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).

Passenger Perceptions, Information Preferences, and Usability of Crowding Visualizations on Public Displays in Transit Stations and Vehicles

Large crowds in public transit stations and vehicles introduce obstacles for wayfinding, hygiene, and physical distancing. Public displays that currently provide on-site transit information could also provide critical crowdedness information. Therefore, we examined people's crowd perceptions and information preferences before and during the pandemic, and designs for visualizing crowdedness to passengers. We first report survey results with public transit users (n = 303), including the usability results of three crowdedness visualization concepts. Then, we present two animated crowd simulations on public displays that we evaluated in a field study (n = 44). We found that passengers react very positively to crowding information, especially before boarding a vehicle. Visualizing the exact physical spaces occupied on transit vehicles was most useful for avoiding crowded areas. However, visualizing the overall fullness of vehicles was the easiest to understand. We discuss design implications for communicating crowding information to support decision-making and promote a sense of safety. © 2023 ACM.

COVID-19 Data Prediction and Visualization in Mainland China

Contemporarily, COVID-19 shows a sign of recurrence in Mainland China. To better understand the situation, this paper investigates the growth pattern of COVID-19 based on the research of past data through regression models. The proposed work collects the data on COVID-19 in Mainland China from January 21st, 2020, to April 30th, 2020, including confirmed, recovered, and death cases. Based on polynomial regression and support vector machine regressor, it predicts the further trend of COVID-19. The paper uses root mean squared error to evaluate the performance of both models and concludes that there is no best model due to the high frequency of daily changes. According to the analysis, support vector machine regressors fit the growth of COVID-19 confirmed case better than polynomial regression does. The best solution is to utilize different types of models to generate a range of prediction result. These results shed light on guiding further exploration of the growth of COVID-19. © 2023 SPIE.

Educational attainment, race, and employability post COVID-19: insights from data visualization

The COVID-19 pandemic caused unemployment rates to reach record highs, adding to an already unequally divided system (Kawohl & Nordt, 2020). Minorities' unemployment rates in the United States were significantly higher in 2020 than the white unemployment rate, regardless of educational attainment. This study draws upon U.S. census data after the onset of the pandemic to investigate the relationship between educational attainment, race, and employment rates in the United States. Logistic regression revealed that the probability of being employed in 2020 was higher for whites than minorities and significantly higher for those with higher levels of education. Based on these preliminary results, we discuss the relationships among race, educational attainment, and employment, and suggest routes for further inquiry. © Issues in Information Systems.

Visual analysis of mask sales data based on crawler

The outbreak of COVID-19 makes people feel distant from each other, and masks have become one of the indispensable articles in People's Daily life. At present, there are many brands of masks with various types and uneven quality. In order to understand the current market of masks and the sales of different brands, users can choose masks with perfect quality. This paper uses Python web crawler technology, based on the input of the word "mask", crawl JD website sales data, through data visualization technology drawing histogram, pie chart, the word cloud, etc., for goods compared with the relationship between price, average price of all brands, brands, average distribution of analysis and evaluation of user information, In this way, the sales situation, price distribution and quality evaluation of each store of the product can be visually displayed. At the same time, it also provides some reference for other users who need to buy the product. © The Authors. Published under a Creative Commons Attribution CC-BY 3.0 License.

Visual Analysis of Social Robots in Twitter on China Agenda Setting

With the development and progress of intelligent algorithms, more and more social robots are used to interfere with the information transmission and direction of international public opinion. This paper takes the agenda of COVID-19 in Twitter as the breakthrough point, and through the methods of web crawler, Twitter robot detection, data processing and analysis, aims at the agenda setting of social robots for China issues, that is, to carry out data visualization analysis for the stigmatized China image. Through case analysis, concrete and operable countermeasures for building the international communication system of China image were provided. © 2022 IEEE.

Integrating deep learning algorithms for the lung segmentation and body-part-specific anatomical classification with Medical Imaging and Data Resource Center (MIDRC)

Medical Imaging and Data Resource Center (MIDRC) has been built to support AI-based research in response to the COVID-19 pandemic. One of the main goals of MIDRC is to make data collected in the repository ready for AI analysis. Due to data heterogeneity, there is a need to standardize data and make data-mining easier. Our study aims to stratify imaging data according to underlying anatomy using open-source image processing tools. The experiments were performed using Google Colaboratory on computed tomography (CT) imaging data available from the MIDRC. We adopted the existing open-source tools to process CT series (N=389) to define the image sub-volumes according to body part classification, and additionally identified series slices containing specific anatomic landmarks. Cases with automatically identified chest regions (N=369) were then processed to automatically segment the lungs. In order to assess the accuracy of segmentation, we performed outlier analysis using 3D shape radiomics features extracted from the left and right lungs. Standardized DICOM objects were created to store the resulting segmentations, regions, landmarks and radiomics features. We demonstrated that the MIDRC chest CT collections can be enriched using open-source analysis tools and that data available in MIDRC can be further used to evaluate the robustness of publicly available tools. © 2023 SPIE.

