Deploying the Minimum Number of Rechargeable UAVs for a Quarantine Barrier

To control the rapid spread of COVID-19, we consider deploying a set of Unmanned Aerial Vehicles (UAVs) to form a quarantine barrier such that anyone crossing the barrier can be detected. We use a charging pile to recharge UAVs. The problem is scheduling UAVs to cover the barrier, and, for any scheduling strategy, estimating theminimum number of UAVs needed to cover the barrier forever. We propose breaking the barrier into subsegments so that each subsegment can be monitored by a single UAV. We then analyze two scheduling strategies, where the first one is simple to implement and the second one requires fewer UAVs. The first strategy divides UAVs into groups with each group covering a subsegment. For this strategy, we derive a closed-form formula for the minimum number of UAVs. In the case of insufficient UAVs, we give a recursive function to compute the exact coverage time and give a dynamic-programming algorithm to allocate UAVs to subsegments to maximize the overall coverage time. The second strategy schedules all UAVs dynamically. We prove a lower and an upper bound on the minimum number of UAVs. We implement a prototype system to verify the proposed coverage model and perform simulations to investigate the performance.

An Intelligent Classification Diagnosis Based on Blood Oxygen Saturation Signals for Medical Data Security Including COVID-19 in Industry 5.0

Obstructive sleep apnea-hypopnea syndrome (OSAHS) is gradually valued due to its high prevalence, high risk, and high mortality. Alternative to the polysomnography (PSG) diagnosis, the proposed method assesses the subject's degree of illness considering the supply chain and Industry 5.0 requirement efficiently and accurately. This article uses the blood oxygen saturation (SpO(2)) signal count of the number of apnea or hypoventilation events during the sleep of the subject, calculating the apnea-hypopnea index (AHI) and the subject's disease level. SpO(2) signals are used to extract 35-D features based on the time domain, including approximate entropy, central tendency measure, and Lempel-Ziv complexity to accelerate the diagnosis process in supply chains. The feature selection process is reduced from 35 to 7 dimensions that benefits to the implementation in the practical supply chains in Industry 5.0 by extracting the extracted features. This article applies Pearson correlation coefficient selection, based on minimum redundancy-maximum correlation algorithm selection, and a wrapper based on the backward search algorithm. The accuracy rate is 86.92%, and the specificity is 90.7% under the selected random forest classifier. A random forest classifier was used to calculate the AHI index, and a linear regression analysis was performed with the AHI index obtained from the PSG. The result reaches a 92% accuracy rate in assessing the prevalence of OSAHS, satisfying the industrial deployment.

What makes nonprofit organizations (NPOs) talk transparently about their connections with businesses on Twitter? Insights from nonprofit-business network portfolios and resource dependencies

Drawing on network portfolio literature and resource dependence theory, this study investigates how a nonprofit's N2B partnership portfolio configurations (i.e., size and industry diversification), reliance on individual donations, and reliance on government grants influence the nonprofit's transparency in disclosing N2B partnerships on Twitter. We manually coded the level of transparency reflected in 911 tweets sent by 81 leading COVID-19 NPOs mentioning 501 companies from March 1 to July 19, 2020. Social network analysis and regression models were performed to answer the research inquiries. Findings indicate that maintaining a large number of business connections is associated with lowered transparency in N2B communication on Twitter, whereas keeping diverse connections with different business industries relates to increased transparency in N2B communication. NPOs with a stronger reliance on government grants signaled more transparency in N2B parentships on Twitter, but the reliance on individual donations did not influence N2B transparency signaling. © 2023 Elsevier Inc.

