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The COVID-19 pandemic has triggered multiple crises-of health, economy, and livelihoods-in India. The restoration of at least a part of the incomes lost by the overwhelmingly informal workforce in the country during the lockdown period should have been a priority for the government. However, the stimulus packages announced by the government have been inadequate, especially given the magnitude of the employment and livelihood crisis. Some of the policies taken in the wake of the crisis, such as the approval to increase daily working hours to twelve, have led to a weakening of labour's position vis-a-vis capital. Rather than boosting economic growth, such measures will only worsen the deficiency in aggregate demand and prolong the recession. India's policy-makers should reconsider the faith they have put in neoclassical economic ideas, which have slowed down employment growth and left millions of people with little access to basic health or education facilities. The pandemic should be an opportunity to build in India an effective and publicly provided social security system as well as rural infrastructure and research institutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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Taking care of one's mental health properly is very important as we are trying to get past the effects caused by the COVID pandemic era, especially since the rate of COVID spread is still persistent. Many organizations, universities, and schools are continuing an online mode of learning or working from home situation to tackle the spreading of the coronavirus. Due to these situations, the user could be using electronic gadgets like laptops for long hours, often without breaks in between. This has eventually affected their mental health. The ‘ViDepBot', Video-Depression-Bot aims in helping the user to maintain their mental health by detecting their depression level early, and taking appropriate actions by faculty/counselors, parents, and friends to help them to come back to normalcy and maintaining a strong mental life. In this work, a system is proposed to determine the depression level from both the facial emotions and chat texts by the user. The FER2013 dataset is trained using deep learning architecture VGG-16 base model with additional layers which acquired an accuracy of around 87% for classifying the live face emotions. Since people tend to post their feelings and thoughts (when feeling down, depressed, or even happy) on social media such as Twitter, the sentiment140 twitter dataset was taken and trained using the machine learning algorithm Bayes theorem which acquired an accuracy of around 80% for classifying the user input texts. The user is monitored through a webcam and the emotions are recognized live. The ViDepBot regularly chats with the user and takes feedback on the mental condition of the user by analyzing the chat texts received. The emotions and chat texts help to find the depression level of the user. After determining the depression level, the ViDepBot framework provides ideal recommendations to improve the user's mood. This ViDepBot can be further developed to keep track of each student/subject person's depression level, where they would be physically present in the classrooms, once the pandemic situation subsides. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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The Coronavirus disease has affected the mental stability of the students since their academic learning has become completely online due to the stay-at-home order implemented over various states. In this work, a system that incorporates the determination of depression level from the facial emotions expressed by a student is proposed where he/she could be working in front of electronic gadgets like laptops for long hours, due to the lockdown situation. The FER2013 data is used to train the deep learning architecture, visual geometry group model with 16 layers (VGG-16) base model with some additional layers. The model has been used to classify the emotions and has acquired an accuracy of 87.76% on the FER2013 dataset. The emotions are then recognized live, monitoring the student through a Webcam. The multi-task cascade convolutional neural network (MTCNN) architecture has been used for detecting the face live. The depression level of the student is determined by calculating the depression coefficient. The dominant emotions in a depression state, the negative ones were captured quickly which helped in determining the depression level. Appropriate remedies are then suggested according to the depression level detected, to improve the student’s mood and also to maintain their mental stability. The calculation of the corresponding depression level in the student will help the faculty, counselor, parents, and friends to take necessary actions to bring the student back to his or her normal mental stage. The system could become more efficient when the activities of the student could be monitored and incorporated into the current system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Aims: The aim of this study was to compare The patient demographics and management of acute manifestations of gallstone disease during The COVID-19 pandemic with an equivalent period in 2019 and assess The differences in recurrence patterns over The period of first and second wave of The pandemic. Method(s): A retrospective cohort study of all adult patients aged >16 years presenting to The Emergency Department at a large District General Hospital with symptoms related to gallstones was conducted. Data was obtained from electronic patient records. Primary outcome assessed were incidence and management of gallstone disease while secondary outcome studied included length of Stay, re-admission rate and recurrence. Data was tabulated and analyzed using Excel (Microsoft, 2016 version). Chi square, t-test and one way ANoVA tests were used. Result(s): 51 patients presented during The period of first wave and 105 patients during second wave as compared to 71 patients in The study period during 2019. The median age of patients during The first wave of COVID was significantly higher than pre COVID and that in second wave. During both The waves of The pandemic, there was a no significant difference in patients presenting with cholecystitis compared with 2019 (47 and 94 versus 60;P value 0.39). There was no significant increase in use of cholecystostomy. There was no significant difference in recurrence and readmissions. Majority of The patients still await surgery. Conclusion(s): During The pandemic, older patients with higher comorbidity presented with acute gallstone disease. Conservative management was effective in The management of these patients.
