Role of Social Entrepreneurs for Economic Growth: A study of selected entrepreneurs from Delhi/NCR during the Covid-19 crisis

The Covid-19 crisis has impacted the world economy in an unprecedented way. The spread and consequences of the deadly virus have disrupted business and human lives globally. The purpose of the paper is to study the role and contributions of social entrepreneurs in the management of Covid-19 crisis to provide innovative solutions and contribute to economic growth for the betterment of society. For this purpose, in-depth semi-structured interviews were conducted with social entrepreneurs actively working during the Covid-19 crisis in Delhi/NCR. Thematic analysis was used to report the findings of the study. The study reveals the motivation factors that influence and push social entrepreneurs to work for people, especially in times of crisis. It also demonstrates the challenges and opportunities faced by social entrepreneurs to manage the crisis and create better social well-being. The paper reflects the understanding of social entrepreneurs' role during pandemic times and thereby provides ways for managing the crisis to accelerate economic growth. © 2023 Thailand and The World Economy. All rights reserved.

Boosting Student Wellbeing Despite a Pandemic: Positive Psychology Interventions and the Impact of Sleep in the United Arab Emirates.

Positive psychology interventions hold great promise as schools around the world look to increase the wellbeing of young people. To reach this aim, a program was developed to generate positive emotions, as well as improve life satisfaction, mental toughness and perceptions of school kindness in 538 expatriate students in Dubai, United Arab Emirates. Starting in September 2019, the program included a range of positive psychology interventions such as gratitude, acts of kindness and mental contrasting as examples. Life satisfaction and mental toughness at mid-year were sustained or grew by the end of the year. Positive affect, emotional wellbeing and social wellbeing increased at post-intervention 1, compared to baseline. However, this improvement reverted to baseline levels at post-intervention 2, when data were collected during the COVID-19 pandemic. Only psychological wellbeing, negative affect, perceptions of control, and school kindness were increased at post-intervention 2. During the lockdown, students moved less, but slept and scrolled more. Those who extended their sleep duration reported greater wellbeing. Boosting wellbeing through the use of positive psychology interventions works - even in a pandemic - and extended sleep duration appears to be a driving factor for this observation.

Comparing Autopsy Thoracic Injury Patterns between In-Hospital CPR Patients Receiving LUCAS Device vs Manual Chest Compressions

Background: Current research comparing CPR-associated injuries between those receiving LUCAS device and manual CPR has primarily focused on patients who suffered out-of-hospital cardiac arrest. During the SARS-CoV-2 pandemic, more hospitals leveraged mechanical CPR devices to provide distant yet high quality chest compressions for in-hospital cardiac arrest (IHCA) patients. We sought to investigate autopsy thoracic injury patterns in in-hospital non-traumatic cardiac arrests, comparing traditional manual compressions with the mechanical LUCAS device compressions. Design: Autopsies were screened for a history of in-hospital cardiopulmonary resuscitation in the absence of prior traumatic injuries at a single, large quaternary care center from 1/1/2018 to 06/30/2021. 20 received LUCAS compressions and 40 received manual compressions. Student's T-Tests were used to compare means for continuous variables, while chi-squared and Fischer's exact tests were used for categorical variables. An alpha of 0.05 was chosen as the threshold for statistical significance. Results: A statistically significant decrease in the rate of sternal fractures and rate of multiple sternal fractures during mechanical CPR was found. A statistically significant increase in other soft tissue injuries, such as pleural wall or lung injuries was seen in mechanical CPR cases, while an increased rate of bilateral rib fractures was noted in manual compression cases. Conversely, no difference in the number or laterality of rib fractures were noted. There was no significant difference in age, biological sex, or rate of scoliosis or kyphosis between cohorts. Results are listed in table 1. (Table Presented) Little research has looked at the injury patterns of mechanical CPR in the IHCA patient population. These results point to a potential difference in thoracic injury patterns from manual compressions when compared to LUCAS device compressions. The statistically significant decrease in sternal fractures with mechanical compressions is noteworthy. Conversely, the increase in other soft tissue injury demands further examination. The decrease in bilateral rib fractures with LUCAS use suggests that placement of the device may play a role in the epidemiology of rib injuries, but not in the number of ribs injured. Further research should examine rib injuries in more detail, and quantify additional comorbidities in both survivors and non-survivors of cardiac arrest.

Smartphones as Chemometric applications

Smartphones first appear in the world around the 1990s. Such devices are made to make and receive calls, messages, and facsimiles and especially in the situations of a pandemic like COVID-19, they are the only source to be connected with the family, friends, and the world as a whole. Their ability to store additional applications and information has made them emerged as an alternative to the primitive analytical instruments. The analytical instruments used so far are expensive and heavy;require trained personnel for their operations. But, the availability of high-quality cameras, low cost, ease of operation of smartphones is making them the easily accessible detection tool. This aspect of smartphone analysis has been taken into a good advantage and varied work has been published in this context. In addition to cost-cutting, it also reduces the time required for the analysis as only a single picture can serve the purpose of repeatability and reproducibility tests. This book chapter provides an overview of recent applications of smartphones in various fields of analytical chemistry along with the future perspective. The rate at which smartphone use is increasing and its technology is advancing, it has a huge potential to replace the analytical instruments. Further, they can be used in the real-time detection of almost all types of forensic exhibits. © 2021 Elsevier Inc. All rights reserved.

