Information Visualization Research Articles

Application and Development of Firefighting Technologies in Industrial Heritage: Experiences and Insights from Macau

Due to the irreversible nature of the consequences of fire, fire protection is a major challenge and source of problems for all types of built heritage. This study aims to establish sustainable fire protection technology strategies by generalizing fire prevention and control technologies and measures against extended burns. This study aims to explore Macau’s industrial heritage’s historical development and technological applications in the field of fire protection using literature analysis, field investigation, and spatial information visualization methods. It will be carried out using the industrial heritage of Macau as the object and systematic analyses from the screening and processing of fire protection historical data, fire risk assessment, and the migration of fire protection focus. The results show that (1) the fire protection of the industrial heritage of Macau has gone through a total of three phases: passive fire protection, transition of fire protection methods, and active fire protection, and the relied-upon fire protection technologies have been iterated and renewed continuously during this period. (2) When the fire load factors of industrial heritage increase, the fire vulnerability assessment substantially changes, and the center of gravity of heritage fire protection shifts from controlling the scope of disaster to reducing the fire risk. (3) The construction of a suitable and effective ecological model of fire protection technology can provide appropriate fire protection solutions for the preservation and reuse of Macau’s industrial heritage in a complex cultural context. Therefore, this study will help to solve the current dilemma of sustainable application and development of fire protection technology for industrial heritage. This study hopes to provide ideas and strategies for reference on industrial heritage fire protection issues in the development of similar world heritage cities.

History and future of business ecosystem: a bibliometric analysis and visualization

AbstractThe business ecosystem theory has developed rapidly in recent years and has become a hot topic in the field of business and management. However, the use of this concept is controversial. This study systematically reviewed literature published spanning nearly three decades from 1993 to 2022. In this paper, researchers designed an improved traceability method to retrieve literature based on data sources form Web of Science. VOSviewer and CiteSpace are adopted as two scientific atlas tools for information processing and visualization to evaluate the relationship between sub fields of business ecosystem. The findings show that the four branches of business ecosystem, i.e., innovation, platform, entrepreneurship and service, absorb theoretical ideas to varying degrees. Among them, the theoretical inheritance relationship of innovation branch is most clear, and gradually grows into the backbone of ecosystem research. Major contribution of this study is reflected in three aspects: Firstly, the improved traceability method provides a repeatable quantitative description process on the basis of significantly reducing researchers’ subjective participation. Secondly, from perspective of bibliometrics, the branch direction and key nodes of theory development are identified. Thirdly, the study helps identify the future development directions of business ecosystem, including innovation, digitalization, entrepreneurship, self-organization and the strategic transformation guided by emerging technologies.

Integrated deep learning model for automatic detection and classification of stenosis in coronary angiography

Coronary artery disease poses a significant threat to human health. In clinical settings, coronary angiography remains the gold standard for diagnosing coronary heart disease. A crucial aspect of this diagnosis involves detecting arterial narrowings. Categorizing these narrowings can provide insight into whether patients should receive vascular revascularization treatment. The majority of current deep learning methods for analyzing coronary angiography are mostly confined to the theoretical research domain, with limited studies offering direct practical support to clinical practitioners. This paper proposes an integrated deep-learning model for the localization and classification of narrowings in coronary angiography images. The experimentation employed 1606 coronary angiography images obtained from 132 patients, resulting in an accuracy of 88.9 %, a recall rate of 85.4 %, an F1 score of 0.871, and a MAP value of 0.875 for vascular stenosis detection. Furthermore, we developed the "Hemadostenosis" web platform (http://bioinfor.imu.edu.cn/hemadostenosis) using Django, a highly mature HTTP framework. Users are able to submit coronary angiography image data for assessment via a visual interface. Subsequently, the system sends the images to a trained convolutional neural network model to localize and categorize the narrowings. Finally, the visualized outcomes are displayed to users and are downloadable. Our proposed approach pioneers the recognition and categorization of arterial narrowings in vascular angiography, offering practical support to clinical practitioners in their learning and diagnostic processes.

The development of information and analytical competencies in the professional training of master's students in the "State and municipal management" direction

Information and analytical competencies are an integral part of the professional skills of state and municipal authorities, as they allow to effectively process and analyze large amounts of information, make informed decisions, and forecast the consequences of the actions taken. Such skills are especially relevant in the conditions of the modern information society, where access to the information has become wider and data volumes are growing every day. The development of information and analytical competencies is a necessary condition for the successful professional activity of graduates of the direction 38.04.04 “State and Municipal Management”. To form and improve these competencies in students it is necessary to use modern methods and tools for working with big data, such as data analysis, information visualization, with databases, and others. It is important to develop the critical thinking and self­control ability when working with information. Using the example of teaching the discipline “Information and analytical technologies in the state and municipal management” at the Peter the Great St. Petersburg Polytechnic University, the author proves the necessity of using B. Bloom’s taxonomy as the basis of the course methodology: thanks to the hierarchy of learning and research objectives, it is possible to effectively form information and analytical competencies and their consistent consolidation in the professional activities of master’s students.

