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To capture the underlying dynamics of the COVID-19 pandemic, we develop a stochastic SEIABR compartmental model, where the concentration of coronaviruses in the environment is considered. This paper is the first attempt to introduce the Black-Karasinski process as the random fluctuations in the modeling of epidemic transmission, and it is shown that Black-Karasinski process is a both mathematically and biologically reasonable assumption compared with existing stochastic modeling methods. We first obtain two critical values R 0 S and R 0 E related to the basic reproduction number R 0 of deterministic system. It is theoretically proved that (i) if R 0 S > 1 , the stochastic model has a stationary distribution ℓ (·) , which implies the long-term persistence of COVID-19; (ii) the disease will go extinct exponentially when R 0 E < 1 ; (iii) R 0 S = R 0 E = R 0 if there is no environmental noise in COVID-19 transmission. Then, we study the local stability of the endemic equilibrium P * of deterministic system under R 0 > 1. By developing an important lemma for solving the relevant Fokker-Planck equation, an approximate expression of probability density function of the distribution ℓ (·) around P * is further derived. Finally, several numerical examples are performed to substantiate our theoretical results. It should be mentioned that the techniques and methods of analysis in this paper can be applied to other complex high-dimensional stochastic epidemic systems. [ABSTRACT FROM AUTHOR]

GLO:NYO/13apr20:36n1.jpg PHOTO (COLOR): . gl THE FIRST week she taught online, Samantha Elkaim planned a lesson on misinformation: Her students collected coronavirus material from social media and analyzed its trustworthiness and accuracy. Elkaim's mother taught kindergarten and first grade for years; Elkaim spent five years as a middle-school teacher before moving to high school last fall. Elkaim felt lucky that her school permitted teachers to do their training from home, and she and her colleagues reassured one another that they just needed to make it through two and a half more weeks of classes-then they'd have spring break to do real lesson planning and get ahead. At Elkaim's school, 84 percent of the students qualify for free or reduced-price lunches. [Extracted from the article]

Purpose: The purpose of this study is to recognise the role of information and communication technology (ICT) tools in different sectors like Education, Health Care, Business, FMCG and Entertainment in the phase of social distancing. This study also attempts to provide a quantitative review of the scholarly literature on this topic. Design/methodology/approach: A comprehensive literature evaluation was undertaken using a database encompassing 150 English-language papers with publication dates ranging from 2019 to 2021. The research profile and thematic analysis are presented through a comprehensive content analysis, resulting in four themes. The study reviews various research articles and reports related to social distancing and opens a discussion on the growing importance of ICT tools during this COVID-19 era. Findings: ICT acts as a surviving tool for the economy by creating a virtual environment and helping people to stay socially connected during this pandemic. There is a lack of empirical evidence to support the facts so further research is required. Research limitations/implications: There are two drawbacks to the current study. Firstly, this study established a rigorous review methodology in which the researchers opted to exclude any grey literature, non-peer-reviewed articles, books, notes and book chapters from consideration. These sources could have had pertinent literature. Secondly, even after protocol's rigour and numerous rounds of checks by a team of academicians and researchers, an anomaly may have sneaked into the evaluation. Originality/value: The current study contributes to the growing literature on ICT tools particularly in this phase of social distancing. This paper highlights the need for future research in this area supported by different statistics. [ABSTRACT FROM AUTHOR]

The article lists policy papers published in Great Britain as of July 2020, including the government's return-to-school plans for colleges, nurseries, and schools, the national professional qualifications (NPQ) reforms, and coronavirus regulations.

The article presents the list of the policy papers being published which include COVID-19: Guidance for Educational Settings, and Child Protection: Duties to report concerns.

Purpose: The purpose of this conceptual paper is to substantiate the idea that clusters will cope with the challenges of Industry 4.0, Industry 5.0 and remain one of the drivers for the development of the local economy. Design/methodology/approach: For the preparation of this paper, the author reviewed the literature on Cluster 4.0, Industry 4.0, Industry 5.0, green and blue recovery, as well as the future of jobs and systems. Data, reports and surveys (UN, OECD, World Economic Forum, McKinsey Global Institute, D2L, etc.) were analysed in detail, in order to create a clear picture related to the research topic. The opinions expressed in the article are depicted graphically, and were developed by the author. Findings: First, predictions on Industry 4.0 challenges are outlined, and the reasons as to why clusters are expected to succeed in the digital era are substantiated; second, the direction of development in the digital era is depicted in a two-dimensional plane; and third, the direction of development in digital era within the clusters is graphically presented as a system. Originality/value: For this purpose, the author has developed a new approach to connect clusters and digitalisation with the self-designed graphical images. [ABSTRACT FROM AUTHOR]

In this paper, we construct a stochastic SAIR (Susceptible–Asymptomatic–Infected–Removed) epidemic model to study the dynamic and control strategy of COVID-19. The existence and uniqueness of the global positive solution are obtained by using the Lyapunov method. We prove the necessary conditions for the existence of extinction and ergodic stationary distribution by defining two new thresholds, respectively. Through the stochastic control theory, the optimal control strategy is obtained. Numerical simulations show the validity of stationary distribution and optimal control. The parameters of the model are estimated by a set of real COVID-19 data. And, the sensitivity of all parameters shows that decreasing physical interaction and screening the asymptomatic as swiftly as possible can prevent the wide spread of the virus in communities. Finally, we also display the trend of the epidemic without control strategies. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this article is to investigate whether the corporate social responsibility (CSR) and innovation activity of Certified B Corps® is comparable to listed companies in the Italian fashion industry. Design/methodology/approach: The study includes a systematic literature review performed on Scopus® and combining keywords related to CSR and the fashion industry. Moreover, the literature review involves empirical analyses performed using a mixed-methods approach. First, a text-based content analysis was carried out on the companies' sustainability reports using the keyword scoring approach. The innovation index was then calculated, and multiple ordinary least squares (OLS) linear regressions were performed. Findings: Based on the data available, it has emerged that Certified B Corps® who pursue a more significant number of SDGs are more likely to implement circular economy principles. Moreover, Certified B Corps® that have higher transparency standards and assume social responsibility practices also have higher innovation activity; in contrast, companies who used CSR only as a marketing tool have lower activity. Research limitations/implications: The analyses were influenced by the coronavirus disease 2019 (COVID-19) pandemic, revealing weaker relationships between the variables, as the analyses involved 2020 data. Furthermore, although the analysis considers the entire population of Certified B Corps® and listed companies in the Italian fashion industry, the size of the sample analyzed influenced the results and insights. Therefore, the analysis does not allow for generalizability. Finally, the study was conducted on a single country whose economic and social contexts have influenced some variables. Practical implications: The paper highlights some managerial implications. Managers should consider CSR to be an investment and an opportunity to survive the post-COVID-19 pandemic by applying the triple bottom line (TBL) approach in formulating strategies and increasing investments to develop sustainable innovations. Originality/value: The article explores B Corps, which has received minimal attention in the literature, to propose a deeper understanding of the topic. [ABSTRACT FROM AUTHOR]

