Smart-thing design by children at a distance: How to engage them and make them learn

In recent years, research in Child-Computer Interaction has shifted the focus from design with children, giving them a voice in the design process, to design by children to bring child participants different benefits, such as engagement and learning. However, design workshops, encompassing different stages, are challenging in terms of engagement and learning, e.g., they require prolonged commitment and concentration. They are potentially more challenging when held at a distance, as in recent years due to the COVID-19 pandemic. This paper explores at-a-distance smart-thing design by children, how it can engage different children and support their learning in programming. The paper reports a series of design workshops with 20 children, aged from 8 to 16 years old, all held at a distance. They were all organised with the DigiSNaP design framework and toolkit. The first workshop enabled children to explore what smart things are, to start ideating their own smart things and to scaffold their programming. The other workshops enabled children to evolve their own smart-thing ideas and programs. Data were gathered in relation to children's engagement and learning from different sources. Results are promising for future editions of smart-thing design at a distance or in a hybrid modality. They are discussed along with guidelines for smart-thing design by children at a distance. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

An Evaluation of Quality Metrics for Distance and Blended Teaching

This study discusses evaluating the service quality in higher education institutions (HEIs) or the teaching quality of hybrid learning. Following COVID-19, most HEIs have shifted from face-to-face learning to blended and online learning in a synchronous or asynchronous form. The disadvantage of online learning is the absence of face-to-face and social interaction, which is why blended learning has become more popular. However, there are methodological and theoretical issues. Methodologically, to evaluate teaching delivery, either qualitative or quantitative research can be employed. Univariate analysis can be utilised to investigate the relationship among metrics. Alternatively, a multivariate analysis such as Structural Equation Modelling could be used when there is a complex interaction between metrics. Other methodological issues relate broadly to sampling, such as which metrics should be evaluated, whose opinions should be shared between students and lecturers and when to evaluate the survey using pre-learning or post-learning metrics in terms of module quality, module relevance, module intellectual, module infrastruc-ture, module success rate, module engagement, module support, module feedback and overall satisfaction. Theoretically, additional metrics such as aesthetic tech-nology and co-creation dynamics should be utilised in evaluating blended learning because institutions and clients simultaneously produce education. Moreover, based on the constructive alignment theory, several metrics can be used to evaluate teaching performance, such as learning-objectives metrics, learning-activities metrics and feedback metrics. In addition, new metrics have also been developed based on the behaviourists, constructivists and social constructionists, including content metrics, instruction-quality metrics, teaching-climate metrics, online-management metrics, professionalism metrics and classroom-management metrics. © Springer Nature Switzerland AG 2022.

Forecasting of COVID-19 Pandemic Using ARIMA and Fb-Prophet Models: UK Case Study

This study aims to provide insights into predicting future cases of COVID-19 infection and rates of virus transmission in the UK by critically analyzing and visualizing historical COVID-19 data, so that healthcare providers can prepare ahead of time. In order to achieve this goal, the study invested in the existing studies and selected ARIMA and Fb-Prophet time series models as the methods to predict confirmed and death cases in the following year. In a comparison of both models using values of their evaluation metrics, root-mean-square error, mean absolute error and mean absolute percentage error show that ARIMA performs better than Fb-Prophet. The study also discusses the reasons for the dramatic spike in mortality and the large drop in deaths shown in the results, contributing to the literature on health analytics and COVID-19 by validating the results of related studies. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Fostering mask-wearing with virality metrics and social media literacy: evidence from the U.S. and Korea.

Although social media can pose threats to the public health by spreading misinformation and causing confusion, they can also provide wider access to health information and opportunities for health surveillance. The current study investigates the ways in which preventive health behaviors and norms can be promoted on social media by analyzing data from surveys and experiments conducted in the U.S. and South Korea. Survey results suggest that the pathway from social media use for COVID-19 information to mask-wearing behavior through mask-wearing norms emerges only among individuals with strong perceived social media literacy in the U.S. Experimental findings show that wear-a-mask campaign posts on social media foster mask-wearing norms and behavioral intention when they come with large (vs. small) virality metrics (e.g., Likes, shares) in both the U.S. and South Korea. Additionally, American users are more willing to engage with posts that come with supportive (vs. mixed) comments by Liking, sharing and commenting. The results highlight the need to cultivate social media literacy and opportunities for exploiting social media virality metrics for promoting public health norms and behaviors.

