Visualization

Cardiac electrophysiology requires the processing of several patient-specific data points in real time to provide an accurate diagnosis and determine an optimal therapy. Expanding beyond the traditional tools that have been used to extract information from patient-specific data, machine learning offers a new set of advanced tools capable of revealing previously unknown data patterns and features. This new tool set can substantially improve the speed and level of confidence with which electrophysiologists can determine patient-specific diagnoses and therapies. The ability to process substantial amounts of data in real time also paves the way to novel techniques for data collection and visualization. Extended realities such as virtual and augmented reality can now enable the real-time visualization of 3-dimensional images in space. This enables improved preprocedural planning and intraprocedural interventions. Machine learning supplemented with novel visualization technologies could substantially improve patient care and outcomes by helping physicians to make more informed patient-specific decisions. This article presents current applications of machine learning and their use in cardiac electrophysiology.

Cardiac electrophysiology requires the processing of several patient-specific data points in real time to provide an accurate diagnosis and determine an optimal therapy. Expanding beyond the traditional tools that have been used to extract information from patient-specific data, machine learning offers a new set of advanced tools capable of revealing previously unknown data patterns and features. This new tool set can substantially improve the speed and level of confidence with which electrophysiologists can determine patient-specific diagnoses and therapies. The ability to process substantial amounts of data in real time also paves the way to novel techniques for data collection and visualization. Extended realities such as virtual and augmented reality can now enable the real-time visualization of 3-dimensional images in space. This enables improved preprocedural planning and intraprocedural interventions. Machine learning supplemented with novel visualization technologies could s...

In mammographic imaging, the ®lm area corresponding to the breast periphery is overexposed, resulting in high optical density and degraded contrast in this region. A digital, model-driven density equalization technique was designed and developed to overcome this overexposure problem, taking into account the non-linear characteristic curve of the ®lm-digitizer system. The method is based on several image processing and analysis techniques, such as thresholding, which is used to segment the pixels of the mammogram belonging to the breast region from the background, and wavelet-based fusion, which is used to equalize the pixels of breast periphery selectively while leaving the remaining breast region unaffected. Initial application of the method resulted in density-equalized mammographic images, characterized by improved contrast at the breast periphery.

The aim of our interdisciplinary project is to characterize the perceptibility and visual effects of figurative architectural terracottas by simulating typical light and shadow patterns. In its initial phase, we focus on the finds from the Republican House I.2 in Fregellae (Latium), early examples of the elaborate relief decorations of the eaves zone in Roman atrium houses. In addition to the digital visualization, our project contributes to current research on light and images in Roman houses, which has so far focused on wall and floor design.

Financial ratios are often used in principal component analysis and related techniques for the purposes of data reduction and visualization. Besides the dependence of results on ratio choice, ratios themselves pose a number of problems when subjected to a principal component analysis, such as skewed distributions. In this work, we put forward an alternative method drawn from compositional data analysis (CoDa), a standard statistical toolbox for use when data convey information about relative magnitudes, as financial ratios do. The method, referred to as the CoDa biplot, does not rely on any particular choice of financial ratio but allows researchers to visually order firms along the pairwise financial ratios for any two accounts. Non-financial magnitudes and time evolution can be added to the visualization as desired. We show an example of its application to the top chains in the Spanish grocery retail sector and show how the technique can be used to depict strategic management diff...

Profound understanding of business processes is a key success factor for Business Process Management (BPM). As more and more analytical information like runtime data from process execution or statistical data from business intelligence are available, the problem of business process complexity becomes apparent. Process-relevant information needs to be provided as fast as possible while considering easy and fast interpretation and dynamic changes in stakeholders' demands. The static and usecase specific creation or modification of process visualizations shown in current approaches and tools, however, is complex, time consuming, inflexible and thus costly. To address these shortcomings, we introduce a template-based approach that decouples the creation of visualization templates from concrete process visualizations. The binding of customization points of visualization templates to analytical process information is supported by a graphical editor that enables customization of visualizations in a fast and flexible manner. Moreover, due to the separation of concerns, our approach improves the usability of process visualizations because templates may be created by graphic experts independently from specific visualization demands. The feasibility of our concept is demonstrated by a prototypical implementation.

Modern in-silico science (or e-Science) is a complex process, often involving multiple steps conducted across different computing environments. Scientific workflow tools help scientists automate, manage and execute these steps, providing a robust and repeatable research environment. Increasingly workflows generate data sets that require scientific visualization, using a range of display devices such as local workstations, immersive 3D caves and large display walls. Traditionally, this display step handled outside the workflow, and output files are manually copied to a suitable visualization engine for display. This inhibits the scientific discovery process disconnecting the workflow that generated the data from the display and interpretation processes. In this paper we present a solution that links scientific workflows with a variety of display devises, including large tiled display walls. We demonstrate the feasibility of the system by a prototype implementation that leverages the Kepler workflow engine and the SAGE display software. We illustrate the use of the system with a case study in workflow driven microscopy.

