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Using the SCOPUS database and VOSviewer, this paper aims to analyze the bibliographic information on three keywords (entrepreneurial intention (EI), human flourishing (HF), and unicorns) to identify relevant areas for current and future research on entrepreneurship by applying a bibliometric and content review approach to 2434 documents for the BMA (business, management, and accounting) and EEF (economics, econometrics, and finance) subject areas to construct and visualize bibliometric networks on the basis of co-citation and co-authorship relations in these items. The main findings of this study are as follows: (1) the number of documents published in the European Union on EI (600) almost doubles those published on this topic in the United States (354); the United States leads the number of papers (113) published on HF, and the number of documents published on Unicorns by BRICS (Brazil, Russia, India, China, and South Africa) countries (22) almost equals the number of documents published on this issue in the United States (25); (2) research on EI during the core years of the COVID-19 pandemic (2019–2022) is of growing interest linked to entrepreneurship education and psychological traits; (3) ethics-related entrepreneurial behavior has historically supported current HF-related research; (4) entrepreneurial ecosystems, leadership, and innovation are critical success factors for born globals to be unicorns; (5) there is a geographic disparity (Spain, India, and the US) in the most cited authors for EI, HF, and unicorns, respectively. [ABSTRACT FROM AUTHOR]

A new mathematical model of Coronavirus (2019-nCov) using piecewise hybrid fractional order derivatives is given in this paper. Moreover, in order to be consistent with the physical model problem, a new parameter μ is presented. The boundedness, existence, and positivity of the solutions for the proposed model are discussed. Two improved numerical methods are presented in this paper. The Caputo proportional constant nonstandard modified Euler–Maruyama method is introduced to study the fractional stochastic model, and the Grünwald–Letnikov nonstandard finite difference method is presented to study the hybrid fractional order deterministic model. Comparative studies with real data from Spain and Wuhan are presented. [ABSTRACT FROM AUTHOR]

The impact of the COVID-19 pandemic on the economy and society has gained the interest of academics and policymakers in recent years. Our paper aims to investigate and systemize the evidence from 1901 publications belonging to the top 1% of worldwide topics by prominence. This paper helps estimate a pandemic's short-run and longer-run effects on energy economics and environmental pollution. By systematizing the literature, we analyze key parameters influencing the deviation of previous worldwide economic and environmental development trajectories due to the COVID-19 pandemic. This paper examines research on the consequences of COVID-19 in five dimensions, particularly the impact of COVID-19 on (1) the environment and climate change, (2) sustainable development, (3) renewable energy and energy policy, and (4) methodology for forecasting and evaluating the energy sector and economic sectors. Our results indicate that the pandemic crisis's impact on achieving sustainable development goals in the context of energy change and pollution is controversial and complex. On the one hand, scientists are unequivocal about the positive impact of the COVID-19 pandemic on improving air quality and reducing CO2 emissions. Nevertheless, the long-term effects are threatened by gaps between countries in economic prosperity and different vaccination rates. Most studies have found that only a joint social effort and international collaboration can move to a clean energy system. In addition, using the Scopus database and modern tools of machine analysis, we determine leading authors in the subject area of "renewable energy, sustainability, and the environment", as well as the top networks and scientific communities that appear within energy. The analysis of this manuscript can be helpful to policymakers and stakeholders in developing comprehensive energy efficiency programs and energy-saving strategies to achieve SDG targets. [ABSTRACT FROM AUTHOR]

A systematic review on the topic of automatic detection of COVID-19 using audio signals was performed. A total of 48 papers were obtained after screening 659 records identified in the PubMed, IEEE Xplore, Embase, and Google Scholar databases. The reviewed studies employ a mixture of open-access and self-collected datasets. Because COVID-19 has only recently been investigated, there is a limited amount of available data. Most of the data are crowdsourced, which motivated a detailed study of the various pre-processing techniques used by the reviewed studies. Although 13 of the 48 identified papers show promising results, several have been performed with small-scale datasets (<200). Among those papers, convolutional neural networks and support vector machine algorithms were the best-performing methods. The analysis of the extracted features showed that Mel-frequency cepstral coefficients and zero-crossing rate continue to be the most popular choices. Less common alternatives, such as non-linear features, have also been proven to be effective. The reported values for sensitivity range from 65.0% to 99.8% and those for accuracy from 59.0% to 99.8%. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has represented a new form of traumatic event, affecting the general population worldwide and causing severe disruption of daily routine. A new urgent concern is related to the burden associated with COVID-19 symptoms that persist beyond the onset of infection, the so-called long-COVID syndrome. The present paper aims to: (1) describe the most frequent psychiatric symptoms reported by patients affected by long-COVID syndrome; (2) evaluate methodological discrepancies among the available studies; (3) inform clinicians and policy-makers on the possible strategies to be promoted in order to manage the psychiatric consequences of long-COVID syndrome. Twenty-one papers have been included in the present review, mostly with a cross-sectional or cohort design. Significant heterogeneity of long-COVID syndrome definitions was found. The presence of psychiatric symptoms was evaluated with very different assessment tools. The most common psychiatric symptoms of the long-COVID syndrome included fatigue, cognitive disturbances/impairment, depression, and anxiety symptoms. The rate of fatigue varied from 93.2–82.3% to 11.5%, cognitive impairment/cognitive dysfunction from 61.4% to 23.5% and depressive-anxiety symptoms from 23.5%to 9.5%. [ABSTRACT FROM AUTHOR]

The rationale for this review paper is to take stock of the current knowledge in the literature on the intersection of telework and work–life balance—an area that has grown in importance due to the impact of the COVID-19 pandemic. The review also considers the context of the government's role in pursuing policies to reduce the impacts of the pandemic in order to improve societal if not personal resilience, as these policies sometimes had unintended adverse impacts. After a section on the literature search method, sections follow on the literature considering telework/working from home, stress, and gender; work–life balance figures prominently in the papers reviewed. An additional category for the government and its role in concerns related to this topic follows. For future research, the differences between groups in responding to the demands of telework and work–life balance, particularly in regard to gender, are worth further investigation, as the COVID-19 pandemic has offered great challenges but also immense opportunities to learn and prepare organizations for future crises. [ABSTRACT FROM AUTHOR]

The importance of the IoT is increasing in every field of life, and it especially has a significant role in improving the efficiency of the healthcare system. Its demand further increased during COVID-19 to facilitate the patient remotely from their home digitally. Every time the COVID-19 patient visited the doctor for minor complications, it increased the risk of spreading the virus and the cost for the patient. Another alarming situation arose when a patient was in a critical position and may not claim an emergency service from the nearby healthcare system, increasing the death rate. The IoT uses healthcare services to properly monitor COVID-19 patients by using the interconnected network to overcome these issues. Through the IoT, the patient is facilitated by the health care system without spreading the virus, decreasing the death ratio during COVID-19. This paper aims to discuss different applications, technologies, and challenges of the IoT healthcare system, related to COVID-19. Different databases were searched using keywords in PubMed, ResearchGate, Scopus, ACM, Springer, Elsevier, Google Scholar, etc. This paper is trying to discuss, identify, and highlight the useful applications of the IoT healthcare system to provide guidelines to the researchers, healthcare institutions, and scientists to overcomes the hazards of COVID-19 pandemics. Hence, IoT is beneficial by identifying the symptoms of COVID-19 patients and by providing better treatments that use the healthcare system efficiently. At the end of the paper, challenges and future work are discussed, along with useful suggestions through which scientists can benefit from the IoT healthcare system during COVID-19 and in a severe pandemic. The survey paper is not limited to the healthcare system and COVID-19, but it can be beneficial for future pandemics or in a worse situation. [ABSTRACT FROM AUTHOR]