Objective. Describe the quality of life of health personnel, the work environment, and interactions between employees and their work environment during the pandemic. Methods. A scoping review was conducted. The electronic databases PubMed, Cumulative Index of Nursing and Allied Literature Complete, and Google Scholar were used, as well as the repositories of the World Health Organization and the Centers for Disease Control and Prevention. Primary, secondary, and grey literature studies published between December 2019 and March 2021 in Spanish, English, and Portuguese were included. Methodological quality was assessed using the Authority, Accuracy, Coverage, Objectivity, Date and Importance (AACODS) checklist; a tool for the measurement of multiple systematic reviews (AMSTAR); and the Critical Appraisal Checklist for Text and Opinion Papers. A thematic analysis was carried out based on the quality-of-life and well-being model. Results. Of a total of 208 articles, 11 were included. The quality of life of health personnel during the COVID-19 pandemic was affected by the characteristics of health personnel, the work environment, and interactions between employees and their work environment. Problems related to psychosocial and occupational factors were observed. Discussion. The quality of life of health personnel was characterized by stigmatization, stress, anxiety, and fatigue. Organizational management and the implementation of psychological interventions appear to affect interactions between employees and their work environment, and improve their quality of life. [ABSTRACT FROM AUTHOR]

In this paper, a three tier mathematical model describing the interactions between susceptible population, Covid-19 infected, diabetic population and Covid-19 infected, non diabetic population is proposed. Basic properties of such a dynamic model, namely, non negativity, boundedness of solutions, existence of disease-free and disease equilibria are studied and sufficient conditions are obtained. Basic reproduction number for the system is derived. Sufficient conditions on functionals and parameters of the system are obtained for the local as well as global stability of equilibria, thus, establishing the conditions for eventual prevalence of disease free or disease environment, as the case may be. The stability aspects are discussed in the context of basic reproduction number and vice versa. An important contribution of this article is that a novel technique is presented to estimate some key, influencing parameters of the system so that a pre-specified, assumed equilibrium state is approached eventually. This enables the society to prepare itself with the help of these key, influencing parameters so estimated. Several examples are provided to illustrate the results established and simulations are provided to visualize the examples. [ABSTRACT FROM AUTHOR]

Purpose: The aim of the current study was to examine the previously unexplored relationship between positive reframing as a mediator between gratitude and technostress in Indian students. By examining this relationship, the authors aim to expand the theoretical domain of gratitude research by examining its potential influence on technology-induced stress. Design/methodology/approach: A cross-sectional survey was used to collect and analyze data from 552 Indian college students who participated in graduate and postgraduate programs across various educational institutions in India. Regression and mediation analyses were performed with both IBM SPSS 25 and AMOS. Findings: This study's data suggest that positive reframing plays an important mediating role between gratitude and technostress. Gratitude also encourages positive reframing, which reduced technostress among the students. Taken together, our data showed that gratitude induces positive reframing, which in turn reduces techno-stress among Indian students in the current study. Research limitations/implications: The sample size in this study is relatively small in relation to the student population in India. The current study relied primarily on quantitative data and analysis and further research could use a mixed-method approach to better understand the underlying mechanisms between positive reframing, gratitude and technostress. The results are derived under an extreme coronavirus disease 2019 (COVID-19) pandemic situation; therefore, the results cannot be generalized to normal times. Practical implications: The paper includes implications for teachers, academic leaders, parents and civil society. Originality/value: Overall, the relationship between positive reframing, gratitude and technostress has not been thoroughly explored. To the best of the authors' understanding, this is the first study to examine the influence of gratitude on technology-induced stress and the role of reframing. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to demonstrate a cloud business intelligence model for industrial SMEs. An initial model was developed to accomplish this, followed by validation and finalization of the cloud business intelligence model. Additionally, this research employs a mixed-techniques approach, including both qualitative and quantitative methods. This paper aims to achieve the following objectives: (1) Recognize the Cloud business intelligence concepts. (2) Identify the role of cloud BI in SMEs. (3) Identify the factors that affect the design and presenting a Cloud business intelligence model based on critical factors affecting SMEs during pandemic COVID-19. (4) Discuss the importance of Cloud BI in pandemic COVID-19 for SMEs. (5) Provide managerial implications for using Cloud BI effectively in Iran's SMEs. Design/methodology/approach: In the current study, an initial model was first proposed, and the cloud business intelligence model was then validated and finalized. Moreover, this study uses a mixed-methods design in which both qualitative and quantitative methods are used. The fuzzy Delphi Method has been applied for parameter validation purposes, and eventually, the Cloud business intelligence model has been presented through exploiting the interpretive structural modeling. The partial least squares method was also applied to validate the model. Data were also analyzed using the MAXQDA and Smart PLS software package. Findings: In this research, from the elimination of synonym and frequently repeated factors and classification of final factors, six main factors, 24 subfactors and 24 identifiers were discovered from the texts of the relevant papers and interviews conducted with 19 experts in the area of BI and Cloud computing. The main factors of our research include drivers, enablers, competencies, critical success factors, SME characteristics and adoption. The subfactors of included competitors pressure, decision-making time, data access, data analysis and calculations, budget, clear view, clear missions, BI tools, data infrastructure, information merging, business key sector, data owner, business process, data resource, data quality, IT skill, organizational preparedness, innovation orientation, SME characteristics, SME activity, SME structure, BI maturity, standardization, agility, balances between BI systems and business strategies. Then, the quantitative part continued with the fuzzy Delphi technique in which two factors, decision-making time and agility, were deleted in the first round, and the second round was conducted for the rest of the factors. In that step, 24 factors were assessed based on the opinions of 19 experts. In the second round, none of the factors were removed, and thus the Delphi analysis was concluded. Next, data analysis was carried out by building the structural self-interaction matrix to present the model. According to the results, adoptability is a first-level or dependent variable. Regarding the results of interpretive structural modeling (ISM), the variable of critical success factors is a second-level variable. Enablers, competencies and SME characteristics are the third-level and most effective variables of the model. Accordingly, the initial model of Cloud BI for SMEs is presented as follows: The results of ISM revealed the impact of SME characteristics on BI critical success factors and adoptability. Since this category was not an underlying category of BI; thus, it played the role of a moderating variable for the impact of critical success factors on adoptability in the final model. Research limitations/implications: Since this study is limited to about 100 SMEs in the north of Iran, results should be applied cautiously to SMEs in other countries. Generalizing the study's results to other industries and geographic regions should be done with care since management perceptions, and financial condition of a business vary significantly. Additionally, the topic of business intelligence in SMEs constrained the sample from the start since not all SMEs use business intelligence systems, and others are unaware of their advantages. BI tools enable the effective management of companies of all sizes by providing analytic data and critical performance indicators. In general, SMEs used fewer business intelligence technologies than big companies. According to studies, SMEs understand the value of simplifying their information resources to make critical business choices. Additionally, they are aware of the market's abundance of business intelligence products. However, many SMEs lack the technical knowledge necessary to choose the optimal tool combination. In light of the frequently significant investment required to implement BI approaches, a viable alternative for SMEs may be to adopt cloud computing solutions that enable organizations to strengthen their systems and information technologies on a pay-per-use basis while also providing access to cutting-edge BI technologies at a reasonable price. Practical implications: Before the implementation of Cloud BI in SMEs, condition of driver, competency and critical success factor of SMEs should also be considered. These will help to define the significant resources and skills that form the strategic edge and lead to the success of Cloud BI projects. Originality/value: Most of the previous studies have been focused on factors such as critical success factors in cloud business intelligence and cloud computing in small and medium-sized enterprises, cloud business intelligence adoption models, the services used in cloud business intelligence, the factors involved in acceptance of cloud business intelligence, the challenges and advantages of cloud business intelligence, and drivers and barriers to cloud business intelligence. None of the studied resources proposed any comprehensive model for designing and implementing cloud business intelligence in small and medium-sized enterprises; they only investigated some of the aspects of this issue. [ABSTRACT FROM AUTHOR]