RCDdb: A manually curated database and analysis platform for regulated cell death

Regulated cell death is a pivotal regulatory mechanism governing the development and homeostasis of multicellular organisms. A comprehensive understanding of RCD's regulatory mechanisms is crucial for developing novel therapeutic strategies against diseases associated with cell death, such as cancer and neurodegenerative diseases. However, existing data repositories support limited types of cell death data and lack comprehensive annotation and analytical functionalities. Thus, establishing an extensive cell death database is an urgent imperative. To address this gap, we developed the Regulated Cell Death Database (RCDdb, chenyclab.com/RCDdb), the first comprehensively manually annotated database designed to support annotations and analytical capabilities across all RCD types. We compiled 3090 marker gene annotations associated with 15 RCD types from 2180 relevant articles. The RCDdb includes annotation data on these marker genes concerning diseases, drugs, pathways, proteins, and gene expressions. Furthermore, it provides 49 diverse visualization methods to present this information. More importantly, the RCDdb features three online analysis tools for identifying and analyzing RCD-related features within user-submitted data. Furthermore, the RCDdb offers a user-friendly interface for querying, browsing, analysis, and visualization of detailed information associated with each RCD category. This resource promises to significantly aid researchers in better understanding the mechanisms of cell death, thereby accelerating progress in research and therapeutic strategies aimed at combating RCD-related diseases.

Design and Application Research of a UAV-Based Road Illuminance Measurement System

This paper presents a UAV-based road illumination measurement system and evaluates its performance through experiments. The system employs a HUBSAN Zino 2+ UAV, STM32F103RCT6 microcontroller, BH1750 illuminance sensor, and GPS and integrates flight, processing, measurement, cloud platform, obstacle avoidance, communication, and power supply units via the OneNET cloud platform. Both hardware and software designs were implemented, using the Z-score algorithm to handle outliers in illumination data. The system showed a single-point measurement error rate of 1.14% and a MAPE of 5.08% for multi-point measurements. In experiments, the system’s horizontal and vertical illuminance RMSE were 1.92 lx and 1.75 lx, respectively. The real-time visualization interface improved operational efficiency, cutting labor costs by half and time costs by nearly four-fifths. UAV control and monitoring from the roadside ensured safety during measurements. The system’s efficiency and wide measurement range enabled extended experiments, collecting illuminance data across multiple horizontal and vertical planes. This resulted in the creation of both horizontal and innovative vertical-plane illuminance distribution maps. These findings provide valuable data for evaluating road lighting quality, enhancing road traffic safety, and improving road illumination design.

Digital twin-driven proactive-reactive scheduling framework for port multi-equipment under a complex uncertain environment

The pervasive uncertainties in multiple port equipment scheduling frequently result in container handling delays or ineffective plans. To address the complexities and uncertainties of port multiple equipment integrated scheduling problem, this paper introduces a Digital Twin-driven (DT-driven) proactive-reactive scheduling framework for the first time. This framework is designed to promptly respond to uncertainties in the scheduling process and provide a transparent visualization of operational information. It specifically tackles the integrated scheduling problem of port quay cranes, Intelligent Guided Vehicles (IGVs), and yard cranes, considering uncertainties such as fluctuations in operating time, equipment failures, and IGV route conflicts. By developing a virtual container port simulation, which features a U-shaped port layout and double-cycling mode drawn from real-world scenarios, the paper evaluates the proposed framework's effectiveness. The experimental results demonstrate that the digital twin framework method significantly improves efficiency and conserves energy. Additionally, in large-scale conditions, the makespan difference between the DT-driven approach and the non-DT-driven approach is as much as 19.56 %. In terms of energy consumption savings, the DT-driven approach's scheduling plan can save 3.67 % of energy consumption under large-scale conditions. Moreover, as the fluctuation index increases, the energy consumption savings become even more significant. This paper also discusses the potential implications of adopting this framework for port companies, highlighting its benefits in enhancing operational and energy efficiency and its incorporation into port management systems. The sensitivity analysis can offer guidance to port companies on optimal equipment allocation strategies.