A Secured Real-Time IoMT Application for Monitoring Isolated COVID-19 Patients using Edge Computing

Internet of Medical Things (IoMT) is an emerging technology whose capabilities to self-organize itself on-the-fly, to monitor the patient's vital health data without any manual entry and assist early human intervention gave birth to smart healthcare applications. The smart applications can be used to remotely monitor isolated patients during this COVID-19 pandemic. Remote patient monitoring provides an opportunity for COVID-19 patients to have vital signs and other indicators recorded regularly and inexpensively to provide rapid and early warning of conditions that require medical attention using secured edge and cloud computing. However, to gain the confidence of the users over these applications, the performance of healthcare applications should be evaluated in real-time. Our real-time implementation of IoMT based remote monitoring application using edge and cloud computing, along with empirical evaluation, show that COVID-19 patients can be monitored effectively not only with mobility but also helps the health care professionals to generate consolidated health data of the patient that can guide them to obtain medical attention. © 2021 IEEE.

Pandemic and community's sense of justice through suo motu in India

An ideal citizen centric society wedded to the democratic governance always keeps its laws in motion to build an egalitarian and just order. Aesthetic virtue of law lies in the realisation of justice and the creation of an egalitarian order. Against this backdrop, the present paper aims to examine the approach of the Supreme Court of India (SCI) towards COVID-19 Pandemic through suo motu proceedings from various perspectives of jurisprudence and constitutionalism. The government claimed to have strived intensively with full vigour but the response due tolack of preparedness and intense gravity of the catastrophe, the efforts of the government appeared negligible. It warranted prompt revisit of priorities which compelled the SCI to intervene and evaluate the legitimacy of the executive action. Furthermore, it impelled to examine the role of the SCI in responding to the community's sense of justice and humanising justice. The paper presents the solution to the paradox generated out of the inherent friction between constitutional authority of judicial review and resistance of judicial review of executive actions by a populist government. The scope of the discussion has primarily been confined to Orders of the SCI in suo motu hearings and examined accordingly.

Nowcasting of COVID-19 Confirmed Cases: Foundations, Trends, and Challenges

The coronavirus disease 2019 (COVID-19) has become a public health emergency of international concern affecting more than 200 countries and territories worldwide. As of September 30, 2020, it has caused a pandemic outbreak with more than 33 million confirmed infections, and more than 1 million reported deaths worldwide. Several statistical, machine learning, and hybrid models have previously been applied to forecast COVID-19 confirmed cases for profoundly affected countries. Future predictions of daily COVID-19 cases are useful for the effective allocation of healthcare resources and will act as an early-warning system for government policymakers. However, due to the presence of extreme uncertainty in these time series datasets, forecasting of COVID-19 confirmed cases has become a very challenging job. For univariate time series forecasting, there are various statistical and machine learning models available in the literature. Still, nowcasting and forecasting of COVID-19 cases are difficult due to insufficient input data, flaw in modeling assumptions, lack of epidemiological features, inadequate past evidence on effects of available interventions, and lack of transparency. This chapter focuses on assessing different short-term forecasting models that are popularly used to forecast the daily COVID-19 cases for various countries. This chapter provides strong empirical evidence that there is no universal method available that can accurately forecast pandemic data. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Comparative Study of Deep Learning Models for COVID-19 Diagnosis

Corona Virus Disease 2019(COVID-19) spread far and wide in numerous nations in early 2020, causing the world to face an existential health crisis. This pandemic continues to have a devastating effect on the global population and by now it has infected more than a few million individuals around the world. One significant obstacle in controlling the spreading of this virus is that the initial system for addressing this infectious disease was not clear. A basic advancement in the struggle opposite the COVID-19 pandemic is early screening and dependable diagnosis utilizing computerized detection of lung infections. Computed Tomography (CT) scans and X-rays imagery offers great potential help to clinical specialists tackling COVID-19. An efficient Deep Learning diagnosis application needs to be developed so that accurate and precise prediction can be done for the disease. This paper introduces dataset analysis and comparative evaluation of deep learning models for creating disease diagnosis application using image processing. Comparison is done using three main deep learning models-Convolutional Neural Network (CNN), Support Vector Machine (SVM) Logistic Regression. Dataset analysis and model selection is a crucial phase for developing a predictive deep learning algorithm. This analysis is done for better results and is done using Orange data mining software. © 2021 IEEE.

A Pre-screening Approach for COVID-19 Testing Based on Belief Rule-Based Expert System

We are living in the digital era, where most of the hospitals in the developed nations are keeping the medical records of the patients and as a result, most of the traits of the COVID-19 infected individuals are present in the digital form. Based upon the data thus generated, which is present on various platforms over the internet. In this chapter, an effort has been made to propose an artificial intelligence-based self-testing technique that can predict the patients who should go for COVID-19 testing. This chapter presents a belief rule-based expert system to predict the likelihood of the person to be tested for COVID-19. The system thus generated can easily pre-screen humans without the intervention of any second individual. Based upon the classification results the individual can be further tested to firm the presence of COVID-19 infection. This method will be cost-effective, plus it will also result in inefficient utilization of the scarce resource of medical testing kits. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.