Application of information visualization design in the popularization of the knowledge of medical waste

With the continuous improvement of living standards, people pay more attention to the knowledge of medical health than before. The knowledge also brings the popularity and development of medical information. However, in the information age of today, tedious and redundant information floods people's lives make it impossible for people to quickly understand and grasp the content they need. Especially after the outbreak of the COVID-19, apart from the epidemic, the large amount of generated medical waste has become an issue of concern, but the current publicity of related knowledge is difficult to resonate with people. After collecting and reconstructing the knowledge about medical waste, a visual information hierarchy design is established to reflect the hierarchical relationships between different medical waste information intuitively and clearly through such a visual presentation. Thus, people better understand and learn them. At the same time, it helps people to put into action together for the disposal of medical waste and provide solutions for the visualization design of rapid and professional sorting and treatment of the increasing amount of medical waste. © 2023 Author(s).

Predicting aspect-based sentiment using deep learning and information visualization: The impact of COVID-19 on the airline industry

This study investigates customer satisfaction through aspect-level sentiment analysis and visual analytics. We collected and examined the flight reviews on TripAdvisor from January 2016 to August 2020 to gauge the impact of COVID-19 on passenger travel sentiment in several aspects. Till now, information systems, management, and tourism research have paid little attention to the use of deep learning and word embedding techniques, such as bidirectional encoder representations from transformers, especially for aspect-level sentiment analysis. This paper aims to identify perceived aspect-based sentiments and predict unrated sentiments for various categories to address this research gap. Ultimately, this study complements existing sentiment analysis methods and extends the use of data-driven and visual analytics approaches to better understand customer satisfaction in the airline industry and within the context of the COVID-19. Our proposed method outperforms baseline comparisons and therefore contributes to the theoretical and managerial literature. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

A Survey on Data-driven COVID-19 and Future Pandemic Management

The COVID-19 pandemic has resulted in more than 440 million confirmed cases globally and almost 6 million reported deaths as of March 2022. Consequently, the world experienced grave repercussions to citizens' lives, health, wellness, and the economy. In responding to such a disastrous global event, countermeasures are often implemented to slow down and limit the virus's rapid spread. Meanwhile, disaster recovery, mitigation, and preparation measures have been taken to manage the impacts and losses of the ongoing and future pandemics. Data-driven techniques have been successfully applied to many domains and critical applications in recent years. Due to the highly interdisciplinary nature of pandemic management, researchers have proposed and developed data-driven techniques across various domains. However, a systematic and comprehensive survey of data-driven techniques for pandemic management is still missing. In this article, we review existing data analysis and visualization techniques and their applications for COVID-19 and future pandemic management with respect to four phases (namely, Response, Recovery, Mitigation, and Preparation) in disaster management. Data sources utilized in these studies and specific data acquisition and integration techniques for COVID-19 are also summarized. Furthermore, open issues and future directions for data-driven pandemic management are discussed. [ FROM AUTHOR]

Visualization and Visual Knowledge Discovery from Big Uncertain Data

In the current uncertain world, data are kept growing bigger. Big data refer to the data flow of huge volume, high velocity, wide variety, and different levels of veracity (e.g., precise data, imprecise/uncertain data). Embedded in these big data are implicit, previously unknown, but valuable information and knowledge. With huge volumes of information and knowledge that can be discovered by techniques like data mining, a challenge is to validate and visualize the data mining results. To validate data for better data aggregation in estimation and prediction and for establishing trustworthy artificial intelligence, the synergy of visualization models and data mining strategies are needed. Hence, in this paper, we present a solution for visualization and visual knowledge discovery from big uncertain data. Our solution aims to discover knowledge in the form of frequently co-occurring patterns from big uncertain data and visualize the discovered knowledge. In particular, the solution shows the upper and lower bounds on frequency of these patterns. Evaluation with real-life Coronavirus disease 2019 (COVID-19) data demonstrates the effectiveness and practicality of our solution in visualization and visual knowledge discovery from big health informatics data collected from the current uncertain world. © 2022 IEEE.