The Evaluation Purposes Visualization

Background: The article outlines the development of a visualization that aimed to represent the purpose of different evaluation theories. This visualization used a stream/river metaphor to show the evolution of different purposes (e.g. utilization, knowledge generation, accountability) of evaluation, and also highlighted the historical context that had influenced their development. The visualization shows that the end goal of all these evaluation approaches (i.e. the ocean in which the rivers flow towards) is societal improvement. Purpose: The article highlights the lessons learned and influences that led to the development of an evaluation theory categorization system. We hope that this would serve as an example (and maybe inspiration) for future efforts that aim to understand how evaluation theories have evolved and developed. Setting: Not applicable Intervention: Not applicable Research Design: Not applicable Data Collection and Analysis: Not applicable Findings: Not applicable Keywords: Evaluation theory, information visualization, evaluation historical context, organizing framework, theory development.

Democratizing cheminformatics: interpretable chemical grouping using an automated KNIME workflow

With the increased availability of chemical data in public databases, innovative techniques and algorithms have emerged for the analysis, exploration, visualization, and extraction of information from these data. One such technique is chemical grouping, where chemicals with common characteristics are categorized into distinct groups based on physicochemical properties, use, biological activity, or a combination. However, existing tools for chemical grouping often require specialized programming skills or the use of commercial software packages. To address these challenges, we developed a user-friendly chemical grouping workflow implemented in KNIME, a free, open-source, low/no-code, data analytics platform. The workflow serves as an all-encompassing tool, expertly incorporating a range of processes such as molecular descriptor calculation, feature selection, dimensionality reduction, hyperparameter search, and supervised and unsupervised machine learning methods, enabling effective chemical grouping and visualization of results. Furthermore, we implemented tools for interpretation, identifying key molecular descriptors for the chemical groups, and using natural language summaries to clarify the rationale behind these groupings. The workflow was designed to run seamlessly in both the KNIME local desktop version and KNIME Server WebPortal as a web application. It incorporates interactive interfaces and guides to assist users in a step-by-step manner. We demonstrate the utility of this workflow through a case study using an eye irritation and corrosion dataset.Scientific contributionsThis work presents a novel, comprehensive chemical grouping workflow in KNIME, enhancing accessibility by integrating a user-friendly graphical interface that eliminates the need for extensive programming skills. This workflow uniquely combines several features such as automated molecular descriptor calculation, feature selection, dimensionality reduction, and machine learning algorithms (both supervised and unsupervised), with hyperparameter optimization to refine chemical grouping accuracy. Moreover, we have introduced an innovative interpretative step and natural language summaries to elucidate the underlying reasons for chemical groupings, significantly advancing the usability of the tool and interpretability of the results.

Mass Spectrometry Imaging of Amino Acids Enabled by Quaternized Pyridinium Salt MALDI Probe.

The distribution of small biomolecules, particularly amino acids (AAs), differs between normal cells and cancer cells. Imaging this distribution is crucial for gaining a deeper understanding of their physiological and pathological significance. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) provides accurate in situ visualization information. However, the analysis of AAs remains challenging due to the background interference by conventional MALDI matrices. On tissue chemical derivatization (OTCD) MSI serves as an important approach to resolve this issue. We designed, synthesized, and tested a series of pyridinium salt probes and screened out the 1-(4-(((2,5-dioxopyrrolidin-1-yl)oxy)carbonyl)phenyl)-2,4,6-triphenylpyridin-1-ium (DCT) probe with the highest reaction efficiency and the most effective detection. Moreover, a quantum chemistry calculation was executed to address mechanistic insight into the chemical nature of the novel probes. DCT was found to map 20 common AAs in normal mouse tissues for the first time, which allowed differentiation of AA distribution in normal, normal interstitium, tumor, and tumor interstitium regions and provided potential mechanistic insights for cancer research and other biomedical studies.

Research on privacy protection in the context of healthcare data based on knowledge map.

With the rapid development of emerging information technologies such as artificial intelligence, cloud computing, and the Internet of Things, the world has entered the era of big data. In the face of growing medical big data, research on the privacy protection of personal information has attracted more and more attention, but few studies have analyzed and forecasted the research hotspots and future development trends on the privacy protection. Presently, to systematically and comprehensively summarize the relevant privacy protection literature in the context of big healthcare data, a bibliometric analysis was conducted to clarify the spatial and temporal distribution and research hotspots of privacy protection using the information visualization software CiteSpace. The literature papers related to privacy protection in the Web of Science were collected from 2012 to 2023. Through analysis of the time, author and countries distribution of relevant publications, we found that after 2013, research on the privacy protection has received increasing attention and the core institution of privacy protection research is the university, but the countries show weak cooperation. Additionally, keywords like privacy, big data, internet, challenge, care, and information have high centralities and frequency, indicating the research hotspots and research trends in the field of the privacy protection. All the findings will provide a comprehensive privacy protection research knowledge structure for scholars in the field of privacy protection research under the background of health big data, which can help them quickly grasp the research hotspots and choose future research projects.

