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The multiple comparison method refers to the hypothesis test of whether there is a significant difference between the means of each sample after the analysis of variance is performed on the samples of each group to be tested. In data analysis, the multiple comparison method can perform a more precise difference analysis based on the analysis of variance. Therefore, this study will select the LSD (Least significant difference) test method in the multiple comparison method to analyze the physical fitness test scores of males and females in the three grades from 2019 to 2021 in the investigated schools. In this way, we can understand the substantial impact of students' home-based learning on students' physical health during the new crown epidemic, so as to make targeted development plans for students' physical health in the current special period, and minimize the adverse impact of the new crown epidemic on students' physical health. © 2023 SPIE.
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This article presents the analysis of the demand and the characterization of mobility using public transportation in Montevideo, Uruguay, during the COVID-19 pandemic. A urban data-analysis approach is applied to extract useful insights from open data from different sources, including mobility of citizens, the public transportation system, and COVID cases. The proposed approach allowed computing significant results to determine the reduction of trips caused by each wave of the pandemic, the correlation between the number of trips and COVID cases, and the recovery of the use of the public transportation system. Overall, results provide useful insights to quantify and understand the behavior of citizens in Montevideo, regarding public transportation during the COVID-19 pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Online health information is critical during pandemics. Previous research has focused on examining antecedents or consequences of particular information behaviors (e.g., seeking, sharing), but the process by which one information behavior influences or transforms into other information behaviors remains poorly understood. Guided by theories of information behavior and the literature on online misinformation, this study proposes an interaction model of online information behaviors that theorizes relationships among online information scanning, misinformation exposure, misinformation elaboration, information sharing, and information avoidance. Conducting a two-wave representative panel survey in Hong Kong during the COVID-19 pandemic (N = 1501), we found that online information scanning at Wave 1 had a direct, positive impact on misinformation exposure and information sharing at Wave 2, while it did not have an impact on information avoidance at Wave 2. Additionally, misinformation exposure was positively related to both information sharing and information avoidance at Wave 2. Importantly, we underlined that evaluations of crisis-related misinformation are aided by misinformation elaboration, which plays a moderating role in catalyzing appropriate information behaviors. Results of this study could help scholars and practitioners propose evidence-based interventions for enhancing the public's ability to manage crisis information on the Internet in times of heightened uncertainty. © 2023 Elsevier Ltd
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Several regional head elections had to be postponed due to the pandemic, including in Indonesia because of the COVID-19 pandemic. Several big cities in Indonesia are of concern because of their large population and GDP. This study conducts analysis and testing of datasets taken from Open Data in a city in Indonesia. In addition to conducting research on regional head elections, we also present information on voters from the category of kids with disabilities. The steps used in this research are using regional mapping data of the city of Surabaya in the Election of the Regional Head. Download the data or dataset for the Regional Head Election ampersand Categories of kids with disabilities. Based on the dataset voters from the category of children with disabilities are more than 5 percent.In this research, we use Python to process our datasets & Big Data technology. Data cleaning or cleansing, Exploratory Data Analysis, and Empirical Cumulative Distribution Functions (ECDF) in python are also needed. Result from ECDF chart with steady increase (increment of 0.1). The highest variance value is in Electoral District 5 = 6.090 and the lowest value is in Electoral District 4 = 0.90. The result of Open Data is graphical data visualization and candidate scores to help as an alternative for the 2024 Regional Head Election and the Category of kids with disabilities. © 2023 IEEE.
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The purpose of this article is to discuss the purchasing behavior (PB), influencing factors, and personal factors of Chinese consumers through literature review, questionnaire survey, and research results. The frequency and amount of purchasing behavior (FPB and APB) are studied in the consumption behavior. In the influencing factors, channel cognitions (CC), product cognitions (PC), and the influence of personal factors on consumer consumption behavior of fresh agricultural products. The FPB and APB are significantly and positively correlated with the CC, PC, and advantage cognition (AC). The FPB and APB are significantly and negatively correlated with disadvantage cognitions (DC). People's CC, PC, and DC significantly affect their FPB and APB. © 2022 IEEE.
