Interactive Visualization Research Articles

Visual analysis of diversity and threat status of natural materials for musical instruments

A classical symphony orchestra consists of up to 29 musical instruments manufactured from up to 758 distinct natural materials. The interrelationships between the extraction of raw materials for instrument making, the international trade conditions, and the protection status of endangered species and their ecosystems are highly complex and have yet to be sufficiently scientifically examined. However, rapidly progressing climate and ecological change call for sustainable solutions. To address this challenging task, we present MusEcology, a new interactive decision support system based on visualizations. The interactive visualizations offer entry points for users of various backgrounds to explore the interrelationships between musical instruments, natural resources and ecosystems. The tool’s fundamental objectives are to guarantee that the (1) data processing correlates related data resources, that (2) visual interfaces and interaction schemes encourage new interdisciplinary research on complex systems interactions, and that (3) high-level decision-making is supported to identify alternative pathways towards sustainable instrument making.

DIORAMA: AS A LEARNING MEDIA FOR SCIENCE AND SOCIAL STUDIES (IPAS) ON ECOSYSTEM MATERIAL IN GRADE IV OF SDI CENDEKIA ASSALAM BANGILAN

Teaching Science and Social Studies (IPAS) in elementary schools often faces challenges in delivering abstract concepts such as ecosystems. Conventional teaching methods, which are less interactive, make it difficult for students to understand the relationships between ecosystem components. This study aims to examine the effectiveness of diorama media in improving students' understanding of ecosystem material in Grade IV of SDI Cendekia Assalam Bangilan. The research used a quantitative approach with a quasi-experimental design, involving 27 students in the experimental group using diorama media and 25 students in the control group using conventional teaching methods. Data were collected through pre-tests and post-tests and analyzed using the t-test. The results showed that the average post-test score of the experimental group was higher (85.4) compared to the control group (73.8). Statistical analysis indicated a significant difference between the two groups (p < 0.05), demonstrating that diorama media is effective in enhancing students' understanding of ecosystem concepts. These findings support constructivist learning theory, which emphasizes the importance of visual experiences and interaction in learning. This study suggests that diorama media should be widely applied to other materials requiring concrete visualization. Additionally, further research can explore the impact of diorama media on other skills such as critical thinking or student collaboration.

Reduced-Order Model of a Time-Trial Cyclist Helmet for Aerodynamic Optimization Through Mesh Morphing and Enhanced with Real-Time Interactive Visualization

Aerodynamics is a key factor in time-trial cycling. Over the years, various aspects have been investigated, including positioning, clothing, bicycle design, and helmet shape. The present study focuses on the development of a methodology for the aerodynamic optimization of a time-trial helmet through the implementation of a reduced-order model, alongside advanced simulation techniques, such as computational fluid dynamics, radial basis functions, mesh morphing, and response surface methodology. The implementation of a reduced-order model enhances the understanding of aerodynamic interactions compared to traditional optimization workflows reported in sports-related research, facilitating the identification of an optimal helmet shape during the design phase. The study offers practical insights for refining helmet design. Starting with a baseline teardrop profile, several morphing configurations are systematically tested, resulting in a 10% reduction in the drag force acting on the helmet. The reduced-order model also facilitates the analysis of turbulent flow patterns on the cyclist’s body, providing a detailed understanding of aerodynamic interactions. By leveraging reduced-order models and advanced simulation techniques, this study contributes to ongoing efforts to reduce the aerodynamic resistance of time-trial helmets, ultimately supporting the goal of improved athlete performance.

CNVizard—a lightweight streamlit application for an interactive analysis of copy number variants

BackgroundMethods to call, analyze and visualize copy number variations (CNVs) from massive parallel sequencing data have been widely adopted in clinical practice and genetic research. To enable a streamlined analysis of CNV data, comprehensive annotations and good visualizations are indispensable. The ability to detect single exon CNVs is another important feature for genetic testing. Nonetheless, most available open-source tools come with limitations in at least one of these areas. One additional drawback is that available tools deliver data in an unstructured and static format which requires subsequent visualization and formatting efforts.ResultsHere we present CNVizard, an interactive Streamlit app allowing a comprehensive visualization of CNVkit data. Furthermore, combining CNVizard with the CNVand pipeline allows the annotation and visualization of CNV or SV VCF files from any CNV caller.ConclusionCNVizard, in combination with CNVand, enables the comprehensive and streamlined analysis of short- and long-read sequencing data and provide an intuitive webapp-like experience enabling an interactive visualization of CNV data.

