Factors Influencing the Success of Online Education during COVID-19: A Case Analysis of Odisha

The COVID-19 pandemic caused by the coronavirus has dramatically changed the lives of students all around the world, with the virus's effects profoundly impacting students' physical and emotional well-being. Due to a series of shutdowns and lockdowns, social distancing, and further closure of schools, colleges, and institutions to ameliorate the pandemic crisis, the teaching and learning process shifted to an online form. As a result, students all over the world have been forced to deal with the problem as a last resort to accepting online education. This study looked at the efficiency of online education in the current situation and the student's reactions. To enhance the online method of education for students, we examined the success characteristics of online education in the Indian state of Odisha. The study's samples were collected from the faculty members of various graduate and post-graduate educational institutions in Odisha, who were recruited by questionnaire to get an expert opinion. © 2023 by the authors.

Repeat Doses of Sotrovimab for Recurrence of COVID-19 in a High-Risk Pregnancy

Background: Myasthenia gravis (MG) is an autoimmune disorder leading to variable degrees of skeletal muscle weakness. During pregnancy, infections can trigger exacerbations and should be treated promptly and aggressively.(1) Sotrovimab is a monoclonal antibody used as monotherapy in high-risk, symptomatic non-hospitalized patients at risk of developing COVID-19 disease. (2) It is thought to have retained activity against SARS-CoV-2 omicron variant. (3) Limited data are available on its use in pregnancy. Case: A 39-year-old woman with severe generalized MG, was referred to our joint neuro-obstetric multidisciplinary service. Her two previous pregnancies were complicated by severe exacerbations of MG necessitating intensive care admissions, and preterm labour. Her long-term therapy included high dose steroids, intravenous immune globulin (IVIG) and plasma exchanges. In this pregnancy, she additionally received rituximab in the first-trimester, allowing her prednisolone to be weaned to 20 mg daily, with ongoing 3-weekly IVIG. She received 3 doses of the Pfizer COVID-19 vaccine. At 19 weeks she developed mild coryzal symptoms, sore throat and myalgia. Lateral flow and polymerase chain reaction tests in the community confirmed infection with SARS-CoV-2. She was treated with sotrovimab with uneventful recovery at home. At 31 weeks, she again tested positive for SARS-CoV-2, after reporting mild COVID-19 symptoms. She received a second dose of sotrovimab and had a quick recovery. Subsequent SARS-CoV-2 genotyping indicated she had contracted the Omicron-BA.2 variant. Fetal surveillance for growth (SARS-CoV-2) and arthrogryposis (MG) did not raise concerns. At 35+3 weeks, she went into spontaneous labour and was delivered by caesarean section for evolving chorioamnionitis, with uneventful recovery for mother and baby. Discussion(s): We report a case of repeated treatment with sotrovimab (in second and third trimesters) of a high-risk, non-hospitalized pregnant woman, who was re-infected with SARS-CoV-2. We identified no immediate maternal, fetal or neonatal complications following two doses of sotrovimab for mild COVID-19.

Exploring the potential therapeutic properties of bee products against COVID-19 (SARS CoV-2)

The new corona virus illness (COVID-19) swept around the world, quickly creating a serious international disaster. For the treatment and prevention of COVID-19, apitherapy appears to be a viable source of pharmacological and nutraceutical medicines. Honey, pollen, propolis, royal jelly, beeswax, and bee venom, for example, have been demonstrated to have significant antiviral action against infections that cause severe respiratory syndromes, including those produced by human corona viruses. Furthermore, many of these natural products are involved in the induction of antibody production, maturation of immune cells, and stimulation of innate and adaptive immunological responses and many of them are involved in the induction of antibody production, maturation of immune cells, and stimulation of innate and adaptive immunological responses.