Purpose: One of the main advantages of the proposed model is that it is flexible to generate n-day pairings simultaneously. It means that, despite previous researches, one-day to n-day pairings can be generated in a single model. The flexibility in generating parings causes that the proposed model leads to better solutions compared to existing models. Another advantage of the model is minimizing the risk of COVID-19 by limitation of daily flights as well as elapsed time minimization. As airports are among high risk places in COVID-19 pandemic, minimization of infection risk is considered in this model for the first time. Genetic algorithm is used as the solution approach, and its efficiency is compared to GAMS in small and medium-size problems. Design/methodology/approach: One of the most complex issues in airlines is crew scheduling problem which is divided into two subproblems: crew pairing problem (CPP) and crew rostering problem (CRP). Generating crew pairings is a tremendous and exhausting task as millions of pairings may be generated for an airline. Moreover, crew cost has the largest share in total cost of airlines after fuel cost. As a result, crew scheduling with the aim of cost minimization is one of the most important issues in airlines. In this paper, a new bi-objective mixed integer programming model is proposed to generate pairings in such a way that deadhead cost, crew cost and the risk of COVID-19 are minimized. Findings: The proposed model is applied for domestic flights of Iran Air airline. The results of the study indicate that genetic algorithm solutions have only 0.414 and 0.380 gap on average to optimum values of the first and the second objective functions, respectively. Due to the flexibility of the proposed model, it improves solutions resulted from existing models with fixed-duty pairings. Crew cost is decreased by 12.82, 24.72, 4.05 and 14.86% compared to one-duty to four-duty models. In detail, crew salary is improved by 12.85, 24.64, 4.07 and 14.91% and deadhead cost is decreased by 11.87, 26.98, 3.27, and 13.35% compared to one-duty to four-duty models, respectively. Originality/value: The authors confirm that it is an original paper, has not been published elsewhere and is not currently under consideration of any other journal. [ABSTRACT FROM AUTHOR]

The article examines whether the U.S. citizens have a special relationship with toilet paper as implicated in the hoarding behavior manifested during the crisis resulting from the COVID-19 pandemic. Topics covered include the origin of the current form of toilet paper, the attitude of the hoarders and how people deal with the shortage of toilet paper.

The article explores possible explanations for toilet-paper hoarding during the time of the coronavirus pandemic. Topics discussed include factors that motivate hoarders including emotional or sentimental attachment, aesthetic appreciation, and utility, the belief that human beings subconsciously equate feces with gold or money, and the political aspects of toilet-paper hoarding.

Purpose: One of the most significant threats of COVID-19 in the world is the closure of universities, schools, training courses and even companies and organizations. In such a situation and with the free time that has arisen, this threat of education closure can become a golden opportunity for learning and progress in virtual education. E-learning uses information technology (IT) to distribute knowledge and information for training and education. Also, cloud computing is a technology utilized in the IT domain. It can be employed in performing e-learning. Therefore, the main goal of this study is to assess the impact of cultural characteristics, economic situations, skills and knowledge on the development and success of CELS in the COVID-19 era. Design/methodology/approach: Cloud-based e-learning system (CELS) provides all e-learning requirements like software and hardware resources to promote conventional e-learning technologies. The CELS stands on several factors of diverse aspects that have been of high significance in CELS success. So, these systems must be checked to analyze their significance rate and successfully carry out their effectiveness. On the other hand, these days, the 2019 coronavirus disease (COVID-19) changes our daily lifestyles. Therefore, the present investigation provides a new model investigating the development and success of CELS in the COVID-19 era. Also, an online questionnaire was used to gather the data. The content validity of the questionnaire was obtained by applying the opinions of ten experts from e-learning specialists. The collected data are analyzed using LISREL and Smart PLS software. Findings: The results from the path coefficient and the sample t-test have implied that skills and knowledge positively influence CELS in the COVID-19 era. In addition, the relationship between cultural characteristics and CELS in the COVID-19 era has been positive and significant. The relationship between the economic situations and the CELS in the COVID-19 era is positive and significant. Practical implications: The proposed model helps managers get a big picture of CELS necessities and more effectively in the COVID-19 era. This research has a unique impact on universities to develop an e-learning platform to facilitate the education process in the COVID-19 era. It provides guidelines for educational institutions to effectively implement the learning management system to facilitate students' education. Originality/value: CELS are getting increasingly essential to offer training courses more efficiently in educational institutions. Although the intersection between cloud computing and e-learning has increasingly grown in both practical and academic contexts, few studies on the impact of cultural characteristics, economic situations, skills and knowledge on the development and success of CELS in the COVID-19 era. This paper explores the ignored but critically important subject of CELS. This paper's main contribution is to present a new and integrated model containing the essential aspects of the development and success of CELS in the COVID-19 era. The proposed framework comprises cultural characteristics, economic situations, skills and knowledge aspects simultaneously, as well as sub-criteria denoting each element. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that can be transmitted through human interaction. In this paper, we present a Piecewise Susceptible–Exposed–Infectious–Unreported–Removed model for infectious diseases and discuss qualitatively and quantitatively. The parameters are explored by mathematical and statistical methods. Numerical simulations of these models are performed on COVID-19 US data and Python is used in the visualization of results. Outbreak factor is generated by piecewise model to explore the future trend of the US pandemic. Several error metrics are given to discuss the accuracy of the models. The main achievement of this paper is to propose the piecewise model and find the relationship between spread of pandemic and mitigation measures to control it by observing the results of numerical simulations. Performance analysis of piecewise model is presented based on COVID-19 data obtained by 'worldmeter'. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to develop a system dynamics approach based on Susceptible, Exposed, Infected, Recovered (SEIR) model to investigate the coronavirus pandemic and the impact of therapeutic and preventive interventions on epidemic disaster. Design/methodology/approach: To model the behavior of COVID-19 disease, a system dynamics model is developed in this paper based on SEIR model. In the proposed model, the impact of people's behavior, contact reduction, isolation of the sick people as well as public quarantine on the spread of diseases is analyzed. In this model, data collected by the Iran Ministry of Health have been used for modeling and verification of the results. Findings: The results show that besides the intervening policies, early application of them is also of utmost priority and makes a significant difference in the result of the system. Also, if the number of patients with extreme conditions passes available hospital intensive care capacity, the death rate increases dramatically. Intervening policies play an important role in reducing the rate of infection, death and consequently control of pandemic. Also, results show that if proposed policies do not work before the violation of the hospital capacity, the best policy is to increase the hospital's capacity by adding appropriate equipment. Research limitations/implications: The authors also had some limitations in the study including the lack of access to precise data regarding the epidemic of coronavirus, as well as accurate statistics of death rate and cases in the onset of the virus due to the lack of diagnostic kits in Iran. These parameters are still part of the problem and can negatively influence the effectiveness of intervening policies introduced in this paper. Originality/value: The contribution of this paper includes the development of SEIR model by adding more policymaking details and considering the constraint of the hospital and public health capacity in the rate of coronavirus infection and death within a system dynamics modeling framework. [ABSTRACT FROM AUTHOR]