Despite the progress of its management, COVID-19 maintains an ominous condition which constitutes a threat, especially for the susceptible population. The cardiac injury occurs in approximately 30% of COVID-19 infections and is associated with a worse prognosis. The clinical presentation of cardiac involvement can be COVID-19-related myocarditis. Our review aims to summarise current evidence about that complication. The research was registered at PROSPERO (CRD42022338397). We performed a systematic analysis using five different databases, including i.a. MEDLINE. Further, the backward snowballing technique was applied to identify additional papers. Inclusion criteria were: full-text articles in English presenting cases of COVID-19-related myocarditis diagnosed by the ESC criteria and patients over 18 years old. The myocarditis had to occur after the COVID-19 infection, not vaccination. Initially, 1588 papers were screened from the database search, and 1037 papers were revealed in the backward snowballing process. Eventually, 59 articles were included. Data about patients' sex, age, ethnicity, COVID-19 confirmation technique and vaccination status, reported symptoms, physical condition, laboratory and radiological findings, applied treatment and patient outcome were investigated and summarised. COVID-19-related myocarditis is associated with the risk of sudden worsening of patients' clinical status, thus, knowledge about its clinical presentation is essential for healthcare workers. [ABSTRACT FROM AUTHOR]

The present outbreak of COVID-19 is a worldwide calamity for healthcare infrastructures. On a daily basis, a fresh batch of perplexing datasets on the numbers of positive and negative cases, individuals admitted to hospitals, mortality, hospital beds occupied, ventilation shortages, and so on is published. Infections have risen sharply in recent weeks, corresponding with the discovery of a new variant from South Africa (B.1.1.529 also known as Omicron). The early detection of dangerous situations and forecasting techniques is important to prevent the spread of disease and restart economic activities quickly and safely. In this paper, we used weekly mobility data to analyze the current situation in countries worldwide. A methodology for the statistical analysis of the current situation as well as for forecasting future outbreaks is presented in this paper in terms of deaths caused by COVID-19. Our method is evaluated with a multi-layer perceptron neural network (MLPNN), which is a deep learning model, to develop a predictive framework. Furthermore, the Case Fatality Ratio (CFR), Cronbach's alpha, and other metrics were computed to analyze the performance of the forecasting. The MLPNN is shown to have the best outcomes in forecasting the statistics for infected patients and deaths in selected regions. This research also provides an in-depth analysis of the emerging COVID-19 variants, challenges, and issues that must be addressed in order to prevent future outbreaks. [ABSTRACT FROM AUTHOR]

In this paper, a machine learning-based system for the prediction of the required level of respiratory support in COVID-19 patients is proposed. The level of respiratory support is divided into three classes: class 0 which refers to minimal support, class 1 which refers to non-invasive support, and class 2 which refers to invasive support. A two-stage classification system is built. First, the classification between class 0 and others is performed. Then, the classification between class 1 and class 2 is performed. The system is built using a dataset collected retrospectively from 3491 patients admitted to tertiary care hospitals at the University of Louisville Medical Center. The use of the feature selection method based on analysis of variance is demonstrated in the paper. Furthermore, a dimensionality reduction method called principal component analysis is used. XGBoost classifier achieves the best classification accuracy (84%) in the first stage. It also achieved optimal performance in the second stage, with a classification accuracy of 83%. [ABSTRACT FROM AUTHOR]

In 2020, COVID-19 took many people by surprise, as did the intercontinental waves of protest triggered by the casual racist murder of George Floyd by a US policeman. The years of 2020 and 2021 will undoubtedly be remembered for massive, unexpected disruptions that require new social normalities to be negotiated. These social disruptions were triggered by unexpected viral pandemics and viral video footage. Yet they built on already existing, entrenched inequities marked by the intersections of racialisation/ethnicisation, social class and gender. It was common, in the early days of the COVID-19 pandemic, for politicians and commentators to espouse a narrative that COVID-19 "does not discriminate". This is, of course, true. However, the research analyses that followed showed that both COVID-19, and the measures taken to arrest it, exacerbated already existing social inequalities. This paper draws on two narratives of the racialized impact of COVID-19 to examine the ways in which the authors mobilise intertextual narratives to protest against racism and call for resistance to the racisms they identify. The paper argues that, while the authors do not overtly position themselves as calling for change, their narratives are crafted in ways that resist current constructions of their racialized or religious groups. [ABSTRACT FROM AUTHOR]

A fluorescence device based on ultraviolet light is proposed in this paper, which currently stands at the design stage with the eventual aim to potentially detect virus/antibody fluorescence reactions. The designed device is proposed to have the characteristics of high reflectivity, low power consumption, wide spectrum of light source, and proper silver coating. For fabrication and raising product quality, 3D printing technology and a sputtering test will be used. In this connection, this paper firstly introduces the design sources; then, the ideas of inventing fluorescence detection devices based on ultraviolet light, followed by the data analysis as well as discussing the results of computer simulations. The design process, materials, methods, and experiments are demonstrated following the reality work procedure. Instead of directly using a virus or antibodies for the experiment, at the current design stage, we focus on using this device to detect the rhodamine B reagent. Experiment shows that this reagent can be successfully detected. With this achievement, we logically believe that such type of an ultraviolet optical sensor, with further development and testing, may have the possible value to detect a single virus such as COVID-19, as well as other viruses or small molecules. Though there is long way to go to achieve such a goal, future works experimenting with the detection device on real virus or antibodies can take place more efficiently with a good foundation. [ABSTRACT FROM AUTHOR]

On 31 May 2021, the Political Bureau of the Central Committee of the Communist Party of China proposed the policy that a couple can have three children, and rolled out more supportive measures to further optimize the fertility policies. However, while the Chinese government is further optimizing its fertility policy, the sudden outbreak of COVID-19 is raging around the world, which threatens the implementation of China's fertility optimization policy. Based on this, this paper firstly explores the impact of COVID-19 on women's fertility intentions. Secondly, based on the Theory of Planned Behavior, this paper constructs a structural equation model to quantitatively reveal the specific factors that affect women's fertility intentions under the epidemic, as well as their impact paths, and then puts forward corresponding suggestions for the government to solve the problem of fertility, aiming at delaying population aging and optimizing population structure. The research results show that: (1) COVID-19 lowers the fertility intentions of women of childbearing age. (2) During the pandemic, economic pressure emerged as the biggest factor affecting women's fertility intentions. The decline in income caused by the pandemic has become an important factor in preventing women from having children. (3) The conflict between work and childbearing is still an important factor affecting the fertility intentions of women of childbearing age. The government's provision of perfect childcare services and their strengthening of the protection of women's employment rights and interests will greatly reduce women's anxiety about childbearing. [ABSTRACT FROM AUTHOR]

