Detection of Antibodies for COVID-19 from Reflectance Spectrum Using Supervised Machine Learning

Since the coronavirus disease 2019 occurred, the lateral flow immunoassay (LFIA) test strip has become a global testing tool for convenience and low cost. However, some studies have shown that LFIA strips perform poorly compared to other professional testing methods. This paper proposes a new method to improve the accuracy of LFIA strips using spectral signals. A spectrochip module is applied to disperse the reflected light from the LFIA strips. The obtained spectral signals will be used for supervised machine learning. After training, the trained model has 93.8% accuracy compared to the standard test. This result indicated that the evaluation method based on the spectrum of LFIA strips could enhance the detection performance. © 2022 IEEE.

Bibliometrics of Latin American research on COVID-19 in the first year of the pandemic: the main trends

Introduction: An outbreak of the COVID-19 was appended in the central Chinese city of Wuhan in December 2019. Lots of related papers were published in the world since then. Objective: This study aimed to identify and analyze the characteristics of COVID-19 publications in the Science Citation Index Expanded (SCI-EXPANDED) published by Latin Americans in 2020. Methods: Documents including searching keywords in their title, , or author keywords from SCI EXPANDED were assessed. The analyzed aspects covered characteristics of document types, languages, Web of Science categories, and journals. Publication performances of countries and institutions were evaluated by six publication indicators and two citation indicators. Results: A lower percentage of articles and a higher percentage of Spanish language were found. Web of Science category of general and internal medicine published the most articles. The Clinics was the most popular journal. The Cadernos de Saude Publica and Revista da Associacao Medica Brasileira published the most publications and reviews, respectively. Brazil took a leading position in the six publication indicators. The University of Sao Paulo in Brazil was the most productive institution. Based on the number of citations from the Web of Science Core Collection since publication to the end of 2020, 10 most frequently cited publications were presented. In addition, the analysis of words in publication titles, author keywords, and KeyWords Plus was performed to find the main research focuses. Conclusions: In 2020, a total of 3 056 COVID-19 documents in SCI-EXPANDED were published by Latin Americans mainly in the Web of Science categories of 'general and internal medicine' and 'public, environmental and occupational health'. More letters and editorial materials and fewer articles were published in the first year of its outbreak. A higher percentage of Spanish and Portuguese publications was found. Brazil dominated the six publication indicators. The University of Sao Paulo in Brazil ranked top in all the six publication indicators while the Technological University of Pereira in Colombia had a higher impact for their first-and corresponding-author publications. Health and infection were the main research focuses.

COVID-19 pandemic: consumers' purchase intention of indoor fitness products during the partial lockdown period in Singapore

Purpose: The study aims to explore how the perception of coronavirus disease 2019 (COVID-19) affects argument quality of advertisement, attitude and purchase intentions of the indoor fitness products based on the elaboration likelihood model (ELM). Moreover, the moderating effect of exercise involvement was examined. Design/methodology/approach: A total of 283 consumers in Singapore were recruited during the partial lockdown period. Data analysis was employed using the partial least squares structural equation modeling (PLS-SEM). Findings: The results of data analysis showed that perception of COVID-19 affected argument quality of advertisement, attitude and purchase intention of indoor fitness products. Meanwhile, argument quality resulted in a favorable attitude toward indoor fitness products, which, ultimately, led to the purchase intention. In addition, exercise involvement positively moderated the influence of argument quality on attitude. Originality/value: The findings provide implications for businesses and researchers to understand sport consumer behavior during the COVID-19 pandemic. © 2021, Emerald Publishing Limited.

Using Augmented Reality as a Powerful and Innovative Technology to Increase Enthusiasm and Enhance Student Learning in Higher Education Chemistry Courses

The promotion of spatial skills is essential in chemistry education. However, the process of acquiring these skills can be monotonous if learning is limited to the memorization of Newman projections or 3D molecular kits. Existing approaches to learning using visualizing tools require physical models which limit learning activities to within the classroom. Augmented reality (AR) in chemistry education allows students to see actual compound representation in a 3D environment, inspect compounds from multiple viewpoints, and control compounds interaction in real-Time in any location. This facilitates the understanding of the spatial relations between compounds. We developed a methodology to use and assess an AR program to teach chemistry to associate degree science students. Figures of small organic molecules together with customized AR cards were used to let students appreciate the complexity of a 3D compound structure by viewing and rotating the depicted compounds. The effectiveness of learning chemistry using AR technology was evaluated. Quantitative questionnaire feedback results from students showed that 87% found that using AR technology for chemistry subjects was an effective teaching method that enhanced their learning, and students were satisfied with the AR educational app and the AR materials used. In a pre-and post-Test evaluation of a group activity, students learned better and remembered more information about functional groups and drawings of complicated compounds after using AR technology. On the basis of our results, we can conclude that using AR has a positive impact on enthusiasm and learning in higher education chemistry courses for subdegree students, and this technology should be broadly used as a digital tool to promote active learning during the COVID-19 pandemic. ©

A Network Analysis of the Cryptocurrency Market

In this study, we conduct a network analysis with centrality measures, using historical daily close prices of top 120 cryptocurrencies between 2013 and 2020, to study and understand the dynamic evolution and characteristics of the cryptocurrency market. Our study has two primary findings: (1) the overall cross-return correlation among the cryptocurrencies is weakening from 2013 to 2016 and then strengthening thereafter;(2) cryptocurrencies that are primarily used for transaction payment, notably BTC, dominate the market until mid-2016, followed by those developed for applications using blockchain as the underlying technology, particularly data storage and recording such as MAID and FCT, between mid-2016 and mid-2017. Since then, ETH, alongside with its strongly correlated cryptocurrencies have replaced BTC to become the benchmark cryptocurrencies. Furthermore, during COVID-19, QTUM and BNB have intermittently replaced ETH to take the leading positions due to their active community engagement during the pandemic.