A high-speed unsupervised hardware architecture for rapid diagnosis of COVID-19

In the diagnosis of COVID-19, investigation, analysis, and automatic counting of blood cell clusters are the most essential steps. Currently employed methods for cell segmentation, identification, and counting are time-consuming and sometimes performed manually from sampled blood smears, which is hard and needs the support of an expert laboratory technician. The conventional method for the blood-count-test is by automatic hematology analyzer which is quite expensive and slow. Moreover, most of the unsupervised learning techniques currently available presume the medical practitioner to have a prior knowledge regarding the number and action of possible segments within the image before applying recognition. This assumption fails most often as the severity of the disease gets increased like the advanced stages of COVID-19, lung cancer etc. In this manuscript, a simplified automatic histopathological image analysis technique and its hardware architecture suited for blind segmentation, cell counting, and retrieving the cell parameters like radii, area, and perimeter has been identified not only to speed up but also to ease the process of diagnosis as well as prognosis of COVID-19. This is achieved by combining three algorithms: the K-means algorithm, a novel statistical analysis technique-HIST (histogram separation technique), and an islanding method an improved version of CCA algorithm/blob detection technique. The proposed method is applied to 15 chronic respiratory disease cases of COVID-19 taken from high profile hospital databases. The output in terms of quantitative parameters like PSNR, SSIM, and qualitative analysis clearly reveals the usefulness of this technique in quick cytological evaluation. The proposed high-speed and low-cost architecture gives promising results in terms of performance of 190 MHz clock frequency, which is two times faster than its software implementation. © 2022 John Wiley & Sons Ltd.

Facilitating a Healthful Culture through Selfcompassion

Background COVID-19 has further highlighted that the physical and emotional wellbeing of healthcare staff must be of equal priority to that of patients. Organisations can do a lot to promote informal support. Whilst facilitating training of staff to become resilience based clinical supervisors, an organisational gap was identified - supporting wellbeing of staff. Organisations need to enable systems to facilitate and encourage reflective practice and reflection of self. High quality care requires investment in staff development and opportunities for staff to take time out to reflect on their practice. Aims 1. Explore self-awareness and how it connects to wellbeing. 2. Look at how mindful self-compassion can help our wellbeing. 3. Identify and discuss potential ways of improving wellbeing and resilience for self and others. Methods A pilot was undertaken following an identified gap in wellbeing delivered by Marie Curie. Qualitative evaluation was undertaken on completion of the course and six months post. Results Given the continued pressures of COVID-19, a high dropout was expected and on reflection worked to the advantage of the facilitators and participants. 10 staff attended, 50% of confirmed attendees. 7 non-clinical staff, 3 clinical. Participants felt the focus on self, including compassion, mindfulness, awareness, and reflection was important and identified that sessions should be kept small. Feedback reinforced the assumption that non-clinical staff needed further support opportunities. Following successful evaluation this is due to be delivered again later in 2022. Conclusion The safe, effective, efficient, and compassionate care that people look to Marie Curie for at the end of life is only possible if staff are physically and emotionally healthy. Having compassion for others entails self-compassion. Research suggests that mindfulness interventions, particularly those with an added loving kindness component, have the potential to increase self-compassion among healthcare workers.

Disruptive Technologies and Digitalization in Insurance: Improving the Value Chain of Insurance

Advancement in technology in the digital era is changing the way businesses used to work. Technology is bringing new opportunities on one hand for the companies and challenges on the other hand that should be carefully delt with. Expectations of the customers are changing with the advancement of technology. Today the customer is becoming more tech oriented and prefers product that are tech savvy too. The distribution of the products has also changed drastically. Many digital channels have emerged in the past few years that aims to provide faster services to the customers and helps in saving cost.The challenges get further compounded with the happening of the COVID-19 Pandemic. The operations of the organizations are getting digitalized and the speed with which these are getting transformed is phenomenal. Emerging technologies like Artificial Intelligence (AI), Machine Learning (ML), Virtual Reality (VR), Augmented Reality (AR), Internet of Things (IoT), Robotic Process Automation (RPA) and Block chain are playing a bigger role in these transformations. Insurance industry is also leveraging the benefits of these technologies especially in improving the value chain of insurance. The objectives of the study were to highlight the process of Digitalization in Insurance;to identify the Disruptive and emerging technologies used by the insurer;and to highlight the role of disruptive technologies in improving the value chain of insurers. This paper highlights the way insurance industry is getting digitalized and how these technologies can improve the value chain of insurance. The paper will help understand the various issues in the value chain of insurance and how the emerging technologies can help the insurers in improving their processes leading to the improvements in their value chain. © 2022 IEEE.