When major emergencies or accidents occur, risk evaluation and prediction are the most important means to reduce their impact. Typical risk evaluation uses the failure mode and effects analysis (FMEA) method for failure-risk ranking and control. However, when faced with severe special infectious diseases such as COVID-19, there are many cognitive and information uncertainties that the FMEA method is unable to effectively handle. To effectively deal with the issue of risk evaluation when major emergencies or accidents occur, this paper integrated the risk-priority number and spherical fuzzy-sets methods to propose a novel emergency-risk-evaluation method. In the numerical verification, this paper applied the example of preventing secondary COVID-19 transmissions in hospitals to explain the calculation procedure and validity of the proposed new emergency-risk-evaluation approach. The calculation results were also compared with the typical RPN, fuzzy-set, and intuitionistic fuzzy-set methods. The calculation results showed that the proposed new emergency-risk-evaluation approach could effectively handle the cognitive and informational uncertainties of emergency-risk-evaluation issues during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Simple Summary: This study follows an improved approach to systematic reviews, called the Systematic Review and Artificial Intelligence Network Meta-Analysis (RAIN), registered within PROSPERO (CRD42021256797), in which, the PRISMA criterion is still considered. Drugs used in the treatment of COVID-19 were searched in the databases of ScienceDirect, Web of Science (WoS), ProQuest, Embase, Medline (PubMed), and Scopus. In addition, using artificial intelligence and the measurement of the p-value between human genes affected by COVID-19 and drugs that have been suggested by clinical experts, and reported within the identified research papers, suitable drug combinations are proposed for the treatment of COVID-19. During the systematic review process, 39 studies were selected. Our analysis shows that most of the reported drugs, such as azithromycin and hydroxyl-chloroquine on their own, do not have much of an effect on the recovery of COVID-19 patients. Based on the result of the new artificial intelligence, on the other hand, at a significance level of less than 0.05, the combination of the two drugs therapeutic corticosteroid + camostat with a significance level of 0.02, remdesivir + azithromycin with a significance level of 0.03, and interleukin 1 receptor antagonist protein + camostat with a significance level 0.02 are considered far more effective for the treatment of COVID-19 and are therefore recommended. COVID-19 affects several human genes, each with its own p-value. The combination of drugs associated with these genes with small p-values may lead to an estimation of the combined p-value between COVID-19 and some drug combinations, thereby increasing the effectiveness of these combinations in defeating the disease. Based on human genes, we introduced a new machine learning method that offers an effective drug combination with low combined p-values between them and COVID-19. This study follows an improved approach to systematic reviews, called the Systematic Review and Artificial Intelligence Network Meta-Analysis (RAIN), registered within PROSPERO (CRD42021256797), in which, the PRISMA criterion is still considered. Drugs used in the treatment of COVID-19 were searched in the databases of ScienceDirect, Web of Science (WoS), ProQuest, Embase, Medline (PubMed), and Scopus. In addition, using artificial intelligence and the measurement of the p-value between human genes affected by COVID-19 and drugs that have been suggested by clinical experts, and reported within the identified research papers, suitable drug combinations are proposed for the treatment of COVID-19. During the systematic review process, 39 studies were selected. Our analysis shows that most of the reported drugs, such as azithromycin and hydroxyl-chloroquine on their own, do not have much of an effect on the recovery of COVID-19 patients. Based on the result of the new artificial intelligence, on the other hand, at a significance level of less than 0.05, the combination of the two drugs therapeutic corticosteroid + camostat with a significance level of 0.02, remdesivir + azithromycin with a significance level of 0.03, and interleukin 1 receptor antagonist protein + camostat with a significance level 0.02 are considered far more effective for the treatment of COVID-19 and are therefore recommended. Additionally, at a significance level of less than 0.01, the combination of interleukin 1 receptor antagonist protein + camostat + azithromycin + tocilizumab + oseltamivir with a significance level of 0.006, and the combination of interleukin 1 receptor antagonist protein + camostat + chloroquine + favipiravir + tocilizumab7 with corticosteroid + camostat + oseltamivir + remdesivir + tocilizumab at a significant level of 0.009 are effective in the treatment of patients with COVID-19 and are also recommended. The results of this study provide sets of effective drug combinations for the treatment of patients with COVID-19. In addition, the new artificial intelligence used in the RAIN method could provide a forward-looking approach to clinical trial studies, which could also be used effectively in the treatment of diseases such as cancer. [ABSTRACT FROM AUTHOR]

Worldwide, COVID-19 is a highly contagious epidemic that has affected various fields. Using Artificial Intelligence (AI) and particular feature selection approaches, this study evaluates the aspects affecting the health of students throughout the COVID-19 lockdown time. The research presented in this paper plays a vital role in indicating the factor affecting the health of students during the lockdown in the COVID-19 pandemic. The research presented in this article investigates COVID-19's impact on student health using feature selections. The Filter feature selection technique is used in the presented work to statistically analyze all the features in the dataset, and for better accuracy. ReliefF (TuRF) filter feature selection is tuned and utilized in such a way that it helps to identify the factors affecting students' health from a benchmark dataset of students studying during COVID-19. Random Forest (RF), Gradient Boosted Decision Trees (GBDT), Support Vector Machine (SVM), and 2- layer Neural Network (NN), helps in identifying the most critical indicators for rapid intervention. Results of the approach presented in the paper identified that the students who maintained their weight and kept themselves busy in health activities in the pandemic, such student's remained healthy through this pandemic and study from home in a positive manner. The results suggest that the 2- layer NN machine-learning algorithm showed better accuracy (90%) to predict the factors affecting on health issues of students during COVID-19 lockdown time. [ABSTRACT FROM AUTHOR]