Environmentally persistent free radicals in PM2.5 from a typical Chinese industrial city during COVID-19 lockdown: The unexpected contamination level variation

The outbreak of COVID-19 has caused concerns globally. To reduce the rapid transmission of the virus, strict city lockdown measures were conducted in different regions. China is the country that takes the earliest home-based quarantine for people. Although normal industrial and social activities were suspended, the spread of virus was efficiently controlled. Simultaneously, another merit of the city lockdown measure was noticed, which is the improvement of the air quality. Contamination levels of multiple atmospheric pollutants were decreased. However, in this work, 24 and 14 air fine particulate matter (PM2.5) samples were continuously collected before and during COVID-19 city lockdown in Linfen (a typical heavy industrial city in China), and intriguingly, the unreduced concentration was found for environmentally persistent free radicals (EPFRs) in PM2.5 after normal life suspension. The primary non-stopped coal combustion source and secondary Cu-related atmospheric reaction may have impacts on this phenomenon. The cigarette-based assessment model also indicated possible exposure risks of PM2.5-bound EPFRs during lockdown of Linfen. This study revealed not all the contaminants in the atmosphere had an apparent concentration decrease during city lockdown, suggesting the pollutants with complicated sources and formation mechanisms, like EPFRs in PM2.5, still should not be ignored. © 2022

The relationship between psychological distress and professional identity in graduate nursing students during COVID-19: A longitudinal cross-lagged analysis

There is growing evidence that the COVID-19 pandemic has had an impact on the mental health and professional identity of nursing students. This study aimed to explore the trajectories of psychological distress and professional identity and examine their causal relationship among graduate nursing students. A prospective longitudinal study was conducted with 556 graduate nursing students in China who were asked to complete the Questionnaires for Emergent Events of Public Health and the Professional Identity Questionnaire for Nursing Students at three timepoints during the pandemic (T1 = onset of the outbreak, T2 = state-wide isolation, T3 = school reopening). The results showed that psychological distress and psychological identity changed over time, with the lowest psychological distress and highest professional identity at T3. The cross-lagged analysis showed that professional identity at T1 and T2 negatively predicted psychological distress at T2 and T3. We concluded that nurse educators should develop programs and strategies to cultivate professional identity in graduate nursing education to improve mental health during public health crises. © 2023: Editum. Universidad de Murcia (Spain).

Research on Quality Improvement Factors of Special Audit for COVID-19 Based on DEMATEL-ISM

In the midst of the COVID-19 pandemic, the standardized management of relief funds and supplies requires audit departments to conduct the special audit and issue audit reports to the public. However, in the context of emergencies, the special audit for COVID-19 is affected by a variety of complex factors, and it is necessary to further identify the factors that affect the quality of the special audit for COVID-19 and clarify the internal logical relationship. Taking the special audit for COVID-19 as the research object, the influencing factors of the special audit quality were identified and extracted through text coding, and the DEMATEL-ISM method of complex factor analysis was used to explain the attributes of factors, sort out the relationship of factors, and divide the hierarchical structure Combined with the results, the improvement direction of the special audit quality for epidemic prevention and control is discussed. © 2022 ACM.

[Kinetics of SARS-CoV-2-specific antibodies among inactivated COVID-19 vaccine recipients, SARS-CoV-2 natural infection cases, and breakthrough cases].