Use of Some Standard Mathematical Models in Physiology and Pathology

Most of the physical phenomena in the actual world exhibit non-linear character. In recent years, significant advances have occurred in the design, construction, and development of mathematical/analytical models for the solution of these physical problems. If appropriate initial and boundary condition(s) are associated with the models, the whole system generates a mathematical problem. In this way, a mathematical model establishes a relationship between mathematics and the rest of the world (the physical world). Analytical models are developed (or modified) based on mathematical concepts and using mathematical languages and symbolism. The three major advantages of using mathematical tools over others in modelling are: • Mathematics can provide well-ordered rules for manipulation -essential in modelling • Mathematics gives unique result for an investigation Mathematics is capable of proving general results from which the results for particular cases may be deduced assigning appropriate values to the parameters (in the admissible range) involved in the investigation. Information about the physical system represented by the model is estimated qualitatively and quantitatively by solving the mathematical problem by applying the best possible mathematical technique. The selection of appropriate mathematical methods for the solution will depend on the purpose for which the model may be applied for investigation. The models are validated by comparing the analytical results of the mathematical problem with the behavior exhibited by the physical problem. If the two do not match, the model is modified by including more parameters or leading to rejection of the model. As the physical models are based on experimental investigation, so they are superior to the mathematical models constructed on the theoretical investigation. But a mathematical model has the advantage of studying the role of key parameters controlling the system within a short period. Mathematical modeling is essential in studying human physiological problems (brain injury, blood flow, population growth, and spread of COVID-19 etc) where experimental investigations cannot be performed to obtain adequate data. So, improved mathematical models are developed (sometimes old models are modified) using suitable symbolism to define the operation of a physical system. As most of the phenomena in the physical world cannot be described by mathematical objects (due to their non-linear character), mathematicians and scientists throughout the globe are continuously developing and modifying mathematical models to solve the practical problems effectively in the domain of physical science, bio-science, social science, medicine, statistics, management, engineering, and technology. They are extensively utilized in making predictions when a particular parameter is varied in a range. The models play a convincing role in health science not only in the prediction of occurrence of critical health irregularities but they may reduce the risk in the treatment of fatal diseases like cardiovascular and neurological dysfunctions, brain injury due to vehicular accidents and soccer games, novel coronavirus infection, absorption of medicine in the human metabolic system leading to morbidity and even death. Our best endeavours is to construct some sophisticated mathematical models for the following types of problems: • in the study of Brain Injury Problems • in the study of Blood Flow through an Atherosclerotic (diseased) Arterial Segment • in Decaying of the absorption of a medicine in human rheological system• in estimating the spread of a disease (COVID 19) growing or decaying exponentially • in estimating the population growth in the coming years.We sincerely believe that the book chapter will throw light on the latest developments in the field of mathematical modelling and thus reinforce and solidify the understanding of this ever expanding domain. © 2022 Nova Science Publishers, Inc.