The article announces the retraction of a research published in the May 2021 issue of the journal "Scientific Reports" on the effect of vitamin D supplementation on COVID-19 prevention or treatment due to concerns about methodology.

Purpose: There has been substantial research on branding globally. However, there is a dearth of studies empirically investigating branding strategies during the pandemic to mitigate COVID-19 effects on the airline industry. The paper considers three factors which are brand familiarity, brand communication and brand reputation to develop the brand trust of customers and that ultimately influence their brand preferences. Design/methodology/approach: The study is based on self-administrative surveys as 450 questionnaires were spread, received 339 responses and a total of 301 questionnaires were selected for data analysis by structural-based modeling after the deletion of outliers and partially filled questionnaires. The data was collected through purposive sampling from Malaysian airports. Findings: The findings confirm the relationship of brand communication, brand familiarity and brand reputation to brand trust and brand preference through mediation and directly except direct relationship of brand familiarity to brand preference. The study is limited to the provided dataset of surveys. The present study couldn't interview respondents which can be done by future studies and also effects of COVID-19 can be examined on related industries or through comparative studies among countries. Originality/value: The present study is the first to investigate the effects of COVID-19 on airline brands and explored the strategies to respond to crises. The study is one of the rare studies that consider branding strategies to the uplift airline industry and mitigate post-pandemic effects from the airline sector. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to analyze how mixes of COVID-19 policy responses are shaping the context in which companies will compete in the following years, defining how the crisis might impact firms' ability to keep their commitments to sustainable practices. Design/methodology/approach: European country-performance data for the years 2019 and 2020 were grouped into indicators of macro sustainability, then cross-analyzed against the policies adopted during the period (also grouped based on their impacts on sustainability pillars), using correlations, factor analysis and clustering. Findings: The influence of traditional sustainability determinants was reframed according to the novel context shaped by the policy responses to the pandemic crisis. The social and digitalization aspects gained the most relevance and appeared interconnected, with digitalization of employment attaining overall more traction. Moreover, changes in the leadership within sustainability domains were observed for each identified country-cluster, due to newly implemented emergency policies. In fact, environmental innovation, digitalization and social support policies appeared to be the main variables to be impacted by the intensity of the policy efforts. Practical implications: Businesses monitoring the developments of sustainability policies closely, will observe novel trends in technological applications. Social implications: Policymakers and researchers may gauge the efficacy of policies against the COVID-19 crisis in the domain of sustainable development and resilience. Originality/value: This paper provides a cross-analysis of quantitative macroeconomic and quantified policy responses to the 2020 pandemic crisis, linking each indicator to the pillars of sustainability that were relevant for companies between the crucial pandemic outbreak years 2019 and 2020. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to examine the role of fear of COVID-19 and job stress on women's turnover intentions in the hospitality industry (travel agencies). Also, the mediating role of work-family conflict was examined. Design/methodology/approach: Three theoretical approaches of importance for framing issues of fear of COVID-19, job stress, work-family conflict and women's turnover intentions. Using the purposive sampling technique, the participants for the current paper were selected from the population of employees of top travel and tour operation firms in Lagos, Nigeria. Findings: Findings from the study indicate that the fear of COVID-19 and job stress was found to be positively related to work-family conflict and work-family conflict was positively related to women's turnover intentions. Work-family conflict mediates the positive relationship between fear of COVID-19 and women's turnover intentions, while against priori; the work-family conflict did not mediate the relationship between job stress and women's turnover intentions. Research limitations/implications: Our study's findings were limited in their generalizability because they focused on a specific operating sector of tourism, travel and tour. Testing the study's model in different tourism operating sectors or mixed industries could offer better insights. A comparative study between this current context and western/non-western contexts to provide more contextual insights. Originality/value: This study considered travel agencies (the pillar of the tourism industry) that have been understudied. The main strength of the study is its female-centric approach to uncovering the influence of the COVID-19 pandemic on hospitality employee outcomes. Specifically, the study used African females in the hospitality settings to investigate the aforementioned relationships. 10; Keywords: COVID-19; job stress; turnover intention; work-family conflict. [ABSTRACT FROM AUTHOR]

Purpose: The fear of COVID-19, being an emerging research variable and a parcel of the ongoing pandemic, has not yet been fully studied with respect to leadership and employee family life. Based on the conservation of resources theory, the current study aims to investigate the relationship between despotic leadership and work-family conflict under the context of fear prevailing due to COVID-19. The paper also highlights the mediating role of emotional exhaustion between the two variables. Design/methodology/approach: Data were collected in two waves by using a time-lagged design from 225 nurses from hospitals in the Central Punjab region in Pakistan. Analysis was done through process macro in SPSS. Findings: Results showed that despotic leadership is positively related to work-family conflict directly and through emotional exhaustion indirectly. Furthermore, the moderation of fear of COVID-19 has also been noted such that its increase strengthened the direct as well as the indirect relationship between despotic leadership and work-family conflict. Originality/value: During the days of COVID-19, the pandemic posed a strong threat to employees' family lives, especially in the presence of despotic leaders at the workplace. Amidst the widespread fear and harmful effects of COVID-19 on economies and organizations, this study provides novel implications for policymakers, researchers and practitioners for mitigating the impact of despotic leadership on employees' family lives. [ABSTRACT FROM AUTHOR]

The article talks about plans and policies of Indian government to prevent the spread of Covid-19 virus' second wave. It mentions that Prime Minister Narendra Modi has warned against lockdowns and called instead for testing, isolating the infected and tracing their contacts; and government has also given special licence for work to continue on a project to overhaul the grand government buildings of the city centre.