Hybrid Emotion-aware Monitoring System based on Brainwaves for Internet of Medical Things

Driven by an increasing number of connected medical devices, Internet of Medical Things (IoMT), as an application of Internet of Things (IoT) in healthcare, is developed to help collect, analyze and transmit medical data. During the outbreak of pandemic like COVID-19, IoMT can be useful to monitor the status of patients and detect main symptoms remotely, by using various smart sensors. However, due to the lack of emotional care in current IoMT, it is still a challenge to reach an efficient medical process. Especially under COVID-19, there is a need to monitor emotion status among particular people like elderly. In this work, we propose an emotion-aware healthcare monitoring system in IoMT, based on brainwaves. With the fast development of EEG (electroencephalography) sensors in current headsets and some devices, brainwave-based emotion detection becomes feasible. The IoMT devices are used to capture the brainwaves of a patient in a scenario of smart home. Also, our system involves the analysis of touch behavior as the second layer to enhance the brainwave-based emotion recognition. In the user study with 60 participants, the results indicate the viability and effectiveness of our approach in detecting emotion like comfortable and uncomfortable, which can complement existing emotion-aware healthcare applications and mechanisms. IEEE

A Short-Term Cryptocurrency Price Movement Prediction Using Centrality Measures

We conduct a network analysis with centrality measures, using historical daily close prices of top 120 cryptocurrencies between 2013 and 2020, to study and understand the dynamic evolution and characteristics of the cryptocurrency market. Our study has three primary findings: (1) the overall cross-return correlation among the cryptocurrencies is weakening from 2013 to 2016 and then strengthening thereafter;(2) cryptocurrencies that are primarily used for transaction payment, notably BTC, dominate the market until mid-2016, followed by those developed for applications using blockchain as the underlying technology, particularly data storage and recording such as MAID and FCT, between mid-2016 and mid-2017. Since then, ETH, alongside with its strongly correlated cryptocurrencies have replaced BTC to become the benchmark cryptocurrencies. Furthermore, during the outbreak of COVID-19, QTUM and BNB have intermittently replaced ETH to take the leading positions due to their active community engagement during the pandemic;(3) centrality measures are useful features in improving the prediction accuracy of the short-term cryptocurrency price movement. © 2020 IEEE.

Characterizing the Cryptocurrency Market During Crisis

We conduct a network analysis with centrality measures, using historical daily close prices of top 120 cryptocurrencies between 2013 and 2020, to analyze the dynamic evolution and characteristics of the current cryptocurrency market. Our study has two primary findings: (1) the overall return correlation among the cryptocurrencies is weakening from 2013 to 2016 and then strengthening thereafter;(2) cryptocurrencies that are primarily used for transaction payment, notably BTC, dominate the market until mid-2016, followed by those developed for applications using blockchain as the underlying technology, particularly data storage and recording such as MAID and FCT, between mid-2016 and mid-2017. Since then, ETH has replaced BTC to become the benchmark cryptocurrencies. Interestingly, during the outbreak of COVID-19, QTUM and BNB have intermittently replaced ETH to take the leading positions, possibly due to their active community engagement during the pandemic. © 2020, Springer Nature Switzerland AG.

PMH4 The Psychological and Behavioral Responses to Covid-19 Epidemic in Pregnant Women in China: A Nationwide Survey

Objectives: To investigate the psychological and behavioral responses of pregnant women to COVID-19 epidemic. Methods: A population-based cross-sectional web-based survey was carried out between Feb 13-16, 2020, where 1908 pregnant women responded. Participants were pregnant women who had registered with the Banmi Online Maternity School, one of the largest national online platforms for maternity college in China. This study used linear and logistic regression to evaluate the influence of demographic factors on psychological and behavioral responses of pregnant women in China to COVID-19 outbreak, and used structural equation modeling (SEM) to evaluate the relative strength of associations between psychological and behavioral responses assessed by PCL-C, EPDS and, stress level as well as preventive behavioral adjustment scales in a sample of 1908 pregnant women in China. Results: Among the 1908 respondents, 1099 met criteria for a positive screening for postpartum depression, and 287 met the criteria for a positive screening for PTSD, where 264 women exceeds the cut-off points for both. We found that women with lower educational level tended to have higher scores of PCL-C, and EPDS scales as well as stress level and behavioral adjustment;and more were regarded as suspected PTSD and probable PPD. Moreover, the SEM analysis showed the highest effect of psychological responses on behavioral responses in the pregnant women was exerted on stress (coefficient =0.376, P<0.001), and Fear of infection (coefficient =-0.747, P<0.001). Conclusions: The psychological states of pregnant women under the COVID-19 epidemic was lower-estimated, and psychoeducation as well as other psychological intervention may be needed to equip both the affected pregnant women and family members with healthy problem-solving and communication skills and provide education and resources about the mental health condition that the pregnant women is experiencing.