From Asymptomatics to Zombies: Visualization-Based Education of Disease Modeling for Children

Throughout the COVID-19 pandemic, visualizations became commonplace in public communications to help people make sense of the world and the reasons behind government-imposed restrictions. Though the adult population were the main target of these messages, children were affected by restrictions through not being able to see friends and virtual schooling. However, through these daily models and visualizations, the pandemic response provided a way for children to understand what data scientists really do and provided new routes for engagement with STEM subjects. In this paper, we describe the development of an interactive and accessible visualization tool to be used in workshops for children to explain computational modeling of diseases, in particular COVID-19. We detail our design decisions based on approaches evidenced to be effective and engaging such as unplugged activities and interactivity. We share reflections and learnings from delivering these workshops to 140 children and assess their effectiveness. © 2023 Owner/Author.

When do data visualizations persuade? The impact of prior attitudes on learning about correlations from scatterplot visualizations

Data visualizations are vital to scientific communication on critical issues such as public health, climate change, and socioeconomic policy. They are often designed not just to inform, but to persuade people to make consequential decisions (e.g., to get vaccinated). Are such visualizations persuasive, especially when audiences have beliefs and attitudes that the data contradict? In this paper we examine the impact of existing attitudes (e.g., positive or negative attitudes toward COVID-19 vaccination) on changes in beliefs about statistical correlations when viewing scatterplot visualizations with different representations of statistical uncertainty. We find that strong prior attitudes are associated with smaller belief changes when presented with data that contradicts existing views, and that visual uncertainty representations may amplify this effect. Finally, even when participants' beliefs about correlations shifted their attitudes remained unchanged, highlighting the need for further research on whether data visualizations can drive longer-term changes in views and behavior. © 2023 ACM.

Misleading Beyond Visual Tricks: How People Actually Lie with Charts

Data visualizations can empower an audience to make informed decisions. At the same time, deceptive representations of data can lead to inaccurate interpretations while still providing an illusion of data-driven insights. Existing research on misleading visualizations primarily focuses on examples of charts and techniques previously reported to be deceptive. These approaches do not necessarily describe how charts mislead the general population in practice. We instead present an analysis of data visualizations found in a real-world discourse of a significant global event - Twitter posts with visualizations related to the COVID-19 pandemic. Our work shows that, contrary to conventional wisdom, violations of visualization design guidelines are not the dominant way people mislead with charts. Specifically, they do not disproportionately lead to reasoning errors in posters' arguments. Through a series of examples, we present common reasoning errors and discuss how even faithfully plotted data visualizations can be used to support misinformation. © 2023 Owner/Author.

Detecting COVID-19 spreading phases with time series analysis

Piecewise linear regression (PLR) method is applied to study cumulative cases of COVID-19 evolving everyday in England up to 6th February 2022 just before travel restrictions are removed and people started not to get tested anymore in the UK and factors e.g. the lockdowns behind the spread COVID-19 are also investigated. It is clear that different periods exhibit distinct patterns depending on variants and government-imposed restriction. Therefore, the effectiveness of lockdown measures is evaluated by comparing the rate of increase after a certain period (delay effect of measures) and that of time before as well as how new variants take over as a dominant variant. In addition, autoregression function is studied to show strong effect of cases in the past on today's cases since the disease is highly infectious. Most of work is carried out thorough python built-in libraries such as pandas for preprocessing data and matplotlib which allows us to gain more insight and better visualization into the real scenario. Visualization is conducted by Geoda showing the regional level of infections. © 2023 SPIE.

Suggesting Assess Queries for Interactive Analysis of Multidimensional Data

Assessment is the process of comparing the actual to the expected behavior of a business phenomenon and judging the outcome of the comparison. The ${{\sf assess}}$assess querying operator has been recently proposed to support assessment based on the results of a query on a data cube. This operator requires (i) the specification of an OLAP query to determine a target cube;(ii) the specification of a reference cube of comparison (benchmark), which represents the expected performance;(iii) the specification of how to perform the comparison, and (iv) a labeling function that classifies the result of this comparison. Despite the adoption of a SQL-like syntax that hides the complexity of the assessment process, writing a complete assess statement is not easy. In this paper we focus on making the user experience more comfortable by letting the system suggest suitable completions for partially-specified statements. To this end we propose two interaction modes: progressive refinement and auto-completion, both starting from an assess statement partially declared by the user. These two modes are evaluated both in terms of scalability and user experience, with the support of two experiments made with real users.