Interactive Geographic Visualization and Unsupervised Learning for Optimal Assignment of Preachers to Appropriate Congregations

Riau Province has a population of 6,642,874 and a diverse geography, which poses significant challenges in optimizing Islamic preaching activities. Traditional assignment methods often lead to inefficiencies due to misalignment between the preacher’s expertise and congregational needs, as well as logistical issues. This study integrates K-Means clustering and DBSCAN algorithms with interactive geographic visualization to optimize the assignment of preachers to mosques. We collected 435 data points, including 185 mosques and 250 preachers. K-Means was evaluated using the Elbow Method and Silhouette Score, identifying 10 clusters as optimal with a Silhouette Score of 0.435654. However, K-Means does not handle outliers effectively, as indicated by zero outliers in all configurations. DBSCAN was tested with various epsilon (eps) and minimum sample values. The optimal configuration with eps of 1.5 and 5 minimum samples resulted in 10 clusters with a Silhouette Score of 0.381108 and 60 outliers. DBSCAN effectively manages outliers and varying densities. Although K-Means is advantageous for its simplicity and higher Silhouette Scores, it is unable to handle outliers effectively. DBSCAN provides robust clustering for noisy data. Therefore, it can be concluded that hybridizing unsupervised learning algorithms with geographic visualization can potentially improve the effectiveness of preaching activities in Riau Province and enhance preacher assignment.

Recognition of Conus species using a combined approach of supervised learning and deep learning-based feature extraction.

Cone snails are venomous marine gastropods comprising more than 950 species widely distributed across different habitats. Their conical shells are remarkably similar to those of other invertebrates in terms of color, pattern, and size. For these reasons, assigning taxonomic signatures to cone snail shells is a challenging task. In this report, we propose an ensemble learning strategy based on the combination of Random Forest (RF) and XGBoost (XGB) methods. We used 47,600 cone shell images of uniform size (224 x 224 pixels), which were split into an 80:20 train-test ratio. Prior to performing subsequent operations, these images were subjected to pre-processing and transformation. After applying a deep learning approach (Visual Geometry Group with a 16-layer deep model architecture) for feature extraction, model specificity was further assessed by including multiple related and unrelated seashell images. Both classifiers demonstrated comparable recognition ability on random test samples. The evaluation results suggested that RF outperformed XGB due to its high accuracy in recognizing Conus species, with an average precision of 95.78%. The area under the receiver operating characteristic curve was 0.99, indicating the model's optimal performance. The learning and validation curves also demonstrated a robust fit, with the training score reaching 1 and the validation score gradually increasing to 95 as more data was provided. These values indicate a well-trained model that generalizes effectively to validation data without significant overfitting. The gradual improvement in the validation score curve is crucial for ensuring model reliability and minimizing the risk of overfitting. Our findings revealed an interactive visualization. The performance of our proposed model suggests its potential for use with datasets of other mollusks, and optimal results may be achieved for their categorization and taxonomical characterization.

Visualization of Protein-Protein Interaction In Situ Using Hybridization-Enhanced Proximity Ligation Assay.

Understanding protein-protein interactions (PPIs) is critical for elucidating cellular functions and disease mechanisms. We present the hybridization-enhanced proximity ligation assay (HPLA), a novel approach incorporating a prehybridization step to improve the ligation efficiency of DNA probes on antibody-oligonucleotide conjugates. This step involves a splint probe hybridizing with complementary sequences on the DNA probes bound to interacting protein complexes, forming V-shaped overhangs that facilitate the circularization of two circle probes. These circularized DNA templates undergo localized rolling circle amplification, producing amplification products that can be visualized as fluorescent dots upon hybridization with dye-labeled detection probes. We demonstrated the utility of HPLA in various biological contexts, including cancer-associated protein interactions and signaling pathways, achieving clearer, more precise visualization of PPIs and enabling robust quantitative analysis.