Flavonoids are a group of natural compounds that have been described in the literature as having anti-inflammatory, antioxidant, and neuroprotective compounds. Although they are considered versatile molecules, little has been discussed about their antiviral activities for neurotropic viruses. Hence, the present study aimed to investigate the pharmacological potential of flavonoids in the face of viruses that can affect the central nervous system (CNS). We carried out research from 2011 to 2021 using the Pubmed platform. The following were excluded: articles not in the English language, letters to editors, review articles and papers that did not include any experimental or clinical tests, and papers that showed antiviral activities against viruses that do not infect human beings. The inclusion criteria were in silico predictions and preclinical pharmacological studies, in vitro, in vivo and ex vivo, and clinical studies with flavonoids, flavonoid fractions and extracts that were active against neurotropic viruses. The search resulted in 205 articles that were sorted per virus type and discussed, considering the most cited antiviral activities. Our investigation shows the latest relevant data about flavonoids that have presented a wide range of actions against viruses that affect the CNS, mainly influenza, hepatitis C and others, such as the coronavirus, enterovirus, and arbovirus. Considering that these molecules present well-known anti-inflammatory and neuroprotective activities, using flavonoids that have demonstrated both neuroprotective and antiviral effects could be viewed as an alternative for therapy in the course of CNS infections. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic caused a sharp increase in the interest in artificial intelligence (AI) as a tool supporting the work of doctors in difficult conditions and providing early detection of the implications of the disease. Recent studies have shown that AI has been successfully applied in the healthcare sector. The objective of this paper is to perform a systematic review to summarize the electroencephalogram (EEG) findings in patients with coronavirus disease (COVID-19) and databases and tools used in artificial intelligence algorithms, supporting the diagnosis and correlation between lung disease and brain damage, and lung damage. Available search tools containing scientific publications, such as PubMed and Google Scholar, were comprehensively evaluated and searched with open databases and tools used in AI algorithms. This work aimed to collect papers from the period of January 2019–May 2022 including in their resources the database from which data necessary for further development of algorithms supporting the diagnosis of the respiratory system can be downloaded and the correlation between lung disease and brain damage can be evaluated. The 10 articles which show the most interesting AI algorithms, trained by using open databases and associated with lung diseases, were included for review with 12 articles related to EEGs, which have/or may be related with lung diseases. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an innovative technology with billions of sensors in various IoT applications. Important elements used in the IoT are sensors that collect data for desired analyses. The IoT and sensors are very important in smart cities, smart agriculture, smart education, healthcare systems, and other applications. The healthcare system uses the IoT to meet global health challenges, and the newest example is COVID-19. Demand has increased during COVID-19 for healthcare to reach patients remotely and digitally at their homes. The IoT properly monitors patients using an interconnected network to overcome the issues of healthcare services. The aim of this paper is to discuss different applications, technologies, and challenges related to the healthcare system. Different databases were searched using keywords in Google Scholar, Elsevier, PubMed, ACM, ResearchGate, Scopus, Springer, etc. This paper discusses, highlights, and identifies the applications of IoT healthcare systems to provide research directions to healthcare, academia, and researchers to overcome healthcare system challenges. Hence, the IoT can be beneficial by providing better treatments using the healthcare system efficiently. In this paper, the integration of the IoT with smart technologies not only improves computation, but will also allow the IoT to be pervasive, profitable, and available anytime and anywhere. Finally, some future directions and challenges are discussed, along with useful suggestions that can assist the IoT healthcare system during COVID-19 and in a severe pandemic. [ABSTRACT FROM AUTHOR]

Financial institutions, investors, central banks and relevant corporations need an efficient and reliable forecasting approach for determining the future of crude oil price in an effort to reach optimal decisions under market volatility. This paper presents an innovative research framework for precisely predicting crude oil price movements and interpreting the predictions. First, it compares six advanced machine learning (ML) models, including two state-of-the-art methods: extreme gradient boosting (XGB) and the light gradient boosting machine (LGBM). Second, it selects novel data, including user search big data, digital currencies and data on the COVID-19 epidemic. The empirical results suggest that LGBM outperforms other alternative ML models. Finally, it proposes an interpretable framework for facilitating decision making to interpret the prediction results of complex ML models and for verifying the importance of various features affecting crude oil price. The results of this paper provide practical guidance for participants in the crude oil market. [ABSTRACT FROM AUTHOR]

The objective of this paper is to investigate the impact of coronavirus disease 2019 (COVID-19) on the financial performance and cash holdings of Chinese agri-food companies. We also examine whether or not company ownership, the affected areas, and leverage level affect this relationship. The empirical results show that the COVID-19 outbreak has had no significant impact on financial performance and the cash-holding level of agri-food companies. In addition, the financial performance of state-owned companies is enhanced during such a crisis, whereas COVID-19 reduced the financial performance and cash-holding level of privately owned companies. In middle- and high-risk areas, the pandemic has had a negative impact on financial performance, while it has had a positive impact on financial performance in low-risk areas. The negative impact of COVID-19 on cash holding is greater in highly leveraged companies than it has been in low-leveraged companies. This paper may provide some new insights for managers to ensure smooth operation and improve firms' performance in order to overcome this crisis. [ABSTRACT FROM AUTHOR]

Disruptions in agri-food systems caused by crises, such as the COVID-19-pandemic, reveal the vulnerability of global food supply chains. Such crises might consequently impact consumer perceptions about the relevance of local food production and consumption. In this light, this study aims to (i) identify whether the COVID-19 outbreak led to short-term changes in perceptions about local food consumption and (ii) capture how the role of local agri-food systems is perceived in times of crisis. For the first purpose, this study analyzes two waves of survey data collected from an Austrian sample (n = 351) to compare pre-and post-COVID-19 levels of consumer values, beliefs, and attitudes towards local food. For the second purpose, the paper assesses consumer perceptions about the reliability and resilience of the local agri-food sector in response to the COVID-19 outbreak. The results reveal that while consumer perceptions driving local food consumption at an early phase of the pandemic remained stable at large, the perceived relevance of the local agri-food sector attenuated. Consumers showed strong beliefs in the local agriculture as a reliable and trustworthy partner during the pandemic guaranteeing food supply security. Based on these findings, the paper discusses how these insights into consumer perceptions in response to macro-level disruptions might help to better understand short-term demand-side implications of other forms of external crises affecting local food production and supply. Finally, the paper provides recommendations for practitioners and avenues for future research to determine implications from a long-term perspective. [ABSTRACT FROM AUTHOR]

Simple Summary: This paper proposes a modified SEIR model to study COVID-19 in Wuhan. The modified model is calibrated by the public number of COVID-19 hospitalization cases in Wuhan. The paper further uses this model to estimate the earliest date of COVID-19 infection in Wuhan, which is in agreement with some existing results. Based on SEIR (susceptible–exposed–infectious–removed) epidemic model, we propose a modified epidemic mathematical model to describe the spread of the coronavirus disease 2019 (COVID-19) epidemic in Wuhan, China. Using public data, the uncertainty parameters of the proposed model for COVID-19 in Wuhan were calibrated. The uncertainty of the control basic reproduction number was studied with the posterior probability density function of the uncertainty model parameters. The mathematical model was used to inverse deduce the earliest start date of COVID-19 infection in Wuhan with consideration of the lack of information for the initial conditions of the model. The result of the uncertainty analysis of the model is in line with the observed data for COVID-19 in Wuhan, China. The numerical results show that the modified mathematical model could model the spread of COVID-19 epidemics. [ABSTRACT FROM AUTHOR]

This paper aims to objectively compare the use of mental health apps between the pre-COVID-19 and during COVID-19 periods and to study differences amongst the users of these apps based on age and gender. The study utilizes a dataset collected through a smartphone app that objectively records the users' sessions. The dataset was analyzed to identify users of mental health apps (38 users of mental health apps pre-COVID-19 and 81 users during COVID-19) and to calculate the following usage metrics; the daily average use time, the average session time, the average number of launches, and the number of usage days. The mental health apps were classified into two categories: guidance-based and tracking-based apps. The results include the increased number of users of mental health apps during the COVID-19 period as compared to pre-COVID-19. Adults (aged 24 and above), compared to emerging adults (aged 15–24 years), were found to have a higher usage of overall mental health apps and guidance-based mental health apps. Furthermore, during the COVID-19 pandemic, males were found to be more likely to launch overall mental health apps and guidance-based mental health apps compared to females. The findings from this paper suggest that despite the increased usage of mental health apps amongst males and adults, user engagement with mental health apps remained minimal. This suggests the need for these apps to work towards improved user engagement and retention. [ABSTRACT FROM AUTHOR]