Application of online-Pilates exercise based on information visualization training feedback in elderly people with low back pain: A randomized controlled trials

Objective: To investigate if remote Pilates exercises for older patients with low back pain(LBP) in the post-COVID-19 era may be successfully performed using a pressure biofeedback unit (PBU)-based information visualization training feedback technology. Design: A total of 40 older patients with LBP were randomly allocated to a control group ($\mathrm{n}=20$) receiving clinical Pilates training instruction via video link or an experimental group ($\mathrm{n}=20$)) with tele-Pilates exercise based on information visualization training feedback. The program had two 60-minute sessions per week for the whole eight-week duration. Pain was assessed by a visual analogue scale(VAS), the Oswestry Disability Index(ODI) was used to evaluate physical function, the modified Schober test was used to measure lumbar range of flexion and extension, and core strength was assessed by the PBU. Results: Between-group analysis showed significant variations in the degree of disability in the intervention group compared to the control group ($\mathrm{p} < 0.001$), lumbar flexibility ($\mathrm{p}=0.02$) and core muscle activation capacity ($\mathrm{p} < 0.001$). And level of pain was significantly decreased in both two groups. Conclusions: In elderly patients with LBP, an 8-week remote Pilates exercise based on information visualization training feedback is beneficial in reducing disability, pain, and enhancing flexibility and core muscle strength. © 2022 IEEE.

Information Visualization Technology and Mathematical Statistics Applied in the Research on Spatiotemporal Clustering and Changing of COVID-19

To analyze the epidemiological and distribution characteristics of COVID-19 in the United States from 2020.1 to 2021.8, which can provide scientific basis for the formulation of epidemic prevention measures. The incidence data of COVID-19 epidemic from 2020.1-2021.8 in the United States were collected for analysis, the spatial autocorrelation was analyzed by using Geoda 1.18.0, SaTScan 10.0 was used to conduct spatial scan statistics, and ArcGIS 10.4 were used to visualize. As of August 26, 2021, the epidemic in the United States is still in a state of high-speed transmission, and the number of cases is concentrated from November 2020 to February 2021 and August 2021;From the perspective of global spatial autocorrelation, COVID-19 in the United States has a high spatial aggregation, and the geographical spatial adjacency of each region has the greatest influence on the intensity of disease aggregation. According to the local spatial self-analysis, most of the agglomerations were in high-high and low-low clusters, and the high-high cluster states showed a patchy distribution, and experienced an increase-decrease-increase in number. According to the spatio-temporal scanning statistics, there were four clusters, of which the first cluster was located in the southeastern United States. In terms of t the mean center of infection, the epidemic moved greatly in the early stage and stabilized in the southeastern part of the United States in the later stage. COVID-19 in the United States has strong aggregation and changes over time. The focus of prevention and control is the southeast of the United States, and the focus of prevention and control is to reduce the population movement of adjacent states. © 2022 IEEE.

Information Visualization Dashboard to Proctor Test-Takers During an Online Language Proficiency Test

As a result of the COVID-19 pandemic, the learning and evaluation processes have been moved to an online modality to keep social distance and reduce the spreading of the virus. The strategies implemented for assessment and proctoring in this online remote teaching and assessment emergency are no exception when proctoring test-takers. This problem is addressed from a practical context of study: the English Language Proficiency Tests of a University in southeast Mexico. Considering an iterative user-centered mixed methodology, a set of dashboards was designed, implemented and evaluated to visualize the information generated by test-takers during the administration process. An increase in the Usability of the dashboards is observed in all heuristic categories, with visual design being greater. The use of the mixed methodology and the constant user feedback during the process helped us to reduce development time compared with other works found in the literature. Moreover, it is possible to use the proposed dashboards in other application domains like medicine, or care facilities where user activity monitoring is needed to make informed decisions. categoryHuman-centered computing;Information visualization

Effectively and Efficiently Supporting Visual Big Data Analytics over Big Sequential Data: An Innovative Data Science Approach

In the current era of big data, huge volumes of valuable data have been generated and collected at a rapid velocity from a wide variety of rich data sources. In recent years, the willingness of many government, researchers, and organizations are led by the initiates of open data to share their data and make them publicly accessible. Healthcare, disease, and epidemiological data, such as privacy-preserving statistics on patients who suffered from epidemic diseases such as Coronavirus disease 2019 (COVID-19), are examples of open big data. Analyzing these open big data can be for social good. For instance, people get a better understanding of the disease by analyzing and mining the disease statistics, which may inspire them to take part in preventing, detecting, controlling and combating the disease. Having a pictorial representation further enhances the understanding of the data and corresponding results for analysis and mining because a picture is worth a thousand words. Hence, in this paper, we present a visual data science solution for the visualization and visual analytics of big sequential data. The visualization and visual analytics of sequences of real-life COVID-19 epidemiological data illustrate the ideas. Through our solution, we enable users to visualize the COVID-19 epidemiological data over time. It also allows people to visually analyze the data and discover relationships among popular features associated with the COVID-19 cases. The effectiveness of our visual data science solution in enhancing user experience in the visualization and visual analytics of big sequential data are demonstrated by evaluation of these real-life sequential COVID-19 epidemiological data. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

TrajectoryVis: a visual approach to explore movement trajectories.