Between August and September, 2021, this study included 605 SARS-CoV-2 natural infection cases and 589 SARS-CoV-2 breakthrough cases from Nanjing and Yangzhou, as well as 690 inactivated COVID-19 vaccine recipients from Changzhou, China. In SARS-CoV-2 natural infection cases, the age range was 19-91 years (median age: 66 year), and the medians(Q1,Q3) of IgG titers were 0.19 (0.06-1.31), 3.70 (0.76-69.48), 15.31 (2.59-82.16), 4.41 (0.99-31.74), 2.31 (0.75-13.83), 2.28 (0.68-9.94) and 2.80 (1.00-9.53) at one to seven weeks after SARS-CoV-2 infection, respectively. In SARS-CoV-2 breakthrough cases, the age range was 18-76 years (median age: 45 year), and the medians(Q1,Q3)of IgG titers were 1.93 (0.34-26.67), 38.87 (7.90-121.0), 75.09 (11.85-123.70), 21.97 (5.20-95.58), 13.97 (3.47-46.82), 9.56 (2.48-33.38) and 4.38 (1.87-11.00) at one to seven weeks after SARS-CoV-2 infection, respectively. In inactivated COVID-19 vaccine recipients, the age range was 18-87 years (median age: 47 years), and the medians(Q1,Q3)of IgG titers were 16.22 (15.84-33.42), 5.35 (2.96-13.23), 3.30 (2.18-6.18), 3.14 (1.16-5.70), 2.77 (1.50-4.52), 2.72 (1.76-4.36), 2.01 (1.27-3.51) and 1.94 (1.35-3.09) at one to eight months after SARS-CoV-2 infection, respectively. The results suggested that IgG antibodies increased gradually within two weeks after SARS-CoV-2 infection, then declined gradually at three to seven weeks in SARS-CoV-2 natural infection cases. In SARS-CoV-2 breakthrough cases, IgG antibodies increased rapidly within two weeks, then declined gradually at three to seven weeks after SARS-CoV-2 infection. Additionally, IgG antibodies decreased rapidly within three months, then decreased gradually and remained at a low level within three months after immunization.

The Internet Design Framework for Improvement of Users' Positive Emotions

This study proposes an internet design framework for users to improve their positive emotions when they are in a negative mood. First, the literature review focuses on the definition of emotion, positive emotional design in internet experiences, and emotion regulation. Second, in order to construct an internet design framework that improves positive emotion, this paper adopts a qualitative analysis method to analyze 70 collected studies in the area of regulating emotion and stimulating positive emotions. Additionally, bibliometrics and statistics are conducted to summarize the framework and strategies. Third, two cases of internet design are presented: (a) Internet design that improves users' positive emotions is examined under the background of extreme rainstorm as an example;an applet service design is provided by case study;(b) in the context of COVID-19, we developed an Internet of things interactive design that improves users' positive emotions. Fourth, the internet design framework and the results of the case studies are analyzed and discussed. Finally, an internet design framework is proposed to improve users' positive emotions when they are in a negative mood, which includes the Detachment-empathy framework, External-protection framework, Ability-strengthen framework, Perspective-transformation framework, and Macro-cognitive framework. The framework can help designers to generate design ideas accurately and quickly when users are in a negative mood, to improve subjective well-being, and contribute to the development of internet experience design. Copyright © 2022 KSII.

One Earth, One Humanity Versus the Virus: Examining the Global COVID-19 Social Partnership Communication Networks on Social Media

The COVID-19 pandemic has posed severe challenges that require collaborative efforts from multi-sector organizations. Guided by an institutional theory framework that considers how both organizational fields and national level contexts affect organizations' social partnership communication, the current study examines the COVID-19-related social partnership communication network on social media. The cross-national study using semantic network analysis and exponential random graph models (ERGMs) first maps the meaning of COVID-19 social partnership network, and then investigates the role of organizational fields and a country's political system, economic system, educational system, and cultural system on the formation of interorganizational communication ties surrounding the relief efforts of COVID-19. Results reveal the importance of the political system-such as the presence of populist government, economic disparity, and uncertainty avoidance cultural orientation in shaping the social media-based social partnership communication network. In addition, NGOs from multiple issue areas are actively engaged in the network, whereas corporations from manufacturing and financial industries are active players.

Tiredness Detection for a Robotic Mobility Aid

The proportion of elderly people in society is predicted to continue to rise in the coming decades. Mobility is a key aspect of many daily activities, but falls become an increasingly significant health risk with age. With the COVID-19 pandemic, many elderly users prefer or require assistive devices, rather than human support, in walking and carrying out daily tasks. However, prior work has shown that when using passive assistive mobility devices, fall risks can actually increase. This presents an opportunity for assistive robots to help maintain and improve the mobility of elderly users, with an additional emphasis on safety, made possible through sensing capabilities. In this paper, we present a computer vision system that detects the eye blink and face angle patterns for exhibiting signs of tiredness. In addition to the frame-based detection, we also introduce a time-window collation with a machine learning classifier. The system proposed here is critical in monitoring the user, performing real-time detection, and recommending they take a break if tiredness is detected. The overall system architecture and algorithmic details are presented, then a series of experiments are conducted to validate the performance of the approach. © 2022 IEEE.

