Notes

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Coronavirus disease-19 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (2019-nCoV or SARS-CoV-2). Genomic analysis has revealed that bat and pangolin coronaviruses are phylogenetically related to SARS-CoV-2. The actual origin and passage history of the virus are unknown, but human-human transmission of the virus has been confirmed. Several diagnostic techniques have been developed to detect COVID-19 in this prevailing pandemic period. PF-06700841 solubility dmso In this review, we provide an overview of SARS-CoV-2 and other coronaviruses. The origin, structure, current diagnostic techniques, such as molecular assays based on oligonucleotides, immunoassay-based detection, nanomaterial-based biosensing, and distinctive sample based detection are also discussed. Furthermore, our review highlights the admissible treatment strategies for COVID-19 and future perspectives on the development of biosensing techniques and vaccines for the diagnosis and prevention of the disease, respectively.Since COVID-19 was declared a pandemic, countries on the same pandemic trajectory have adopted very different lockdown strategies. Using data for over 132 countries, and employing an event-study design, this paper identifies the role of political, economic and institutional factors in explaining the differential timing and intensity of stringency measures undertaken.This study develops a theoretical model that highlights the determinants of actual social media (SM) usage for travel planning by combining theoretical frameworks from the marketing, psychology and information systems literature. The data was collected through field as well as online survey in India. An online survey questionnaire link was shared on different social media platforms and social networking sites. Besides, field visits were carried out to collect data in-person through face-to-face interviews. The final sample consists of 539 observations. Structural Equation Modelling (SEM) was applied to validate the hypothesized relationships among constructs. The results suggest that technological convenience and perceived enjoyment influence the perceived ease of using SM for travel planning. In turn, perceived ease of use impacts perceived usefulness, along with media richness. Perceived ease of use and perceived usefulness, along with trust positively influence intentions to use SM for travel planning, while perceived risk inhibits those intentions. However, trust increases perceived usefulness and mitigates perceived risk. Importantly, intentions exert a strong impact on actual use. This study contributes to the literature by presenting and validating a theory-driven framework that unveils the factors influencing actual usage of SM for travel planning. The proposed theoretical framework emphasizes the key relationships among factors and provides a research basis for development in other contexts.This study examines the influence of domestic tourism on economic vulnerability index (EVI). Domestic tourism spending has a significant effect in reducing EVI. These results are consistent with two sub-indices of EVI (shock index and exposure index). Interestingly, it is found that (i) this impact is consistent in low- and lower-middle-income countries, while domestic tourism has a non-significantly effect in upper-middle and high-income countries of increasing EVI; (ii) these results are consistent in the long-run; and (iii) the impact of domestic tourism is consistent in both the 2002-2007 and 2008-2012 periods, but is statistically non-significant in the 2013-2017 period. Notably, we find that domestic tourism spending has a U-shape effect on EVI; while international tourism has an increasing effect.Mental and social health outcomes from a portfolio of women's outdoor tourism products, with ~100,000 clients, are analysed using a catalysed netnography of >1000 social media posts. Entirely novel outcomes include psychological rescue; recognition of a previously missing life component, and flow-on effects to family members. Outcomes reported previously for extreme sports, but not previously for hiking in nature, include psychological transformation. Outcomes also identified previously include happiness, gratitude, relaxation, clarity and insights, nature appreciation, challenge and capability, and companionship and community effects. Commercial outdoor tourism enterprises can contribute powerfully to the wellbeing of women and families. This will be especially valuable for mental health recovery, following deterioration during COVID-19 coronavirus lockdowns worldwide.•Lack of social support and panic about the pandemic result in impaired well-being.•Unemployed and furloughed employees are more negatively impacted by the crisis.•Women and younger employees experience more psychological distress.•The note compares and synthesizes six national Covid-19 exit strategies for tourism.•Only 8% of the recommendations proposed by the UNWTO (2020) were fully implemented.•Italy adopted relatively more recommendations than the other countries studied.•Exit strategies tend to be short-term, local solutions.•Exit strategies differ from country to country and are not evidence-based policies.This research note focuses on the impact of tourism development from disaster capitalism as expressed by post-disaster land grabs and forced population displacement. Case studies highlighted are India, Thailand and Sri Lanka following the 2004 Indian Ocean tsunami; Honduras after Hurricane Mitch in 1998; and Barbuda following Hurricane Irma in 2017, demonstrating how disaster capitalism continues to be in play. The examples draw on disaster research to show how tourism development from disaster capitalism leads to more disasters. Long-standing disaster research can assist tourism researchers in identifying how to counter harmful post-disaster tourism development.This study explores the interrelationship between FDI, institutional factors, financial development and sustainability by revisiting the pollution haven (or halo) hypotheses. The data is sourced from the World Development Indicators (WDI) database over the period of 1990-2016, covering 21 developed and developing countries with high carbon emissions. The study uses dynamic panel data estimations by applying the generalized method of moments (GMM) and system-generalized methods of moments (Sys-GMM) over sample countries. The results indicate that FDI has a significant positive impact on environmental degradation. There is evidence of pollution haven hypotheses, especially in developing countries. We contribute to existing literature by revisiting the Environment Kuznets Curve (EKC) hypothesis and presenting the effect of FDI on carbon intensity in the light of institutional factors and financial development. The findings relating to FDI, institutional factors and financial development may cause researchers and policymakers to reiterate the sustainability dimension of foreign capital inflows in both developed and developing countries. We propose the policy framework to include a mandatory Statement of Environmental Disclosures for both listed and unlisted home and host companies at the time of their origin, expansion and fund raising in order to achieve sustainable business goals (SBGs).This research examines how artificial intelligence may contribute to better understanding and to overcome over-indebtedness in contexts of high poverty risk. This research uses Automated Machine Learning (AutoML) in a field database of 1654 over-indebted households to identify distinguishable clusters and to predict its risk factors. First, unsupervised machine learning using Self-Organizing Maps generated three over-indebtedness clusters low-income (31.27%), low credit control (37.40%), and crisis-affected households (31.33%). Second, supervised machine learning with exhaustive grid search hyperparameters (32,730 predictive models) suggests that Nu-Support Vector Machine had the best accuracy in predicting families' over-indebtedness risk factors (89.5%). By proposing an AutoML approach on over-indebtedness, our research adds both theoretically and methodologically to current models of scarcity with important practical implications for business research and society. Our findings also contribute to novel ways to identify and characterize poverty risk in earlier stages, allowing customized interventions for different profiles of over-indebtedness.Financial services organisations facilitate the movement of money worldwide, and keep records of their clients' identity and financial behaviour. As such, they have been enlisted by governments worldwide to assist with the detection and prevention of money laundering, which is a key tool in the fight to reduce crime and create sustainable economic development, corresponding to Goal 16 of the United Nations Sustainable Development Goals. In this paper, we investigate how the technical and contextual affordances of machine learning algorithms may enable these organisations to accomplish that task. We find that, due to the unavailability of high-quality, large training datasets regarding money laundering methods, there is limited scope for using supervised machine learning. Conversely, it is possible to use reinforced machine learning and, to an extent, unsupervised learning, although only to model unusual financial behaviour, not actual money laundering.
Although cognitive-behavioral therapy (CBT) techniques are well known for targeting psychological distresses, to date, no study has investigated their effectiveness in relieving death anxiety and ageism among nurses.