Requirement Engineering (RE) is a Software Engineering (SE) process of defining, documenting, and maintaining the requirements from a problem. It is one of the most complex processes of SE because it addresses the relation between customer and developer. RE learning may be abstract and complex for most students because many of them cannot visualize the subject directly applied. Through the advancement of technology, Virtual Reality (VR) hardware is becoming increasingly more accessible, and it is not rare to use it in education. Little research and systematic studies explain the integration between SE and VR, and even less between RE and VR. Hence, this systematic review proposes to select and present studies that relate the use of VR applications to teach SE and RE concepts. We selected nine studies to include in this review. Despite the lack of articles addressing the topic, the results from this study showed that the use of VR technologies for learning SE is still very seminal. The projects based essentially on visualization. There are lack of tasks to build modeling artifacts, and also interaction with stakeholders and other software engineers. Learning tasks and the monitoring of students' progress by teachers also need to be considered.

Deepwater channel, levee, overbank, and lobe assemblages of turbidite origin are complex depositional systems with significant facies variation. This can result in a high degree of reservoir compartmentalization within relatively short distances. In the Gulf of Mexico, we often find that our excitement in discovering high quality reservoir sand is diluted by the risk of reservoir compartmentalization. This lack of connectivity reduces recoverable resource and rate of return, and complicates development scenarios. Failure to recognize the degree of compartmentalization can result in overestimation of total recoverable resources and adversely impact the economic viability of the development projects. Therefore, the better the reservoir complexity can be assessed, the fewer surprises will have to be dealt with during field development processes. In this paper, the integration of 3D seismic visualization, wireline and logging-while-drilling (LWD) data, modular formation dynamics tester (MDT) pressure data, paleontological data, and comprehensive geochemical analyses is employed to understand the depositional processes, construct the stratigraphic framework and build a reservoir model of the study area. Seismic and well data are applied to define the stratigraphic bounding surfaces and propose secondary (internal) subsequences, while paleontological data are used as high level biostratigraphic control points. Pressure and geochemical analyses are then utilized to refine the detailed reservoir compartments. The results allow generation of field development scenarios and well completion schemes based on the characteristics of each individual reservoir compartment.

Deepwater channel, levee, overbank, and lobe assemblages of turbidite origin are complex depositional systems with significant facies variation. This can result in a high degree of reservoir compartmentalization within relatively short distances. In the Gulf of Mexico, we often find that our excitement in discovering high quality reservoir sand is diluted by the risk of reservoir compartmentalization. This lack of connectivity reduces recoverable resource and rate of return, and complicates development scenarios. Failure to recognize the degree of compartmentalization can result in overestimation of total recoverable resources and adversely impact the economic viability of the development projects. Therefore, the better the reservoir complexity can be assessed, the fewer surprises will have to be dealt with during field development processes. In this paper, the integration of 3D seismic visualization, wireline and logging-while-drilling (LWD) data, modular formation dynamics tester ...

Nowadays recent advances in Geomatics and Computer Science led specialists to explore new methodologies for the preservation and valorization of Built Heritage (BH). In fact, if on one hand the preservation of monumental complexes needs to remotely manage virtual representations of the real environments to be preserved, on the other hand the valorization allows visitors to virtually explore BH sites in remote, thanks to the spread of web technologies. The main challenges about the virtual fruition of BH sites are related to the management of 3D big data digitized geometries and the requirement for property software-based solutions. This work explores a possible strategy, based on open-source technologies for the web fruition of 3D geometric and semantic information of BH sites from 3D digitization. The first results of this research are related to the cloister of the Cathedral of Cefalù (Italy), which represents a classic example of a BH site that, for its extraordinary importance and its state of preservation, could benefit from the application of virtual technologies for conservation and fruition purposes. The work describes the acquisition of the 3D geometric dataset through laser scanner and photogrammetric surveys, its optimisation, and the design of a web-based platform for the fruition online of 3D geometric and semantic information. The web navigation system was developed through Three.js JavaScript WebGL libraries, inside a HTML5 compliant environment, connected in real-time to Postgres Relational Database Management System through the activation of a Python module in an Anaconda platform. In this way, the geometric information, hosted by the WebGL environment, was connected in real-time to the semantic information stored in the database. This system represents a prototypical structure for the future development of a full platform for the web exploration and management of BH sites developed with low-cost technologies and useful for a large variety of stakeholders (from municipalities to private companies) who manage BH sites.

