Attitude and influencing factors to receive the COVID-19 vaccine among university students in Sichuan Province, China: A cross-sectional study.

AIMS: To explore the university students' attitude and the potential influencing factors to receive the coronavirus disease 2019 (COVID-19) vaccine in Sichuan Province, China. DESIGN: A cross-sectional study. METHODS: The self-designed questionnaire was distributed among university students online in June 2021. SPSS software was used for statistical analysis of the data. Descriptive statistics, Chi-square, two independent samples t-tests, one-way analysis of variance (ANOVA), multivariate linear regression, and content analysis were performed. RESULTS: A total of 397 questionnaires were analysed, involving 316 (79.6%) respondents have received at least one dose of a COVID-19 vaccine and 81 (20.4%) have not taken the vaccine. The total mean score of university students' vaccination attitude was 25.97 (standard deviation [SD] = 3.720), and the total scoring rate was 74.2%. Main factors influencing students' attitude included education level, major, living style, with chronic disease or not, self-reported vaccination status, and number of medical units that can provide vaccination within 3 km of residence. Students were more willing to choose Chinese-manufactured vaccines (66.8%) and participate in collective vaccination programs organized by the school (71.3%). The desired vaccine protection period was 5-10 years (42.1%). The top three reasons for refusing the vaccine or vaccine hesitancy were as follows: concern about the side effects of vaccine (44.8%), lack of information about vaccine (31.0%), and concern about the efficacy of vaccine (29.3%). CONCLUSION: In general, most of the participants had relatively high level of positive attitude to receive the COVID-19 vaccine. Nevertheless, more attention should be paid to postgraduate students, non-medical students, those living alone, those with chronic disease, those have not received the COVID-19 vaccine, and those living far away from the vaccination medical units. Findings of this study can help educational institutions in developing effective interventions to improve the vaccination rate in the university student population.

Emotion regulation difficulties and sleep quality in adolescence during the early stages of the COVID-19 lockdown (preprint)

Introduction: Adolescence is a period of vulnerability for emotion regulation and sleep difficulties, risks that might be compounded by intense COVID-19 lockdowns and challenges. The aim of this study was to investigate how sleep quality related to emotion regulation difficulties in adolescents during lockdown in Perú. Methods: Participants were 2563 adolescents enrolled in Innova school in Perú (11 – 17 years) in May 2020. Hypotheses were derived from exploring one half of the sample, preregistered at https://osf.io/fuetz/, and then confirmed in the second half of the sample. Participants completed subjective surveys of sleep quality (short PSQI) and the Difficulties in Emotion Regulation Scale Short Form (DERS-SF). Results: Worse sleep quality was robustly associated with more difficulties in emotion regulation across both samples. The association was found particularly for emotional regulation subscales related to the ability to engage in goal directed behavior in the face of distress, emotional clarity and strategies to deal with feeling distressed. In contrast, there was no robust association between sleep and the ability to regulate impulses in the context of negative emotions, and no association with the ability to accept emotions. Girls and older adolescents robustly endorsed worse sleep quality and more difficulties in emotion regulation. Limitations: The cross-sectional nature of this study prevents us from determining the direction of the association. Data was collected using adolescent self-report which, while informative of adolescent perceptions, might diverge from objective measures of sleep or emotion regulation difficulties. Conclusions: Our findings with adolescents in Perú contribute to our understanding of the association between sleep an emotion regulation at a broader global scale.

Effects of the COVID-19 pandemic on public bus occupancy and real-world tailpipe emissions of gaseous pollutants per passenger kilometer traveled

Public buses typically have less emission per passenger kilometer traveled (PKT) than private cars and motorcycles, and the emission benefit of public buses increases with ridership. However, the drop in public bus usage during the novel coronavirus (COVID-19) pandemic could lead to an increase in air pollutant emissions per PKT, making the emission benefits of public buses questionable. This study investigated the effects of the COVID-19 pandemic on public bus occupancy rates in Taichung City, Taiwan, and also compared real-world emissions per PKT of carbon monoxide (CO), total hydrocarbons (THC), nitric oxide (NO), and carbon dioxide (CO 2 ) of a public bus before and during the pandemic. Mean bus occupancy rates were 11–25% on different bus routes before the pandemic, indicating that only a fourth or less of the bus passenger capacity was utilized. During the pandemic, mean bus occupancy rates dropped to 4–15%. Moreover, the public bus was less polluting based on CO and THC emissions than the car and motorcycle, even at the low passenger occupancy rates observed during the pandemic. However, NO and CO 2 emissions per PKT of the bus were remarkably higher during the pandemic than those of the car and motorcycle. Furthermore, we estimated the break-even passenger occupancy rate for buses as 15%, which was the minimum threshold occupancy rate below which the buses would be more polluting than cars and motorcycles in terms of CO, THC, and CO 2 emissions per PKT. Our findings will help transport management authorities and policymakers to optimize bus route designs and frequencies and implement anti-pandemic measures to maximize the environmental benefits of the public bus transit systems.

