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In this chapter we extend earlier work (Vinuesa et al., Nat Commun 11, 2020) on the potential of artificial intelligence (AI) to achieve the 17 Sustainable Development Goals (SDGs) proposed by the United Nations (UN) for the 2030 Agenda. The present contribution focuses on three SDGs related to healthy and sustainable societies, i.e., SDG 3 (on good health), SDG 11 (on sustainable cities), and SDG 13 (on climate action). This chapter extends the previous study within those three goals and goes beyond the 2030 targets. These SDGs are selected because they are closely related to the coronavirus disease 19 (COVID-19) pandemic and also to crises like climate change, which constitute important challenges to our society. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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The impact of Covid around the globe is imperative all around the world. Though the pandemic had glaring effects on the market, sectors have been showing a reviving trend post-covid. This is possible with the right mapping and affective forecasting. The majorly hit sector was the hotel industry, and situations showed a positive surge with potential sales incrementing post covid. Hospitality industry in particular is much prone to act post covid after easing of travel restrictions and social distancing. Sustainability seems to be the difficult arena with many hotels having low profile financial conditions. The aim of the research is to understand the potential indicators in hotels in India which are expected to boost the sales and sustain the sector. The authors have used a combination of literature review to identify the potential indicators through past studies. Further the qualitative opinion polls, focus group and interviews were conducted to evaluate the indicators and their comparative ranking in the industry. Data were gathered through hotel employees representing various domains like Front office, F & B, Marketing, and promotions department of the premium leisure hotel properties. Further the analysis has been employed for classifying the potential indicators with their relative impact, examining their cause-and-effect phenomena. The study has been validated by expert opinion poll supporting the hypothesis tested. Mapping such inclusive indicators;the hotel industry may be well prepared for the coming future. The most significant indicators have been identified and the mutual interdependence between the potential indicators have been seen through the analysis. will be highly beneficial for the hotel industry to have strong strategic preparation post covid. It also shows that estimating the critical elements at the right time can lead to vowing revenues, saving the industry. The study has practical application for promoting the hotel industry. © 2022 IEEE.
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Background: COVID-19 is still going on with lots of uncertainties. There is a need for early and effective marker for prognostication of COVID-19 patients. Coagulation dysfunction and increased D-dimer levels are seen in this disease. Thus, present study was aimed to compare D-dimer value in relation to disease severity and disease mortality, and to evaluate prognostic significance of D-dimer. Method(s): All symptomatic SARS-CoV-2 RT-PCR confirmed COVID-19 patients admitted in Hospital from March 31, 2021, to May 31, 2021 were evaluated and clinical, demographical and laboratory findings were collected and analysed. According to disease severity patients were grouped and death events and D-dimer value were assessed. Optimal D-dimer cut off point in all groups were evaluated. Result(s): 388 patients were included in the study out of which 142 (36.5%) died during hospital stay. Mean D-dimer value in mild disease was 1.17+/-0.21, moderate disease was 1.47+/-0.17 and in severe disease was 2.92+/-0.23 FEUmicrog/ml. Mean D-dimer value in non-survivors were significantly (P<0.001) different and higher (59.3%) as compared to survivors. ROC curve analysis showed a prognostic value of D-dimer in mild (AUC=0.755, Z=3.30, P=0.001), moderate (AUC=0.762, Z=4.65, P<0.001) and severe (AUC=0.694, Z=5.08, P<0.001) patients. Optimal cut off of D-dimer between survivor and non survivors was >0.78 in mild cases, >1.05 in moderate patients and >2.11 in severe cases. Conclusion(s): Mean D-dimer value showing increasing trend with increase disease severity and prognostic significance was found highest in moderate patients followed by mild patients and least in severe patients in terms of mortality.Copyright © 2023, Dr Yashwant Research Labs Pvt Ltd. All rights reserved.
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The rapid rate of mutation of the RNA genome of the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is responsible for the emergence of viral variants, leading to the enhanced survivability of the virus. Hence, searching for new drugs that can restrict new viral infections by interacting with wild-type and mutated viral proteins is important. However, new drug development's economic and time-constraining nature makes drug repurposing a more viable solution to address the problem. In this work, we conducted a computational study to screen 23 Non-Steroidal Anti-Inflammatory Drugs (NSAID) interactions with 5 major viral proteins of SARS-CoV-2 that are mainly involved in host infection. Our in-silico results establish a database that shows that different NSAID ligands interact with the different viral proteins with good binding affinities. Stabilizing point mutations were introduced within the conserved amino acids involved in ligand-protein interactions. Redocking the NSAID ligands with these mutated viral proteins showed that the NSAID ligands could bind with the mutated and wild-type viral proteins with comparable binding affinities. We conclude that the NSAID ligands could be repurposed as therapeutic drugs against the SARS-CoV-2 virus. Additionally, our work generated a repository that includes binding affinities, possible modes of interaction, and specific interacting residues of the protein (wild-type and mutated) ligand complexes that could be used for future validation studies. Further, our results point to the potential of these drugs to treat other viral infections with similar disease etiology.Copyright All © 2023 are reserved by International Journal of Pharmaceutical Sciences and Research.
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Tourism is one of the most competitive, dynamic, and responsive industries that can easily be influenced by changes caused by environmental factors, both internal and external. Social, economic, political, and technological changes have a large scale impact on the future of tourism. Changes in the environment and technology bring new and often unseen threats, opportunities, and challenges. The growth of Asian Tourism is expected to grow continuously at a sustained pace, and sustainability will remain mainstream in the future. Innovation, technology, and education will be the main driver that will help in tourism growth in Asia. Digital transformation, Artificial Intelligence (AI), Virtual Reality (VR), Artificial Reality (VR), etc. are changing the mode of interaction with the tourist. Thus what tourism is today may not be the same tomorrow. This chapter attempts for reviewing the major tendencies that will shape the development and growth and tourism in Asia An attempt is also made to analyze the effect of the pandemic hit on Asian Tourism and post COVID-19 travel trends. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.