Environmental data visualization tools are used for data validation, maintenance, and assessment of status and trends of environmental variables. However, currently available visualization tools do not meet all the requirements needed for efficient data management. In this paper, we propose a new approach and present a web-based implementation of a visualization tool that focuses on efficient visualization and seamless integration of maps and graphs. Our approach emphasizes the spatio-temporal relationship of environmental data stored in a relational database. Several new advantages emerge with this approach. Through a case study of two oceanographic datasets—the HarmoNIA project database and the Croatian national monitoring database—we show how this approach enables intuitive status and trend assessments, simplifies data validation, enables the updating of corrections, and is suitable for a web-based implementation that works efficiently even with large datasets.

With the advent of the high-performance graphics and networking technologies that enable us to create virtual worlds networked via the Internet, various virtual environments have been developed to support mathematics education at around the beginning of the 21st century. In the environments that have been inherently two-or threedimensional Euclidean, students have discovered and experienced mathematical concepts and processes in almost the same ways that they can do in real life. Although elementary mathematics, for instance, calculus and linear algebra, plays an essential role in areas of understanding and knowledge to solve real-world problems, there are traditionally three general areas in pure mathematics for advanced problem-solving techniques: algebra, analysis, and geometry. So, using Virtual Reality (VR) as a general and advanced tool for mathematics education to support students not only in the primary and secondary, also in higher education, the virtual environment ideally provides a wide variety of mathematical domains as possible. We present an immersive virtual environment that allows the user to set environmental limits beyond three-dimensional Euclidean space. More specifically, by setting the limits to n-dimensional complex projective space, an element of both complex and infinitely distant domain can be naturally visualized as a recognizable form in the Euclidean 3-space. The problem here is that the higher the level of mathematics, the more the visualization method tends to become abstract that only experts with advanced degrees can fathom. We also show how our figurative approach is essential for bridging the gap between elementary and more advanced mathematical visualizations.

In museums, it is critical to properly conserve the existing artwork. For this purpose, it is fundamental to continuously monitor its environment, either in storage or exhibition rooms. Contrarily to traditional measuring equipments and procedures used in museums, the deployment of a Wireless Sensor Network (WSN) can help to implement these measurements continuously, in a real-time basis, and in a much easier and cheaper way. This is the main objective of the WISE-MUSE project, which proposes the use of WSNs for museums’ environmental and structural monitoring, and automatic environmental control. In this paper, the implementation and the main results of the WISE-MUSE project, which was carried out in a contemporary art museum, are described. Among other important contributions that will also be described in this paper, the development of a new wireless sensor node and a Web-based visualization tool, which bring some considerable advantages when compared with other commercially avai...

The big data revolution is an exciting opportunity for universities, which typically have rich and complex digital data on their learners. It has motivated many universities around the world to invest in the development and implementation of learning analytics dashboards (LADs). These dashboards commonly make use of interactive visualisation widgets to assist educators in understanding and making informed decisions about the learning process. A common operation in analytical dashboards is a 'drill-down', which in an educational setting allows users to explore the behaviour of sub-populations of learners by progressively adding filters. Nevertheless, drill-down challenges exist, which hamper the most effective use of the data, especially by users without a formal background in data analysis. Accordingly, in this paper, we address this problem by proposing an approach that recommends insightful drill-downs to LAD users. We present results from an application of our proposed approach using an existing LAD. A set of insightful drill-down criteria from a course with 875 students are explored and discussed.

The big data revolution is an exciting opportunity for universities, which typically have rich and complex digital data on their learners. It has motivated many universities around the world to invest in the development and implementation of learning analytics dashboards (LADs). These dashboards commonly make use of interactive visualisation widgets to assist educators in understanding and making informed decisions about the learning process. A common operation in analytical dashboards is a 'drill-down', which in an educational setting allows users to explore the behaviour of sub-populations of learners by progressively adding filters. Nevertheless, drill-down challenges exist, which hamper the most effective use of the data, especially by users without a formal background in data analysis. Accordingly, in this paper, we address this problem by proposing an approach that recommends insightful drill-downs to LAD users. We present results from an application of our proposed approach using an existing LAD. A set of insightful drill-down criteria from a course with 875 students are explored and discussed.

Process mining is a specialised form of data analytics that aims to provide data-driven improvement recommendations, derived from event logs. These event logs contain information about the execution of real-world processes, which may be complex. Cohort identification recommends drill-down filters for process mining, based on differences in process. In this paper, we describe its integration in three process mining tools: as a stand-alone ProM plug-in, as part of the visual Miner and (planned) as part of Course Insights.

The purpose of the study was to assess the output of research on maritime accidents and citations from 2000 to 2022 through a bibliometric analysis. Utilizing the visualization and mapping program VOSviewer 1.6.18, the relevant data was extracted from the Web of Science (WoS) database and analyzed. The findings indicated important study fields, country contributions, productive journals, as well as the most cited authors’ articles. The primary findings were as follows: The most influential journal was Safety Science. One of the most common topics of study for maritime accidents was the human factor. The most productive country was the People’s Republic of China. The findings of the study can assist researchers in conducting their studies more effectively by providing information about the journals they may use, the authors who contributed to it, current research trends, countries, and keywords.