Vaccination is an important tool in the fight against the COVID-19 pandemic in patients with haematologic malignancies. The paper provides an analysis of the course of breakthrough SARS-CoV-2 infection in a group of vaccinated patients with haematological malignancy and a comparison with a historical cohort of 96 non-vaccinated patients with haematologic malignancies and bone marrow failure syndromes (two patients) in the treatment of COVID-19. A severe or critical course of COVID-19 was significantly less frequent in the group of vaccinated patients (10.2% vs. 31.4%, p = 0.003). The need for hospitalisation due to COVID-19 was significantly lower in vaccinated patients (27.1% vs. 72.6%, p < 0.0001) and the duration of hospitalisation was significantly shorter (10 vs. 14 days, p = 0.045). Vaccinated patients were insignificantly less likely to require oxygen therapy during infection. COVID-19 mortality was significantly higher in non-vaccinated patients (15.6% vs. 5.1%, p = 0.047). The paper demonstrated a significant positive effect of vaccination against COVID-19 on a less severe clinical course of infection, lower need for hospitalisation and mortality. However, the results need to be evaluated even in the context of new antivirals and monoclonal antibodies against SARS-CoV-2 or virus mutations with different biological behaviour. [ABSTRACT FROM AUTHOR]

During the last three years, numerous research papers have been reported which use social media data to explore several issues related to the COVID-19 pandemic. Bibliometric methods in this work are used to analyze 1427 peer-reviewed documents from the last three years extracted from the Web of Science database. The results of this study show that there was high growth in publications in open access journals with an annual rate reaching 19.3% and they also identify the top cited journals and research papers. The thematic analysis of papers shows that research topics related to social media for surveillance and monitoring of public attitudes and perceptions, mental health, misinformation, and fake news are important and well-developed, whereas topics related to distance-learning education with social media are emerging. The results also show that the USA, China, and the UK have published many papers and received a high number of citations because of their strong international collaboration. [ABSTRACT FROM AUTHOR]

Objective: To investigate post-acute laryngeal injuries and dysfunctions (PLID) in coronavirus disease 2019 (COVID-19) patients. Methods: Three independent investigators performed a systematic review of the literature studying PLID in patients with a history of COVID-19. The review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Statement. Epidemiological, clinical, hospitalization features, laryngeal diseases, and voice outcomes were extracted from the included papers. Results: Eight papers met our inclusion criteria (393 patients) corresponding to five uncontrolled prospective and three retrospective studies. The most prevalent PLID were vocal fold dysmotility (65%), vocal fold edema (35%), muscle tension dysphonia (21%), and laryngopharyngeal reflux (24%). Posterior glottic stenosis (12%), granuloma (14%), and posterior glottic diastasis (12%) were the most common injuries. Most patients with PLID were obese and had a history of intensive care unit hospitalization, and orotracheal intubation. The delay between the discharge and the laryngology office consultation ranged from 51 to 122 days. The mean duration of intubation ranged from 10 to 34 days. Seventy-eight (49%) intubated patients were in the prone position. The proportion of patients requiring surgical treatment ranged from 39% to 70% (mean = 48%). There was an important heterogeneity between studies about inclusion, exclusion criteria, and outcomes. Conclusions: COVID-19 appeared to be associated with PLID, especially in patients with a history of intubation. However, future controlled studies are needed to evaluate if intubated COVID-19 patients reported more frequently PLID than patients who were intubated for other conditions. [ABSTRACT FROM AUTHOR]

This paper investigates the effects of coronavirus disease 2019 (COVID-19) on the performance of arbitrage trading in the energy market using daily data covering the period between 1 January 2015 and 5 December 2021. The investigation was achieved by utilizing a parametric pairs-trading model, where pairs of energy-related securities, including futures, stocks and ETFs traded in the United States, are formed. The empirical results suggest that the out-of-sample performances of pair trading declined sharply in the face of COVID-19. Dividing the whole data sample into two sub-samples, we found that the strategy performed well before COVID-19 but yielded poor results in the pandemic era. The analysis presented in this paper could serve as a benchmark for arbitrage-based trading models in the energy market during the pandemic. [ABSTRACT FROM AUTHOR]

Studying people's opinions is a growing research field. This area of research is known as sentiment analysis. The COVID-19 pandemic changed everything around the world and reduced social contact among people. Citizens and residents of Saudi Arabia experienced high stress during the pandemic, seeking entertainment via games and publishing their activities on social media platforms such as Twitter. In this paper, we focus on applying the Mazajak sentiment analyzer on tweets containing game keywords in Arabic collected using Twitter API during the lockdown period to decide whether users preferred playing individually or in groups. This can help designers and developers, as well as the Saudi General Entertainment Authority (GEA), focus on creating the most interesting games for individuals and improving them to meet users' expectations. Our approach has three main stages: tweet collection, tweet preparation, and finally, the application of sentiment analysis to get the desired goal based on people's behavior toward the games. The result of this paper confirms that people, in general, preferred playing in groups, instead of alone, during this period. [ABSTRACT FROM AUTHOR]

It has been validated beyond doubt that High-Resolution Computed Tomography (HRCT) chest and to some extent chest radiographs have a role in corona virus disease-19 (COVID-19). Much less is known about the role of lung ultrasonography (LUS) in COVID-19. In this paper, our main purpose was to gauge the relationship between LUS and chest HRCT in reverse transcriptase polymerase chain reaction (RT–PCR) documented cases of COVID-19, as well as in those with high suspicion of COVID-19 with negative RT–PCR. It was a prospective study carried out at our tertiary care hospital, namely, SKIMS Soura. The total number of patients in this study were 152 (200 patients were selected out of which only 152 had undergone both LUS and chest HRCT). The patients were subjected to both LUS and chest HRCT. The radiologist who performed LUS was blinded to clinical findings and HRCT was evaluated by a radiologist with about a decade of experience. The LUS findings compatible with the disease were subpleural consolidations, B-lines and irregular pleural lines. Findings that were compatible with COVID-19 on chest HRCT were bibasilar, subpleural predominant ground glass opacities, crazy paving and consolidations. COVID-19-positive patients were taken up for chest HRCT for disease severity stratification and were also subjected to LUS. On HRCT chest, the imaging abnormalities compatible with COVID-19 were evident in 110 individuals (72.37%), and on Lung Ultrasound they were observed in 120 individuals (78.95%). Imaging of COVID-19 patients assessed by both LUS and HRCT chest,, showed a positive correlation (p < 0.0001). The study revealed a sensitivity of 88%, a specificity of 76.62%, a positive predictive value of 78.57% and a negative predictive value of 86.76%. None of the individuals with a diagnosis of COVID-19 on HRCT were missed on LUS. An excellent correlation was derived between the LUS score and CT total severity score (p < 0.0001 with a kappa of 0.431). Similar precision compared with chest HRCT in the detection of chest flaws in COVID-19 patients was obtained on LUS. [ABSTRACT FROM AUTHOR]