Research on the Efficiency Improvement of Human-Machine Collaboration in Manufacturing Production Process

In modern manufacturing, human-computer collaboration has become an important means to improve productivity and product quality. However, the existing human-computer collaboration process and human-computer interaction interface design still has many deficiencies, which affects its efficiency and safety. This paper aims to explore how to improve the efficiency and safety of human-computer collaboration in the production process by optimising the human-computer collaboration process and designing the human-computer interaction interface. It is found that the efficiency and safety of human-computer collaboration can be significantly improved by optimising the collaboration process, improving the working environment and standardising the workflow through the lean manufacturing theory, and adopting a user-friendly interface design with high information visualisation and ease of operation.

Tiramisù: making sense of multi-faceted process information through time and space

AbstractKnowledge-intensive processes represent a particularly challenging scenario for process mining. The flexibility that such processes allow constitutes a hurdle as they are hard to capture in a single model. To tackle this problem, multiple visual representations of the same processes could be beneficial, each addressing different information dimensions according to the specific needs and background knowledge of the concrete process workers and stakeholders. In this paper, we propose, describe, and evaluate a framework, named , that leverages visual analytics for the interactive visualization of multi-faceted process information, aimed at supporting the investigation and insight generation of users in their process analysis tasks. is based on a multi-layer visualization methodology that includes a visual backdrop that provides context and an arbitrary number of superimposed and on-demand dimension layers. This arrangement allows our framework to display process information from different perspectives and to project this information onto a domain-friendly representation of the context in which the process unfolds. We provide an in-depth description of the approach’s founding principles, deeply rooted in visualization research, that justify our design choices for the whole framework. We demonstrate the feasibility of the framework through its application in two use-case scenarios in the context of healthcare and personal information management. Plus, we conducted qualitative evaluations with potential end users of both scenarios, gathering precious insights about the efficacy and applicability of our framework to various application domains.

PungentDB: Bridging traditional Chinese medicine of medicine food homology and modern food flavor chemistry

Merging traditional Chinese medicine's (TCM) principles of medicine-food homology with modern flavor chemistry, this research unveils PungentDB (http://www.pungentdb.org.cn/home), a database documenting 205 unique pungent flavor compounds from 231 TCMs. It provides detailed insights into their chemical attributes, biological targets (including IC50/EC50 values), and molecular structures (2D/3D), enriched with visualizations of target organ distribution and protein structures, exploring the pungent flavor space with the help of a feature-rich visual interface. This collection, derived from over 3249 sources and highlighting 9129 targets, delves into the compounds' unique pungent flavors—taste, aroma, and thermal sensations—and their interaction with taste and olfactory receptors. PungentDB bridges ancient wisdom and culinary innovation, offering a nuanced exploration of pungent flavors' role in enhancing food quality, safety, and sensory experiences. This initiative propels flavor chemistry forward, serving as a pivotal resource for food science advancement and the innovative application of pungent flavors.

Development of a robust and generalizable algorithm "gQuant" for accurate normalizer gene selection in qRT-PCR analysis

The emergent role of nucleic acid-based biomarkers—microRNAs(miRNAs), long non-coding RNAs(lncRNAs), and messenger RNAs(mRNAs), is becoming increasingly prominent in disease diagnostics and risk assessment. qRT-PCR is the primary analytical method for quantitative measurement of biomarkers. Yet, the relative infancy of non-coding RNAs recognition as biomarkers poses a challenge due to the absence of a consensus on a universally accepted normalizer gene, an absolute requirement for accurate quantification. Current tools normalizer selection are fraught with statistical limitations and suboptimal graphical user interface for data visualisation. These deficiencies underscore the necessity for a balanced tool tailored to handle qRT-PCR datasets. Addressing the identified challenges, we have developed 'gQuant' tool crafted to address these limitations. We employed voting classifiers that combine predictions from multiple statistical methods. Tool's efficacy was validated through different available and in house data derived from urinary exosomal miRNAs datasets. Comparative analysis with existing tools revealed that their integrated methodologies could skew the ranking of normalizer genes, whereas 'gQuant' consistently yielded rankings characterised by lower standard-deviation, reduced covariance, and enhanced kernel density estimation values. Given 'gQuant's' promising performance, normalizer gene identification will be greatly improved, improving precision of gene expression quantification in a variety of research scenarios. The gQuant tool developed for this study is available for public use and can be accessed at [https://github.com/ABHAYHBB/gQuant-Tool]."