Stem cell-derived trophoblast organoids model human placental development, X chromosome inactivation, and susceptibility to emerging pathogens

Problem: Trophoblast organoids derived from human placental villi provide a powerful 3D model system of placental development, but access to first-trimester tissues is limited due to ethical and legal restrictions. Here we sought to establish a methodology for establishing 3D trophoblast organoids from naïve human pluripotent stem cells (hPSCs), which have an expanded potential for extraembryonic differentiation. Method of Study: We previously demonstrated that naïve hPSCs readily give rise to self-renewing human trophoblast cells (hTSCs) that resemble post-implantation cytotrophoblast (CTB) progenitors and can further differentiate into specialized trophoblast lineages. Here we examined whether hTSCs derived from three distinct sources (naïve hPSCs, human blastocysts, and first-trimester placental tissues) have the potential to self-organize into 3D trophoblast organoids by transfer to Matrigel droplets in the presence of trophoblast organoid medium. The expression of protein markers in the resulting stem cellderived trophoblast organoids (SC-TOs) was examined by immunofluorescence and light-sheet microscopy, while their single cell transcriptome was analyzed using the 10X Genomics platform. We also investigated the X chromosome inactivation (XCI) status of organoids derived from female naïve hPSCs and their ability to differentiate into invasive extravillous trophoblast (EVT) organoids. Finally, we evaluated whether SC-TOs are susceptible to infection by various emerging pathogens (SARS-CoV-2 and Zika virus), as a basis for establishing a stem cell-based model system of placental infections during the first trimester. Results: Trophoblast organoids generated from naïve and primary hTSCs displayed comparable tissue architecture, placental hormone secretion, microRNA expression, and capacity for long-term selfrenewal. In-depth single cell transcriptome profiling revealed that SCTOs encompass a variety of trophoblast identities that closely correspond to CTB progenitor, syncytiotrophoblast (STB) and EVT cell types found in human post-implantation embryos. Interestingly, the cellular composition in trophoblast organoids derived from naïve and primary hTSCs was highly similar, which suggests that trophoblast organoid culture represents a powerful attractor state in which the influence of subtle epigenetic differences between naïve and primary hTSCs is mitigated. These organoid cultures displayed clonal XCI patterns previously described in the human placenta.Upon differentiation into specialized EVT organoids, extensive trophoblast invasion was observed in co-culture assays with human endometrial cells. We further demonstrated that SC-TOs display selective vulnerability to infection by SARS-CoV-2 and Zika virus, which correlated with the expression levels of their respective entry factors. Conclusions: The generation of trophoblast organoids from naïve hPSCs provides an accessible and patient-specific 3D model system of the developing placenta and its susceptibility to emerging pathogens. The ability to genetically manipulate naïve hPSCs prior to differentiation into SC-TOs enables functional interrogation of regulatory factors implicated in placental organogenesis.

An Online Experiment Evaluating the Effects of Social Endorsement Cues, Message Source, and Responsibility Attribution on Young Adults’ COVID-19 Vaccination Intentions

Adopting the theory of planned behavior framework, this online experiment investigated the effects of social endorsement cues, message source, and responsibility attribution on young adults’ perceptions of COVID-19 vaccination and intentions to get vaccinated. Four major findings were identified. First, social endorsement cues positively affect attitude, subjective norms, and vaccination intentions. Second, individuals perceive an expert source as the most credible, but a media outlet source results in the most positive subjective norms. Third, responsibility attributions do not generate significant effects on the dependent variables. Finally, social endorsement cues and message source both have some interaction effects with perceived susceptibility to COVID-19 on message outcomes. © The Author(s) 2022.

