MR.Brick: Designing A Remote Mixed-reality Educational Game System for Promoting Children's Social & Collaborative Skills

Children are one of the groups most influenced by COVID-19-related social distancing, and a lack of contact with peers can limit their opportunities to develop social and collaborative skills. However, remote socialization and collaboration as an alternative approach is still a great challenge for children. This paper presents MR.Brick, a Mixed Reality (MR) educational game system that helps children adapt to remote collaboration. A controlled experimental study involving 24 children aged six to ten was conducted to compare MR.Brick with the traditional video game by measuring their social and collaborative skills and analyzing their multi-modal playing behaviours. The results showed that MR.Brick was more conducive to children's remote collaboration experience than the traditional video game. Given the lack of training systems designed for children to collaborate remotely, this study may inspire interaction design and educational research in related fields. © 2023 ACM.

Ways to promote intra-regional trade and investment in Central Asia to boost the green recovery

The economic recession caused by the restrictions on the spread of the Coronavirus disrupted the economic life of many countries. The primary purpose of the paper is to measure the impacts of intra-regional trade and investment on the green recovery of five Central Asian economies over 2010–2021 through employing annual data and the panel co-integration approach. The results confirmed that the degree of green trade openness positively and significantly affects the green recovery. The estimated coefficient of direct foreign investment is negative. The patent coefficient is not statistically significant, and the official exchange rate has a positive effect. Financial development has a positive and significant coefficient. The paper has several policy implications, such as enhancing green FDI (Foreign Direct Investment), supporting green innovation, and promoting green trade liberalism for scholars and policymakers. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Angry, Sad, or Scared? Within-valence Mapping of Emotion Words to Facial and Body Cues in 2 to 4-Year-Old Children

The acquisition of emotion words is critical to children's socio-emotional development. Previous studies report that children acquire emotion words gradually during ages 3-5 and beyond. The majority of this work, however, has used demanding tasks for young children (e.g., asking children to label emotion-related facial configurations) and has predominantly relied on facial configurations. Here we designed a child-friendly, word-comprehension task incorporating both facial configurations and body language. In two preregistered online experiments, we asked two to four-year-olds (N = 96) to connect emotion words-happy, sad, angry, and scared-to either facial configurations (Experiment 1) or combined facial and body cues (Experiment 2). We found relatively early competence in understanding emotion words, especially those of the same-valence. All age groups, including 2-year-olds, successfully linked emotion words to corresponding facial configurations (Experiment 1). Experiment 2 replicated this pattern and further showed that children performed equally well (though not substantially better) when given additional body cues. Parental reports of children's exposure to and use of masks during the COVID-19 pandemic did not correlate with children's performance in either experiment. Even before children can produce emotion words in an adult-like manner, they possess at least a partial understanding of those words and can map them to emotion cues within valence domains. © 2023 University of California Press. All rights reserved.

Informational inefficiency on bitcoin futures

This paper investigates the dynamics and drivers of informational inefficiency in the Bitcoin futures market. To quantify the adaptive pattern of informational inefficiency, we leverage two groups of statistics which measure long memory and fractal dimension to construct a global-local market inefficiency index. Our findings validate the adaptive market hypothesis, and the global and local inefficiency exhibits different patterns and contributions. Regarding the driving factors of the time-varying inefficiency, our results suggest that trading activity of retailers (hedgers) increases (decreases) informational inefficiency. Compared to hedgers and retailers, the role played by speculators is more likely to be affected by the COVID-19 crisis. Extremely bullish and bearish investor sentiment has more significant impact on the local inefficiency. Arbitrage potential, funding liquidity, and the pandemic exert impacts on the global and local inefficiency differently. No significant evidence is found for market liquidity and policy uncertainty related to cryptocurrency.

An Improved Soft Subspace Clustering Algorithm for Brain MR Image Segmentation

In recent years, the soft subspace clustering algorithm has shown good results for high-dimensional data, which can assign different weights to each cluster class and use weights to measure the contribution of each dimension in various features. The enhanced soft subspace clustering algorithm combines interclass separation and intraclass tightness information, which has strong results for image segmentation, but the clustering algorithm is vulnerable to noisy data and dependence on the initialized clustering center. However, the clustering algorithm is susceptible to the influence of noisy data and reliance on initialized clustering centers and falls into a local optimum;the clustering effect is poor for brain MR images with unclear boundaries and noise effects. To address these problems, a soft subspace clustering algorithm for brain MR images based on genetic algorithm optimization is proposed, which combines the generalized noise technique, relaxes the equational weight constraint in the objective function as the boundary constraint, and uses a genetic algorithm as a method to optimize the initialized clustering center. The genetic algorithm finds the best clustering center and reduces the algorithm's dependence on the initial clustering center. The experiment verifies the robustness of the algorithm, as well as the noise immunity in various ways and shows good results on the common dataset and the brain MR images provided by the Changshu First People's Hospital with specific high accuracy for clinical medicine.