The effects of COVID-19 on Chinese commuting patterns in early 2020

We build mobility networks from Chinese commuting data and track network metrics for the two months before the WHO pandemic announcement. The Wuhan travel ban on 23 January imposed changes to the level of importance of some central cities in the commuting patterns. While Beijing was the most important city in both the inflows and outflows, Wuhan and other cities became more relevant after the transition. © 2022 National Institute for Space Research, INPE. All rights reserved.

Innovations in Ambulatory Stewardship

Ambulatory antibiotic use accounts for most of the global consumption of antibiotics leading to selection pressure, multidrug resistance, and significant healthcare costs (https://www.cdc.gov/antibiotic-use/community/pdfs/16_268900-A_CoreElementsOutpatient_508.pdf) The Centers for Disease Control and Prevention established the Core Elements of outpatient antimicrobial stewardship in 2016 as a framework to develop, expand, and evaluate ambulatory stewardship programs, which must address overuse in multiple settings (e.g., urgent care centers, adult and pediatric outpatient practices, dental practices, and retail clinics). As such, we present examples of innovative yet adaptable outpatient stewardship initiatives encompassing a variety of settings. We also address patterns of ambulatory antibiotic prescribing and novel stewardship initiatives implemented during the novel coronavirus disease 2019 (COVID-19) pandemic. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Analysis of mobility network metrics in the spread of COVID-19 in the Metropolitan Region of Vale do Paraíba and Litoral Norte (RMVPLN)

In 2019, a pandemic of the so-called new coronavirus (SARS-COV-II) began, which causes the disease COVID-19. In a short time after the first case appeared, hundreds of countries began to register new cases every day. Mapping and analyzing the flow of people, regardless of the mode of transport, can help us to understand and prevent several phenomena that can affect our society in different ways. Graphs are complex networks made up of points and edges. The (geo)graphs are graphs with known spatial location and, in the case of our study, the edges represent the flow between them. The (geo)graphs proved to be a promising tool for such analyses. In the study region, municipalities that first registered their COVID-19 cases are also municipalities that have the highest mobility indices analyzed: degree, betweenness and weight of edges. © 2022 National Institute for Space Research, INPE. All rights reserved.

Characterizing the nature of trust & misinformation on Twitter

We analyze a dataset from Twitter of misinformation related to the COVID-19 pandemic. We consider this dataset from the intersection of two important but, heretofore, largely separate perspectives: misinformation and trust. We apply existing direct trust measures to the dataset to understand their topology, and to better understand if and how trust relates to spread of misinformation online. We find evidence for small worldness in the misinformation trust network;outsized influence from broker nodes;a digital fingerprint that may indicate when a misinformation trust network is forming;and, a positive relationship between greater trust and spread of misinformation. © 2022 IEEE.

Impact of COVID-19-Related Travel Restrictions on the Environment and Travel Time Reliability

This study analyzes the effect of the restrictions in traffic movement enforced in order to combat the spread of coronavirus on air quality and travel time reliability under heterogeneous and laneless traffic conditions. A comparative analysis was conducted to examine quantity of pollutants, average travel time distributions (TTD), and their associated travel time reliability (TTR) metrics during the COVID-19 pandemic, postpandemic, and during partial restrictions. Pollutants data (PM2.5, NO2, and NOX) and travel time data for selected locations from Chennai City in India were collected for a sample period of one week using Wi-Fi sensors and state-run air quality monitoring stations. It was observed that the average quantity of PM2.5, NO2, and NOX were increased by 433.1%, 681.4%, and 99.2%, respectively, during the postlockdown period. Correlation analysis also indicated that all considered air pollutants are moderately correlated to Wi-Fi hits, albeit to varied degrees. From the analysis, it was also found that average TTD mean and interquartile range values were increased by 47.2% and 105.2%. In addition, the buffer time index, planning time index, travel index, and capacity buffer index associated with these TTD metrics were increased by 148.1%, 63.7%, 42.8%, and 202.9%, respectively, soon after relaxing travel restrictions. © 2023 American Society of Civil Engineers.