For many in the West, paternalism manifest as state interference carries a pejorative connotation, as it is often taken to entail unjustified restrictions on autonomy and self-determination and frequently believed to precipitate bureaucracy, corruption, and inefficiency. Meanwhile, uncritical deference to policies in which individual liberties remain essentially unchecked by state oversight has faced renewed scrutiny since the outbreak of the COVID-19 pandemic, as many across the globe are now coming to believe that we must accept greater governmental intervention in our lives, particularly during times of widespread health crises. This paper explores normative considerations justifying state intervention with respect to public health policies in response to the COVID-19 pandemic through the lens of Confucian paternalism, which is distinguished from a more general concept of paternalism widely used in contemporary philosophical discourse. It argues that the "soft" paternalism apropos to Confucianism has pragmatic benefits for the development of healthcare policies due to which it is not only morally warranted but even preferable to alternatives in terms of safeguarding population health. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had a devastating impact on the world at large with over 750 million cases and almost 7 million deaths reported thus far. Of those, over 100 million cases and 1 million deaths have occurred in the United States of America (USA). The mental health of the general population has been impacted by several aspects of the pandemic including lockdowns, media sensationalism, social isolation, and spread of the disease. In this paper, we examine the associations that social isolation and COVID-19 infection and related death had with the prevalence of anxiety and depression in the general population of the USA in a state-by-state multiple time-series analysis. Vector Error Correction Models are estimated and we subsequently evaluated the coefficients of the estimated models and calculated their impulse response functions for further interpretation. We found that COVID-19 incidence was positively associated with anxiety across the studied period for a majority of states. Variables related to social isolation had a varied effect depending on the state being considered. [ABSTRACT FROM AUTHOR]

This paper presents a novel numerical technique for the identification of effective and basic reproduction numbers, R e and R 0 , for long-term epidemics, using an inverse problem approach. The method is based on the direct integration of the SIR (Susceptible–Infectious–Removed) system of ordinary differential equations and the least-squares method. Simulations were conducted using official COVID-19 data for the United States and Canada, and for the states of Georgia, Texas, and Louisiana, for a period of two years and ten months. The results demonstrate the applicability of the method in simulating the dynamics of the epidemic and reveal an interesting relationship between the number of currently infectious individuals and the effective reproduction number, which is a useful tool for predicting the epidemic dynamics. For all conducted experiments, the results show that the local maximum (and minimum) values of the time-dependent effective reproduction number occur approximately three weeks before the local maximum (and minimum) values of the number of currently infectious individuals. This work provides a novel and efficient approach for the identification of time-dependent epidemics parameters. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) plays a fundamental role in monitoring applications; however, existing approaches relying on cloud and edge-based IoT data analysis encounter issues such as network delays and high costs, which can adversely impact time-sensitive applications. To address these challenges, this paper proposes an IoT framework called Sazgar IoT. Unlike existing solutions, Sazgar IoT leverages only IoT devices and IoT data analysis approximation techniques to meet the time-bounds of time-sensitive IoT applications. In this framework, the computing resources onboard the IoT devices are utilised to process the data analysis tasks of each time-sensitive IoT application. This eliminates the network delays associated with transferring large volumes of high-velocity IoT data to cloud or edge computers. To ensure that each task meets its application-specific time-bound and accuracy requirements, we employ approximation techniques for the data analysis tasks of time-sensitive IoT applications. These techniques take into account the available computing resources and optimise the processing accordingly. To evaluate the effectiveness of Sazgar IoT, experimental validation has been conducted. The results demonstrate that the framework successfully meets the time-bound and accuracy requirements of the COVID-19 citizen compliance monitoring application by effectively utilising the available IoT devices. The experimental validation further confirms that Sazgar IoT is an efficient and scalable solution for IoT data processing, addressing existing network delay issues for time-sensitive applications and significantly reducing the cost related to cloud and edge computing devices procurement, deployment, and maintenance. [ABSTRACT FROM AUTHOR]

Dust or condensed water present in harsh outdoor or high-humidity human breath samples are one of the key sources that cause false detection in Micro Electro-Mechanical System (MEMS) gas sensors. This paper proposes a novel packaging mechanism for MEMS gas sensors that utilizes a self-anchoring mechanism to embed a hydrophobic polytetrafluoroethylene (PTFE) filter into the upper cover of the gas sensor packaging. This approach is distinct from the current method of external pasting. The proposed packaging mechanism is successfully demonstrated in this study. The test results indicate that the innovative packaging with the PTFE filter reduced the average response value of the sensor to the humidity range of 75~95% RH by 60.6% compared to the packaging without the PTFE filter. Additionally, the packaging passed the High-Accelerated Temperature and Humidity Stress (HAST) reliability test. With a similar sensing mechanism, the proposed packaging embedded with a PTFE filter can be further employed for the application of exhalation-related, such as coronavirus disease 2019 (COVID-19), breath screening. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease (SARS-CoV-2) has caused many infections and deaths throughout the world; the spread of the coronavirus pandemic is still ongoing and continues to affect healthcare systems and economies of countries worldwide. Mathematical models are used in many applications for infectious diseases, including forecasting outbreaks and designing containment strategies. In this paper, we study two types of SIR and SEIR models for the coronavirus. This study focuses on the discrete-time and fractional-order of these models; we study the stability of the fixed points and orbits using the Jacobian matrix and the eigenvalues and eigenvectors of each case; moreover, we estimate the parameters of the two systems in fractional order. We present a statistical study of the coronavirus model in two countries: Saudi Arabia, which has successfully recovered from the SARS-CoV-2 pandemic, and China, where the number of infections remains significantly high. [ABSTRACT FROM AUTHOR]

As the number of reports of post-acute COVID-19 musculoskeletal manifestations is rapidly rising, it is important to summarize the current available literature in order to shed light on this new and not fully understood phenomenon. Therefore, we conducted a systematic review to provide an updated picture of post-acute COVID-19 musculoskeletal manifestations of potential rheumatological interest, with a particular focus on joint pain, new onset of rheumatic musculoskeletal diseases and presence of autoantibodies related to inflammatory arthritis such as rheumatoid factor and anti-citrullinated protein antibodies. We included 54 original papers in our systematic review. The prevalence of arthralgia was found to range from 2% to 65% within a time frame varying from 4 weeks to 12 months after acute SARS-CoV-2 infection. Inflammatory arthritis was also reported with various clinical phenotypes such as symmetrical polyarthritis with RA-like pattern similar to other prototypical viral arthritis, polymyalgia-like symptoms, or acute monoarthritis and oligoarthritis of large joints resembling reactive arthritis. Moreover, high figures of post-COVID-19 patients fulfilling the classification criteria for fibromyalgia were found, ranging from 31% to 40%. Finally, the available literature about prevalence of rheumatoid factor and anti-citrullinated protein antibodies was largely inconsistent. In conclusion, manifestations of rheumatological interest such as joint pain, new-onset inflammatory arthritis and fibromyalgia are frequently reported after COVID-19, highlighting the potential role of SARS-CoV-2 as a trigger for the development of autoimmune conditions and rheumatic musculoskeletal diseases. [ABSTRACT FROM AUTHOR]