Heterojen ozellik tasiyan geomateryallerin ic yapilarinin goruntulenmesi, numunelerin mekanik ozelliklerinin degerlendirilmesinde arastirmaciya yeni bir bakis acisi saglamaktadir. Ic yapinin tahribatsiz goruntulenmesi icin farkli metotlar kullanilmaktadir. Fakat bu metotlar bircok arastirmaci icin pahali ve ulasilmaz bir durumdadir. Ic yapilarin incelenmesi icin bir diger yontem ise tahribatli kesme ve dilimleme yontemidir. Bu yontemde dogrudan kesitlere ulasilmasina ragmen detayli bir uc boyutlu gorsellestirme icin yeterli sayida ornekleme yapmak mumkun olamamaktadir. Ic yapinin daha iyi degerlendirilebilmesi icin seri dilimleme yonteminden daha basarili bir yonteme ihtiyac duyulmaktadir. Bu amacla, bu calismada secilmis bir geomateryalin Bohme asindirma deney cihazi kullanilarak dilimleme ile elde edilemeyecek siklikta kesit goruntusu sayisallastirilmis ve detayli bir uc boyutlu gorsellestirme gerceklestirmek icin MATLAB teknik programlama dili kullanilmistir. Bu sekilde ornegin i...

The study is geographically focused on the two deep-sea trenches: Vanuatu and Vityaz, Fiji Basin, East Australia, located in the area of the double convergent complex boundary of Pacific and Indo-Australian plates in the North Fiji backarc basin. Complex geophysical settings results in the formation of the trenches, seismicity, geodynamic complexity and instability of the region. The work aimed at modelling and comparative statistical analysis of the bathymetry and geomorphology of two trenches. Technically, the methodology of this work is based on the Generic Mapping Tools (GMT) using cartographic mapping of the geophysical, tectonic and geological settings, 2D and 3D modelling, visualizing raster grids GEBCO, ETOPO1, ETOPO5. Histogram topographic equalization (equalized, normalized, quadratic) was done using module 'grdhisteq'. Modelling geoid and gravity was based on EGM96 by sequence of GMT modules. The cross-spectrals of 2D binary raster grids were compared and their coherency plotted by 'grdfft'. The automated cross-sectioning was done using 'grdtrack' module. The results include modelled transects visualizing trenches asymmetry, bathymetric and geomorphic variations. Vityaz Trench is shallower with depths <-6,100 m, has 'bell-shape' data distribution with two peaks at 700 m,[0][1][2][3][4][5][6][7][0][1][2][3][4][5],000 m to -3,000 m, even data distribution with depth increasing at -2,200 m to -800 m. Geomorphic form of both trenches varies: Vityaz has flat wide bottom similar to a trough, steeper gradient eastern slope. Vanuatu has V-form classic shape with gentle shapes on both slopes. Selected segment of Vityaz has shallower depths with maximal <-5,000 m while Vanuatu is deeper (-6,000 m). Cartographic functionality of the GMT is presented by various cartographic projections: Mercator, Behrman, Cylindrical Stereographic, Hammer retroazimuthal. 3D modelling was done using GMT module 'grdview'. Trenches were visualized and rotated at 160°/45°azimuth. Presented maps, 2D and 3D models and graphics are plotted and visualized by GMT with selected code snippets explained. Current work contributes to the methods of the modelling of deep-sea trenches geomorphology.

