ABSTRACT
Purpose: COVID-19 impacted the tourism sector, and its ripple effect is equally evident in tourism academia at all levels. Since innovation in tourism pedagogy is considered an epicentre of quality education, this study proposes an integrated model to identify the degree of pedagogical innovation adapted by tourism educators. The model is an amalgamation of innovation indicators in teaching practices developed by Sigala (2021), a futurist model developed by Wassler and Fan (2021) and a model of innovation developed by Brooker and Joppe (2014). Design/Methodology: The study is exploratory, and an online qualitative survey was used to collect data. Data were analysed using the Nvivo 12 software and three themes were drawn: Painters, Artists and Artisans. Findings: The study found that the majority of the tourism educators are painters as they adopted minor changes in their pedagogy. They follow the conventional methods of teaching by incorporating ICT into their pedagogy. Whereas a smaller group of tourism educators introduced innovative tools to encourage and equip students with professional skills (artists/artisans). Practical Implications: The study suggests practical implications for tourism educators to embrace and innovate their pedagogy to become 'artists/artisans'. The support of Higher Education Institutions (HEIs) and industry professionals are equally crucial for bringing innovation in tourism pedagogy and academia, in general, artisans. Originality: Given that tourism education has scantly been discussed following the breakout of COVID-19 (Sigala, 2020), the study addresses that resurrection of tourism pedagogy through an integrated model. © 2022 Pinaz Tiwari, Hugues Seraphin and Vanessa Gowresunkar.
ABSTRACT
The unprecedented growth of the tourism and hospitality sector globally correlates with the advancement of digital media and technological tools. The dominance of information and communication technology is prevalent at every stage of travelers' decision making process (i.e., from searching for a suitable destination to posting feedback on social media platforms). Not only the travelers' behavior patterns are influenced by technology, but destinations also utilize technology for marketing and enhancing consumers' experience. Nevertheless, the advancement of technology has acted like a double-axed sword for the tourism sector. Frequently, digital media is held accountable for popularising a destination to an extent that it becomes a hub for mass-tourism. Issues like tourismphobia, anti-tourism movements, and touristification etc. are gaining hype through technology and online social platforms. Alternatively, destination managers utilise technological tools to sustain tourism growth and visitor experience for better management. Information and communication technology (ICT) has played a key role in influencing tourists to visit popular destinations that led to the issue of overtourism. Likewise, the incorporation of technology is equally vital in managing the tourists' flow, and subsequently, avoiding crowding and overtourism. The chapter aims to highlight the ambidextrous role of technology in overtourism. The study is conceptual and uses short cases of various popular destinations affected by overtourism and how technology served as an emancipator to combat the unsustainable consumption patterns. The chapter discusses the practical implications of utilising technology to combat issues leading to unsustainability in tourism. It also highlights the emerging role of technology in enhancing visitors' experience in the post-COVID-19 scenario. This study presents a holistic perspective and the relationship between technology and tourism. Several studies have discussed the bright side of technology in the tourism and hospitality sector. However, the darker side is less acknowledged. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.
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This paper reports a study on the statistics for particulate matter pollution (PM2.5) and the COVID-19 lockdown in the Kathmandu valley. The PM2.5 decreased during the COVID-19 pandemic lockdown periods 2020 compared to the average value of the previous three years (2017, 2018, and 2019). Further, analysis of active fire and air mass trajectory for April and May in 2019 and 2020 shows that the particulate matter trend associated with Kathmandu is not directly influenced by the long-range transport of wind carrying aerosols from the active fire regions. Statistical tests indicate a reduction of particulate matter pollution during the period.