In this paper, we propose a fractional-order COVID-19 epidemic model with a quarantine and standard incidence rate using the Caputo fractional-order derivative. The model consists of six classes: susceptible (S), exposed (E), infected (I), quarantined (Q), recovered (R), and deceased (M). In our proposed model, we simultaneously consider the recovery rate and quarantine rate of infected individuals, which has not been considered in other fractional-order COVID-19 epidemic models. Furthermore, we consider the standard incidence rate in the model. For our proposed model, we prove the existence, uniqueness, non-negativity, and boundedness of the solution. The model has two equilibrium points: disease-free equilibrium and endemic equilibrium. Implementing the spectral radius of the next-generation matrix, we obtain the basic reproduction number ( R 0 ). The disease-free equilibrium always exists and is locally and globally asymptotically stable only if R 0 < 1 . On the other hand, endemic equilibrium exists and is globally asymptotically stable if R 0 > 1 . Our numerical simulation confirms the stability properties of the equilibrium. The smaller the order of the derivative, the slower the convergence of the solution of the model. Both the recovery rate and quarantine rate of the infected class are important parameters determining the stability of the equilibrium point. Based on parameter estimation from COVID-19 data in Indonesia, the fractional-order model has better performance than the first-order model for both the calibration and 20-day forecasting of confirmed daily active cases of COVID-19. [ABSTRACT FROM AUTHOR]

The author will outline the research history of the main issues addressed in this paper. The author has worked on this research himself. XDH, which is responsible for purine degradation, is present in various organisms. However, conversion to XO only occurs in mammals. The molecular mechanism of this conversion was elucidated in this study. The physiological and pathological significance of this conversion is presented. Finally, enzyme inhibitors were successfully developed, two of which are used as therapeutic agents for gout. Their wide application potential is also discussed. [ABSTRACT FROM AUTHOR]

As the COVID-19 pandemic begins to abate and national public health systems are treating the SARS-CoV-2 virus as endemic, many public health measures are no longer mandated but remain recommended with voluntary participation. One of these is the wearing of fitted face masks, initially mandated to contain, or at least slow, the spread of SARS-CoV-2, which is primarily transmitted via aerosols emitted while breathing, coughing, or sneezing. Although the habit of once wearing fitted face masks recedes into memory for much of the population, so does the knowledge of the various types of masks that were once en vogue. To create a record for the future, this paper provides the first comprehensive documentation of the nature and range of fitted facemasks that circulated during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Since 2020, COVID-19 has repeatedly arisen around the world, which has had a significant impact on the global economy and culture. The prediction of the COVID-19 epidemic will help to deal with the current epidemic and similar risks that may arise in the future. So, this paper proposes a hybrid prediction model based on particle swarm optimization variational mode decomposition (PSO-VMD), Long Short-Term Memory Network (LSTM) and AdaBoost algorithm. To address the issue of determining the optimal number of modes K and the penalty factor (α) in the variational mode decomposition (VMD), an adaptive value for particle swarm optimization (PSO) is proposed. Specifically, the weighted average sample entropy of the relevant coefficients is utilized to determine the adaptive value. First, the epidemic data are decomposed into multiple modal components, known as intrinsic mode functions (IMFs), using PSO-VMD. These components, along with policy-based factors, are integrated to form a multivariate forecast dataset. Next, each IMF is predicted using AdaBoost-LSTM. Finally, the prediction results of all the IMF components are reconstructed to obtain the final prediction result. Our proposed method is validated by the cumulative confirmed data of Hubei and Hebei provinces. Specifically, in the case of cumulative confirmation data, the coefficient of determination ( R 2 ) of the mixed model is increased compared to the control model, and the average mean absolute error (MAE) and root mean square error (RMSE) decreased. The experimental results demonstrate that the VMD–AdaBoost–LSTM model achieves the highest prediction accuracy, thereby offering a new approach to COVID-19 epidemic prediction. [ABSTRACT FROM AUTHOR]

Considering the sensitivity of data in medical scenarios, federated learning (FL) is suitable for applications that require data privacy. Medical personnel can use the FL framework for machine learning to assist in analyzing large-scale data that are protected within the institution. However, not all clients have the same distribution of datasets, so data imbalance problems occur among clients. The main challenge is to overcome the performance degradation caused by low accuracy and the inability to converge the model. This paper proposes a FedISM method to enhance performance in the case of Non-Independent Identically Distribution (Non-IID). FedISM exploits a shared model trained on a candidate dataset before performing FL among clients. The Candidate Selection Mechanism (CSM) was proposed to effectively select the most suitable candidate among clients for training the shared model. Based on the proposed approaches, FedISM not only trains the shared model without sharing any raw data, but it also provides an optimal solution through the selection of the best shared model. To evaluate performance, the proposed FedISM was applied to classify coronavirus disease (COVID), pneumonia, normal, and viral pneumonia in the experiments. The Dirichlet process was also used to simulate a variety of imbalanced data distributions. Experimental results show that FedISM improves accuracy by up to 25% when privacy concerns regarding patient data are rising among medical institutions. [ABSTRACT FROM AUTHOR]

Bulgaria has the lowest COVID-19 vaccination rate in the European Union and the second-highest COVID-19 mortality rate in the world. That is why we think it is important better to understand the reason for this situation and to analyse the development of the disease over time. In this paper, an extended time-dependent SEIRS model SEIRS-VB is used to investigate the long-term behaviour of the COVID-19 epidemic. This model includes vaccination and vital dynamics. To apply the SEIRS-VB model some numerical simulation tools have been developed and for this reason a family of time-discrete variants are introduced. Suitable inverse problems for the identification of parameters in discrete models are solved. A methodology is proposed for selecting a discrete model from the constructed family, which has the closest parameter values to these in the differential SEIRS-VB model. To validate the studied models, Bulgarian COVID-19 data are used. To obtain all these results for the discrete models a mathematical analysis is carried out to illustrate some biological properties of the differential model SEIRS-VB, such as the non-negativity, boundedness, existence, and uniqueness. Using the next-generation method, the basic reproduction number associated with the model in the autonomous case is defined. The local stability of the disease-free equilibrium point is studied. Finally, a sensitivity analysis of the basic reproduction number is performed. [ABSTRACT FROM AUTHOR]