The study presents the 2D and 3D mapping of the Mesopotamia Foredeep Basin, Iraq, and the adjacent areas using integrated cartographic approach of multi-source data visualization by Generic Mapping Tools (GMT), R language and QGIS. The geomorphological and geological mapping is based on multiple datasets containing raster and vector layers of the geological, geophysical, topographic, lithological and geodetic data captured from the open source repositories and resulted in 8 new maps. This study develops and presents an integrated cartographic method combining the use of various tools for different tasks in data visualization: QGIS for geological mapping, GMT for mapping geoid, gravity anomalies, 3D mesh visualization and 2D topographic mapping, R language (libraries 'raster' and 'tmap') for modelling relief (slope and aspect) of Iraq. The topography shows variation in data from minimum at -69 m below sea level in the Arabian Gulf, to maximum at 3754 m asl in the NE of Iraq. The map of geologic units of Iraq shows dominating types are Quaternary (Q) and Tertiary (T) that roughly divide the country into two segments (SW and NE). The Mesopotamia Foredeep Basin is dominated by Quaternary (Q). The 3D model of the Iraqi relief presented perspective view of the Mesopotamia Foredeep Basin and Zagros Mts with azimuth rotation at 165°/30°. The geoid undulations increase in NW direction: the lowest, negative values of geoid (-17 m) are over the Arabian Gulf which reflects the continuum of the Indian geopotential minimum. Maximal values of geoid reach 19 m in the SW and NW. The Mesopotamia Foredeep Basin has geoid variations ranging from -15 to 5 m showing correlation with topographic depressions. Free-air gravity model shows correlation of the values with topography proving general trend of the deficit of landmasses causing the negative or lower values in gravity. The values of Faye's gravity anomalies differ from minimum at -124.945 mGal to maximum at 327.724 mGal. The Mesopotamia Foredeep Basin has clearly negative values. The generated cartographic outputs (8 new maps) contribute to the increase of knowledge on the geomorphology, geology and distribution of geophysical anomalies of Iraq with a special focus on Mesopotamia Foredeep Basin. The technical research questions covered by this study relate to the cartographic data formatting, processing, integration and modelling using thematic multi-source datasets covering Iraq. The paper contributes both to the cartographic aspect of integrated mapping and to the regional studies of Iraq, Middle East and the Mesopotamia Foredeep Basin.

Construction project management requires precise scheduling, efficient resource allocation, and effective risk mitigation to prevent delays and cost overruns. Traditional methods such as Microsoft Project and manual Gantt charts often lack the flexibility needed to adapt to real-time project changes. This study evaluates the effectiveness of Primavera P6 in optimizing project timelines, improving resource utilization, and enhancing risk management. A comparative analysis between Primavera P6 and Microsoft Project revealed that Primavera P6 reduced project duration from 933 days to 739 days, demonstrating its superior scheduling capabilities. Additionally, Primavera P6 optimized resource allocation, minimizing idle time and reducing labor costs by 20%. The software's real-time risk assessment tools further enhanced project efficiency by identifying and mitigating delays proactively. These findings highlight Primavera P6 as a powerful tool for modern construction project management, offering significant advantages in scheduling accuracy, cost control, and decision-making. The study concludes that adopting Primavera P6 can lead to greater efficiency, reduced delays, and improved overall project performance, making it a valuable asset in the construction industry.

Network visualization is one of the most widely used tools in digital humanities research. The idea of uncertain or "fuzzy" data is also a core notion in digital humanities research. Yet network visualizations in digital humanities do not always prominently represent uncertainty. In this article, we present a mathematical and logical model of uncertainty as a range of values which can be used in network visualizations. We review some of the principles for visualizing uncertainty of di erent kinds, visual variables that can be used for representing uncertainty, and how these variables have been used to represent di erent data types in visualizations drawn from a range of non-humanities fields like climate science and bioinformatics. We then provide examples of two diagrams: one in which the variables displaying degrees of uncertainty are integrated/pinto the graph and one in which glyphs are added to represent data certainty and uncertainty. Finally, we discuss how probabilistic data and what-if scenarios could be used to expand the representation of uncertainty in humanities network visualizations.

This paper presents exploratory research regarding the design and evaluation of a dashboard supporting the advising of aspiring university students incorporating a black-box predictive model for student success. While black-box predictive models can provide accurate predictions, incorporating them in dashboards is challenging as the blackbox nature can threaten the interpretability and negatively impact trust of end-users. Explainable Learning Analytics aims to provide insights to black-box predictions by for instance explaining how the input features impact the prediction made. Two dashboards were designed to visualize the prediction and the outcome of the explainer. The dashboards supplemented the explainer with an interactive visualisation allowing to simulate how changes in the student's features impact the prediction. Both dashboards were evaluated in user tests with 13 participants. The results show the potential of explainable AI techniques to bring predictive models to advising practice. We found that the combination of the explainer with the simulation helped users to compare the predictive model with their mental models of student success, challenging understanding of users and influencing trust in the predictive model.

This paper describes the novel use of volume rendering for the visualization of MICOM oceanographic data and the tracking of mesoscale eddies. We present methods for generating 3D volumetric data and describe how to use VIRVO to build an animation for MICOM Gulf Stream motion data. This put the data in a format that easily gives oceanographers the ability to visualize mesoscale eddies from any angle, including an undersea aspect, over any duration, and by combinations of spatial dimensions. One ...

The objective of this project is to build a low-cost system for surveying, modeling, prototyping and interactive visualizing aimed at the enhancement of islamic pottery finds of X – XI centuries. The system will allow various applications: the creation of systems for displaying artifacts from the museum alongside the exhibition, the virtual view of restorations from fragments, including AR and VR, the physical reconstruction of the original form using 3D printer to show the pieces in their entirety, as well as the construction of interactive virtual archives to be made available to scholars and visitors.