The article offers information on the effects of Covid-19 across the world and on the health workers. It further discusses announcement of the U.S. President Donald Trump on his European travel ban; challenges like anxiety and disappointment faced by health workers; and also mentions about role of critical services such as healthcare workers, 999 staff, food manufacturers/suppliers, cleaners and refuse collectors.

COMMENT Over the past few months, as the effects of the COVID-19 coronavirus on Australian communities have grown, we've seen people respond in very different ways. We've also seen worried people panic-buying groceries, clearing supermarket shelves. Already, our experts have been hard at work explaining whether you should take out health insurance for COVID-19, whether travel insurance covers you and how the coronavirus affects your super. [Extracted from the article]

The article focuses on policy papers on reopening of schools, children and family analysis and COVID-19 Recovery Strategy. Topics discussed include working document set out thinking for changing operations of schools and providers time in response to Covid-19, focusing on adults running out of school activities, and schools, and questions relating to childcare during the coronavirus outbreak.

Purpose: This is mainly because the restrictive condition of intuitionistic hesitant fuzzy number (IHFN) is relaxed by the membership functions of Pythagorean probabilistic hesitant fuzzy number (PyPHFN), so the range of domain value of PyPHFN is greatly expanded. The paper aims to develop a novel decision-making technique based on aggregation operators under PyPHFNs. For this, the authors propose Algebraic operational laws using algebraic norm for PyPHFNs. Furthermore, a list of aggregation operators, namely Pythagorean probabilistic hesitant fuzzy weighted average (PyPHFWA) operator, Pythagorean probabilistic hesitant fuzzy weighted geometric (PyPHFWG) operator, Pythagorean probabilistic hesitant fuzzy ordered weighted average (PyPHFOWA) operator, Pythagorean probabilistic hesitant fuzzy ordered weighted geometric (PyPHFOWG) operator, Pythagorean probabilistic hesitant fuzzy hybrid weighted average (PyPHFHWA) operator and Pythagorean probabilistic hesitant fuzzy hybrid weighted geometric (PyPHFHWG) operator, are proposed based on the defined algebraic operational laws. Also, interesting properties of these aggregation operators are discussed in detail. Design/methodology/approach: PyPHFN is not only a generalization of the traditional IHFN, but also a more effective tool to deal with uncertain multi-attribute decision-making problems. Findings: In addition, the authors design the algorithm to handle the uncertainty in emergency decision-making issues. At last, a numerical case study of coronavirus disease 2019 (COVID-19) as an emergency decision-making is introduced to show the implementation and validity of the established technique. Besides, the comparison of the existing and the proposed technique is established to show the effectiveness and validity of the established technique. Originality/value: Paper is original and not submitted elsewhere. [ABSTRACT FROM AUTHOR]

With the prevalence of COVID-19, the modeling of epidemic propagation and its analyses have played a significant role in controlling epidemics. However, individual behaviors, in particular the self-protection and migration, which have a strong influence on epidemic propagation, were always neglected in previous studies. In this paper, we mainly propose two models from the individual and population perspectives. In the first individual model, we introduce the individual protection degree that effectively suppresses the epidemic level as a stochastic variable to the SIRS model. In the alternative population model, an open Markov queueing network is constructed to investigate the individual number of each epidemic state, and we present an evolving population network via the migration of people. Besides, stochastic methods are applied to analyze both models. In various simulations, the infected probability, the number of individuals in each state and its limited distribution are demonstrated. [ABSTRACT FROM AUTHOR]

• A novel lightweight DL technique is presented for fine-tuning lung infection segmentation. • The down-sampling module is innovatively designed to effectively capture lesion features from cross-domain CT images. • A novel contextual enrichment module is introduced to empower the network to capture the combinations of static. • A novel federated periodic client selection protocol is proposed to select the training participants fairly and periodically. The automatic segmentation of COVID-19 pneumonia from a computerized tomography (CT) scan has become a major interest for scholars in developing a powerful diagnostic framework in the Internet of Medical Things (IoMT). Federated deep learning (FDL) is considered a promising approach for efficient and cooperative training from multi-institutional image data. However, the nonindependent and identically distributed (Non-IID) data from health care remain a remarkable challenge, limiting the applicability of FDL in the real world. The variability in features incurred by different scanning protocols, scanners, or acquisition parameters produces the learning drift phenomena during the training, which impairs both the training speed and segmentation performance of the model. This paper proposes a novel FDL approach for reliable and efficient multi-institutional COVID-19 segmentation, called MIC-Net. MIC-Net consists of three main building modules: the down-sampler, context enrichment (CE) module, and up-sampler. The down-sampler was designed to effectively learn both local and global representations from input CT scans by combining the advantages of lightweight convolutional and attention modules. The contextual enrichment (CE) module is introduced to enable the network to capture the contextual representation that can be later exploited to enrich the semantic knowledge of the up-sampler through skip connections. To further tackle the inter-site heterogeneity within the model, the approach uses an adaptive and switchable normalization (ASN) to adaptively choose the best normalization strategy according to the underlying data. A novel federated periodic selection protocol (FED-PCS) is proposed to fairly select the training participants according to their resource state, data quality, and loss of a local model. The results of an experimental evaluation of MIC-Net on three publicly available data sets show its robust performance, with an average dice score of 88.90% and an average surface dice of 87.53%. [ABSTRACT FROM AUTHOR]

• Logic AND operation in FNNs did not depict the uncertainty of pixels in images well. • A new fuzzy rule is proposed for the uncertainty using the similarity between pixels. • A novel SR method is proposed by combining fuzzy set theory and deep learning. • Some deep learning modules are designed to extract more effective image features. • The proposed method has better performance in SR medical image reconstruction. The clarity of medical images is crucial for doctors to identify and diagnose different diseases. High-resolution images have more detailed information and clearer content than low-resolution images. It is well known that medical images can frequently have some blurred object boundaries, and that traditional deep learning models cannot adequately describe the uncertainty of these blurred boundaries. This paper proposes a new fuzzy metric to characterize the uncertainty of pixels and designs a fuzzy hierarchical fusion attention neural network based on multiscale guided learning. Specifically, a fuzzy neural information-processing block is proposed, which converts an input image into a fuzzy domain using fuzzy membership functions. The uncertainty of the pixels is processed using the proposed fuzzy rules, and then the output of the fuzzy rule layer is fused with the result of the convolution in the neural network. Simultaneously, a multiscale guided-learning dense residual block and pyramidal hierarchical attention module are designed to extract more effective hierarchical image information. Finally, a recurrent memory module with a residual structure is used to process the output features of the hierarchical attention modules. A recursive sub-pixel reconstruction module is used at the tail of the network to reconstruct the images. Compared with existing super-resolution methods using the public COVID-CT dataset, the proposed method demonstrated superior performance in high-resolution medical image reconstruction and reduced the number of parameters and analysis time of the models. [ABSTRACT FROM AUTHOR]