Extracellular Disintegration of Viral Proteins as an Innovative Strategy for Developing Broad-Spectrum Antivirals Against Coronavirus

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has claimed millions of lives and caused innumerable economic losses worldwide. Unfortunately, state-of-the-art treatments still lag behind the continual emergence of new variants. Key to resolving this issue is developing antivirals to deactivate coronaviruses regardless of their structural evolution. Here, we report an innovative antiviral strategy involving extracellular disintegration of viral proteins with hyperanion-grafted enediyne (EDY) molecules. The core EDY generates reactive radical species and causes significant damage to the spike protein of coronavirus, while the hyperanion groups ensure negligible cytotoxicity of the molecules. The EDYs exhibit antiviral activity down to nanomolar concentrations, and the selectivity index of up to 20,000 against four kinds of human coronavirus, including the SARS-CoV-2 Omicron variant, suggesting the high potential of this new strategy in combating the COVID-19 pandemic and a future "disease X."

Clinical Guidelines for Dentistry in China During the Coronavirus Disease 2019 Pandemic

Preventing the spread of Coronavirus Disease 2019 (COVID-19) has become the focus of epidemiologists as the highly infectious respiratory disease spreads primarily by close, person-to-person contact via droplets or the skin. Aerosol dissemination may occur in a closed, high-aerosol environment. The aerosols generated in dental procedures can pollute surrounding air and device surfaces. In this paper, we summarize prevention and control measures relating to dentistry. We focus on the relationship between COVID-19 and dental disease prevention and control in dental treatment procedures and imaging examinations, oral health education and perspectives, and guidance for the practice of dentistry during the COVID-19 pandemic to provide a consistent and broadly endorsed standard for dental hospital and clinics. Copyright © 2021 Luo, Wang, Tang, Peng, Ma and Wu.

Evaluation of the Canadian government policies on controlling the COVID-19 outbreaks

In this paper, we investigate the COVID-19 pandemic in Canada and evaluate the Canadian government policies on controlling COVID-19 outbreaks. The first case of COVID-19 was reported in Ontario on 25 January 2020. Since then, there have been over million cases by now. During this time period, the federal, provincial and local governments have implemented regulations and policies in order to control the pandemic. To evaluate these government policies, which may be done by analysing the infection rate, infection period and reproductive number of COVID-19, we approach the problem by introducing an extended susceptible-exposed-infectious-removed (SEIR) model and conducting the model inference by using the iterated filter ensemble adjustment Kalman filter (IF-EAKF) algorithm. We first divide the time period into phases according to the policy intensities in each province by segmenting the time period from 4 March 2020 to 31 October 2020 into three time phases: the exploding phase, the strict policy implementation phase, and the provincial reopening phase. We then use IF-EAKF algorithm to obtain the estimates of the model parameters. We show that the infection rate in the second phase is lower than that in both first and third phases. We also discuss the number of new COVID-19 cases under different policy intensities and different policy durations in the third wave of the pandemic. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

The Chinese International Engineering Program: History, development, and curriculum refinement

This chapter describes the history and development of a unique educational model, the Chinese International Engineering Program (CIEP) at the University of Rhode Island. It discusses the necessary cornerstones of how to get a program off the ground which creates a bridge between STEM and Humanities disciplines, while providing an integrated year of study and an internship in China. The authors also detail the expansion of the CIEP to other institutions and other disciplines (e.g., through the Chinese Language Flagship Program). Special focus is placed on the creation of a corporate network of internship hosts and a Chinese internship course which creates interventions to move learners along in their language proficiency and cultural competency development. Several career success stories and strategies for program adjustments during the Covid-19 pandemic are provided. © 2023 selection and editorial matter, Haidan Wang and Christine Uber Grosse;individual chapters, the contributors

Trans/feminist collaborative autoethnographic storying of gender-based violence, during the COVID-19 pandemic