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The perception of smart cities encompasses a strategy that uses different types of technologies, artificial intelligence (AI), and machine learning and in which, through the internet of things (IoT) and sensor-based data collection, the strategy extrapolates information using insights gained from that data to manage or monitor or track assets, resources, and services efficiently in an urban area. Both these models deeply affect the localities where they are applied and can create together immense possibilities for urban recovery, better quality of life, physical and mental health protection, and economic and social redevelopment. Smart Cities and Machine Learning in Urban Health promotes interdisciplinary work that develops and illustrates the concept of resilience in relation to smart city and machine learning. The book examines the ability of an area and its communities to recover quickly from difficulties;the rigidness and resistance of an area and its communities to possible crisis;the ability of an area, its communities, infrastructure, and business to spring back into shape;and the responsiveness and mitigation towards the crisis with a special look at the impact of the COVID-19 pandemic. The research's theoretical foundation rests on a wide range of non-architectural sources, primarily AI, sociology, urban studies, and technological development, but it explores everything on cases taken from real cities, thus transforming them into pieces of architectural interest. Covering topics such as carbon emissions, digital healthcare systems, and urban transformation, this book is an essential resource for graduate and post-graduate students, policymakers, researchers, university faculty, engineers, public management, hospital administration, professors, and academicians. © 2022 by IGI Global. All rights reserved.
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Objective: The aim of this study is to investigate the postoperative outcomes in post COVID versus non-COVID patients undergone cardiac surgery. Materials and Methods: A retrospective cohort study to analyze the impact of COVID-19 in patients undergoing elective or emergency cardiac surgeries. A total of 512 patients were included in the study over a period of 6 months. The study consists of 35 post-COVID patients and 477 non-COVID patients. All data were collected from previous medical records and hospital database. The clinical outcomes and mortality of post-COVID patients were compared with a cohort of non-COVID patients. The endpoints were compared using t-test or Chi-squared test. Results: Among the post-COVID patients, 54.3% (19) of the post-COVID patients were under COVID category A followed by category B 28.6% (10) and category C 17.1% (6). About 50% of post-COVID patients had complications, especially pneumonia and myocardial infarction following COVID-19. Around 43% of patients showed fibrotic changes in computed tomography (CT) Thorax at the time of admission for surgery. 63% showed CT score in between 1 and 5. The mean COVID antibody titer was 158 U/ml. Majority of the surgeries were coronary artery bypass graft and significant difference was observed in the requirement of intra-aortic balloon pump in post-COVID patients (P < 0.0001). No postoperative mortality reported in post-COVID patients. The postoperative outcomes and survival rates were almost similar in both groups. Conclusion: In our study, the post-COVID patients were recover in a similar way as non-COVID patients after cardiac surgery.
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INTRODUCTION: The novel coronavirus pandemic raises great concern due to its spread and collateral effects on the society. Nearly 30,000 cases are reported from India by the beginning of May 2020. The current pandemic is associated with a sudden surge of false information termed as infodemic. This study attempts to understand the root causes of COVID-19 infodemic. METHODS: This cross-sectional online study was conducted from April 20, 2020, to April 30, 2020, to collect information on the possible causes of COVID-19 infodemic. A fishbone diagram was developed from the data through iterative process to illustrate the root causes of the infodemic. RESULTS: The total of 179 people responded to the online survey. Among them, 99 were health-care professionals and 75 were representatives of the general public. The mean age of the respondents was 28.93 +/- 9.99 years. The root causes for the COVID-19 infodemic were classified into five domains, namely, social media-associated causes, behavioral aspects, the novelty of the virus and related challenges, causes due to lacunae in policies and health systems, and difficulties in the verification of information. CONCLUSION: A comprehensive action plan has to be developed to contain the infodemic through adequate education of all stakeholders, warnings and legal actions, improvements in policy and health systems. The authorities should brainstorm to design activities that contain the spread of false information through social media at the origin itself.