ABSTRACT
The emergence of COVID-19 pandemic has been a major social as well as economic challenges around the globe. Infections from the infected surfaces have also been identified as drivers of COVID-19 transmission, but most of the epidemic models do not include the effect of environmental contamination to account for the indirect transmission of the disease. The present study is devoted to the investigation of the effect of environmental contamination on the spread of the coronavirus pandemic by means of a mathematical model. We also consider the impact of vaccination coverage as an effective control measure against COVID-19. The proposed model is analyzed to discuss the feasibility as well as stability of the disease-free and endemic equilibria;an epidemic threshold in the form of basic reproduction number is obtained. Further, we incorporate the effect of seasonal periodic changes by letting the rate of direct transmission of disease as time dependent, and find sufficient conditions for the global attractivity of the positive periodic solution. We employ sophisticated techniques of sensitivity analysis to identify model parameters which significantly alter the epidemic threshold and the disease prevalence. We find that by enhancing the vaccination of the susceptible population and hospitalization of the symptomatic/asymptomatic individuals, the basic reproduction number can be lowered to a value less than unity. The findings show that the prevalence of disease can be potentially suppressed by increasing the vaccination of susceptible population, hospitalization of infected people and depletion of environmental contamination. Moreover, we observe that seasonal pattern in the disease transmission causes persistence of the pandemic in the population for a longer period. © 2023 John Wiley & Sons, Ltd.
ABSTRACT
The pandemic during COVID-19 has had a negative influence on the world's fabric, including health systems, travel, living and working habits, and economies in numerous countries throughout the world. Furthermore, it has had a significant negative impact on continuing global attempts to curb excessive usage of plastic materials. The extensive usage by healthcare professionals and the overall community, of masks, sanitizers, and synthetic-based personal protective equipment (PPE) kits, has resulted in massive amounts of plastic trash, with no effective measures or policies in place to reduce its severity. Wearing a face mask as a way of protection against COVID-19 has become commonplace. However, because present mask disposal techniques (i.e., burning and reclamation) produce dangerous chemicals, huge production of contaminated face masks causes environmental difficulties. Furthermore, disposable masks are prepared of a variety of materials that are either non-recyclable or difficult to recycle. Therefore, as a result, it is critical to comprehend the scope of the problem and, equally essential, to devise a viable solution to contribute to the creation of a sustainable civic society. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Background: The present radiological COVID literature is mainly confined to the CT findings. Using High Resolution Computed tomography (HRCT) as a regular 1st line investigation put a large burden on radiology department and constitute a huge challenge for the infection control in CT suite. Materials and Methods: A prospective study of 700 consecutive COVID positive cases who underwent Chest Xray (CXR) and HRCT thorax were included in the study. Many of these CXR were repeated and followed up over a duration of time to see the progression of disease. Results: 392/700 (56%) were found to be negative for radiological thoracic involvement. 147/700 (21%) COVID positive patients showed lung consolidations, 115/700 (16.5%) presented with GGO, 40/700 (5.7%) with nodules and 42/700 (6%) with reticular–nodular opacities. 150/700 patients (21.4 %) had mild findings with total RALE severity score of 1-2. More extensive involvement was seen in 104/700 (14.8 %) and 43/700 (6.2%) patients, who had severity scores of 3-4 and 5-6 respectively. 11/700 patients had a severity score of >6 on their baseline CXR. Those with severity score of 5 or more than 5 (54/700, 7.7%) required aggressive treatment with mean duration of stay of 14 days, many of them died also (23/54, 42.5%). Conclusion: In cases of high clinical suspicion for COVID-19, a positive CXR may obviate the need for CT. Additionally, CXR utilization for early disease detection and followup may also play a vital role in areas around the world with limited access to CT and RT-PCR test.
ABSTRACT
A novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a global pandemic that started in China (Wuhan, Hubei region) in December 2019, called Coronavirus disease. This systematic review intends to evaluate the correlation of pre-existing particulate matter (PM2.5) induced comorbidities along with COVID-19 spread and mortality. A search was operated to report the association between PM2.5 and COVID-19 outbreak and evaluating the PM2.5 related disease affected by COVID-19 infection. The research was conducted in consent with the criteria of PRISMA (Preferred Reporting Items for Systematic Reviews, and Meta-Analyses). We filtered the review and research articles published only in the English language and selected these keywords: air pollution, particulate matter, COVID-19, health impact. We obtained a total of 27 appropriate published articles in their final version. Additional articles were rectified by searching through Scopus, PubMed and Google Scholar. We concluded that the values of coagulation biomarkers in all SARS-CoV-2 patients were considerably higher as compared with healthy people. It was noted that Hypertension, Diabetes, COPD, CVD, Asthma and Cancer possess an evident relation with COVID-19 severity. Globally, air pollutants affect the body's immunity, leading to people being more susceptible to pathogens. In addition, transmission from person-to-person dynamic of the new respiratory virus was considered the environmental factors' role in accelerating coronavirus spread and its lethality. COVID-19 patients with pre-existing comorbidities induced by particulate matter show a high risk of mortality as compared to COVID-19 patients without these comorbidities. © 2022 Technoscience Publications. All rights reserved.