A Case Study of University Students' Performance in Arabic and Islamic Culture Courses Before and During the Pandemic (Face-to-Face and Online Instruction)

In the present study, we examined whether students' academic success in courses devoted to Arabic and Islamic culture changed when the familiar face-to-face delivery format (before the Covid-19 pandemic) was discarded in favor ofan online synchronous delivery format (during the pandemic). The final class grades of students enrolled in one of four courses in a sequence devoted to Arabic culture and religion were compared while holding constant the variable instructor. The ability of early performance indicators to predict final class grades was also examined to assess whether there were differences between instructional deliveries. Superior performance and lower failure rates were observed online for courses at the beginning of the sequence, but not at the end of the sequence. These findings suggest that the impact of instructional delivery might vary depending on the students' accumulated academic experience.

The Relationship Between Financial Indicators and Financial Intermediaries' Stock Price Volatility Listed on the BM&FBOVESPA Index in the Crisis Period of 2008 and 2020 (Covid-19): GCG

The study goal was to verify the relationship among financial indicators and intermediaries' volatility stock price listed on the BM&FBovespa Index in the crisis period from 2008 and 2020 (COVID-19). The methods used for analysis were Spearman's correlation, multiple linear regression, and Test T. The analyzed period refers to the year 2008, the second semester of 2019 and the first semester of 2020, which include the periods before and during the crises of 2008 and 2020. The results found show that only the indicator of the assets total turnover rate has a significant relationship with the stock price volatility.Alternate :O estudo tem como objetivo verificar a relação entre os indicadores com a volatilidade das ações das intermediadoras financeiras listadas no Índice BM&FBovespa no período das crises de 2008 e 2020 (COVID-19). Os métodos utilizados para análise foram de correlação de Spearman, regressão linear múltipla e Teste T. O período analisado refere-se ao ano de 2008, segundo semestre de 2019 e primeiro semestre de 2020, onde englobam os períodos pré e durante as crises de 2008 e 2020. Os resultados encontrados apontam que apenas o indicador taxa total de rotatividade dos ativos possui relação significativa com a volatilidade do preço das ações.Alternate :El estudio tiene como objetivo verificar la relación entre los indicadores y la volatilidad de las acciones de los intermediarios financieros listados en el Índice BM&FBovespa en el período de las crisis de 2008 y 2020 (COVID-19). Los métodos utilizados para el análisis fueron la correlación de Spearman, la regresión lineal múltiple y la prueba T. El período analizado se refiere al año 2008, la segunda mitad de 2019 y la primera mitad de 2020, que incluyen los períodos antes y durante las crisis de 2008 y 2020. Los resultados encontrados indican que solo el indicador de tasa de rotación de activos totales tiene una relación significativa con la volatilidad del precio de las acciones.

Airline catering supply chain performance during pandemic disruption: a Bayesian network modelling approach

PurposeThe supply chain (SC) encompasses all actions related to meeting customer requests and transferring materials upstream to meet those demands. Organisations must operate towards increasing SC efficiency and effectiveness to meet SC objectives. Although most businesses expected the coronavirus disease 2019 (COVID-19) pandemic to severely negatively impact their SCs, they did not know how to model disruptions or their effects on performance in the event of a pandemic, leading to delayed responses, an incomplete understanding of the pandemic's effects and late deployment of recovery measures. Therefore, this study aims to consider the impact of implementing Bayesian network (BN) modelling to measure SC performance in the airline catering context.Design/methodology/approachThis study presents a method for modelling and quantifying SC performance assessment for airline catering. In the COVID-19 context, the researchers proposed a BN model to measure SC performance and risk events and quantify the consequences of pandemic disruptions.FindingsThe study simulates and measures the impact of different triggers on SC performance and business continuity using forward and backward propagation analysis, among other BN features, enabling us to combine various SC perspectives and explicitly account for pandemic scenarios.Originality/valueThis study's findings offer a fresh theoretical perspective on the use of BNs in pandemic SC disruption modelling. The findings can be used as a decision-making tool to predict and better understand how pandemics affect SC performance.