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The problem of identification of unknown epidemiological parameters (contagiosity, the initial number of infected individuals, probability of being tested) of an agent-based model of COVID-19 spread in Novosibirsk region is solved and analyzed. The first stage of modeling involves data analysis based on the machine learning approach that allows one to determine correlated datasets of performed PCR tests and number of daily diagnoses and detect some features (seasonality, stationarity, data correlation) to be used for COVID-19 spread modeling. At the second stage, the unknown model parameters that depend on the date of introducing of containment measures are calibrated with the usage of additional measurements such as the number of daily diagnosed and tested people using PCR, their daily mortality rate and other statistical information about the disease. The calibration is based on minimization of the misfit function for daily diagnosed data. The OPTUNA optimization framework with tree-structured Parzen estimator and covariance matrix adaptation evolution strategy is used to minimize the misfit function. Due to ill-posedness of identification problem, the identifiability analysis is carried out to construct the regularization algorithm. At the third stage, the identified parameters of COVID-19 for Novosibirsk region and different scenarios of COVID-19 spread are analyzed in relation to introduced quarantine measures. This kind of modeling can be used to select effective anti-pandemic programs. © 2023 Walter de Gruyter GmbH, Berlin/Boston 2023.
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Many countries have implemented school closures due to the outbreak of the COVID-19 pandemic, which has inevitably affected children's physical and mental health. It is vital for parents to pay special attention to their children's health status during school closures. However, it is difficult for parents to recognize the changes in their children's health, especially without visible symptoms, such as psychosocial functioning in mental health. Moreover, healthcare resources and understanding of the health and societal impact of COVID-19 are quite limited during the pandemic. Against this background, we collected real-world datasets from 1,172 children in Hong Kong during four time periods under different pandemic and school closure conditions from September 2019 to January 2022. Based on these data, we first perform exploratory data analysis to explore the impact of school closures on six health indicators, including physical activity intensity, physical functioning, self-rated health, psychosocial functioning, resilience, and connectedness. We further study the correlation between children's contextual characteristics (i.e., demographics, socioeconomic status, electronic device usage patterns, financial satisfaction, academic performance, sleep pattern, exercise habits, and dietary patterns) and the six health indicators. Subsequently, a health inference system is designed and developed to infer children's health status based on their contextual features to derive the risk factors of the six health indicators. The evaluation and case studies on real-world datasets show that this health inference system can help parents and authorities better understand key factors correlated with children's health status during school closures. © 2023 ACM.
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In this modern and digital era, digital transformation is echoed as one of the organization's efforts to survive through Business Intelligence (BI). BI has become a buzzword even among business actors or organizations, not least for Small and Medium Enterprises (SMEs). SMEs are one of the sectors affected by the COVID-19 pandemic, namely the number of SME players who have lost their income and are finally forced to go out of business. BI is a combination of techniques and methods in terms of fulfilling access to information and a concise data management mechanism to be able to have a positive influence on SME business activities. It is because the strength of BI significantly impacts strategic decision-making using processing tools from Microsoft, namely SQL Server Integration Services (SSIS) and SQL Server Reporting Services (SSRS). This study aims to see the extent of BI as an alternative solution in decision-making by all SMEs in Indonesia. This research contributes to SMEs through the implementation of BI;SMEs get explicit knowledge about the factors that affect the performance of SMEs to help SMEs in making decisions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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The healthcare services across the world have been badly affected by the pandemic since December 2019. People have suffered in terms of medical supplies and treatments because existing medical infrastructure has failed to accommodate huge number of COVID infected patients. Further, patients with existing morbidities have been the worst hit so far and need attention. Therefore, there is a need of post-COVID care for such patients which can be achieved by using technologies such as Internet of Things (IoT) and data analytics. This paper presents medical IoT-based data analysis for post-COVID care. This paper, further, presents post-COVID data analysis to get an insight into the various symptoms across the different perspectives. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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The outbreak of the coronavirus disease in Nigeria and all over the world in 2019/2020 caused havoc on the world's economy and put a strain on global healthcare facilities and personnel. It also threw up many opportunities to improve processes using artificial intelligence techniques like big data analytics and business intelligence. The need to speedily make decisions that could have far-reaching effects is prompting the boom in data analytics which is achieved via exploratory data analysis (EDA) to see trends, patterns, and relationships in the data. Today, big data analytics is revolutionizing processes and helping improve productivity and decision-making capabilities in all aspects of life. The large amount of heterogeneous and, in most cases, opaque data now available has made it possible for researchers and businesses of all sizes to effectively deploy data analytics to gain action-oriented insights into various problems in real time. In this paper, we deployed Microsoft Excel and Python to perform EDA of the covid-19 pandemic data in Nigeria and presented our results via visualizations and a dashboard using Tableau. The dataset is from the Nigeria Centre for Disease Control (NCDC) recorded between February 28th, 2020, and July 19th, 2022. This paper aims to follow the data and visually show the trends over the past 2 years and also show the powerful capabilities of these data analytics tools and techniques. Furthermore, our findings contribute to the current literature on Covid-19 research by showcasing how the virus has progressed in Nigeria over time and the insights thus far. © 2022 IEEE.