ABSTRACT
Background: The present radiological COVID literature is mainly confined to the CT findings. Using High Resolution Computed tomography (HRCT) as a regular 1st line investigation put a large burden on radiology department and constitute a huge challenge for the infection control in CT suite. Material(s) and Method(s): A prospective study of 700 consecutive COVID positive cases who underwent Chest Xray (CXR) and HRCT thorax were included in the study. Many of these CXR were repeated and followed up over a duration of time to see the progression of disease. Result(s): 392/700 (56%) were found to be negative for radiological thoracic involvement. 147/700 (21%) COVID positive patients showed lung consolidations, 115/700 (16.5%) presented with GGO, 40/700 (5.7%) with nodules and 42/700 (6%) with reticular-nodular opacities. 150/700 patients (21.4 %) had mild findings with total RALE severity score of 1-2. More extensive involvement was seen in 104/700 (14.8 %) and 43/700 (6.2%) patients, who had severity scores of 3-4 and 5-6 respectively. 11/700 patients had a severity score of >6 on their baseline CXR. Those with severity score of 5 or more than 5 (54/700, 7.7%) required aggressive treatment with mean duration of stay of 14 days, many of them died also (23/54, 42.5%). Conclusion(s): In cases of high clinical suspicion for COVID-19, a positive CXR may obviate the need for CT. Additionally, CXR utilization for early disease detection and followup may also play a vital role in areas around the world with limited access to CT and RT-PCR test. Copyright © 2022, Dr Yashwant Research Labs Pvt Ltd. All rights reserved.
ABSTRACT
The pandemic during COVID-19 has had a negative influence on the world's fabric, including health systems, travel, living and working habits, and economies in numerous countries throughout the world. Furthermore, it has had a significant negative impact on continuing global attempts to curb excessive usage of plastic materials. The extensive usage by healthcare professionals and the overall community, of masks, sanitizers, and synthetic-based personal protective equipment (PPE) kits, has resulted in massive amounts of plastic trash, with no effective measures or policies in place to reduce its severity. Wearing a face mask as a way of protection against COVID-19 has become commonplace. However, because present mask disposal techniques (i.e., burning and reclamation) produce dangerous chemicals, huge production of contaminated face masks causes environmental difficulties. Furthermore, disposable masks are prepared of a variety of materials that are either non-recyclable or difficult to recycle. Therefore, as a result, it is critical to comprehend the scope of the problem and, equally essential, to devise a viable solution to contribute to the creation of a sustainable civic society.
