume data set so as to reduce the user's privacy concern when the volume data set is to be uploaded to a remote server. In addition, a privacy-aware transfer function adjustment is proposed so that not only the privacy is protected during the rendering process, but also the computational loading could be leveraged to the server side as much as possible. Experimental results show that the proposed method yields visually satisfactory results compared with a normal direct volume rendering approach. Moreover, the decrease of the rendering efficiency caused by the proposed method is still controlled within an acceptable range. A case study proves that the proposed approach can be adopted in practice. This work explores the possibility of rendering a volume data set through remote server(s) while the privacy of data is still maintained. [ABSTRACT FROM AUTHOR]

This paper presents a new unified approach to adapt scientific visualization systems to third-party solvers implemented on different software and hardware platforms. This approach allows building multiplatform visualization systems, enables automatic conversion of input and output data from any solver into a rendering-compatible format, and provides real-time generation of high-quality images. The automated adaptation of visualization systems to third-party solvers is based on ontological engineering methods. Multiplatform portability is provided by the automatic generation of a graphical user interface (GUI) for each particular operating system and by preprocessing the data to be rendered by using heuristic-based tools, which ensures compatibility with different hardware and software platforms, including desktop computers and mobile devices. In addition, an original anti-aliasing algorithm is proposed to ensure high quality of resulting images. Based on the proposed approach, a multiplatform scientific visualization system called SciVi is developed, which is successfully used for solving various real-world scientific visualization problems from different application domains. [ABSTRACT FROM AUTHOR]

Pictures of an electric arc burning in argon, obtained by means of a digital camera, within the different domains of wavelengths of the visible spectrum are presented. Maps of the thermal fields of the heating spot are plotted, and the plasma temperature in the anode arc zone is calculated. The application potential of the digital image technology in the visible wavelength domain for analysis of the processes directly in the anode arc zone and for estimation of the arc column parameters is shown. [ABSTRACT FROM AUTHOR]