Early detection of fraudulent COVID-19 products from Twitter chatter (preprint)

BackgroundSocial media have served as lucrative platforms for spreading misinformation and for promoting fraudulent products for the treatment, testing, and prevention of COVID-19. This has resulted in the issuance of many warning letters by the United States Food and Drug Administration (FDA). While social media continue to serve as the primary platform for the promotion of such fraudulent products, they also present the opportunity to identify these products early by employing effective social media mining methods. In this study, we employ natural language processing and time series anomaly detection methods for automatically detecting fraudulent COVID-19 products early from Twitter. Our approach is based on the intuition that increases in the popularity of fraudulent products lead to corresponding anomalous increases in the volume of chatter regarding them. ResultsWe utilized an anomaly detection method on streaming COVID-19-related Twitter data to detect potentially anomalous increases in mentions of fraudulent products. We compared the anomaly signal generation date for each product with the corresponding FDA letter issuance date. Issue dates ranged from March 6, 2020 to June 22, 2021, and 44 key phrases representing fraudulent products were included. From 577,872,350 posts made between February 19, 2020 to December 31, 2020, our unsupervised approach detected 34/44 (77.3%) signals about fraudulent products earlier than the FDA letter issuance dates, and an additional 6/44 (13.6%) within a week following the corresponding FDA letters.  ConclusionsOur proposed method is simple, effective, and easy to deploy, and does not require high-performance computing machinery, unlike deep neural network-based methods. The method can be easily extended to other types of signal detection from social media data.

Early detection of fraudulent COVID-19 products from Twitter chatter (preprint)

Social media have served as lucrative platforms for misinformation and for promoting fraudulent products for the treatment, testing and prevention of COVID-19. This has resulted in the issuance of many warning letters by the United States Food and Drug Administration (FDA). While social media continue to serve as the primary platform for the promotion of such fraudulent products, they also present the opportunity to identify these products early by employing effective social media mining methods. In this study, we employ natural language processing and time series anomaly detection methods for automatically detecting fraudulent COVID-19 products early from Twitter. Our approach is based on the intuition that increases in the popularity of fraudulent products lead to corresponding anomalous increases in the volume of chatter regarding them. We utilized an anomaly detection method on streaming COVID-19-related Twitter data to detect potentially anomalous increases in mentions of fraudulent products. Our unsupervised approach detected 34/44 (77.3%) signals about fraudulent products earlier than the FDA letter issuance dates, and an additional 6/44 (13.6%) within a week following the corresponding FDA letters. Our proposed method is simple, effective and easy to deploy, and do not require high performance computing machinery unlike deep neural network-based methods.

Shearing Characteristics of Cu-Cu Joints Fabricated by Two-Step Process Using Highly <111>-Oriented Nanotwinned Cu

Cu-Cu bonding has the potential to break through the extreme boundary of scaling down chips’ I/Os into the sub-micrometer scale. In this study, we investigated the effect of 2-step bonding on the shear strength and electrical resistance of Cu-Cu microbumps using highly <111>-oriented nanotwinned Cu (nt-Cu). Alignment and bonding were achieved at 10 s in the first step, and a post-annealing process was further conducted to enhance its bonding strength. Results show that bonding strength was enhanced by 2–3 times after a post-annealing step. We found 50% of ductile fractures among 4548 post-annealed microbumps in one chip, while the rate was less than 20% for the as-bonded counterparts. During the post-annealing, interfacial grain growth and recrystallization occurred, and the bonding interface was eliminated. Ductile fracture in the form of zig-zag grain boundary was found at the original bonding interface, thus resulting in an increase in bonding strength of the microbumps.

Effects of case- and population-based COVID-19 interventions in Taiwan (preprint)

In the first wave of the COVID-19 pandemic, broad usage of non-pharmaceutical interventions played a crucial role in controlling epidemics. However, the substantial economic and societal costs of continuous use of border controls, travel restrictions, and physical distancing measures suggest that these measures may not be sustainable and that policymakers have to seek strategies to lift the restrictions. Taiwan was one of the few countries that demonstrated initial success in eliminating the COVID-19 outbreak without strict lockdown or school closure. To understand the key contributors to the successful control, we applied a stochastic branching model to empirical case data to evaluate and compare the effectiveness of more targeted case-based (including contact tracing and quarantine) and less targeted population-based interventions (including social distancing and face mask use) in Taiwan. We found that case-based interventions alone would not be sufficient to contain the epidemic, even in a setting where a highly efficient contact tracing program was in place. The voluntary population-based interventions have reduced the reproduction numbers by more than 60% and have likely played a critical role at the early stage of the outbreak. Our analysis of Taiwan's success highlights that coordinated efforts from both the government and the citizens are indispensable in the fight against COVID-19 pandemic.

Self-reported COVID-19 symptoms on Twitter: An analysis and a research resource (preprint)

Objective To mine Twitter to quantitatively analyze COVID-19 symptoms self-reported by users, compare symptom distributions against clinical studies, and create a symptom lexicon for the research community. Materials and methods We retrieved tweets using COVID-19-related keywords, and performed semi-automatic filtering to curate self-reports of positive-tested users. We extracted COVID-19-related symptoms mentioned by the users, mapped them to standard concept IDs (UMLS), and compared the distributions to those reported in early studies from clinical settings. Results We identified 203 positive-tested users who reported 1002 symptoms using 668 unique expressions. The most frequently-reported symptoms were fever/pyrexia (66.1%), cough (57.9%), body ache/pain (42.7%), fatigue (42.1%), headache (37.4%), and dyspnea (36.3%) amongst users who reported at least 1 symptom. Mild symptoms, such as anosmia (28.7%) and ageusia (28.1%) were frequently reported on Twitter, but not in clinical studies. Conclusion The spectrum of COVID-19 symptoms identified from Twitter may complement those identified in clinical settings.