ABSTRACT
COVID-19, a brand-new coronavirus, was found in Wuhan, China, in December 2019 and has since spread to 24 additional nations as well as numerous locations in China. The number of confirmed cases continues to rise every day, reaching 34,598 on February 8, 2021. We present our findings a new method was used in this investigation, predictive framework, for such number of reported COVID-19 cases in the China. During the next 10 days, predicated on recently known cases in China. The suggested upgraded adaptable neuro-fuzzy powerful instrument (ANFIS) with an updated floral modeling is used in this model. The salp swarm algorithm (SSA) was used to implement the pollination algorithm (FPA). Generally, SSA is used to enhance FPA in order to minimize its shortcomings. The fundamental theme of the essay FPASSA-ANFIS seems to be a proposed paradigm of improving ANFIS effectiveness through determining FPASSA which was used to determine the ANFIS specifications. The world is also used to analyze the FPASSA-ANFIS model. Statistical figures from the World Health Organization (WHO) on the COVID-19 pandemic for forecast the cases reported these following are indeed the cases for the next 10 days. Most specifically, the FPASSA-ANFIS model in comparison to such a number of other models outperformed them in terms of computing time, root mean squared error (RMSE), and mean absolute percentage (MAP). Researchers also put the suggested model to the tests utilizing two distinct datasets of week pandemic confirmed cases from two or more countries: the USA and China. These results also indicated incredible performance. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Preventing the transmission of COVID-19 necessitates diagnosis and identification. Researchers have developed algorithms to detect the presence of COVID-19 in X-ray and CT scans and images. These methodologies produce skewed data and incorrect disease detection. So, in the case of COVID-19 forecasting utilizing CT scans in an IoT setting, the current study paper established an oppositional-based deep dense convolutional neural network (DDCNN) and chimp optimization algorithm. The framework proposed is divided into two stages: preprocessing and estimation. Previously, a CT scan pictures generated from anticipated COVID-19 are acquired utilizing IoT devices from an open-source system. After that, the photos are preprocessed with a Gaussian function. A Gaussian filter can be used to remove undesirable noise from CT scan pictures that have been obtained. The preprocessed photos are then transmitted to the prediction process. DDCNN is applied to the images preprocessed in this step. The recommended classifier is designed to be as efficient as possible using the oppositional-based chimp optimization algorithm (OCOA). This approach is used to choose the best classifier parameters under consideration. Furthermore, the suggested method is applied to forecast COVID-19 and categorizes the findings as COVID-19 or non-COVID-19. The proposed technique was used in Python, and results were assessed using statistical analysis. CNN-EPO and CNN-FA were compared to the new method. The results proved that the proposed model was optimal. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Offering fresh insights into the key emerging issues in the field, including the changing socio-economic contexts brought about by the rise of the millennial generation and the creative class, the Covid-19 pandemic, and a greater emphasis on social responsibility, this forward-looking Research Agenda critically debates and rethinks theories and practices in the property sector. © Piyush Tiwari and Julie T. Miao 2022. All rights reserved.
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Context: The COVID-19 pandemic burdened the healthcare systems and led to unprecedented impact leading to global economic crisis. In India, the vaccines given emergency use authorization for restricted use were Covishield and Covaxin. The majority of the known adverse effects of COVID vaccine were reported to be mild but there are some serious and severe adverse events reported in COVID-19 vaccine trials including deaths. Aim: To make estimates of the adverse events following immunization (AEFI) burden in India based on Vaccine Adverse Effect Reporting System data model from the USA. Settings and Designs: The descriptive study was carried out in March-May 2022. Data were extracted from report of National AEFI Committee, Ministry of Health and Family Welfare (MOHFW), Immunization Division, Government of India. This study made different hypothesis based on assumption of levels of underreporting ranging from 0.1 to 5%. From these hypotheses, projected deaths and those requiring hospitalization because of AEFI were calculated. Results: More than half (51.34%) of adverse events following immunization were classified as coincidental by National AEFI Committee. Actual reported deaths by MOHFW were 387 but estimated deaths may be around 38,700-41,400 or even more. From the total AEFI deaths, 77.78% were labelled as coincidental deaths. A Maximum of 53.82% coincidental death were because of cardiac cause. Conclusion: Deaths reported by MOHFW are likely to represent gross underestimate of the real scenario in India. © Medical Journal of Dr. D.Y. Patil Vidyapeeth 2022.