• Logic AND operation in FNNs did not depict the uncertainty of pixels in images well. • A new fuzzy rule is proposed for the uncertainty using the similarity between pixels. • A novel SR method is proposed by combining fuzzy set theory and deep learning. • Some deep learning modules are designed to extract more effective image features. • The proposed method has better performance in SR medical image reconstruction. The clarity of medical images is crucial for doctors to identify and diagnose different diseases. High-resolution images have more detailed information and clearer content than low-resolution images. It is well known that medical images can frequently have some blurred object boundaries, and that traditional deep learning models cannot adequately describe the uncertainty of these blurred boundaries. This paper proposes a new fuzzy metric to characterize the uncertainty of pixels and designs a fuzzy hierarchical fusion attention neural network based on multiscale guided learning. Specifically, a fuzzy neural information-processing block is proposed, which converts an input image into a fuzzy domain using fuzzy membership functions. The uncertainty of the pixels is processed using the proposed fuzzy rules, and then the output of the fuzzy rule layer is fused with the result of the convolution in the neural network. Simultaneously, a multiscale guided-learning dense residual block and pyramidal hierarchical attention module are designed to extract more effective hierarchical image information. Finally, a recurrent memory module with a residual structure is used to process the output features of the hierarchical attention modules. A recursive sub-pixel reconstruction module is used at the tail of the network to reconstruct the images. Compared with existing super-resolution methods using the public COVID-CT dataset, the proposed method demonstrated superior performance in high-resolution medical image reconstruction and reduced the number of parameters and analysis time of the models. [ABSTRACT FROM AUTHOR]

In the present paper, a reaction–diffusion epidemic mathematical model is proposed for analysis of the transmission mechanism of the novel coronavirus disease 2019 (COVID-19). The mathematical model contains six-time and space-dependent classes, namely; Susceptible, Exposed, Asymptomatically infected, Symptomatic infected, Quarantine, and Recovered or Removed (SEQI a I s R). The threshold number R 0 is calculated by utilizing the next-generation matrix approach. In addition to the simple explicit procedure, the mathematical epidemiological model with diffusion is simulated through the operator splitting approach based on finite difference and meshless methods. Stability analysis of the disease free and endemic equilibrium points of the model is investigated. Simulation results of the model with and without diffusion are presented in detail. A comparison of the obtained numerical results of both the models is performed in the absence of an exact solution. The correctness of the solution is verified through mutual comparison and partly, via theoretical analysis as well. [ABSTRACT FROM AUTHOR]

Purpose: Community governance plays an important role in the prevention and control of the Coronavirus disease 2019 (COVID-19) pandemic in China. Community workers, the main executors in community governance, experience a huge amount of stress, which affects their physical and mental health. Thus, it is crucial to pay more attention to the stressors and stress responses of community workers and propose strategies to alleviate such responses. This paper aims to analyze the work stress of community workers during the COVID-19 pandemic in China. Design/methodology/approach: Based on a questionnaire survey of 602 community workers during COVID-19 in China, the four main stressors and 14 stress factors of community workers were identified and six factors at three levels of stress responses were defined. A stress analysis model is proposed that tests the mediating role of psychological capital and the moderating role of organizational climate. Findings: The results show that stressors influence stress responses through the moderating role of psychological capital, organizational climate plays a negative mediator role between stressors and psychological capital and the main stressors for community workers are work, safety and performance stress. Originality/value: This paper contributes to existing research because it offers suggestions for reducing the impact of stress on the community workers during the COVID-19 pandemic. Further, it can promote the control and prevention of the COVID-19. [ABSTRACT FROM AUTHOR]

The early and accurate detection of COVID-19 is vital nowadays to avoid the vast and rapid spread of this virus and ease lockdown restrictions. As a result, researchers developed methods to diagnose COVID-19. However, these methods have several limitations. Therefore, presenting new methods is essential to improve the diagnosis of COVID-19. Recently, investigation of the electrocardiogram (ECG) signals becoming an easy way to detect COVID-19 since the ECG process is non-invasive and easy to use. Therefore, we proposed in this paper a novel end-to-end deep learning model (ECG-COVID) based on ECG for COVID-19 detection. We employed several deep Convolutional Neural Networks (CNNs) on a dataset of 1109 ECG images, which is built for screening the perception of COVID-19 and cardiac patients. After that, we selected the most efficient model as our model for evaluation. The proposed model is end-to-end where the input ECG images are fed directly to the model for the final decision without using any additional stages. The proposed method achieved an average accuracy of 98.81%, Precision of 98.8%, Sensitivity of 98.8% and, F1-score of 98.81% for COVID-19 detection. As cases of corona continue to rise and hospitalizations continue again, hospitals may find our study helpful when dealing with these patients who did not get significantly worse. [ABSTRACT FROM AUTHOR]

• In the proposed IVIF-BWM, we build weight-determining models concerning expert's attitudes. • A bi-objective programming model is proposed to derive experts' dynamic weights. • Develop an IVIF-DBWM to determine the global weights of all criteria. • IVIF-SPA is proposed to evaluate the overall resumption risk of each alternative. Currently, China has achieved a remarkable achievement on the containment of COVID-19, which creates a favorable condition for the gradual resumption of normal life. However, COVID-19 infections continue to rise in many nations and some sporadic cases occur from time to time in China, which still poses some risks to the resumption. Hence, it is imperative to develop some reasonable techniques to assess the resumption risk. This paper aims to investigate an integrated interval-valued intuitionistic fuzzy (IVIF) technique to adroitly assess the resumption risk based on DEMATEL (decision making trial and evaluation laboratory), BWM (best-worst method) and SPA (set pair analysis). This integrated technique is called IVIF-DBWM-SPA, where the IVIF-DBWM (combined by the IVIF-DEMATEL and IVIF-BWM) is used to determine the global criteria weights and the IVIF-SPA is employed to generate the ranking order of the alternatives. The IVIF-DEMATEL and IVIF-BWM are used to determine the weights of dimensions and the weights of criteria under each dimension, respectively. In this IVIF-BWM, two bi-objective programming models are constructed by regarding experts' pessimistic and optimistic attitudes, respectively. Combined experts' intrapersonal and interpersonal uncertainties simultaneously, a bi-objective programming model is proposed to derive the dynamic weights of experts. Based on the determined weights of experts and criteria, an IVIF-SPA is developed to assess the risk levels of all alternatives. The validity of the proposed technique is demonstrated with a real case of college resumption risk assessment amid COVID-19. Some sensitivity and comparison analyses are provided to show the merits of the proposed technique. [ABSTRACT FROM AUTHOR]