COVID-19: Social Inequalities and Human Possibilities

COVID-19: Social Inequalities and Human Possibilities examines the unequal impact of the COVID-19 pandemic on individuals, communities, and countries, a fact seldom acknowledged and often suppressed or invisible. Taking a global approach, this book demonstrates how the impact of the pandemic has differed as a result of social inequalities, such as economic development, social class, race and ethnicity, sex and gener, age, and access to health care and education. Economic inequality between and within nations has significantly contributed to the chances of individuals contracting and dying from the virus. Developing nations with weak health care systems, workers whose jobs cannot be performed remotely, the differences between those with and without access to soap and water to wash their hands, or the ability to practice physical distancing also account for the unequal impact of the virus. Racial and ethnic minorities experience higher death rates from the virus, which has also unequally affected indigenous peoples and urban and foreign migrants around the world. Inequality is also embedded in national and international responses to the pandemic, as giving and receiving aid is often impacted by inequalities of demographic and national power and influence, resulting in national and global competition rather than the collaboration needed to end the pandemic. Along with the other titles in Routledge’s COVID-19 Pandemic series, this book represents a timely and critical advance in knowledge related to what many believe to be the greatest threat to global ways of being in more than a century. COVID-19: Social Inequalities and Human Possibilities is therefore indispensable for academics, researchers, and students as well as activists and policy makers interested in understanding the social impact of the COVID-19 pandemic and eradicating the inequalities it has exacerbated. © 2022 J. Michael Ryan and Serena Nanda.

The Care Policy Scorecard: a new tool to shift progress towards a caring economy

COVID-19 has highlighted the centrality of care and women’s labour (paid and unpaid). While there is a growing body of literature on specific policy measures to address care work in different contexts, there is no global practical tool to track progress against key policy areas. This article introduces the Care Policy Scorecard, an evidence-based policy tool developed through extensive collaboration between several institutions, care policy advocates, policymakers, and researchers in the global South and North. The Scorecard helps care advocates to assess how care-related policies are adopted, budgeted for, and implemented by governments, and to what extent they can transform the social organisation of care. The paper also includes preliminary results from the application of this tool in Kenya, and shares learnings from the use of the findings for national-level care policy advocacy. © 2022 Oxfam KEDV.

Emerging health start-ups for economic feasibility: opportunities during COVID-19

The Indian population has a potential threat of communicable and noncommunicable diseases. The low preventive health measure is a cause of significant loss to the economy. The integration of the cloud platform with remote wearable sensors not only helps the health stakeholders to capture the patient’s vitals but also perform predictive analysis during COVID-19. Raising timely alarms through the Internet of Medical Things and artificial intelligence (AI) has complete preventive care applications through real-time analytics. However, health merchandise start-ups using AI and machine learning for timely device delivery face delay in making themselves available and affordable for remote patients of Tiers II and III. This study takes a health service provider perspective and seeks to study the problem situation using a causal loop model. Finally, analysis of the feedback loops and medical device import data is done to develop suitable strategies for COVID-19 patients of remote locations. © 2022 Elsevier Inc. All rights reserved.

Emerging real time analytics based health start-ups: opportunities during covid-19

Indian population has potential threat of communicable and non-communicable diseases. The low preventive health measure is a cause of major loss to the economy. Integration of the cloud platform with remote wearable sensors not only helps the health stakeholders to capture the patient vitals but also perform predictive analysis during COVID-19. Raising timely alarms through Internet of Medical Things and Artificial Intelligence has wide application in preventive care through real time analytics. However, Health Merchandise Startups using artificial intelligence and machine learning for timely device delivery face delay in making themselves available and affordable for Remote patients of Tier II and III. This study takes a health service provider perspective and seeks to study problem situation systemically by using a casual loop model. Finally, analysis of the feedback loops is done to be able to come out with suitable strategies for COVID-19 patients of Remote locations. © MEDIC SCIENTIFIC, All rights reserved.