A Novel Hybrid Based Method in Covid 19 Health System for Data Extraction with Blockchain Technology

Millions of people have been afflicted by the COVID-19 epidemic, which has resulted in hundreds of thousands of fatalities throughout the world. Extracting correct data on patients and facilities with and without COVID-19 with high confidence for medical specialists or the government is extremely difficult. As a result, utilizing blockchain technology, a reliable data extraction methodology for the COVID-19 database is constructed. In this accurate data extraction model development and validation study in blockchain technology for COVID analysis, here a novel Hybrid Deep Belief Lionized Optimization (HDBLO) approach is proposed. The weights of the deep model are optimized by the fitness of lion optimization. The implementation of this work is executed using MATLAB software. The simulation outcomes shows the effective performance of proposed model in blockchain technology in COVID paradigm in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), accuracy, F-measure, Processing time, precision and error. Consequently, the proposed approach is compared with the conventional strategies for significant validation. © 2023 Authors. All rights reserved.

Computational Intelligence and Soft Computing Paradigm for Cheating Detection in Online Examinations

Covid-19 has been a life-changer in the sphere of online education. With complete lockdown in various countries, there has been a tumultuous increase in the need for providing online education, and hence, it has become mandatory for examiners to ensure that a fair methodology is followed for evaluation, and academic integrity is met. A plethora of literature is available related to methods to mitigate cheating during online examinations. A systematic literature review (SLR) has been followed in our article which aims at introducing the research gap in terms of the usage of soft computing techniques to combat cheating during online examinations. We have also presented state-of-the-art methods followed, which are capable of mitigating online cheating, namely, face recognition, face expression recognition, head posture analysis, eye gaze tracking, network data traffic analysis, and detection of IP spoofing. A discussion on improvement of existing online cheating detection systems has also been presented.

Impacts of Daily Travel by Distances on the Spread of COVID-19: An Artificial Neural Network Model.

The continued spread of COVID-19 poses significant threats to the safety of the community. Since it is still uncertain when the pandemic will end, it is vital to understand the factors contributing to new cases of COVID-19, especially from the transportation perspective. This paper examines the effect of the United States residents' daily trips by distances on the spread of COVID-19 in the community. The artificial neural network method is used to construct and test the predictive model using data collected from two sources: Bureau of Transportation Statistics and the COVID-19 Tracking Project. The dataset uses ten daily travel variables by distances and new tests from March to September 2020, with a sample size of 10,914. The results indicate the importance of daily trips at different distances in predicting the spread of COVID-19. More specifically, trips shorter than 3 mi and trips between 250 and 500 mi contribute most to predicting daily new cases of COVID-19. Additionally, daily new tests and trips between 10 and 25 mi are among the variables with the lowest effects. This study's findings can help governmental authorities evaluate the risk of COVID-19 infection based on residents' daily travel behaviors and form necessary strategies to mitigate the risks. The developed neural network can be used to predict the infection rate and construct various scenarios for risk assessment and control.

Building an infrastructure to support the development, conduct, and reporting of informative clinical studies: The Rockefeller University experience.

Introduction: Clinical trials are a vital component of translational science, providing crucial information on the efficacy and safety of new interventions and forming the basis for regulatory approval and/or clinical adoption. At the same time, they are complex to design, conduct, monitor, and report successfully. Concerns over the last two decades about the quality of the design and the lack of completion and reporting of clinical trials, characterized as a lack of "informativeness," highlighted by the experience during the COVID-19 pandemic, have led to several initiatives to address the serious shortcomings of the United States clinical research enterprise. Methods and Results: Against this background, we detail the policies, procedures, and programs that we have developed in The Rockefeller University Center for Clinical and Translational Science (CCTS), supported by a Clinical and Translational Science Award (CTSA) program grant since 2006, to support the development, conduct, and reporting of informative clinical studies. Conclusions: We have focused on building a data-driven infrastructure to both assist individual investigators and bring translational science to each element of the clinical investigation process, with the goal of both generating new knowledge and accelerating the uptake of that knowledge into practice.

Mobility Dynamics amid COVID-19 with a Case Study in Tennessee.