THE NEW PATTERN OF ONLINE CREDIT LOAN IN THE POST-EPIDEMIC ERA

This paper is one of the few studies that discusses the impacts and challenges of banking business, especially online credit loans (also known as Internet loans or consumption loans) from the aspects of social environment, economic development, public policy, and technological environment changes under the impact of COVID-19 epidemic. Based on authors' years of experiences in banking industry, they find that online loan products are facing with numerous problems, such as unclear position of online loan products, management dilemma of employees, dilemma between social responsibilities and debt collection, mismatch between risk and income, high bad-loan ratio issues and so on. To find effective ways to solve those problems, authors combined experience with practice and put forward several possible directions, such as seizing the opportunities of digital transformation to develop financial technology, expanding online products, establishing a new financial environment under changed consuming scene, and utilizing the strict supervision mechanism to expand traditional banking business.

The role of evidence-based medicine in guiding clinical research and practice: opinion based on studies on antiviral drugs for the treatment of COVID-19

This paper analyzed clinical studies, systematic reviews and clinical practice guidelines on the treatment of COVID-19 with antiviral drugs. It elaborated on the evidence ecosystems in these studies following the "efficacy, evidence-based practice, effectiveness" (3E) model that considered five elements: research hypothesis, research evidence, big data from healthcare facilities, real-world data, and real-world evidence. Finally, this paper summarized the experience in the production, transformation and application of evidence. This paper could help clinicians conduct highquality clinical research and provide good clinical practice based on the best currently available evidence.

Utility of exercise testing to assess athletes for post COVID-19 myocarditis.

Purpose: This study assessed a functional protocol to identify myocarditis or myocardial involvement in competitive athletes following SARS-CoV2 infection. Methods: We prospectively evaluated competitive athletes (n = 174) for myocarditis or myocardial involvement using the Multidisciplinary Inquiry of Athletes in Miami (MIAMI) protocol, a median of 18.5 (IQR 16-25) days following diagnosis of COVID-19 infection. The protocol included biomarker analysis, ECG, cardiopulmonary stress echocardiography testing with global longitudinal strain (GLS), and targeted cardiac MRI for athletes with abnormal findings. Patients were followed for median of 148 days. Results: We evaluated 52 females and 122 males, with median age 21 (IQR: 19, 22) years. Five (2.9%) had evidence of myocardial involvement, including definite or probable myocarditis (n = 2). Three of the 5 athletes with myocarditis or myocardial involvement had clinically significant abnormalities during stress testing including ventricular ectopy, wall motion abnormalities and/or elevated VE/VCO2, while the other two athletes had resting ECG abnormalities. VO2max, left ventricular ejection fraction and GLS were similar between those with or without myocardial involvement. No adverse events were reported in the 169 athletes cleared to exercise at a median follow-up of 148 (IQR108,211) days. Patients who were initially restricted from exercise had no adverse sequelae and were cleared to resume training between 3 and 12 months post diagnosis. Conclusions: Screening protocols that include exercise testing may enhance the sensitivity of detecting COVID-19 related myocardial involvement following recovery from SARS-CoV2 infection.

An Intelligent Classification Diagnosis based on Blood Oxygen Saturation Signals for Medical Data Supply Chain Including COVID-19 in Industry 5.0