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The Covid-19 Pandemic that broke out in late December 2019 has had a widespread negative effect on the mental health of people around the world. This work aims to elicit features that had a major influence on mental health during the pandemic to better understand preventive measures and remedial actions that can be taken to help individuals in need. Along with factors such as demographic age, gender, marital status, and employment status, additional information such as the effect of media used as a source of information, coping methods, trust in the country's government, and healthcare organizations was analyzed to find their correlation (if any) to the perceived stress of the individual. Machine Learning techniques such as XGBoost, AdaBoost, Decision Trees, Ordinal regression, k-Nearest Neighbors, Lasso and Ridge regression were used to arrive at a relationship between the perceived stress scores and the features considered. On interpreting results from the different models, we conclude that the main factor influencing stress scores was loneliness followed by features indicating trust in government, compliance with Covid-19 preventive measures and concerns regarding the pandemic. © 2022 IEEE.
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In order to study the overall situation of college English online teaching and students' online learning satisfaction during the COVID-19 epidemic, this paper made a questionnaire from four aspects: learning environment, teachers' teaching activities, students' learning activities, and online learning effect. Through SPSS23.0 software, the questionnaire data were analyzed by reliability analysis, validity analysis, principal component analysis, regression analysis and other methods, and the impacts of learning environment, teaching activities, learning activities, learning evaluation on students' learning satisfaction were studied. © 2022 IEEE.
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The most common technique of analyzing data assembled to draw conclusions concerning the knowledge they contain, a lot of} with the utilization of specialized frameworks and programmes, is data examination. Researchers and professionals often use data analysis techniques and innovations to validate or, on the opposite hand, refute logical models, enabling organizations to form higher business decisions. Information analysis is turning into more and more popular in several fields, together with healthcare. Not solely will illustration play an enormous role in naturally displaying the results of knowledge analysis, however additionally throughout the whole method of collecting, cleaning, Associate in Nursing analyzing, and sharing information. this text outlines an approach for victimization Tableau as a business insight tool to represent and analyses aid data intelligently. Strategies: beginning with making the Tableau Work Space Individual ability 10.3, this analysis illustrates the foremost prevailing model-based technique of comprehending and visualizing Coronavirus data. © 2022 IEEE.
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COVID-19, the novel coronavirus that has disrupted lives around the world, continues to challenge how humans interact in public and shared environments. Repopulating the micro-spatial setting of an office building, with virus spread and transmission mitigation measures, is critical for a return to normalcy. Advice from public health experts, such as maintaining physical distancing from others and well-ventilated spaces, are essential, yet there is a lack of sound guidance on configuring office usage that allows for a safe return of workers. This paper highlights the potential for decision-making and planning insights through location analytics, particularly within an office setting. Proposed is a spatial analytic framework addressing the need for physical distancing and limiting worker interaction, supported by geographic information systems, network science, and spatial optimization. The developed modeling approach addresses dispersion of assigned office spaces as well as associated movement within the office environment. This can be used to support the design and utilization of offices in a manner that minimizes the risk of COVID-19 transmission. Our proposed model produces two main findings: (1) that the consideration of minimizing potential interaction as an objective has implications for the safety of work environments, and (2) that current social distancing measures may be inadequate within office settings. Our results show that leveraging exploratory spatial data analyses through the integration of geographic information systems, network science, and spatial optimization, enables the identification of workspace allocation alternatives in support of office repopulation efforts. © 2022 held by the owner/author(s).
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Social media have the potential to provide timely information about emergency situations and sudden events. However, finding relevant information among the millions of posts being added every day can be difficult, and in current approaches developing an automatic data analysis project requires time and technical skills. This work presents a new approach for the analysis of social media posts, based on configurable automatic classification combined with Citizen Science methodologies. The process is facilitated by a set of flexible, automatic and open-source data processing tools called the Citizen Science Solution Kit. The kit provides a comprehensive set of tools that can be used and personalized in different situations, particularly during natural emergencies, starting from images and text contained in the posts. The tools can be employed by citizen scientists for filtering, classifying, and geolocating the content with a human-in-the-loop approach to support the data analyst, including feedback and suggestions on how to configure the automated tools, and techniques to gather inputs from citizens. Using flooding scenario as a guiding example, this paper illustrates the structure and functioning of the different tools proposed to support citizens scientists in their projects, and a methodological approach to their use. The process is then validated by discussing three case studies based on the Albania earthquake of 2019, the Covid-19 pandemic, and the Thailand floods of 2021. The results suggest that a flexible approach to tools composition and configuration can support a timely setup of an analysis project by citizen scientists, especially in case of emergencies in unexpected locations. © 2013 IEEE.