The year 2020 has marked the spread of a global pandemic, COVID-19, challenging many aspects of our daily lives. Different organizations have been involved in controlling this outbreak. The social distancing intervention is deemed to be the most effective policy in reducing face-to-face contact and slowing down the rate of infections. Stay-at-home and shelter-in-place orders have been implemented in different states and cities, affecting daily traffic patterns. Social distancing interventions and fear of the disease resulted in a traffic decline in cities and counties. However, after stay-at-home orders ended and some public places reopened, traffic gradually started to revert to pre-pandemic levels. It can be shown that counties have diverse patterns in the decline and recovery phases. This study analyzes county-level mobility change after the pandemic, explores the contributing factors, and identifies possible spatial heterogeneity. To this end, 95 counties in Tennessee have been selected as the study area to perform geographically weighted regressions (GWR) models. The results show that density on non-freeway roads, median household income, percent of unemployment, population density, percent of people over age 65, percent of people under age 18, percent of work from home, and mean time to work are significantly correlated with vehicle miles traveled change magnitude in both decline and recovery phases. Also, the GWR estimation captures the spatial heterogeneity and local variation in coefficients among counties. Finally, the results imply that the recovery phase could be estimated depending on the identified spatial attributes. The proposed model can help agencies and researchers estimate and manage decline and recovery based on spatial factors in similar events in the future.

Ontology-Based Interestingness in COVID-19 Data

This paper describes an approach to mining interestingness in data by designing domain ontology, COKPME and populating it with anonymized COVID-19 data from private hospital in Karnataka State, India. In general, association rules applied to healthcare data generate a large number of rules. These generated rules may not guarantee interestingness of the generated knowledge. To address this, we propose an ontology-based interestingness measure using the association rule mining algorithm. With the association rule, the implicit relationship between different categories of data attributes is captured. Our approach is to design the domain ontology, populate with data instances and operate association rules for semantic and non-semantic data to discover interesting facts.

COVID-19 detection from lung ultrasound images

Early-stage detection of Coronavirus Disease 2019 (COVID-19) is crucial for patient medical attention. Since lungs are the most affected organs, monitoring them constantly is an effective way to observe sickness evolution. The most common technique for lung-imaging and evaluation is Computed Tomography (CT). However, its costs and effects over human health has made Lung Ultrasound (LUS) a good alternative. LUS does not expose the patient to radiation and minimizes the risk of contamination. Also, there is evidence of a relation between different artifacts on LUS and lung's diseases coming from the pleura, whose abnormalities are related with most acute respiratory disorders. However, LUS often requires an expert clinical interpretation that may increase diagnosis time or decrease diagnosis performance. This paper describes and compares machine learning classification methods namely Naive Bayes (NB) Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) and Random Forest (RF) over several LUS images. They obtain a classification between lung images with COVID-19, pneumonia, and healthy patients, using image's features previously extracted from Gray Level Co-Occurrence Matrix (GLCM) and histogram's statistics. Furthermore, this paper compares the above classic methods with different Convolutional Neural Networks (CNN) that classifies the images in order to identify these lung's diseases.

Sexual health indicators for the United States: Measuring progress and documenting public health needs.

Introduction: Today, we are facing increased and continued adverse sexual health outcomes in the United States, including high post-COVID-19 pandemic rates of sexually transmitted infections (STIs). For the past 20 years, there have been calls for a national health strategy and a more comprehensive sexual health approach to address the myriad of persistent sexual health problems in this country. Employing a sexual health approach requires shifting from a longstanding, stigmatizing focus on morbidity toward a holistic and integrated focus on health rather than disease. While strategies are being implemented by multisectoral stakeholders, it is also important to establish a core set of indicators that broadly describe the state of sexual health in the U.S. and allow for measurement across time. The development of a comprehensive scorecard with key sexual health indicators has been proposed by other entities (e.g., Public Health England, World Health Organization), but such an attempt has not been made in the U.S. Methods: A review of national U.S. surveys and surveillance systems with items related to sexual health was conducted for years 2010-2022 to develop an inventory of existing data that yield national estimates for potential indicators of sexual health. Results: We selected 23 sexual health indicators in four broad domains including: (1) knowledge; communication and attitudes (five indicators); (2) behaviors and relationships (four indicators); (3) service access and utilization (seven indicators); and (4) adverse health outcomes (seven indicators). Recent data for each indicator are provided. Discussion: A growing body of evidence shows the positive effects of moving away from a morbidity focus toward an integrated, health-promoting approach to sexual health. Yet, not much has been done in terms of how we implement this national shift. We argue that measurement and monitoring are key to future change. We envision these core sexual health indicators would be published in the form of an index that is publicly available and updated frequently. These sexual health indicators could be used for ongoing monitoring, and to guide related research, programming, and policy development to help promote sexual health in coming years.