Obstructive sleep apnea-hypopnea syndrome (OSAHS) has been gradually valued due to its high prevalence, high risk, and high mortality. This article is to find an alternative to the polysomnography (PSG) OSAHS diagnosis method and assesses the subject's degree of illness considering the supply chain and Industry 5.0 requirement, efficiently, accurately and easily. The blood oxygen saturation (SpO2) signal is used to count the number of apnea or hypoventilation events. It extracts 35-dimensional features based on the time domain to enhance the process resilience, including approximate entropy, Centralized Trend Measurement (CTM), and LZ complexity for the diagnosis process in supply chains. This article summarizes the Oxygen Desaturation Index (ODI) characteristics. The feature selection process is reduced from 35 to 7 dimensions and benefits the implementation in the practical supply chains in industry 5.0. A 92% accuracy rate is reached in assessing the prevalence of OSAHS, satisfying the industrial deployment. IEEE

COVID pandemic versus pre-pandemic care of stroke patients within the Florida Stroke Registry

Background: The coronavirus 2019 (COVID-19) pandemic has affected all aspects of stroke care delivery and resource allocation. We sought to study this effect utilizing the Florida Stroke Registry (FSR), which collects data from hospitals in large metropolitan cities and small communities, each facing pandemic peaks at different timepoints and within various healthcare system organizations. Methods: From March 2019 to March 2021, the FSR identified 82,899 patients with the final diagnosis of ischemic stroke and TIA. Stroke care metrics were compared in patients enrolled during the COVID-19 pandemic (March 2020 to February 2021) to those enrolled in the immediate pre-pandemic year. These metrics included utilization of intravenous thrombolytic (IVT), Endovascular therapy (EVT), Door-To-Needle time (DTN), Door-To-Puncture time (DTP), Door-To-Computed Tomography time (DTCT) and overall Defect-Free Care (DFC). Results: Pre-pandemic patients (n= 41,929, 49.0% female, mean age 70.1 ± 14.6 years, 64.3% white, 20.4% black, 15.3% Hispanic) had similar demographics to pandemic patients (48.8% female, mean age 69.9 ± 14.4 years, 65.4% white, 19.9% black, 14.7% Hispanic). Pandemic stroke patients had more severe presentations (median NIHSS 3 [IQR 8] vs 3 [7], p < .0001), longer onset-to-arrival time (242 [677] vs 229 [654] minutes, p = 0.002), and were more likely to arrive via emergency medical services (62.3% vs. 60.8%, p < .0001) than pre-pandemic stroke patients. Although both groups received IVT equally (13.4% vs. 13.5%, p = 0.67), pandemic stroke patients were more likely to receive EVT (7.0% vs. 6.5%, p = 0.005) and had longer DTP (84 [60] vs. 81 [64] minutes, p = 0.01), shorter DTCT (22 [52] vs 23 [56] minutes, p = 0.01) and similar DTN (36 [22] vs. 37 [22] minutes, p = 0.05) times, with an increased DFC rate of 2.2% (86.6% vs. 84.4%, p < .0001). Conclusions: In this large registry based study, we found that compared to pre-pandemic care, ischemic stroke patients treated during the COVID19 pandemic presented sicker and later to the hospital and were more likely to receive EVT, but had longer door-to-puncture times. Despite many healthcare delivery challenges imposed by COVID19, Florida hospitals within the FSR maintained high quality of stroke care overall.

Why Do People Not Install Corona-Warn-App? Evidence from Social Media

This study investigates why the Corona-Warn-App, which was meticulously designed in Germany to interrupt the COVID-19’s chain of infection, was not installed by the majority of the population and therefore failed to achieve what it was created to do. We collect natural language data by scraping 70,529 related comments from Twitter, and apply sentiment analysis to understand the content. We distinguish negative comments into two categories: technical issues, e.g. crashes and errors, and trust-related issues, e.g. concerns about privacy protection. After a more detailed manual check, we find that some criticisms of the app are not accurate. Surprisingly, more than 40% of trust-related denunciations are based purely on misinformation spread by users. For example, a user complains about a violation of data privacy, when, in fact, the app is fully GDPR-compliant. Our study provides evidence for the intentional promulgation of misinformation to lower trust in life-saving technologies during a pandemic, and calls for a more careful evaluation of the technology’s performance. © 2022, Springer Nature Switzerland AG.