In the past decade ultrasound (US) has become the preferred modality for a number of interventional procedures, offering excellent soft tissue visualization. The main limitation however is limited visualization of surgical tools. A new method is proposed for robust 3D tracking and US image enhancement of surgical tools under US guidance. Small US sensors are mounted on existing surgical tools. As the imager emits acoustic energy, the electrical signal from the sensor is analyzed to reconstruct its 3D coordinates. These coordinates can then be used for 3D surgical navigation, similar to current day tracking systems. A system with realtime 3D tool tracking and image enhancement was implemented on a commercial ultrasound scanner and 3D probe. Extensive water tank experiments with a tracked 0.2mm sensor show robust performance in a wide range of imaging conditions and tool position/orientations. The 3D tracking accuracy was 0.36 ± 0.16mm throughout the imaging volume of 55 • × 27 • × 150mm. Additionally, the tool was successfully tracked inside a beating heart phantom. This paper proposes an image enhancement and tool tracking technology with sub-mm accuracy for US-guided interventions. The technology is non-disruptive, both in terms of existing clinical workflow and commercial considerations, showing promise for large scale clinical impact.

Introduction Today&#x27;s aerospace products are more complex while being developed by teams located across the globe. The challenge is to optimally organize, direct, and manage these teams, their interactions, dependencies, and priorities during the program. The complexity ...

This article describes the development of a platform designed to visualize the 3D motion of the tongue using ultrasound image sequences. An overview of the system design is given and promising results are presented. Compared to the analysis of motion in 2D image sequences, such a system can provide additional visual information and a quantitative description of the tongue 3D motion. The platform can be useful in a variety of fields, such as speech production, articulation training, etc.

We applied t-distributed stochastic neighbor embedding (t-SNE) to visualize Urdu handwritten numerals (or digits). The data set used consists of 28 × 28 images of handwritten Urdu numerals. The data set was created by inviting authors from different categories of native Urdu speakers. One of the challenging and critical issues for the correct visualization of Urdu numerals is shape similarity between some of the digits. This issue was resolved using t-SNE, by exploiting local and global structures of the large data set at different scales. The global structure consists of geometrical features and local structure is the pixel-based information for each class of Urdu digits. We introduce a novel approach that allows the fusion of these two independent spaces using Euclidean pairwise distances in a highly organized and principled way. The fusion matrix embedded with t-SNE helps to locate each data point in a two (or three-) dimensional map in a very different way. Furthermore, our prop...

This research article considers why Kanban, a widely used tool for work and workflow management through visualization, in many instances is not enough alone for complex projects. Kanban is universally valued for its simplicity in representing work progression and in providing groups with a mechanism for work ordering, but it can fall short when complex planning, resource assigning, and timelines for a project enter into consideration. Citing current studies and observations and interviews with leaders of projects, this study identifies both positive and negative factors in using Kanban alone. Whereas Kanban can allow groups to effectively monitor work for a single day, we have found that it is deficient in important factors for deeper requirements in a project, such as following a project's milestones, dealing with a range of groups, and communicating a general direction for a project to everyone involved in it. As a result, many leaders of a project have found that using Kanban in combination with other methodologies, such as with Scrum or traditional methodologies for planning a project, can make larger, complex projects work effectively. In this analysis, the study identifies Kanban's specific strengths, as well as, weaknesses and suggests improvements in its use through integration with the additional tools and techniques.

Electroencephalography (EEG) is a technique used to record brain electrical activity through sensors placed on the scalp. Motor imagery (MI) involves the ability to imagine a movement without any external visual stimulus. The complex processing of EEG signals in MI scenarios requires advanced computational approaches to recognize the patterns that emerge during this imagery process. This study aims to compare the performance of four popular deep learning architectures namely EEGNet, EEGConformer, EEGInception, and EEGITNet in classifying EEG data within the MI context. The EEGConformer and EEGNet are the most effective models with average accuracies of 72.41% and 71.88%, respectively. Beside, EEGInception and EEGITNet recorded lower accuracies, particularly EEGInception, which lagged behind with an average accuracy of 55.59%. These findings indicate that simple architectures like EEGNet remain competitive, although more complex models like EEGConformer offer a slight performance advantage. This study also highlights the importance of subject-specific factors in enhancing model performance, which can be addressed through adaptive or personalized approaches. The results of this study are expected to provide deeper insights into the most accurate models for motor imagery EEG tasks and contribute to the development of Brain Computer Interface (BCI) applications.

Power quality problems are not new to power systems, but they cannot be overlooked. In the context of Smart Grids, power systems are undergoing a transformation characterized by the high penetration of renewable sources and electronic devices in the grids, in addition to greater computerization of operations. Thus, alternatives in the representation and visualization of these integrated quality parameters become more and more necessary, both for a better understanding of these phenomena and for advanced applications with the use of images. With this in mind, this paper aims to present an alternative for visualizing PQ disturbances through 2-D images from scalograms based on the continuous wavelet transform (CWT) and multiresolution analysis. For this, signals from three different sources, mathematical equations, models of transmission and distribution of energy in MATLAB / Simulink, and real signals from a database were used. For the creation of the scalogram images, the signal proc...