A high-speed unsupervised hardware architecture for rapid diagnosis of COVID-19

In the diagnosis of COVID-19, investigation, analysis, and automatic counting of blood cell clusters are the most essential steps. Currently employed methods for cell segmentation, identification, and counting are time-consuming and sometimes performed manually from sampled blood smears, which is hard and needs the support of an expert laboratory technician. The conventional method for the blood-count-test is by automatic hematology analyzer which is quite expensive and slow. Moreover, most of the unsupervised learning techniques currently available presume the medical practitioner to have a prior knowledge regarding the number and action of possible segments within the image before applying recognition. This assumption fails most often as the severity of the disease gets increased like the advanced stages of COVID-19, lung cancer etc. In this manuscript, a simplified automatic histopathological image analysis technique and its hardware architecture suited for blind segmentation, cell counting, and retrieving the cell parameters like radii, area, and perimeter has been identified not only to speed up but also to ease the process of diagnosis as well as prognosis of COVID-19. This is achieved by combining three algorithms: the K-means algorithm, a novel statistical analysis technique-HIST (histogram separation technique), and an islanding method an improved version of CCA algorithm/blob detection technique. The proposed method is applied to 15 chronic respiratory disease cases of COVID-19 taken from high profile hospital databases. The output in terms of quantitative parameters like PSNR, SSIM, and qualitative analysis clearly reveals the usefulness of this technique in quick cytological evaluation. The proposed high-speed and low-cost architecture gives promising results in terms of performance of 190 MHz clock frequency, which is two times faster than its software implementation. © 2022 John Wiley & Sons Ltd.

Work Environment,Stress andburnout among Security Personnel during Covid 19 Pandemic

Background: Odisha was also affected by the spread of the novel coronavirus (SARS-Cov-2), responsible for the COVID-19 disease. For its mitigation the health system in a tertiary care institution was frantically deploying all personnel like from health , security, food handlers , dealing with transportation. Objective: Our aim was to assess exposure, perceptions, workload, and possible burnout of Security personnel during the COVID-19 pandemic and to suggest specific recommendations based on the study findings. Methodology: The type of study was a cross sectional study, placed at Cuttack district in Odisha. Time Period of this study was Aprilto June 2021. 465 were finally included in the study. On the days of the mental health status assessment security personnel's were appraised and accordingly a predesigned, pretested questionnaire was implemented to them. Results: 57.5% males and 67.2% females were at the risk of burn out while 38.4% and 32.4% were at the edge of severe burn out. There was a significant difference in the mean score between those aged less than 30 years at F (5.434, 2) and those between 30 to 50 years as well as those aged above 50 years with p- value 0.008 and 0.009 respectively. Conclusions: Specific strategies have to be recommended and adapted like changing work pattern taking breaks, avoiding overtime, balance work with life. The task should include different skills to cope with stress, time managementand social support from family, friends and peer. Besides this various relaxation strategies to promote fitness, developunderstanding of life, counseling, better sleep, exercise, and good balanced nutrition.

Examining Risk Absorption Capacity as a Mediating Factor in the Relationship between Cognition and Neuroplasticity in Investors in Investment Decision Making

The encouragement of potential investors who are emotionally broken by past losses and market experiences is crucial to the sustainable flow of funds to the stock market. This can be established by building a knowledge-creating mechanism among investors in their cognitive dimensions, which, in turn, can develop their risk-bearing potential to reach the optimum level so that emotionally broken investors can use their cognitive abilities with their developed risk-absorption potential to further invest in the market in the near future. This study investigates the mediating effect of risk-absorption attitudes in the relationship between cognition and neuroplasticity in investors. Data for the study collected from 506 individual retail investors' samples using a stratified random sampling technique were analyzed through covariance-based structural equation modeling. The findings of the study indicate that the constructs, viz., the investors' cognition, risk absorption, and neuroplasticity, are valid and reliable. The structural model also supports the notion that risk absorption mediates the relationship between the investors' cognition and neuroplasticity. The outcomes of the study are expected to aid in the policy formulation for equity-related financial product marketers, such as depository participants, brokers, mutual funds and SIP institutions, and to help in healing psychological trauma that potential investors suffered from due to losses in the past and overcoming reluctances to further invest in stock markets. The investors' terrible psychological health developed because of past loss experience can be restored through the concept of neuroplasticity, in which different cognitive dimensions are used, while also enhancing risk absorption in potential investors.