Visual Representation and Cognitive Models in Information Visualization

As a branch of computer science, information visualization aims to help users understand and analyze complex data through graphical interfaces and interactive technologies. Information visualization primarily includes various visual structures such as time-series structures, spatial relationship structures, statistical distribution structures, and geographic map structures, each with unique functions and application scenarios. To better explain the cognitive process of visualization, researchers have proposed various cognitive models based on interaction mechanisms, visual perception steps, and novice use of visualization. These models help understand user cognition in information visualization, enhancing the effectiveness of data analysis and decision-making.

Research methods and the use of visual representation in library and information science research

AbstractThe increasing variety of research strategies and data collection techniques in information science, the access to large secondary data sets, and the ubiquity of information visualization call for expanding the classification of research methods and exploring how research is communicated visually. This study examined the relationship between types of data used in empirical research, visualizations, and research methods applied in information science studies. It analyzed 751 research articles published in the Journal of the Association for Information Science and Technology (JASIST) using content analysis and machine learning techniques. The study finds that most empirical studies adopted a quantitative design with data mining, bibliometrics, experiments, and surveys as dominant strategies. The substantial use of secondary data points to the shift in how data are collected in empirical research. The JASIST articles used a variety of visualizations to present research designs and findings, with quantitative and mixed methods studies employing primarily tables and charts and qualitative studies relying more on tables and diagrams. This study uniquely explores the relationship between research methods and visualization. It contributes to the classification of the methods in information science by expanding the range of strategies within the quantitative, qualitative, and mixed methods designs.

Cell Formation Problem with Alternative Routes and Machine Reliability: Review, Analysis, and Future Developments

The Cell Formation Problem (CFP) is a widely studied issue that aims to group machines effectively based on criteria such as productivity, lower costs, and greater efficiency. In recent years, more characteristics were summarized relating to this problem. This paper provides a bibliographic examination of methodologies addressing the CFP in cellular manufacturing, focusing on novel approaches such as alternative routes and machine reliability. The articles were obtained from Scopus and Web of Science and filtered using the PRISMA methodology. Classification based on objective functions, constraints, and methodologies facilitated informative visualizations for analysis. Findings indicate a focus on capital utilization optimization, with cost reduction via intercellular moves minimization as the primary objective. Common constraints include limits on the number of machines per cell, restricting machines to a single cell and singular production routes per part. The genetic algorithm predominates as a non-exact solution approach, while the “ε-constraint” method is commonly used. This study offers insights into contemporary trends in solving the CFP with alternative routings and machine reliability, aiding researchers and professionals in the field to improve the quality of their investigations.

VoiceBack: Design of Artificial Intelligence-Driven Voice-Based Feedback System for Customer-Agency Communication in Online Travel Services

Online travel booking has become increasingly popular; however, most travel websites do not yet offer voice interaction. This study introduces VoiceBack, an artificial intelligence (AI)-driven voice-based feedback system conceptualized to support both customers and online travel agencies during the booking process. It proposes a theoretically and empirically underpinned design concept that involves a voice user interface (VUI) for customer feedback. This feedback, collected by an AI agent, is analyzed and converted into actionable statistics, which are then presented to online travel agencies through a visual interface. The interface is designed to highlight problem areas and usability issues during the booking process. This study contributes to the field of human-centered AI, by offering insight into the complex process of designing and integrating voice, emotion, and feedback within user interfaces. This integrated approach can enrich the user experience of customers when booking travel online, and pave the way for more intuitive and responsive interaction designs in the future.

In vivo imaging of microvasculature in human finger skin using SV-OCT

Abstract Speckle variance optical coherence tomography (SV-OCT) enables non-invasive visualization of the three-dimensional vascular information within the microcirculatory tissue bed by utilizing flowing red blood cells as intrinsic contrast agents, without the need for dye injection. This study evaluated the feasibility of using the SV-OCT device for monitoring the microvascular system in human finger skin. An adaptive wavelet Fourier transform filtering algorithm was applied to remove stripe noise from the images of finger vasculature. The results demonstrate that SV-OCT systems can be used to extract the vascular system of finger skin. By employing an adaptive wavelet Fourier transform filtering (AWFTF)algorithm to process the vascular data, stripe noise can be effectively removed, thereby enhancing the imaging quality of the blood vessels.