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Many companies use video advertising during the covid pandemic. Video advertising has a positive effect on the industry but also has a negative impact (inherent risk) such as time, physical, financial, and social risk. Video advertising content generally follows information quality characteristics to achieve the maximum result. This study will explore on how Video Advertising's Information Quality Content (VAIQC) affects social media risk, customer trust and intention to buy. The study was conducted using the Structural Equation Model and Partial Lease Square (SEM-PLS) techniques with 246 respondents. Several factors have a significant influence, such as customer trust on intention to buy, financial risk on intention to buy, Video's advertising information quality content (VAIQC) on customer trust, financial risk, physical risk, social risk and time risk. This study also looks at the effect of gender on the research model. The results of this research are very useful for the industry and future digital advertising development. © 2022 IEEE.
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The world is witnessing a pandemic of SARS-CoV2 infection since the first quarter of the twenty-first century. Ever since the first case was reported in Wuhan city of China in December 2019, the virus has spread over 223 countries. Understanding and predicting the dynamics of COVID-19 spread through data analysis will empower our administrations with insights for better planning and response against the burden inflicted on our health care infrastructure and economy. The aim of the study was to analyze and predict COVID-19 spread in Ernakulam district of Kerala. Data was extracted from lab data management system (LDMS), a government portal to enter all the COVID-19 testing details. Using the EpiModel package of R-mathematical modeling of infectious disease dynamics, the predictive analysis for hospitalization rate, percentage of patients requiring oxygen and ICU admission, percentage of patients getting admitted, duration of hospital stay, case fatality rate, age group and gender-wise fatality rate, and hospitalization rate were computed. While calculating the above-said variables, the percentage of vaccinated population, breakthrough infections, and percentage of hospitalization among the vaccinated was also taken into consideration. The time trend of patients in ICU showed men outnumbered women. Positive cases were more among 20–30 years, while 61–70 years age group had more risk for ICU admission. An increase in CFR with advancing age and also a higher CFR among males were seen. Conclusions: Analyzing and predicting the trend of COVID-19 would help the governments to better utilize their limited healthcare resources and adopt timely measures to contain the virus. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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School lecture halls are often designed as confined spaces. During the period of COVID-19, indoor ventilation has played an even more important role. Considering the economic reasons and the immediacy of the effect, the natural ventilation mechanism becomes the primary issue to be evaluated. However, the commonly used CO2 tracer gas concentration decay method consumes a lot of time and cost. To evaluate the ventilation rate fast and effectively, we use the common methods of big data analysis - Principal Component Analysis (PCA), K-means and linear regression to analyze the basic information of the lecture hall to explore the relation between variables and air change rate. The analysis results show that the target 37 lecture halls are divided into two clusters, and the measured 11 lecture halls contributed 64.65%. When analyzing the two clusters separately, there is a linear relation between the opening area and air change rate (ACH), and the model error is between 6% and 12%, which proves the feasibility of the basic information of the lecture hall by calculating the air change rate. © 2023 Elsevier Ltd
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Learning habits among students in higher institution, has radically changed over the last two decades, partially due to the features of the information and digital society, wide scale broadband internet access, proliferation of smart devices and consequently, available online mobile applications. Thus, in higher education, e-learning systems are essential, and the COVID-19 pandemic era has seen an increase in popularity of e-learning. Thus, it is critical to establish the extent to which students in higher education adopt e-learning. This paper focuses at developing a framework for e-learning technologies through the Technology Acceptance Model (TAM) by incorporating system characteristics as its' extended variable, which consists of three main constructs: system quality, content quality and information quality among university students in Malaysia. Regarding the system characteristics, the findings suggested that all three components that make up system characteristics had a significant positive influence on students' perceived ease of use of the e-learning system, whereas only system quality and information quality had a significant positive impact on students' perceived usefulness of the e-learning system. With respect to TAM, all relationships were found to be significant. © 2022 IEEE.
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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.