Integrated Blockchain and Digital Twin Framework for Sustainable Building Energy Management

The building sector remains a major contributor to increasing energy consumption and emissions. Meanwhile, the energy system is becoming more complex due to the transition to clean energy sources. Current tools and policies struggle to manage this complexity, as the existing infrastructure was not designed for such large dynamic distributed energy resources. This creates an urgent need to adopt emerging technologies for enhancing building energy management systems. The objective of this research is to develop a framework that integrates Blockchain and Digital Twin technologies to provide an efficient and trusted energy management platform that supports smart cities communities and to effectively contribute to enhancing the progress of UN Sustainability Development Goals (SDGs), specifically SDG11 and SDG13. The proposed framework comprises four main elements: Blockchain platform, Digital Twin platform, Application Program Interfaces (APIs), and building energy model. Blockchain platform automates energy billing by utilizing digital currency and smart contracts with pre-set pricing tiers and feed in tariffs. Digital Twin platform provides interactive communication and visualization with physical assets. APIs enables seamless interconnectivity between both platforms. The building energy model acts as a prediction tool, and the simulation results are fed to Digital Twin platform to alert system participants in case actual consumption deviates from optimum values. The viability of the proposed framework is demonstrated using a case study of a residential apartment.

Comunicazione Digitale XR: Assessing the Impact of Extended Reality Technologies on Learning

“Comunicazione Digitale XR” is a project that exploits the EON-XR platform to enrich Information Technology education by incorporating interactive 360-degree visuals and 3D models to complement traditional teaching methods. This study probes the efficacy of Extended Reality (XR) technologies in augmenting educational experiences through the integration of gamification techniques within the curriculum. The evaluation, involving twenty-six students, delved into factors such as engagement levels, comprehension of material, and the contributory role of XR in exam readiness. Data collection was conducted using electronic log files, which provided detailed quantitative metrics on student interactions, and a post-intervention survey that assessed changes in comprehension and engagement. Additionally, qualitative data was gathered through semi-structured interviews. Quantitative data from surveys and log files were analyzed using descriptive statistics, while qualitative data from interviews were subjected to thematic analysis. This investigation has demonstrated substantial enhancements in student comprehension and engagement with the educational content facilitated by XR technologies. These findings underscore the potential of XR to augment the educational paradigm and improve students’ preparedness for academic evaluations.

Beyond Vision Impairments: Redefining the Scope of Accessible Data Representations.

The increasing ubiquity of data in everyday life has elevated the importance of data literacy and accessible data representations, particularly for individuals with disabilities. While prior research predominantly focuses on the needs of the visually impaired, our survey aims to broaden this scope by investigating accessible data representations across a more inclusive spectrum of disabilities. After conducting a systematic review of 152 accessible data representation papers from ACM and IEEE databases, we found that roughly 78% of existing articles center on vision impairments. In this article, we conduct a comprehensive review of the remaining 22% of papers focused on underrepresented disability communities. We developed categorical dimensions based on accessibility, visualization, and human-computer interaction to classify the papers. These dimensions include the community of focus, issues addressed, contribution type, study methods, participants involved, data type, visualization type, and data domain. Our work redefines accessible data representations by illustrating their application for disabilities beyond those related to vision. Building on our literature review, we identify and discuss opportunities for future research in accessible data representations.

Multi-Scent Olfactory Display with Visual and Sound Interaction

Multi-Scent Olfactory Display with Visual and Sound Interaction

A physiologically based toxicokinetic model for microplastics and nanoplastics in mice after oral exposure and its implications for human dietary exposure assessment

Evidence of microplastics (MPs) and nanoplastics (NPs) in foods and daily-use products, along with their frequent detection in the human body, has raised concerns regarding their potential impact on human health through dietary ingestion. However, there is a lack of quantitative tools to simulate their bioaccumulation and tissue distribution following environmental exposure. To address this gap, we developed the first physiologically based toxicokinetic (PBTK) model for predicting the biodistribution of MPs and NPs in mice following oral exposure under various exposure scenarios. This novel model incorporated key kinetic mass transport processes, such as membrane permeability, albumin binding, and cellular uptake. We identified that the absorption rate in the gastrointestinal tract and fecal excretion rate constant had significant impacts on organ dosimetry. Our regression analysis indicated that the size-dependent dissociation constant and urine clearance rate constant sharply increased by a factor of 3 as NPs particle size increased to 1µm. Finally, we developed a graphical user interface to enable interactive visualization and analysis for future applications, supporting human dietary exposure and risk assessment using available food consumption data and MPs/NPs residue data. The simulation results offer a mechanistic perspective, enhancing understanding of the internal organ dosimetry burden and health impacts from dietary exposure to MPs and NPs.

Analysis of Interaction Features of Cyclo[13]carbon with Small Molecules and Formation Mechanism of Its Dimer.

