ABSTRACT
Globalisation, industrialisation, and ease of international travel are making a huge impact on urban ecology. It is also influencing the climate, exaggerated by increased pollution, global temperature, and greenhouse gasses. All these factors together play a pivotal role in the increased emergence of infectious human diseases like fungal, bacterial, and viral. Many microorganisms are commensal and coâ€exist as microflora of healthy individuals. However, when the immune system is compromised due to disruption of mucosal barriers or presence of other environmental factors including increased air pollution, these microorganisms get an opportunity to invade and cause infections. In a similar way, the ongoing COVIDâ€19 pandemic, mediated by SARSâ€CoVâ€2, has also led to an increase in a number of nosocomial infections and secondary infections or superinfections like bacterial, fungal, and viral. Several studies have suggested that adaptation to higher temperatures is one of the key contributors to the emergence of many fungus like Candida auris and mucormycetes. Even bacteria like Acinetobacter baumannii, Mycobacterium tuberculosis, etc. or viral infections mediated by Ebola, H1N1, Encephalitis, and SARSâ€CoVâ€2 become more virulent with change in climate and urban ecology. Lifestyle change including uncontrolled intake of animal foods which possibly resulted in the zoonotic transfer of SARSâ€CoVâ€2 to humans, an increase in sexually transmitted diseases including AIDS due to uncontrolled lifestyles in a modern urban society are few other examples. Although these are the results of humans’ need and greed, it is not too late to take actions towards sustainable developments and adopting preventive plans to save the future world. This chapter mainly attempts to understand how such changes in environmental factors, altered climate, and urban ecology can lead to emergence of several infectious diseases, followed by a discussion of preventive measures to keep a steady but sustainable global development of human lives. © 2022 John Wiley & Sons Ltd.
ABSTRACT
The year 2020 brought a big concern for the global community because of COVID-19, which affected every sector of society, and tourism is no exception. Researchers across the globe are publishing their studies related to different dimensions of tourism in the context of COVID-19, and images have formed an essential component of their research. In tourism, images related to COVID-19 can open new dimensions for scholars. The main aim of the research is to measure the retrieval effectiveness of three image search engines (ISEs), that is, Bing Images, Google Images and Yahoo Image Search, concerning images related to COVID-19 and tourism. The study attempts to identify the capability of the ISEs to retrieve the desired and actual images related to COVID-19 and tourism. The PubMed Central (PMC) Database was consulted to retrieve the desired images and develop a testbed. The advanced search feature of PMC Database was explored by typing the search terms ‘COVID-19' and ‘Tourism' using ‘AND' operator to make the search more comprehensive. Both the terms were searched against the ‘Figure/Table' caption to retrieve papers carrying images related to COVID-19 and tourism. Queries were executed across the select ISEs, that is, Bing Images, Google Images and Yahoo Image Search. Retrieved images were individually analysed against the original image from the articles to determine the Precision, Relative Recall, F-Measure and Fallout Ratio. The format of the images in JPG/JPEG, besides checking the original image rank in the retrieved lot, was also ascertained. Bing Images scores more in terms of Mean Precision, followed by Google Images and Yahoo Image Search. For the Relative Recall measure, Google Images scores high, followed by Bing Images and Yahoo Image Search, respectively. Regarding F-Measure and Fallout Ratio, Bing Images outperforms Google Images and Yahoo Image Search. In retrieving the sought format of JPG/JPEG, Google Images performs best, followed by Yahoo Image Search and Bing Images. Google Images produces the original image at the first rank on more than one occasion. In contrast, Bing Images retrieves the original image at the first rank in two instances. Yahoo Images performs poorly over this metric as it does not retrieve any original image at the first rank on any other instance. The study cannot be generalised as the scope is only limited to the images indexed by PMC. Furthermore, the retrieval effectiveness of only three ISEs is measured. The study is the first to measure the retrieval effectiveness of ISEs in retrieving images related to the COVID-19 pandemic and tourism. The study can be extended across other image-indexing databases pertinent to tourism studies, and the retrieval effectiveness of other ISEs can also be considered. © The Author(s) 2023.
