Résumé
During the COVID-19 pandemic era that began in 2020, there has been a growing trend in the literature to tackle the problem of health stress (HS) for promoting a sense of public health. In turn, this developing area of research has a high level of relevancy linked to business and economic recovery (Cvirik, 2020). Since HS has increased sharply during the COVID-19 pandemic era, there has been a need to further investigate the balance between coping with HS and the positive continuous intention to use mobile health applications (mHealth apps) among the public. This is the first study that takes the Asia-Pacific region as its case study and empirically investigates the validity of extensions based on the theories of expectation confirmation theory (ECT) (Bhattacherjee, 2001) on user continuous behavior relating to mHealth apps during the COVID-19 pandemic. Results reveal that HS as an emotion can positively affect perceived usefulness and satisfaction in relation to the continuous intention to use mHealth apps. The differences between new and frequent users are confirmed. Discussion and implications for practices are provided in the end. (c) 2022 The Authors. Published by Elsevier B.V. on behalf of College of Management, National Cheng Kung University.
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Background: SARS-CoV-2 is known to infect the testes and reduce the quality of spermatozoa so that it is at risk of causing male infertility. Data related to the effect of SARS-CoV-2 infection on sperm parameters from Southeast Asia, especially Indonesia, are still very limited, so it is necessary to investigate further which parameters are most affected by COVID-19. Destination: This study aims to compare the concentration, motility, morphology, and DNA fragmentation of spermatozoa in the COVID-19 survivor group and the non-COVID-19 survivor group in Indonesia. Method: This research was conducted at two fertility centers in Indonesia, namely BOCAH Indonesia, Primaya Hospital Tangerang, and the Laboratory of Biomedical Sciences, Faculty of Medicine, Airlangga University, Surabaya. The research subjects were taken from two groups of volunteers, COVID-19 survivors, and non-COVID-19 survivors. The semen analysis was carried out including for all samples, including concentration, motility, and morphology-based on guidelines from the World Health Organization (WHO) 2010. Meanwhile, spermatozoa DNA fragmentation was assessed using the sperm chromatin dispersion (SCD) method.
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Covid-19 further revealed the significance of ventilation by air conditioning systems. Most common split heaters and resistance heaters recirculate the indoor air without ventilation process. Ventilation wastes energy consumption by the building. However, adding an air-to-air heat recovery unit seems a quick solution to reduce the wasted heat of the ventilation process. Nonetheless, recovery unit means further pumping power (pressure drop through the air-to-air heat exchanger), capital cost, additional fans and their electricity consumption, exergy costs and so on. Hence, the profitability of the recovery unit depends on outdoor temperature, desired indoor temperature, electricity price of the region, exergy loss and also the aforementioned factors. In this research the general standard Specific Exergy Costing theory is employed and simplified as an economic strategy for recovery ventilation. The model not only is able to predict the profitability of the ventilation process using air-to-air heat exchanger, but also it is an optimization tool for air-to-air heat recovery units as provided as a case study in this paper.
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Because predictions of transportation system reliability can provide useful information for intelligent transportation systems (ITS), evaluation of them might be viewed as a beneficial activity for reducing traffic congestion. This evaluation procedure could include some alternatives and criteria in a discrete decision space. To handle this evaluation process in an uncertain environment, a novel uncertain multi-criteria decision-making (MCDM) method is put forward in this paper. Considering the validity of uncertainty theory as a measure of epistemic uncertainty, we first introduce it into analytic hierarchy process (AHP) and provide the whole calculation procedure of the approach. The proposed approach is employed to evaluate regional travel time belief reliability in a case study. Additionally, a comparison is performed between the results of uncertain AHP and other MCDM methods to examine the efficiency of this method. These analyses show that uncertainty theory is particularly suited to be employed combination with the AHP method.
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Background: Obesity, physical inactivity, excessive alcohol consumption, and poor diet are all modifiable risk factors for cancer. These unhealthy behaviors are disproportionally concentrated in racial and ethnic minorities and these disparities may have been exacerbated during the COVID-19 pandemic. This study examined racial and ethnic disparities in weight gain and other undesirable lifestyle changes during the COVID-19 pandemic and explored mechanisms underlying these lifestyle disparities. Methods: We used data from the 2020 Health, Ethnicity and Pandemic Study, a national survey representative of US households conducted in October 2020. Racial and ethnic minorities were oversampled. Participants were asked to report lifestyle behaviors before and during the COVID-19 pandemic. We examined two outcomes in this study: weight gain and experiencing any undesirable lifestyle changes (i.e., reduced exercise time, increased alcohol drinking, or increased fast-food meal consumption). The primary exposure was race-ethnicity (non-Hispanic (NH) white, NH black, Hispanic, NH Asian, NH other race). Four sets of mediators were examined: socioeconomic status (education, household income, and undesirable job changes), family and friend social relationship change, perceived and experienced racism, and psychological distress. Weighted multivariable logistic regression models were performed. Mediation effects were examined with variance decomposition method. Results: A total of 2,709 participants were included in our sample. Compared with white respondents, black (OR = 1.71;p < 0.001) and Hispanic respondents (OR = 2.17;p < 0.001) were more likely to experience weight gain, controlling for age and sex. Among the hypothesized mediators, undesirable job changes during the pandemic, experiencing worse family relationship, and higher levels of psychological distress were all linked to higher odds of weight gain, but none of these variables played a salient role in mediating the black-white and Hispanic-white disparities in weight gain during the pandemic. As to the odds of experiencing undesirable lifestyle changes, black (OR = 1.76;p < 0.001), Hispanic (OR = 2.12;p < 0.001), and Asian respondents (OR = 1.42;p < 0.01) all exhibited disadvantages relative to white respondent. These disadvantages were largely attributable to perceived racism toward one's own group and psychological distress for all three minority groups. Conclusions: Racial and ethnic minorities were more likely to experience unhealthy lifestyle changes relative to white individuals during the pandemic in the United States, which can be partly attributable to higher levels of perceived racism and psychological distress. The long-term effects of racial/ethnic disparities of lifestyle change during the pandemic on cancer prevention warrant further research.