Purpose: The paper aims to disentangle the factors behind territorial disparities in the coronavirus disease 2019 (COVID-19) case fatality ratio, focusing on the pressure put by the pandemic on healthcare services and adopting a spatial perspective. Design/methodology/approach: Multiscale geographically weighted regression (MGWR) models have been used for uncovering the spatial variability in the impact of healthcare services on COVID-19 case fatality ratio, allowing authors to better capture the real spatial patterns at local level. The authors proved that this approach yields better results, and the MGWR model outperforms traditional regression methods. The selected case studies are two of the biggest UE countries, among the first affected by a high incidence of COVID-19 cases, namely Italy and Germany. Findings: The authors found sizeable regional differences in COVID-19 mortality rates within each of the analysed countries, and the stress borne by local healthcare systems seems to be the most powerful factor in explaining them. In line with other studies, the authors found additional factors of influence, such as age distribution, gender ratio, population density and regional development. Originality/value: This research clearly indicated that COVID-19 related deaths are strongly associated with the degree of resilience of the local healthcare systems. The authors supply localized results on the factors of influence, useful for assisting the decision-makers in prioritizing limited healthcare resources. The authors provide a scientific argument in favour of the decentralization of the pandemic management towards local authorities not neglecting, however, the necessary regional or national coordination. [ABSTRACT FROM AUTHOR]

Pregnant people with coronavirus disease 2019 (COVID-19) have a higher risk of adverse maternal and fetal outcomes compared with pregnant people without COVID-19. In 2021, large increases in maternal mortality were reported in Jamaica, almost half of which were attributable to COVID-19. COVID-19 vaccination has been shown to reduce these risks, but low- and middle-income countries lack free, publicly available data, known as open data, on COVID-19 vaccine uptake for their pregnant populations. The objectives of this paper were to: review how high-income countries use open data to detect trends in COVID-19 vaccine uptake among pregnant people and develop vaccination distribution strategies; outline barriers to making open data available for maternal COVID-19 vaccination in the Caribbean; and propose a multipronged strategy that would increase the availability of open data on maternal COVID-19 vaccination in the Caribbean. A multipronged strategy to fill the data void would involve: (i) utilizing existing Caribbean maternal immunization data collection entities; (ii) adapting digital software tools to establish maternal electronic immunization registries; and (iii) collaborating with local partners skilled in data analytics. Making open data available for COVID-19 vaccine uptake among pregnant people in the Caribbean could offer substantial benefits, including the development of measurable maternal COVID-19 vaccination goals and the facilitation of vaccine decision-making discussions between providers and pregnant people. [ABSTRACT FROM AUTHOR]

Digital contact tracing (DCT) is one of the weapons to be used against the COVID-19 pandemic, especially in a post-lockdown phase, to prevent or block foci of infection. As DCT systems can handle highly private information about people, great care must be taken to prevent misuse of the system and actions detrimental to people's privacy, up to mass surveillance. This paper presents a new centralized DCT protocol, called ZE2-P3T (Zero Ephemeral Exchanging Privacy-Preserving Proximity Protocol), which relies on smartphone localization but does not give any information about the user's location and identity to the server. Importantly, the fact that no exchange of ephemeral identities among users is required is the basis of the strong security of the protocol, which is proven to be more secure than the state-of-the-art protocol DP-3T/GAEN. [ABSTRACT FROM AUTHOR]

• A novel reaction-diffusion COVID-19 model with direct and aerosol transmission is proposed. • The basic reproduction number R 0 of spatial diffusion system is obtain and confirm that R 0 is the threshold for predicting whether COVID-19 is prevalent. • Lyapunov methods and some novel differential inequality techniques for PDE. • Develop a new numerical simulation method to accurately fit the development of COVID-19 in Putian and Yangzhou, China, respectively. The COVID-19 epidemic has infected millions of people and cast a shadow over the global economic recovery. To explore the epidemic's transmission law and provide theoretical guidance for epidemic prevention and control. In this paper, we investigate a novel SEIR-A reaction-diffusion COVID-19 system with direct and aerosol transmission. First, the solution's positivity and boundedness for the system are discussed. Then, the system's the basic reproduction number is defined. Further, the uniform persistence of disease when R 0 > 1 is explored. In addition, the system equilibrium's global stability based on R 0 is demonstrated. Next, the system's NSFD scheme is investigated and the discrete system's positivity, boundedness, and global properties are studied. Meantime, global sensitivity analysis on threshold R 0 is investigated. Interestingly, the effects of three strategies, including vaccination, receiving treatment, and wearing a mask, are evaluated numerically. The results suggest that the above three strategies can effectively control the peak and final scale of infection and shorten the duration of the epidemic. Finally, theoretical simulations and instance predictions are used to give several key indicators of the epidemic, including threshold R 0 , peak, time to peak, time to clear cases, and final size. The instance prediction results are as follows: (1) The basic reproduction numbers of Yangzhou and Putian in China are R 0 = 2.5107 and R 0 = 1.8846 , respectively. (2) This epidemic round in Yangzhou will peak at 56 new daily confirmed cases on the 9th day (August 5), and Putian will peat at 37 new daily confirmed cases on the 6th day (September 15). (3) The final scale of infections in Yangzhou and Putian reached 570 and 205 cases, respectively. (4) The Yangzhou epidemic is expected to be completely cleared on the 25th day (August 21). In addition, the Putian epidemic will continue for 15 days and be cleared on September 24. The analysis results mean that we should improve our immunity by actively vaccinating, reducing the possibility of aerosol transmission by wearing masks. In particular, people should maintain proper social distance, and the government should strengthen medical investment and COVID-19 project research. [ABSTRACT FROM AUTHOR]

Purpose: The paper focuses on reviewing and theorizing the factors that affect the adoption of cloud computing in the education sector narrowing the focus to developing countries such as India. Design/methodology/approach: Through an extensive literature survey, critical factors of cloud computing for education were identified. Further, the fuzzy DEMATEL approach was used to define their interrelationship and its cause and effect. Findings: A total of 17 factors were identified for the study based on the literature survey and experts' input. These factors were classified as causes and effects and ranked and interrelated. "Required Learning Skills and Attitude," "Lack of Infrastructure," "Learners' Ability" and "Increased Investment" are found to be the most influential factors. Practical implications: The resultant ranking factors can be used as a basis for managing the process of cloud adoption in several institutions. The study could guide academicians, policymakers and government authorities for the effective adoption of cloud computing in education. Originality/value: The study investigates interdependency amongst the factors of cloud computing for education in context with developing economy. This is one of first study in higher education institutes of India. [ABSTRACT FROM AUTHOR]