The newly discovered cyclo[13]carbon, the first artificially synthesized odd-numbered carbon ring, is an intriguing carbon isomer that provides a valuable subject for studying low-symmetry carbon materials. In this work, we employed first-principles calculations to explore the geometric structure and electronic properties of cyclo[13]carbon through various techniques such as vibrational mode analysis, bond order analysis, spin density analysis, electron localization analysis, electrostatic potential and van der Waals potential analysis, visualization of weak interactions, and energy decomposition analysis. We investigated the interaction characteristics of cyclo[13]carbon with small molecules and examined its dimer formation mechanism and dynamics features using ab initio molecular dynamics. Our study reveals the unique physicochemical properties of this novel carbon ring system. The antiaromaticity of the low-symmetry cyclo[13]carbon sets it apart from previously synthesized even-numbered carbon rings, with van der Waals interactions playing a crucial role in its binding with small molecules and in the formation of C13 dimers. This research provides theoretical insights that complement experimental observations and theoretical studies, aiding further investigation into the diverse properties of fresh carbon material isomers and promoting the synthesis and application of novel molecular materials in molecular electronics and nanotechnology.

The Application and Effects of Augmented Reality in Geometry Learning

With the development of new technology, augmented reality (AR) technology is revolutionizing the educational landscape, particularly in geometry. This innovative approach deepens their comprehension of geometric principles and stimulates their motivation to learn. This paper aims to provide a comprehensive introduction to ARs application in the educational field, especially the topic of geometry, and evaluate its effectiveness in improving users in two aspects: motivation and academic results. The study applies the Attention, Relevance, Confidence, and Satisfaction (ARCS) Model to assess the impact of AR in education compared to traditional teaching. It finds that AR boosts student motivation, especially for those with lower academic performance. While AR enhances learning experiences, its effect on improving grades is not immediate. The method that is mainly utilized in this study is reviewing the literature, summarizing, and analyzing critical information from them. And it turns out that short-term grade improvements are not significantly observable, AR technology offers long-term benefits by enhancing engagement and conceptual comprehension through interactive and spatial visualizations.

Markarian Multiwavelength Data Center (MMDC): A Tool for Retrieving and Modeling Multitemporal, Multiwavelength, and Multimessenger Data from Blazar Observations

The Markarian Multiwavelength Data Center (MMDC) is a web-based tool designed for accessing and retrieving multiwavelength and multimessenger data from blazar observations. MMDC facilitates the construction and interactive visualization of time-resolved multiband spectral energy distributions of blazars by integrating (i) archival data from over 80 catalogs and databases, (ii) optical data from all-sky survey facilities such as the All-Sky Automated Survey for Supernovae, Zwicky Transient Facility, and Panoramic Survey Telescope and Rapid Response System, and (iii) newly analyzed data sets in the optical/UV band from Swift-UVOT, in the X-ray band from Swift-X-Ray Telescope and NuSTAR observations, and the high-energy γ-ray band from Fermi-Large Area Telescope observations. MMDC distinguishes itself from other online platforms by the large quantity of available data. For instance, it includes data from all blazar observations by Swift and NuSTAR, as well as the results of detailed spectral analysis in the γ-ray band during different emission states, covering the period from 2008 to 2023. Another important distinguishing feature of MMDC is its ability to enable precise, self-consistent theoretical modeling of the observed data using machine learning algorithms trained on leptonic and lepto-hadronic models, which consider the injection of particles and all relevant cooling processes. MMDC is an innovative tool that significantly enhances blazar research by providing a comprehensive framework for data accessibility, analysis, and theoretical interpretation, thereby advancing our understanding of blazar emissions and the underlying astrophysical processes.

Protomix: A Python Package for 1H-NMR Metabolomics Data Preprocessing

Abstract Motivation NMR-based metabolomics is a field driven by technological advancements, necessitating the use of advanced preprocessing tools. Despite this need, there is a remarkable scarcity of comprehensive and user-friendly preprocessing tools in Python. To bridge this gap, we have developed Protomix—a Python package designed for metabolomics research. Protomix offers a set of automated, efficient, and user-friendly signal-preprocessing steps, tailored to streamline and enhance the preprocessing phase in metabolomics studies. Results This package presents a comprehensive preprocessing pipeline compatible with various data analysis tools. It encompasses a suite of functionalities for data extraction, preprocessing, and interactive visualization. Additionally, it includes a tutorial in the form of a Python Jupyter notebook, specifically designed for the analysis of 1D 1H-NMR metabolomics data related to prostate cancer and benign prostatic hyperplasia. Supplementary information Supplementary data are available at Bioinformatics Advances online.