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In the tough COVID-19 pandemic, wearing a mask in daily life becomes an important habit. However, sometimes people forget to wear a mask or wear a mask incorrectly in careless. Hence, alarming the problem of protecting ourselves from COVID-19 becomes a key challenge. Unfortunately, at home security or oce security systems, wearing a mask lets the person identication lost its function. Hence, masked-person identication becomes an essential issue. This paper proposes an AI-based mask-wearing status recognition and person identication system for solving the above problems. The system consists of three stages, face detection based on MTCNN, mask-wearing status recognition, and person identication using MobileNetV2. Masked-person identication is one of the functions of the proposed system. The experimental results show that the face detector reaches almost 100% accuracy among 3000 images. The mask-wearing status recognition has a 96.1% test accuracy in 300 test images, and person identication achieves a 98% recognition rate. In summary, the eectiveness of the proposed system is proved by the high accuracy recognition rate. © 2021 CEUR-WS. All rights reserved.
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MOOC is widely used and more popular after COVID-19.In order to improve the learning effect, MOOC is evolving with XR technologies such as avatars, virtual scenes and experiments. This paper proposes a novel visual analytics system GazeMOOC, that can evaluate learners’ learning engagement in MOOC with XR content. For same MOOC content, gaze data of all learners are recorded and clustered. By differentiating gaze data of distracted learners and active learners, GazeMOOC can help evaluate MOOC content and learners’ learning engagement. © 2021 Copyright held by the owner/author(s).
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Due to line-of-sight communication links and distributed deployment, Unmanned Aerial Vehicles (UAVs) have attracted substantial interest in agile Mobile Edge Computing (MEC) service provision. In this paper, by clustering multiple users into independent communities based on their geographic locations, we design a 5G-enabled UAV-to-community offloading system. A system throughput maximization problem is formulated, subjected to the transmission rate, atomicity of tasks and speed of UAVs. By relaxing the transmission rate constraint, the mixed integer non-linear program is transformed into two subproblems. We first develop an average throughput maximization-based auction algorithm to determine the trajectory of UAVs, where a community-based latency approximation algorithm is developed to regulate the designed auction bidding. Then, a dynamic task admission algorithm is proposed to solve the task scheduling subproblem within one community. Performance analyses demonstrate that our designed auction bidding can guarantee user truthfulness, and can be fulfilled in polynomial time. Extensive simulations based on real-world data in health monitoring and online YouTube video services show that our proposed algorithm is able to maximize the system throughput while guaranteeing the fraction of served users.
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Abstract Background From December 2019 to February 2020, 2019 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a serious outbreak of coronavirus disease 2019 (COVID-19) in Wuhan, China. Related clinical features are needed. Methods We reviewed 69 patients who were hospitalized in Union hospital in Wuhan between January 16 to January 29, 2020. All patients were confirmed to be infected with SARS-CoV-2 and the final date of follow-up was February 4, 2020. Results The median age of 69 enrolled patients was 42.0 years (IQR 35.0-62.0), and 32 patients (46%) were men. The most common symptoms were fever (60[87%]), cough (38[55%]), and fatigue (29[42%]). Most patients received antiviral therapy (66 [98.5%] of 67 patients) and antibiotic therapy (66 [98.5%] of 67 patients). As of February 4, 2020, 18 (26.9%) of 67 patients had been discharged, and five patients had died, with a mortality rate of 7.5%. According to the lowest SpO2 during admission, cases were divided into the SpO2≥90% group (n=55) and the SpO2<90% group (n=14). All 5 deaths occurred in the SpO2<90% group. Compared with SpO2≥90% group, patients of the SpO2<90% group were older, and showed more comorbidities and higher plasma levels of IL6, IL10, lactate dehydrogenase, and c reactive protein. Arbidol treatment showed tendency to improve the discharging rate and decrease the mortality rate. Conclusions COVID-19 appears to show frequent fever, dry cough, and increase of inflammatory cytokines, and induced a mortality rate of 7.5%. Older patients or those with underlying comorbidities are at higher risk of death.