A parallel randomized controlled trial was conducted according to the CONSORT guidelines during October 2019 at the university hospital. A total of 110 nurses were selected through proportional stratified sampling and randomly assigned to the experimental and control groups. The intervention consisted of six two-hour training sessions delivered over five modules with the integration of different CBT exercises. The effect of CBT was assessed by measuring the differences in the students' responses to a series of validated questionnaires of study variables pre-test (before the training sessions) and post-test (after the training sessions). Clinical registration was completed at ClinicalTrial.gov (ID NCT04319393).

Overall, using CBT techniques led to significant improvements in the study outcom investigate the effectiveness of CBT on other forms of discrimination, such as racism and sexism in healthcare settings, are recommended.The Covid-19 pandemic has precipitated the global race for essential personal protective equipment in delivering critical patient care. This has created a dearth of personal protective equipment availability in some countries, which posed particular harm to frontline healthcare workers' health and safety, with undesirable consequences to public health. Substantial discussions have been devoted to the imperative of providing adequate personal protective equipment to frontline healthcare workers. The specific legal obligations of hospitals towards healthcare workers in the pandemic context have so far escaped important scrutiny. This paper endeavours to examine this overlooked aspect in the light of legal actions brought by frontline healthcare workers against their employers arising from a shortage of personal protective equipment. By analysing the potential legal liabilities of hospitals, the paper sheds light on the interlinked attributes and factors in understanding hospitals' obligations towards healthcare workers and how such duty can be justifiably recalibrated in times of pandemic.
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