ABSTRACT
This paper proposed a model that deals with automatic prediction of the disease given the medical imaging. While most of the existing models deals with predicting disease in one part of the body either brain, heart or lungs, this paper focuses on three different organs brain, chest, and knee for better understanding the real word challenge where problems do not include crisp classification but the multiclass classification. For simplicity this paper focuses on just determining whether that organ is affected with the disease or not and future work can be done by further expanding the model for multiple disease detection of that organ. We have used CNN for multiclass image classification to determine the input medical image is brain, chest or knee and then SVM is used for binary classification to determine whether that input image is detected with the disease or not. Three different datasets from Kaggle are used: Brain Tumor MRI Dataset, COVID-19 Chest X-ray Image Dataset and Knee Osteoarthritis Dataset with KL Grading. Images from these datasets are used to make fourth datasets for training and testing the CNN for the prediction of the three different organs and after that output will be the input of respective SVM classifier based on the output result and predict the weather it is diagnostic with the disease or not. The proposed model can be employed as an effective and efficient method to detect different human diseases associated with different parts of the body without explicitly giving the input that it belongs to that part. For the transparency this model displays the accuracy of prediction made for the input image. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Aim and background: Coronavirus disease 2019 [SARS-CoV-2] is a serious infectious disease which can cause multiple organ failures especially the lungs. Supportive treatment including invasive and non-invasive oxygen support remains a common therapy. High-flow nasal cannula [HFNC], a non-invasive oxygen support method, has emerged as effective treatment option. Despite its significance in SARS-CoV-2 infection, there is a possible adverse effect of pneumothorax. Many cases of pneumothorax are reported as an initial presentation of COVID-19 infection, but in this report, we present two cases of spontaneous pneumothorax on HFNC in COVID-19 infection. Case 1: A 47-year-old patient, known case of hypertension, got admitted for COVID treatment at our hospital. His PaO2/FiO2 index was 47 on admission and the specific treatment started including non-invasive ventilation. Subsequently, he was put on HFNC to maintain oxygen support. He developed newlyonset cough 4 days prior to pneumothorax. After 13 days on HFNC, right-sided spontaneous pneumothorax developed as a complication. Chest X-ray and lung ultrasound were done to confirm pneumothorax, and a tube thoracostomy was done. However, patient had to be intubated the next day because of decreased saturation on NIV and he died after 2 days of intubation. Case 2: A 34-year-old patient got admitted for COVID-19 pneumonia, without any comorbidities. His PaO2/FiO2 index was 86 on admission and the specific treatment started including non-invasive ventilation. Thereafter, he was put on HFNC to maintain oxygen support. The patient developed cough 5 days prior to pneumothorax. After 8 days on HFNC, patient's oxygen saturation dropped suddenly. He was intubated in emergency, however, suffered cardiac arrest, a few minutes after intubation. Chest X-ray done later showed leftsided massive pneumothorax. Conclusion: Patients on mechanical ventilation are at risk of developing spontaneous pneumothorax. However, HFNC may also be associated with higher chances of barotrauma than other low-flow oxygen therapies, especially in addition to cough. Rapid deterioration of oxygen in a patient on HFNC should be vigilantly monitored for pneumothorax.
ABSTRACT
Up to now, the coronavirus disease 2019 (COVID-19) has been sweeping across all over the world, which has affected individual's lives in an overwhelming way. To fight efficiently against the COVID-19, radiography and radiology images are used by clinicians in hospitals. This article presents an integrated framework, named COVIDNet, for classifying COVID-19 patients and healthy controls. Specifically, ResNet (i.e., ResNet-18 and ResNet-50) is adopted as a backbone network to extract the discriminative features first. Second, the spatial pyramid pooling (SPP) layer is adopted to capture the middle-level features from the features of ResNet. To learn the high-level features, the NetVLAD layer is used to aggregate the features representation from middle-level features. The context gating (CG) mechanism is adopted to further learn the high-level features for predicting the COVID-19 patients or not. Finally, extensive experiments are conducted on the collected database, showing the excellent performance of the proposed integrated architecture, with the sensitivity up to 97% and specificity of 99.5% of the ResNet-18, and with the sensitivity up to 99% and specificity of 99.4% of the ResNet-50. © 2014 IEEE.