Представлено доказательство от противного гипотезы Коллатца на основе конструктивно-топологического подхода с использованием средних геометрических свойств структур сети, порожденной алгоритмом 3n+1. Ключевое противоречие выявлено методом «конструктивной индукции» и связано с обнаруженным инвариантом - «делимостью сети». Доказательство переносимо и на другие алгоритмы, что дало основание сформулировать расширение оригинальной гипотезы на алгоритмы типа Коллатца, но с операцией деления на любое целое число, не только 2. Содержание 1. Введение 2. Среднегеометрические свойства 3. Техника доказательства 4. Логика доказательства 5. Расширение гипотезы Коллатца 6. Заключение

A proof by contradiction of the Collatz conjecture is presented based on the constructive-topological approach using the geometric mean properties of the network structures generated by the 3n+1 algorithm. The key contradiction is revealed by the method of "constructive induction" and is related to the discovered invariant - "network divisibility." The proof is transferable to other algorithms as well, which gave grounds to formulate an extension of the original conjecture to Collatz-type algorithms, but with the operation of division by any integer, not just 2. Contents 1. Introduction 2. Geometric mean properties 3. Proof technique 4. Logic of the proof 5. Extension of the Collatz conjecture 6. Conclusion

The expanding availability of high bandwidth networking is heralding a new generation of resources targeting very tightly-coupled distributed interactions. A case in point is the virtualization of critical tasks: advanced training, hazardous industrial operations, or minimally invasive surgery all require that complex entities -mechanical, thermal, physiological, etc. -be blended into cohesive "worlds" and made available to multiple actors requiring different levels of representation. In this paper we discuss issues related to the integration of computer vision and behavioral modeling, hard real-time compliance, the architecture of distributed simulation and an object-oriented framework with an extensible knowledge of physics, leading to the design of Extreme Virtual Environments. We also present current exemples targeting underwater robotics and computer-aided surgery.

Visual Analytics seeks to combine automatic data analysis with visualization and human-computer interaction facilities to solve analysis problems in applications characterized by occurrence of large amounts of complex data. The financial data analysis domain is a promising field for research and application of Visual Analytics technology, as it prototypically involves the analysis of large data volumes in solving complex analysis tasks. We introduce a Visual Analytics system for supporting the analysis of large amounts of financial time-varying indicator data. A system, driven by the idea of extending standard technical chart analysis from one to two-dimensional indicator space, is developed. The system relies on an unsupervised clustering algorithm combined with an appropriately designed movement data visualization technique. Several analytical views on the full market and specific assets are offered for the user to navigate, to explore, and to analyze. The system includes automatic screening of the potentially large visualization space, preselecting possibly interesting candidate data views for presentation to the user. The system is applied to a large data set of time varying 2-D stock market data, demonstrating its effectiveness for visual analysis of financial data. We expect the proposed techniques to be beneficial in other application areas as well.

E↵ective visual exploration of large data sets is an important problem. A standard technique for mapping large data sets is to use hierarchical data representations (trees, or dendrograms) that users may navigate. If the data sets get large, so do the hierarchies, and e↵ective methods for the navigation are required. Traditionally, users navigate visual representations using desktop interaction modalities, including mouse interaction. Motivated by recent availability of lowcost eye-tracker systems, we investigate application possibilities to use eye-tracking for controlling the visual-interactive data exploration process. We implemented a proof-ofconcept system for visual exploration of hierarchic data, exemplified by scatter plot diagrams which are to be explored for grouping and similarity relationships. The exploration includes usage of degree-of-interest based distortion controlled by user attention read from eye-movement behavior. We present the basic elements of our system, and give an illustrative use case discussion, outlining the application possibilities. We also identify interesting future developments based on the given data views and captured eye-tracking information.

In this paper, we propose an interactive visualization called VERMONT which tackles the problem of visualizing mutations and infers their possible effects on the conservation of physicochemical and topological properties in protein families. More specifically, we visualize a set of structure-based sequence alignments and integrate several structural parameters that should aid biologists in gaining insight into possible consequences of mutations. VERMONT allowed us to identify patterns of position-specific properties as well as exceptions that may help predict whether specific mutations could damage protein function.