Social networks are a dominant data source for sharing, participation, and exchanging information. For example, Twitter is a microblogging site that enables users to express opinions by transmitting brief messages (i.e., Tweets). Tweets can be used to extract information on users' movements or trajectories over time. Information visualization (InfoVis) is helpful to understand, analyze, and make decisions about these trajectories. To better understand and compare existing visual encoding methods in InfoVis, we propose TrajectoryVis, a generic trajectory visualization tool to represent social network datasets (e.g., Twitter). Individual and aggregated trajectories can be visualized using different visual coding approaches. Our approach is assessed using a user and a COVID-19 case study to prove its effectiveness.

Technology Enhanced Learning Analytics Dashboard in Higher Education

During the COVID-19 pandemic period, all the Sri Lankan universities delivered lectures in fully online mode using Virtual Learning Environments. In fully online mode, students cannot track their performance level, their progress in the course, and their performances compared to the rest of the class. This paper presents research work conducted at the University of Colombo School of Computing (UCSC), Sri Lanka, to solve the above problems and facilitate students learning in fully online and blended learning environments using Learning Analytics. The research objective is to design and create a Technology Enhanced Learning Analytics (TELA) dashboard for improving students’ motivation, engagement, and grades. The Design Science research strategy was followed to achieve the objectives of the research. Initially, a literature survey was conducted analyzing features and limitations in current Learning Analytic dashboards. Then, current Learning Analytic plugins for Moodle were studied to identify their drawbacks. Two surveys with 136 undergraduate students and interviews with 12 lecturers were conducted to determine required features of the TELA system. The system was designed as a Moodle Plugin. Finally, an evaluation of the system was done with third-year undergraduate students of the UCSC. The results showed that the TELA dashboard can improve students' motivation, engagement, and grades. As a result of the system, students could track their current progress and performance compared to the peers, which helps to improve their motivation to engage more in the course. Also, the increased engagement in the course enhances the student’s self-confidence since the student can see continuous improvement of his/her progress and performance which in turn improves the student’s grades. ©The Authors.

Using smart glasses for monitoring cyber threat intelligence feeds

The surge of COVID-19 has introduced a new threat surface as malevolent actors are trying to benefit from the pandemic. Because of this, new information sources and visualization tools about COVID-19 have been introduced into the workflow of frontline practitioners. As a result, analysts are increasingly required to shift their focus between different visual displays to monitor pandemic related data, security threats, and incidents. Augmented reality (AR) smart glasses can overlay digital data to the physical environment in a comprehensible manner. However, the real-life use situations are often complex and require fast knowledge acquisition from multiple sources. In this study we report results from an experiment with six subjects using an AR overlaid information interface coupled with traditional computer monitors. Our goal was to evaluate a multi tasking setup with traditional monitors and an AR headset where notifications from the new COVID-19 MISP instance were visualized. Our results indicate that better situational awareness does translate to increased task performance, but at the cost of a gender gap that requires further attention. © 2021 ACM.

Designing an information visualization dashboard to proctor test-takers during language certification online testing

The learning mechanisms and the evaluation process have been moved to an online mode in order to maintain social distancing and reduce the spread of the COVID-19. E-learning initiatives (including assessment and proctoring) produce a large amount of data, so visualization mechanisms are required to support the decision-making process. This problem is addressed from a practical context of study: the English Language Certification Tests of a University in the southeast of Mexico. The conceptual design of four conceptual dashboards are presented using a mixed methodology: the UCD process and a conceptual model for a dashboard generator process. The four conceptual dashboards were evaluated by five experts. Although the design proposals were simple and reflected most initial user requirements, the experts suggested to include more elements in all the dashboards to provide the information needed for the intended users and improve the decision-making process. © 2021 ACM.

Predicting aspect-based sentiment using deep learning and information visualization: The impact of COVID-19 on the airline industry

This study investigates customer satisfaction through aspect-level sentiment analysis and visual analytics. We collected and examined the flight reviews on TripAdvisor from January 2016 to August 2020 to gauge the impact of COVID-19 on passenger travel sentiment in several aspects. Till now, information systems, management, and tourism research have paid little attention to the use of deep learning and word embedding techniques, such as bidirectional encoder representations from transformers, especially for aspect-level sentiment analysis. This paper aims to identify perceived aspect-based sentiments and predict unrated sentiments for various categories to address this research gap. Ultimately, this study complements existing sentiment analysis methods and extends the use of data-driven and visual analytics approaches to better understand customer satisfaction in the airline industry and within the context of the COVID-19. Our proposed method outperforms baseline comparisons and therefore contributes to the theoretical and managerial literature.