ABSTRACT
The Internet of Things (IoT) has made it possible for health institutions to have remote diagnosis, reliable, preventive, and real-time decision-making. However, the anonymity and privacy of patients are not considered in IoT. Therefore, this article proposes a blockchain-based anonymous system, known as GarliMediChain, for providing anonymity and privacy during COVID-19 information sharing. In GarliMediChain, garlic routing and blockchain are integrated to provide low-latency communication, privacy, anonymity, trust, and security. Also, COVID-19 information is encrypted multiple times before transmitting to a series of nodes in the network. To ensure that COVID-19 information is successfully shared, a blockchain-based coalition system is proposed. The coalition system enables health institutions to share information while maximizing their payoffs. In addition, each institution uses the proposed fictitious play to study the strategies of others in order to update its belief by selecting the best responses from them. Furthermore, simulation results show that the proposed system is resistant to security-related attacks and is robust, efficient, and adaptive. From the results, the proposed proof-of-epidemiology-of-interest consensus protocol has 15.93%less computational cost than 26.30%of proof-of-work and 57.77%proof-of-authority consensus protocol, respectively. Nonetheless, the proposed GarliMediChain system promotes global collaborations by combining existing anonymity and trust solutions with the support of blockchain technology. IEEE
ABSTRACT
The immune system is comprised of lymph glands, lymph nodes, thymus gland, spleen, bone marrow, lymphocytes, and molecules such as antibodies and cytokines. It has a vast array of functionally different cells such as T and B lymphocytes, macrophages, neutrophils and mast cells. The ontogenesis of the immune system is comprised of the innate immune cells and the adaptive immune cells, where innate immune cells are the first defense mechanisms to respond to pathogenic environmental factors. There are multiple components of the adaptive immune cells, including immunoglobulins (Igs), T-cell receptors (TCR), and major histocompatibility complex (MHC) responsible for adaptive immunity. However, many elements of both the innate and adaptive immune systems are conserved in our bodies. The adaptive immunity is a type of immunity that develops when a person’s immune cells respond to a pathogen such as microorganism or vaccination. Environmental factors such as pathogenic bacteria or viruses, solar exposure, age, exercise, stress, diet, sleep quality and air pollutants can influence the immune system. There may be marked decline in the immune function due to attack of COVID-19. Most patients with mild COVID-19 develop an appropriate immune response that culminates with viral clearance. However, severe disease manifestations have been linked to lymphopenia and immune hyper-responsiveness leading to cytokine storm. It has been observed that in COVID-19, alveolar macrophages are epigenetically altered after inflammation, leading to long-term lung immune-paralysis. Western diets are known to have adverse effects on the immune function. However, Mediterranean-type diets rich in short-and long-chain polyunsaturated fatty acids (PUFA), vegetables, nuts and fruits, dairy products and fish and red wine, due to high content of vitamins, minerals and flavonoids may be useful in boosting immunity. Moderate physical activity may also cause an extensive increase in numerous and varied lipid super-pathway metabolites, including oxidized derivatives called oxylipins. Emerging evidence suggests that dietary supplements containing flavonoids, carotenoids, coenzyme Q10 (CoQ10), vitamins, minerals and antioxidants modulate gene and protein expression and thereby modify endogenous metabolic pathways, and consequently enhance the immunity. Mediterranean-type diet and multiple bioactive nutrients, fatty acids, amino acids, vitamins and minerals as well as moderate physical activity may be crucial for enhancing immunomodulation.
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Today social media plays a very important role in everyone life through which we create online communities to share every kind of information. No one can be one sure about the news they are receiving is true or not? In India, WhatsApp has limited that a person cannot forward a text to more than 5 people at once [1]. This was done to curb the rise of false information. In this paper a machine learning models is create to segregate false and real news. A performance comparison for all the model has been performed in the terms of accuracy. The present article also explores the application of the fake news detector in the real world application. © 2021 IEEE.
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The COVID-19 pandemic has had a severe impact on humans' lives and and healthcare systems worldwide. How to early, fastly and accurately diagnose infected patients via multimodal learning is now a research focus. The central challenges in this task mainly lie on multi-modal data representation and multi-modal feature fusion. To solve such challenges, we propose a medical knowledge enriched multi-modal sequence to sequence learning model, termed MedSeq2Seq. The key components include two attention mechanisms, viz. intra-modal (Ia) and inter-model (Ie) attentions, and a medical knowledge augmentation mechanism. The former two mechanisms are to learn multi-modal refined representation, while the latter aims to incorporate external medical knowledge into the proposed model. The experimental results show the effectiveness of the proposed MedSeq2Seq framework over state-of-the-art baselines with a significant improvement of 1%-2%. © 2021 IEEE.