In this paper, we reported on the lattice distortion, surface morphologies, vacancy defects and electrochemical performance that had been observed in Na3V2(PO4)2F3 prepared at different annealing temperatures. X-ray diffraction indicated that all the samples were single phase materials with tetragonal structure and exhibited lattice distortion with the increase of annealing temperatures. A possible mechanism causing the strain-induced lattice distortion had been discussed. Moreover, scanning electron microscopy and positron annihilation techniques were used to study the grain size and vacancy defects as a function of annealing temperatures. The superior electrochemical performance of Na3V2(PO4)2F3 electrode was obtained at the annealing temperature of 350 degrees C with 167.73 F center dot g-1 specific capacitance and 85% capacitance retention. The better electrochemical performance was due to the synergistic effects of grain size and vacancy defect regulated by the annealing temperatures. These results could provide experimental basis for enhancing electrochemical performance of Na3V2(PO4)2F3 in sodium-ion battery area applications. (c) 2023 Elsevier B.V. All rights reserved.

Evaluation of airborne infection risk with different window airing strategies in classroom

The coronavirus disease may spread by airborne aerosols, especially in a poorly ventilated enclosure. Natural ventilation can reduce the transmission of infection. The WHO suggested the minimum ventilation rate of 10 L/s/person in non-residential settings. The objective was to evaluate risk of airborne infection with different settings in natural ventilated classroom. The risk was evaluated by using the modified Wells-Riley equation associated with the variation of contaminant concentration simulated by a multi-zone airflow model. The results provide the guidance of natural ventilation strategy in the classroom to reduce the transmission of airborne infection disease. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Heterogeneous influence of individuals' behavior on mask efficacy in gathering environments

Wearing masks is an easy way to operate and popular measure for preventing epidemics. Although masks can slow down the spread of viruses, their efficacy in gathering environments involving heterogeneous person-to-person contacts remains unknown. Therefore, we aim to investigate the epidemic prevention effect of masks in different real-life gathering environments. This study uses four real interpersonal contact datasets to construct four empirical networks to represent four gathering environments. The transmission of COVID-19 is simulated using the Monte Carlo simulation method. The heterogeneity of individuals can cause mask efficacy in a specific gathering environment to be different from the baseline efficacy in general society. Furthermore, the heterogeneity of gathering environments causes the epidemic prevention effect of masks to differ. Wearing masks can greatly reduce the probability of clustered epidemics and the infection scale in primary schools, high schools, and hospitals. However, the use of masks alone in primary schools and hospitals cannot control outbreaks. In high schools with social distancing between classes and in workplaces where the interpersonal contact is relatively sparse, masks can meet the need for prevention. Given the heterogeneity of individual behavior, if individuals who are more active in terms of interpersonal contact are prioritized for mask-wearing, the epidemic prevention effect of masks can be improved. Finally, asymptomatic infection has varying effects on the prevention effect of masks in different environments. The effect can be weakened or eliminated by increasing the usage rate of masks in high schools and workplaces. However, the effect on primary schools and hospitals cannot be weakened. This study contributes to the accurate evaluation of mask efficacy in various gathering environments to provide scientific guidance for epidemic prevention. © 2022, Higher Education Press.

Vaccine Uptake in a Predominantly Black and Latinx Cohort Hospitalized for Covid-19

Background: Vaccine uptake has been notably lower in minoritized populations in the United States. The impact of previous infection with SARSCoV- 2, disease severity, and persistent symptoms on the uptake of COVID-19 vaccines and boosters in predominantly Black and Latinx communities has not been examined. We aimed to describe correlates of vaccine uptake in a minoritized cohort hospitalized for COVID-19 during the first pandemic wave in New York City, and investigate whether those with more severe initial COVID-19 and persistent symptoms would be less likely to get vaccinated. Method(s): This retrospective cohort study included the electronic medical records of the first 894 consecutive adult patients who survived hospitalization for COVID-19 at a large quaternary care medical center in Northern Manhattan between 1 March and 8 April 2020. We ed data regarding demographics, comorbidities, oxygen requirements during hospitalization, persistence of symptoms at 3- and 6-months after admission, COVID-19 vaccinations through November 2022, and influenza vaccination during the 2018-2019 through 2021-2022 seasons. Unadjusted and adjusted logistic regression analyses were conducted to describe the predictors of COVID-19 vaccination, delayed vaccination (first dose after 6 May 2021), and receipt of a booster vaccine. Statistical analyses were performed using R V.4.2.1. Result(s): The cohort of 894 patients was predominantly Latinx (54%) and Non- Hispanic Black (15%). 41% received at least one influenza vaccine pre-COVID, and 67% had at least one comorbidity. 22% (199/894) remained COVID-19 unvaccinated. Of the individuals who received at least one dose of COVID-19 vaccine, 57% (397/695) received at least one booster. Exactly 31% (212/695) delayed vaccination. 25% (27/106) of unvaccinated individuals reported persistent generalized symptoms compared to 18% (78/436) of vaccinated individuals. Multiple logistic regression showed that Hispanic/Latinx ethnicity, age 35-64, and concurrent influenza vaccination were associated with increased COVID-19 vaccine uptake. No association was found between vaccine uptake and disease severity or persistence of symptoms. Conclusion(s): Achieving a deeper understanding of the factors driving vaccine hesitancy is critical to increasing and sustaining acceptance of COVID-19 vaccination especially in communities with historically low uptake of annual vaccines.