Concepts Towards Nation-Wide Individual Tree Data and Virtual Forests

Individual tree data could offer potential uses for both forestry and landscape visualization but has not yet been realized on a large scale. Relying on 5 points/m2 Finnish national laser scanning, we present the design and implementation of a system for producing, storing, distributing, querying, and viewing individual tree data, both in a web browser and in a game engine-mediated interactive 3D visualization, “virtual forest”. In our experiment, 3896 km2 of airborne laser scanning point clouds were processed for individual tree detection, resulting in over 100 million trees detected, but the developed technical infrastructure allows for containing 10+ billion trees (a rough number of log-sized trees in Finland) to be visualized in the same system. About 92% of trees wider than 20 cm in diameter at breast height (corresponding to industrial log-size trees) were detected using national laser scanning data. Obtained relative RMSE for height, diameter, volume, and biomass (stored above-ground carbon) at individual tree levels were 4.5%, 16.9%, 30.2%, and 29.0%, respectively. The obtained RMSE and bias are low enough for operational forestry and add value over current area-based inventories. By combining the single-tree data with open GIS datasets, a 3D virtual forest was produced automatically. A comparison against georeferenced panoramic images was performed to assess the verisimilitude of the virtual scenes, with the best results obtained from sparse grown forests on sites with clear landmarks. Both the online viewer and 3D virtual forest can be used for improved decision-making in multifunctional forestry. Based on the work, individual tree inventory is expected to become operational in Finland in 2026 as part of the third national laser scanning program.

A Comparison of Open Data Observatories

Open Data Observatories refer to online platforms that provide real-time and historical data for a particular application context, e.g., urban/non-urban environments or a specific application domain. They are generally developed to facilitate collaboration within one or more communities through reusable datasets, analysis tools, and interactive visualizations. Open Data Observatories collect and integrate various data from multiple disparate data sources—some providing mechanisms to support real-time data capture and ingest. Data types can include sensor data (soil, weather, traffic, pollution levels) and satellite imagery. Data sources can include Open Data providers, interconnected devices, and services offered through the Internet of Things. The continually increasing volume and variety of such data require timely integration, management, and analysis, yet presented in a way that end-users can easily understand. Data released for open access preserve their value and enable a more in-depth understanding of real-world choices. This survey compares thirteen Open Data Observatories and their data management approaches - investigating their aims, design, and types of data. We conclude with research challenges that influence the implementation of these observatories, outlining some strengths and limitations for each one and recommending areas for improvement. Our goal is to identify best practices learned from the selected observatories to aid the development of new Open Data Observatories.

Touching the City: Tactility and Affect of Public Art

Touching objects of public art is one of the most frequently recorded and at the same time superficially described practices of interaction with the urban environment. I propose to look at this practice through the lens of the “affective atmospheres” approach, which allows us to problematize tactile contact as a means and result of affective attunement with space. Analyzing the affective-discursive canon, emerging around the interaction with a new art object (the sculpture “Big Clay No. 4” in Moscow), I demonstrate how, on the one hand, the transition from digital to visual and tactile interaction with the materiality of space creates collective affect online; on the other hand, how this affect is transmitted to social media, making the public art object the center of a phygital hybrid space.

Mapping of Health System Performance Indicators to the WHO HSPA Framework.

Healthcare systems worldwide face escalating costs and demographic changes, necessitating effective evaluation tools to understand their underlying challenges. Switzerland's high-quality yet costly healthcare system underscores the need for robust assessment methods. Existing international rankings often lack transparency and comparability, highlighting the value of structured frameworks like the Health System Performance Assessment (HSPA) by the World Health Organization (WHO). This framework evaluates healthcare systems across multiple dimensions including governance, resource generation, financing, and service delivery. This paper aims to integrate Swiss healthcare indicators from the Swiss Health Observatory (Obsan) into the HSPA framework, addressing the central research question: How can these indicators be mapped to the HSPA framework, and what insights does this integration provide? Our methodology includes selecting and categorizing Obsan indicators, manually mapping them to HSPA sub-functions, and validating these mappings using word embeddings and cosine similarity. An R Shiny application was developed for interactive visualization. Results demonstrate accurate indicator assignment, enabling intuitive visualization and enhancing data structuring.