Learning Topical Structured Interfaces from Medical Research Literature

Accessing large-scale structured datasets such as WDC or CORD-191 is very challenging. Even if one topic (e.g. COVID-19 vaccine efficacy) is of interest, all topical tables in different sources/papers have hundreds of different schemas, depending on the authors, which significantly complicates both finding and querying them. Here we demonstrate a scalable Meta-profiler system, capable of constructing a structured standardized interface to a topic of interest in large-scale (semi-)structured datasets. This interface, that we call Meta-profile represents a multi-dimensional meta-data summary for a selected topic of interest, accumulating all differently structured representations of the topical tables in the dataset. Such Meta-profiles can be used as a rich visualization as well as a robust structural query interface simplifying access to large-scale (semi-)structured data for different user segments, such as data scientists and end users. © 2023 Owner/Author.

Towards Augmented Reality Guiding Systems: An Engineering Design of an Immersive System for Complex 3D Printing Repair Process

Over the past decade, additive manufacturing (AM) has become widely adopted for both prototyping and, more recently, end-use products. In particular, fused deposition modeling (FDM) is the most widespread form of additive manufacturing due to its low cost, ease of use, and versatility. While additive processes are relatively automated, many steps in their operation and repair require trained human operators. Finding such operators can be difficult, as highlighted during the recent COVID-19 pandemic. Augmented reality (AR) systems could significantly help address this challenge by automating the training for 3D printer operators. Given multidimensional design choices, however, a research gap exists in the system requirements for such immersive guidance. To address this need, we explore the applicability of AR to guide users through a repair process. In that context, we report on the system design as well as the results of the AR system assessment in a qualitative study with experts. © 2023 IEEE.

It's about Time: Analytical Time Periodization

This paper presents a novel approach to the problem of time periodization, which involves dividing the time span of a complex dynamic phenomenon into periods that enclose different relatively stable states or development trends. The challenge lies in finding such a division of the time that takes into account diverse behaviours of multiple components of the phenomenon while being simple and easy to interpret. Despite the importance of this problem, it has not received sufficient attention in the fields of visual analytics and data science. We use a real-world example from aviation and an additional usage scenario on analysing mobility trends during the COVID-19 pandemic to develop and test an analytical workflow that combines computational and interactive visual techniques. We highlight the differences between the two cases and show how they affect the use of different techniques. Through our investigation of possible variations in the time periodization problem, we discuss the potential of our approach to be used in various applications. Our contributions include defining and investigating an earlier neglected problem type, developing a practical and reproducible approach to solving problems of this type, and uncovering potential for formalization and development of computational methods.

Development of a Web-Portal Health Information System for Barangay

In the Philippines, a barangay is the smallest administrative unit serving as suburban neighborhoods' first line of defense. According to Bautista, barangays conduct a manual file-based process of storing the community's health information. Therefore, the need for a single platform enables a small government unit to manage its resources while being transparent to its community. The study aims to develop a web- based barangay health information system portal for Barangay 69 District 1 in Tondo Manila. The system would be a reference tool for barangays as their platform provides inventory management, the barangay's health programs, and a dashboard for data visualization inventory management, tracking of Covid cases, administration of health activities, and a dashboard for data visualization. As a result, the web portal is functional, and different test scenarios show above-average results. The study concludes that the system provided a platform for the barangay and its residents. It also concludes that it is user-friendly and efficiently disseminates the barangay's health programs and activities. © 2023 IEEE.

A bibliometric analysis of publications on obsessive-compulsive disorder using VOSviewer.

Background: Obsessive-compulsive disorder (OCD) is one of the top ten disabling diseases seriously affecting the health of population. Recently, studies on this disease significantly increased. However, only a few bibliometric analyses concerning this area have been reported. In this study, we used bibliometrics and visualization tools to examine the current state, hot topics and future trends in OCD research. Methods: Scientific publications regarding OCD were retrieved from the Web of Science Core Collection (WoSCC) database. The features of OCD research were further analyzed using VOSviewer. Results: A total of 24,552 publications and 65,296 authors in the field of OCD were retrieved from 2000 to 2022, showing an overall upward trend in publications over the past 22 years. One hundred and thirteen countries around the world had participated in the research. Among these countries, the developed countries such as the United States, England, and Canada were the crucial productive nations in this subject. As for institutions, the Harvard University, the University of London, and the University of California system were the leading institutions. Authors including Storch EA, Mataix-Cols D, and Stein DJ were the prolific authors. 1,949 journals are contributing to the OCD field, of which the top three are Biological Psychiatry (831 articles), European Neuropsychopharmacology (776 articles) and Psychiatric Research (648 articles). Research hotspots of OCD included pathogenesis, epidemiology, comorbidities, clinical features, and evaluation methods. COVID-19, mental health, functional connectivity, and genome-wide association were emerging trends in the field of OCD. Conclusion: This study integrates the bibliometric information on the current research status and emerging trends in OCD from a macro perspective. The findings can provide valuable insights into further research on OCD.