This paper presents the report resulting from the questionnaire launched by the “Grupo de Trabajo de Análisis y Prospectiva de las Bibliotecas en Andalucía” (Workgroup of Analysis and Prospective of the Libraries in Andalusia) of the AAB (Andalusian Librarians Association), in May and June 2021. It shows the situation of the Andalusian libraries during the lockdown and during the current world health alert situation caused by the COVID-19 pandemic. The data analyze the situation from the perspective of the transformation of the services and their innovation: changes in the opening times of libraries, new work modes, among others. The paper analyzes a representative sample of Andalusian libraries and documentation centers extracted from the regional administration official directories. The report ends with some conclusions and thoughts, which hopefully will help to understand and verify the new Andalusian library reality, with the aim that they are taken into account and that they help in the future to make decisions regarding the profession. [ABSTRACT FROM AUTHOR]

The impact of the COVID-19 pandemic on libraries is possibly the most important event in recent years. The aim of this paper is to analyze the thoughts in the professional field of libraries (especially within public libraries) prior to and during the pandemic, and to provide an outline of the responses and the impact on library services. The paper wraps up with an overview of the initial situation and how the public libraries of Galicia reacted. Through these analyses, the goal is to foresee possible ways which could guide the future library services. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to present reflections on the COVID-19 pandemic impacts in the search for the targets of Sustainable Development Goal 8 (SDG 8) disseminated by the United Nations. These reflections are made considering the Brazilian scenario. Design/methodology/approach: As this paper is a viewpoint, several information presented here are based on authors' opinion. However, to enhance the information basis and present examples, a bibliographic research was also conducted. Findings: Analyzing the targets of SDG 8 and the probable crisis in the Brazilian economy as a consequence of the pandemic, the authors believe that this crisis will impact the achievement of these targets. Practical implications: The reflections presented here can contribute with the debates about the search for decent work in Brazil. It is worth to remember that the development of actions aligned with SDG 8 targets generates more than only economic gains, as these contribute for building a more just, egalitarian and inclusive society. Originality/value: This viewpoint calls attention to issues that demand urgent public policies; otherwise, these issues can generate losses to an entire generation of Brazilians. [ABSTRACT FROM AUTHOR]

Purpose: Central to problems affecting effective university–business collaboration (UBC) is the lack of intensive communication in addition to some administrative issues. This paper aims to resolve this issue by use of a uniquely designed "UBC system." It suggests establishing a unified business analytics portal as one of the most convenient ways of facilitating UBC. This research paper describes the development and functioning of the "UBC system," which is capable of supporting universities and businesses in terms of the COVID-19 crisis and in enabling the implementation of innovations in their activities. Design/methodology/approach: Several dozen organizations and students and graduates of different universities were selected to test the "UBC system." This research was conducted in several phases. The "UBC system" was developed on the basis of Microsoft 365, which is a solution made on a single platform with the integrated operation of several applications (SharePoint [Easy-Quiz and Survey], Outlook, Teams, Power Automate and Power BI). The system collects, processes and compares the data; schedules online interviews; conducts intelligence quotient testing and surveys; and its business analytic reports and dashboards are shared on the internet and are accessible from any location and from any device. Findings: The results of the given study suggest that establishing reliable and convenient online coordination through the "UBC system" can provide help in enabling efficient collaboration between universities and businesses – something that has gained special importance in the time of the COVID-19 pandemic. This paper provides a number of conclusions and recommendations to improve online cooperation between universities and businesses. Practical implications: The system can be administered by state entities (such as the Ministry of Education) and by career development centers within universities. The use of these kinds of systems can become a permanent part of effective UBC. The "UBC system" will not only help overcome employment problems in times of crisis but also make it easier to analyze the real situation and to introduce and develop innovative trends by both businesses and universities through mutual cooperation in an automated mode. Originality/value: The proposed "UBC system" platform was developed by the authors. [ABSTRACT FROM AUTHOR]

The article informs about panic-buying toilet paper back in March 2002, which reveals a lot about one's personality, as reported by researchers from the Max Planck Institute for Evolutionary Anthropology, and found that people who rated higher on scales of emotionality and conscientiousness.

The article offers a statement from the Great Britain Department for Work and Pensions (DWP) comenting that the British government's workplace health Green Paper is expected to be published "later this year."

In response to fighting COVID-19 pandemic, researchers in machine learning and artificial intelligence have constructed some medical knowledge graphs (KG) based on existing COVID-19 datasets, however, these KGs contain a considerable amount of semantic relations which are incomplete or missing. In this paper, we focus on the task of knowledge graph embedding (KGE), which serves an important solution to infer the missing relations. In the past, there have been a collection of knowledge graph embedding models with different scoring functions to learn entity and relation embeddings published. However, these models share the same problems of rarely taking important features of KG like attribute features, other than relation triples, into account, while dealing with the heterogeneous, complex and incomplete COVID-19 medical data. To address the above issue, we propose a graph feature collection network (GFCNet) for COVID-19 KGE task, which considers both neighbor and attribute features in KGs. The extensive experiments conducted on the COVID-19 drug KG dataset show promising results and prove the effectiveness and efficiency of our proposed model. In addition, we also explain the future directions of deepening the study on COVID-19 KGE task. [ABSTRACT FROM AUTHOR]

The dynamics of COVID-19 pandemic varies across countries and it is important for​ researchers to study different kind of phenomena observed at different stages of the waves during the epidemic period. Our interest in this paper is not to model what happened during the endemic state but during the epidemic state. We proposed a continuous formulation of a unique maximum reproduction number estimate with an assumption that the epidemic curve is in form of the Gaussian curve and then compare the model with the discrete form and the observed basic reproduction number during the contagiousness period considered. Furthermore, we estimated the transmission rate from identification of the first inflection point of a wave of the curve of daily new infectious cases using the Bernoulli S–I (Susceptible–Infected) equation. We applied this new method to the real data from Cameroon COVID-19 outbreak both at national and regional levels. High correlation was observed between the socio-economic parameters and epidemiology parameters at regional level in Cameroon. Also, the method was applied to the second wave COVID-19 outbreak for the world data which is a period the phenomena we are considering were observed. Lastly, it was observed that the models presented results correspond with the epidemic dynamics in Cameroon and World data. We recommend that it is important to study what happened during the growth inflection point as some countries data did not climax. [ABSTRACT FROM AUTHOR]