Diagnostics of SARS-CoV-2 infection using electrical impedance spectroscopy with an immunosensor to detect the spike protein.

Mass testing for the diagnostics of COVID-19 has been hampered in many countries owing to the high cost of the methodologies to detect genetic material of SARS-CoV-2. In this paper, we report on a low-cost immunosensor capable of detecting the spike protein of SARS-CoV-2, including in samples of inactivated virus. Detection is performed with electrical impedance spectroscopy using an immunosensor that contains a monolayer film of carboxymethyl chitosan as matrix, coated with an active layer of antibodies specific to the spike protein. In addition to a low limit of detection of 0.179 fg/mL within an almost linear behavior from 10-20 g/mL to 10-14 g/mL, the immunosensor was highly selective. For the samples with the spike protein could be distinguished in multidimensional projection plots from samples with other biomarkers and analytes that could be interfering species for healthy and infected patients. The excellent analytical performance of the immunosensors was validated with the distinction between control samples and those containing inactivated SARS-CoV-2 at different concentrations. The mechanism behind the immunosensor performance is the specific antibody-protein interaction, as confirmed with the changes induced in C-H stretching and protein bands in polarization-modulated infrared reflection absorption spectra (PM-IRRAS). Because impedance spectroscopy measurements can be made with low-cost portable instruments, the immunosensor proposed here can be applied in point-of-care diagnostics for mass testing even in places with limited resources.

Visualizing dynamic data with heat triangles.

In this paper, an overview-based interactive visualization for temporally long dynamic data sequences is described. To reach this goal, each data object at a certain time point can be mapped to a number value based on a given property. Among others, a property is application-dependent and can be number of vertices, number of edges, average degree, density, number of self-loops, degree (maximum and total), or edge weight (minimum, maximum, and total) for dynamic graph data, but it can as well be the number of ball contacts in a football match, or the time-dependent visual attention paid to a stimulus in an eye tracking study. To achieve an overview over time, an aggregation strategy based on either the mean, minimum, or maximum of two values is applied. This temporal value aggregation generates a triangular shape with an overview of the entire data sequence as the peak. The color coding can be adjusted, forming visual patterns that can be rapidly explored for certain data features over time, supporting comparison tasks between the properties. The usefulness of the approach is illustrated by means of applying it to dynamic graphs generated from US domestic flight data as well as to dynamic Covid-19 infections on country levels.

Can scientists use simple infographics to convince? Effects of the "flatten the curve" charts on perceptions of and behavioral intentions toward social distancing measures during the COVID-19 pandemic.

Infographics of modest complexity are commonly used to convey knowledge to non-experts. However, little is known regarding how the use of infographics may convince the public and lead to massive behavioral changes in response to an acute cause. In March 2020, scientists and journalists revamped a scholarly published graph into the "flatten the curve" (FTC) mantra that defined the United States' initial response to the COVID-19 pandemic. This study examined how Americans' awareness of the flatten the curve charts relates to their perceived effectiveness of social distancing measures, perceived controllability of the pandemic, and behavioral intentions toward social distancing measures. Implications on visual communication of science are discussed.

KnetMiner: a comprehensive approach for supporting evidence-based gene discovery and complex trait analysis across species.

The generation of new ideas and scientific hypotheses is often the result of extensive literature and database searches, but, with the growing wealth of public and private knowledge, the process of searching diverse and interconnected data to generate new insights into genes, gene networks, traits and diseases is becoming both more complex and more time-consuming. To guide this technically challenging data integration task and to make gene discovery and hypotheses generation easier for researchers, we have developed a comprehensive software package called KnetMiner which is open-source and containerized for easy use. KnetMiner is an integrated, intelligent, interactive gene and gene network discovery platform that supports scientists explore and understand the biological stories of complex traits and diseases across species. It features fast algorithms for generating rich interactive gene networks and prioritizing candidate genes based on knowledge mining approaches. KnetMiner is used in many plant science institutions and has been adopted by several plant breeding organizations to accelerate gene discovery. The software is generic and customizable and can therefore be readily applied to new species and data types; for example, it has been applied to pest insects and fungal pathogens; and most recently repurposed to support COVID-19 research. Here, we give an overview of the main approaches behind KnetMiner and we report plant-centric case studies for identifying genes, gene networks and trait relationships in Triticum aestivum (bread wheat), as well as, an evidence-based approach to rank candidate genes under a large Arabidopsis thaliana QTL. KnetMiner is available at: https://knetminer.org.