ABSTRACT
Corona virus has spread the Covid-19 pandemic to the whole world resulting in the loss of about 3.8 million people. Nearly 156.5 million people have recovered from this disease by timely diagnostic using primary symptoms, which include lethargy caused by muscular weakness. Post Covid-19 patients also face myalgia, which is caused by the abnormal neural action potential. Electromyography (EMG) has been used for years to detect the neural communication and the action potential caused by it. Biomedical experts prefer EMG over other methods due to its ability to capture and conserve the data which helps in detecting major muscular disorders. This paper depicts multiple approaches to diagnose current Covid-19 patients or post Covid-19 patients using the EMG data of lower limb using Machine Learning. These approaches vary from each other in the form of the information conserved in the training data. The proposed method achieves the highest accuracy of 93.8% along with increasing the computational efficiency, as compared to the conventional methods. The dataset used is a publically available dataset, provided by University of California, by the name of Irvine (UCI) EMG lower limb dataset.
ABSTRACT
As of August 9, 2021, there have been around 203 million confirmed cases of coronavirus disease (2019) COVID-19, including 4.3 million deaths. Adverse psychological effects are expected to be long-lasting in vulnerable groups, especially among frontline healthcare workers, given the magnitude of the crisis. Observing strict quarantine and social distancing measures, while being an important strategy to curb the spread, have also led to a significant negative impact on mental health indicators;the long-term consequences are yet to be assessed on a global scale. A medical crisis may become a mental health crisis and the updated findings are reviewed in this paper to provide an updated brief for immunological, occupational, socioeconomic, racial/ethnic, psychological predictors while commenting on care recommendations to prevent psychological trauma from progressing to PTSD.
ABSTRACT
Depression is a medical illness that affects the way you think and how you react. It is a serious medical issue that impacts the stability of the mind. Depression occurs at many stages and situations. With the help of classification, the stage of depression the person is in can be tried to categorize. Nowadays, many users are sharing their views on social media, and it became a platform for knowing people around us. From the data that is shared on social media, the depressing posts are being classified using machine learning techniques. With these reports collected, the depressed person might be helped from making any sudden decisions. So, in our research study, the large datasets of the people in depression during the COVID-19 pandemic situations is analyzed and not in pandemic situations. Here to analyze the data, the neural networks have been trained with the current pandemic analysis report, and it has given a prediction that the people are less likely to get depressed when they are not in a pandemic situation like COVID-19. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
A drastic decline in the sources of emissions of pollutants under COVID-19 induced lockdown resulted in an unprecedented trends in most hazardous pollutants PM2.5, PM10 and NO2 in India. To realize the impact of lockdown in the concentrations of PM2.5, PM10 and NO2, we compared the trend of lockdown period (20nd March to 15th April) with several (3–7) years of past data in four Indian mega cities (Delhi, Pune, Mumbai, and Ahmedabad) of different micro-climate and geography. The significant reduction in the concentrations of NO2 in the ranges of ~60–65% is noticed in four megacities within the lockdown period when compared with the averaged data of past years. However, relatively low reduction in PM2.5 (~25–50%) and PM10 (~36–50%) is observed and city to city variation is found to be significant. The prevailing secondary aerosol formation and enhancement of any natural source of emissions could be some factors preventing PM2.5 levels to go down significantly. Under near negligible fossil fuel emission, contrary to the expectation, an increase in the ratio as compared to normal scenario is observed in Delhi on some days whereas on some selected days, PM2.5/PM10 ratio is found to decline significantly.
ABSTRACT
The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is rapidly spreading across the globe due to its contagion nature. We hereby report the baseline permanent levels of two most toxic air pollutants in top ranked mega cities of India. This could be made possible for the first time due to the unprecedented COVID-19 lockdown emission scenario. The study also unfolds the association of COVID-19 with different environmental and weather markers. Although there are numerous confounding factors for the pandemic, we find a strong association of COVID-19 mortality with baseline PM2.5 levels (80% correlation) to which the population is chronically exposed and may be considered as one of the critical factors. The COVID-19 morbidity is found to be moderately anti-correlated with maximum temperature during the pandemic period (-56%). Findings although preliminary but provide a first line of information for epidemiologists and may be useful for the development of effective health risk management policies.