Assessing the spread risk of COVID-19 associated with multi-mode transportation networks in China

The spatial spread of COVID-19 during early 2020 in China was primarily driven by outbound travelers leaving the epicenter, Wuhan, Hubei province. Existing studies focus on the influence of aggregated out-bound popula-tion flows originating from Wuhan;however, the impacts of different modes of transportation and the network structure of transportation systems on the early spread of COVID-19 in China are not well understood. Here, we assess the roles of the road, railway, and air transportation networks in driving the spatial spread of COVID-19 in China. We find that the short-range spread within Hubei province was dominated by ground traffic, notably, the railway transportation. In contrast, long-range spread to cities in other provinces was mediated by multiple factors, including a higher risk of case importation associated with air transportation and a larger outbreak size in hub cities located at the center of transportation networks. We further show that, although the dissemination of SARS-CoV-2 across countries and continents is determined by the worldwide air transportation network, the early geographic dispersal of COVID-19 within China is better predicted by the railway traffic. Given the recent emergence of multiple more transmissible variants of SARS-CoV-2, our findings can support a better assessment of the spread risk of those variants and improve future pandemic preparedness and responses.

Urban Form and Spatiotemporal Vulnerability of Local Communities to Covid-19

This paper investigates spatiotemporal dynamics of the effects of urban form on the Covid-19 spread within local communities in Salt Lake County, Utah, in the United States. We identify three types of communities-minority, traditional urban and suburban, and new suburban-and three stages throughout March 2020-September 2021, reflecting the initial, outbreak, and recovery stages. While the traditional urban and suburban communities experience the least risk of Covid-19, minority communities are severely impacted in the initial and outbreak stages, and remote suburban communities are primarily affected in the outbreak and recovery stages. The regression further reveals the role of urban form in the pandemic. High-density urban land use is the main density factor contributing to the disease's spread. In the initial stage, mobility factors such as street connectivity and walkability contribute to the local spread, while land use mixture is the catalyst in the outbreak stage. A comprehensive compact development might offset these negative effects on local public health, and its contribution to local resilience in the recovery stage is also confirmed. Thus, compact development is still valuable for building urban resilience, and proper planning and policies can offset the potential adverse effects of pandemics.

Exploring the impact of social media exposure patterns on people?s belief in fake news during COVID-19: A cross-gender study

During COVID-19, fake news on social media seriously threatened public health. As a solution to this problem, this study examined how social media exposure patterns affect people being deeply harmed by fake news. Based on cognitive dissonance theory, this study investigated the effect of intentional and incidental exposure on belief in fake news through the mediating role of confirmation bias. The results show that intentional exposure positively influences confirmation bias and belief in fake news. Incidental exposure is the opposite. Our results also show that intentional exposure and confirmation bias negatively influence incidental exposure. Furthermore, these relationships remain unchanged by gender. This study provides theoretical and empirical contributions to reducing people's belief in fake news.

Social Media Driven Big Data Analysis for Disaster Situation Awareness: A Tutorial

Situational awareness tries to grasp the important events and circumstances in the physical world through sensing, communication, and reasoning. Tracking the evolution of changing situations is an essential part of this awareness and is crucial for providing appropriate resources and help during disasters. Social media, particularly Twitter, is playing an increasing role in this process in recent years. However, extracting intelligence from the available data involves several challenges, including (a) filtering out large amounts of irrelevant data, (b) fusion of heterogeneous data generated by the social media and other sources, and (c) working with partially geo-tagged social media data in order to deduce the needs of the affected people. Spatio-temporal analysis of the data plays a key role in understanding the situation, but is available only sparsely because only a small fraction of people post relevant text and of those very few enable location tracking. In this paper, we provide a comprehensive survey on data analytics to assess situational awareness from social media big data.