[Optimisation of risk and crisis communication of governments, authorities and public health institutions-challenges in long-lasting crises illustrated by the COVID-19 pandemic]. / Optimierung der Risiko- und Krisenkommunikation von Regierungen, Behörden und Organisationen der Gesundheitssicherung ­ Herausforderungen in lang anhaltenden Krisen am Beispiel der COVID-19-Pandemie.

The COVID-19 pandemic demonstrates the great importance of risk and crisis communication. In a dynamic situation, authorities and policymakers face the challenge of dealing with a large amount of data, reviewing it and communicating it in a way that is appropriate for diverse target groups. Comprehensible and unambiguous information on risks and options for action make a significant contribution to the objective and subjective safety of the population. Hence, there is a great need to use the experience gained from the pandemic to optimize risk and crisis communication.Digitalization enables multimodal arrangements - that is, the combination of text, graphics, icons, images, animations and sound. These arrangements play an increasingly important role in risk and crisis communication. It is of interest to what extent the communicative interaction of authorities, media and other public actors in crisis preparation and management in view of a complex public can be improved with the help of target group-specific communication and how legal certainty can be ensured for official and media practice. Accordingly, the article pursues three objectives:1. It describes the challenges faced by authorities and media actors in pandemic communication.2. It shows the role of multimodal arrangements as well as the necessary research perspectives to grasp the complexity of communicative crisis management in the federal system.3. It provides a rationale for how an interdisciplinary research network from the fields of media, communication and law can gain insights into the evidence-based use of multimodal communication.

Visual analysis of the prevention and control measures of COVID-19 in Chinese ports.

In 2022, COVID-19 solutions in China have entered a normal stage, and the solutions imported from ports have been transformed from emergency prevention and control measures to investigative long-term prevention and control measures. Therefore, it is necessary to study solutions for COVID-19 at border ports. In this study, 170 research papers related to the prevention and control measures of COVID-19 at ports from 2020 to September 2022 were retrieved from Wanfang database, HowNet database, Wip database, and WoS core collection. Citespace 6.1.R2 software was used to research institutions visualize and analyze researchers and keywords to explore their research hotspots and trends. After analysis, the overall volume of documents issued in the past 3 years was stable. The major contributors are scientific research teams such as the Chinese Academy of Inspection and Quarantine Sciences (Han Hui et al.) and Beijing Customs (Sun Xiaodong et al.), with less cross-agency cooperation. The top five high-frequency keywords with cumulative frequency are as follows: COVID-19 (29 times), epidemic prevention and control (29 times), ports (28 times), health quarantine (16 times), and risk assessment (16 times). The research hotspots in the field of prevention and control measures for COVID-19 at ports are constantly changing with the progress of epidemic prevention and control. Cooperation between research institutions needs to be strengthened urgently. The research hotspots are the imported epidemic prevention and control, risk assessment, port health quarantine, and the normalized epidemic prevention and control mechanism, which is the trend of research and needs further exploration in the future.

Determining Required Teaching Methods in Distance Undergraduate Medical Education Based on the Non-University Learning Methods Medical Students Used During Covid-19: An Exploratory Factor Analysis.

In order to improve the distance learning experience for undergraduate medical education, this study aims to evaluate the teaching methods used by universities in Jordan during the distance learning period and identify the best methods in this situation based on non-university educational avenues utilized by medical students during COVID-19. We conducted a survey of 195 medical students from universities across the country using a questionnaire that measures how dependent students are on educational resources provided by universities before and during the distance learning condition and looks into medical students' most used non-university learning methods in face-to-face and distance learning conditions, and the extent to which medical students used them. We found that the main methods used by medical students for non-university learning were non-university educational videos like YouTube videos (92.8%) and non-university textual explanations (i.e., explanations on websites and summaries of materials made by other students) (67.7%). Before the remote learning situation, there was a large reliance on non-university learning materials, which rose significantly during the distance learning situation (p<0.001, r=0.54). We conducted a polychoric correlations-based Exploratory Factor Analysis (EFA) on 10 items, 7 of which were retained in the final model that revealed 2 factors, to analyze the relationship between the universities' educational methods used in distance learning and the non-university methods medical students used. The first factor reflected the change in "students' use of non-university visualization learning methods in distance learning" (external videos, general dependence on non-university methods, and simulation apps had the highest significant loadings (>0.3)). The second factor reflected the change in "universities' use of visualization and interactive learning methods in distance learning" (deductive discussions, educational videos, and practical methods had significant loadings). A moderately negative correlation was detected between the two factors after applying a Promax rotation (r=-0.41), indicating that the decrease in universities' use of visualization and interactive learning aids in connection with insufficient visualization in the distance educational sessions increased students' use of the aforementioned visualized learning methods in distance learning. This study identifies the optimal visual teaching aids to improve distance undergraduate medical education.