The novel coronavirus (COVID-19) pandemic still has a significant impact on the worldwide population's health and well-being. Effective patient screening, including radiological examination employing chest radiography as one of the main screening modalities, is an important step in the battle against the disease. Indeed, the earliest studies on COVID-19 found that patients infected with COVID-19 present with characteristic anomalies in chest radiography. In this paper, we introduce COVID-ConvNet, a deep convolutional neural network (DCNN) design suitable for detecting COVID-19 symptoms from chest X-ray (CXR) scans. The proposed deep learning (DL) model was trained and evaluated using 21,165 CXR images from the COVID-19 Database, a publicly available dataset. The experimental results demonstrate that our COVID-ConvNet model has a high prediction accuracy at 97.43% and outperforms recent related works by up to 5.9% in terms of prediction accuracy. [ABSTRACT FROM AUTHOR]

In Africa, refusal of COVID-19 and other vaccines is widespread for different reasons, including disbelief in the existence of the virus itself and faith in traditional remedies. In sub-Saharan countries, refusal is often made worse by opposition to vaccines by the religious establishments. This is a pressing problem, as Africa has the highest vaccine-avoidable mortality rate for children under the age of five in the world. Dialogue between those wishing to promote vaccines and those who resist them is essential if the situation is to be improved. This article argues that Western and other aid agencies seeking to promote vaccination programs need to develop a dialogue with resisters, and in this process to embrace and commend the ancient African philosophical tradition of Ubuntu, incorporating it into these programs as a way to overcome such entrenched resistance. The paper concludes with concrete recommendations for how to accomplish this goal. [ABSTRACT FROM AUTHOR]

COVID-19, which broke out globally in 2019, is an infectious disease caused by a novel strain of coronavirus, and its spread is highly contagious and concealed. Environmental vectors play an important role in viral infection and transmission, which brings new difficulties and challenges to disease prevention and control. In this paper, a type of differential equation model is constructed according to the spreading functions and characteristics of exposed individuals and environmental vectors during the virus infection process. In the proposed model, five compartments were considered, namely, susceptible individuals, exposed individuals, infected individuals, recovered individuals, and environmental vectors (contaminated with free virus particles). In particular, the re-positive factor was taken into account (i.e., recovered individuals who have lost sufficient immune protection may still return to the exposed class). With the basic reproduction number R 0 of the model, the global stability of the disease-free equilibrium and uniform persistence of the model were completely analyzed. Furthermore, sufficient conditions for the global stability of the endemic equilibrium of the model were also given. Finally, the effective predictability of the model was tested by fitting COVID-19 data from Japan and Italy. [ABSTRACT FROM AUTHOR]

The disaster of the COVID-19 pandemic has claimed numerous lives and wreaked havoc on the entire world due to its transmissible nature. One of the complications of COVID-19 is pneumonia. Different radiography methods, particularly computed tomography (CT), have shown outstanding performance in effectively diagnosing pneumonia. In this paper, we propose a spatial attention and attention gate UNet model (SAA-UNet) inspired by spatial attention UNet (SA-UNet) and attention UNet (Att-UNet) to deal with the problem of infection segmentation in the lungs. The proposed method was applied to the MedSeg, Radiopaedia 9P, combination of MedSeg and Radiopaedia 9P, and Zenodo 20P datasets. The proposed method showed good infection segmentation results (two classes: infection and background) with an average Dice similarity coefficient of 0.85, 0.94, 0.91, and 0.93 and a mean intersection over union (IOU) of 0.78, 0.90, 0.86, and 0.87, respectively, on the four datasets mentioned above. Moreover, it also performed well in multi-class segmentation with average Dice similarity coefficients of 0.693, 0.89, 0.87, and 0.93 and IOU scores of 0.68, 0.87, 0.78, and 0.89 on the four datasets, respectively. Classification accuracies of more than 97% were achieved for all four datasets. The F1-scores for the MedSeg, Radiopaedia P9, combination of MedSeg and Radiopaedia P9, and Zenodo 20P datasets were 0.865, 0.943, 0.917, and 0.926, respectively, for the binary classification. For multi-class classification, accuracies of more than 96% were achieved on all four datasets. The experimental results showed that the framework proposed can effectively and efficiently segment COVID-19 infection on CT images with different contrast and utilize this to aid in diagnosing and treating pneumonia caused by COVID-19. [ABSTRACT FROM AUTHOR]

Intraocular foreign body injuries (IOFB) can lead to a number of intraocular pathologies; the visual results depend on the mechanism of the injury, the type of foreign body and the subsequent complications. The presence of intraocular cilia (eye lashes) following penetrating injury or surgical intervention is uncommon. In the present paper, we present a case of a 58-year-old woman with a history of eye trauma and a perforated corneal wound in the left eye that occurred 50 years ago. On the ophthalmological exam we noticed in the anterior chamber a straight linear extension, resembling cilia, extending behind the iris. The patient reports that it appeared during COVID-19 infection, after repeated episodes of coughing. After a follow-up period, we decided to remove the eyelash; 24 h after surgery, the patient complained of severe eye pain. Intraocular pressure (IOP) in LE was 54 mmHg. The slit-lamp examination showed perikeratic congestion, corneal edema and mydriasis. Eye hypotensive treatment was started immediately and the patient's general condition slightly improved. Intraocular cilia can be tolerated for many years without causing any ocular reaction. The decision for surgical intervention must be taken according to the individual needs of the patient and his ocular characteristics with careful pre- and post-operative follow up. [ABSTRACT FROM AUTHOR]

COVID-19, a viral respiratory illness, is caused by Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2), which was first identified in Wuhan, China, in 2019 and rapidly spread worldwide. Testing and isolation were essential to control the virus's transmission due to the severity of the disease. In this context, there is a global interest in the feasibility of employing nano-biosensors, especially those using graphene as a key material, for the real-time detection of the virus. The exceptional properties of graphene and the outstanding performance of nano-biosensors in identifying various viruses prompted a feasibility check on this technology. This paper focuses on the recent advances in using graphene-based electrochemical biosensors for sensing the SARS-CoV-2 virus. Specifically, it reviews various types of electrochemical biosensors, including amperometric, potentiometric, and impedimetric biosensors, and discusses the current challenges associated with biosensors for SARS-CoV-2 detection. The conclusion of this review discusses future directions in the field of electrochemical biosensors for SARS-CoV-2 detection, underscoring the importance of continued research and development in this domain. [ABSTRACT FROM AUTHOR]

Predicting energy demand in adverse scenarios, such as the COVID-19 pandemic, is critical to ensure the supply of electricity and the operation of essential services in metropolitan regions. In this paper, we propose a deep learning model to predict the demand for the next day using the "IEEE DataPort Competition Day-Ahead Electricity Demand Forecasting: Post-COVID Paradigm" database. The best model uses hybrid deep neural network architecture (convolutional network–recurrent network) to extract spatial-temporal features from the input data. A preliminary analysis of the input data was performed, excluding anomalous variables. A sliding window was applied for importing the data into the network input. The input data was normalized, using a higher weight for the demand variable. The proposed model's performance was better than the models that stood out in the competition, with a mean absolute error of 2361.84 kW. The high similarity between the actual demand curve and the predicted demand curve evidences the efficiency of the application of deep networks compared with the classical methods applied by other authors. In the pandemic scenario, the applied technique proved to be the best strategy to predict demand for the next day. [ABSTRACT FROM AUTHOR]