Figure 1: Our Level-of-Detail (LOD) framework enables efficient digitization of high heritage ceilings, demonstrated through the 4.8-meterhigh historical ceiling (37.5m length) at the National Trust's Blickling Hall, United Kingdom. Our approach combines 3D Gaussian Splatting (3DGS) [KKLD23] reconstruction with high-resolution 3D scannings of intricate emblematic patterns, creating a hybrid visualization system in Virtual Reality (VR). The VR environment enables seamless transitions between architectural-scale visualization and immersive inspection of fine details, enhanced by integrated narrative storytelling.

The DOSoReMI.hu (Digital, Optimized, Soil Related Maps and Information in Hungary) project was started intentionally for the renewal of the national spatial soil infrastructure in Hungary partly inspired by GlobalSoilMap (GSM) initiative. During our activities we have significantly extended the potential, how soil information requirements could be satisfied. Soil property maps have been compiled partly according to GSM specifications, partly by slightly or more strictly changing some of their predefined parameters (depth intervals, pixel size, property etc.) according to the specific demands on the final products. Based upon the collected experiences the full range of GSM products were also targeted. The on-line publishing of the results has also been elaborated creating a proper web map service. Our paper summarizes, what can the users expect from the renewed national soil spatial infrastructure.

The purpose of this paper is to highlight the use of digital mapping and GIS in combination with 3D geo-visualization in depicting landscapes and cultural heritage sites. As computers evolve in their processing capabilities (graphics card, processor, memory speed), so does the graphical output representing our world that can be done in less time. Short processing times and available highly sophisticated software create conditions to test existing models of our 3D world or best create alternative renderings of monuments and landscapes. A study area has been chosen to present such a methodology. Ancient Pylos was selected as the study area due to the abundance of bibliographic references available, facilitating the creation of a cartographic representation of the region based on archaeological discoveries and historical sources. The advantages of employing 3D geo-visualizations in archaeology are numerous and are examined within this article. Visual imaging aids archaeologists in elucidating intricate or deficient details pertaining to a monument, simultaneously streamlining archaeological data for easier comprehension by the general public. The use of 3D geo-visualization to represent data and other non-photorealistic details is expected to dominate in the near future. This is an open-access article under the CC-BY-SA license.

The paper is expansive on the psychology of visual perception in dashboards in a push to design for impact. Visualization is a game of storytelling that ensures everything meets human perception in their designs. It is based on building and developing better data visualizations to show how everything would be designed. It is explicit in terms of interpreting and communicating information in making sure that everything is applied to how it works in understanding different psychological requirements to show its application and use, based on improving how information can be applied and improved to make sure that it fits what is expected, based on improved visual impacts. Data interpretation works on processing data requirements to balance information delivery and aesthetics in the data visualizations. The paper is key in providing information that starts with improving compelling visuals, which come with improving the focus on data improvement and improving the essence that works on cognitive load and balance for understanding and reducing clutter or distractions of the data visuals. Organizing and improving information is an important aspect in ensuring that data visualization is effective and reliable, with related data points being explicit and improved in how viewers or the audience view and relate to the information being viewed. Case findings are that there lacks more knowledge in terms of how designing for impact is the future for data visualizations to make visual impact key.

Computational and data grids represent an emerging technology that allows geographically and organizationally distributed resources (e.g.computing and storage resources) to be linked and accessed in a fashion that is transparent to the user, presenting an extension of the desktop for users whose computational, data and visualization needs extend beyond their local systems. The New York State Grid is an integrated computational and data grid that provides web-based access for users from around the world to computational, application and data storage resources. This grid is used in a ubiquitous fashion, where the users have virtual access to their data sets and applications, but do not need to be made aware of the details of the data storage or computational devices that are specifically employed. Two of the applications that users worldwide have access to on a variety of grids, including the New York State Grid, are theSnBandBnPprograms, which implement theShake-and-Bakemethod of mol...

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Agent-monitored multisimulation provides a powerful paradigm to conceive and perform experimentations not possible by traditional simulation techniques. Multisimulation was developed for simulation-based conflict management and training purposes. Anticipatory behavior has several advantages over reactive behavior. Monitoring multisimulation studies by agents bring additional benefits. First, overviews of several types of threats and the relevance and importance of anticipation are given. Then, the possibilities offered by multisimulation and agent-monitored anticipatory multisimulation are presented. Afterwards, the appropriateness of agent-monitored anticipatory multisimulation is shown for several types of synchronous or asynchronous threat management and training.

Summary Rapid progress in genome science requires equally rapid visualization software development so that researchers can better explore and understand novel datasets. To make developing new visualizations faster and easier, we previously re-factored the Integrated Genome Browser (IGB), a desktop Java application with dozens of features, into a pluggable application framework that can accept new functionality as plug-ins, called IGB Apps. However, developers lacked a centralized location for sharing Apps, making it hard to connect with potential users. To fill this gap, we created an App Store for IGB, a user-friendly Web site for developers to release and document Apps, and for users to find them. Availability and implementation The IGB App Store is available from https://bioviz.org.