Obesity and severity of COVID-19 infection

Background: This prospective longitudinal study evaluated the relationship of obesity among other clinical factors in relation to the subsequent development of severe COVID-19 infection in a young and an unvaccinated cohort. Methods: COVID-19 infection was diagnosed in 572 young patients with a positive reverse-transcriptase- polymerase- chain- reaction assay performed on a standard naso-pharyngeal swab. Demographic and medical data were obtained at the time of the diagnosis, and patients were followed until recovery or death. Variables studies included age, sex, ethnicity, race, smoking status, alcohol status, hypertension, diabetes, asthma/COPD, heart dissease, immunosuppressive therapy, and obesity defined as a Body Mass Index = or > 30 Kg/m2. Subjects who developed decompensated hypoxia, associated with clinical manifestations and then hospitalized, were considered to have severe disease. Results: The cohort was limited to patients 18 to 40 yrs old,and consisted of 51.9% women. Forty three percent were white non-Hispanic or Latino, while 30 1% were black or African American, and 7% Asian. The mean age was 29.3 (SD +/-5.9) yrs, and mean BMI was 28.7 (+/-7.1) kg/m2. In a multivariable model patients with Asthma and Obesity had a significantly higher likelihood of developing severe Covid-19 infection and were both significant predictors of severe Covid-19 infection (HR = 3.25;95% CI = 1.4, 7.40;P < .005), and (HR = 2.1;95% CI = 1.3, 3.5;P < .005) respectively. Other factors including Age did not predict severe Covid-19 infection in this cohort. Conclusions: In patients younger than 40 years old, Obesity and Asthma predict the subsequent development of severe Covid-19 infection after diagnosis. This increased risk is regardless of age.

VGG-16-Based Framework for Identification of Facemask Using Video Forensics

In the context of the COVID-19 disease outbreak, organizations such as the universities are at risk of being essentially shut around the world if the overall condition does not improve. The other name for COVID-19 is a serious acute respiratory syndrome, a virus that causes serious respiratory problems. Corona virus-2 is a contagious agent spread through droplets in the air from an affected patient. This spreads easily by direct contact with affected patients or touching the objects which all already touched by the affected patients. Even if there are many vaccines available to defend against COVID-19 across the globe, still there is a high necessity to consider the precautions for avoiding infection. The major aspect for preventing the infection using a facemask that protects a person from entering the virus into the body through the nose and mouth of a person. The other major aspect for preventing the infection by washing hands using and washes or sanitizers. In the present article, the major and popular advanced technique used for image-based detection and classification is the Deep Learning-based VGG-16 technique. The deep learning technology is used in the analysis to identify face mask recognition and determine whether or not the individual is carrying a facemask. VGG-16 is the CNN (Convolutional Neural Network) framework is utilized for the present study. The Kaggle dataset considered consists of 25,000 images with each of the images having 225 × 225 pixels as the resolution, and the proposed model performed with a 96% accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Inequalities and covid-191

This chapter examines the unequal impact of COVID-19 on individuals, communities, and nations, a fact often suppressed or invisible. Economic inequality between and within nations significantly contributes to the chances of contracting and dying from the coronavirus. Developing nations with weak healthcare systems, workers whose jobs cannot be performed remotely, the differences between those with and without access to soap and water to wash their hands, and the ability to practice social distancing also account for the unequal impact of the coronavirus. Racial and ethnic minorities experience higher death rates from COVID-19, which has also unequally affected Indigenous peoples and urban and foreign migrants around the world. Inequality is also embedded in international responses to COVID-19, as giving and receiving aid is often impacted by inequalities of national power and influence, resulting in global competition rather than the collaboration needed to end the pandemic. © 2021 selection and editorial matter, J. Michael Ryan;individual chapters, the contributors.

COVID-19 risk assessment using the C4.5 algorithm

The number of confirmed cases of COVID-19 is increasing exponentially day by day across the world because of its super spreading nature. It was started in China and took a very less time to spread all over the globe. Due to its mortality rate, spreading nature, and unavailability of proper medicine and vaccination, it is declared as a pandemic by the World Health Organization (WHO) in March 2020. In this crisis time of the COVID-19 outbreak, technologists try to smooth the lives by minimizing the infection rate and facilitating in-time quality treatment. In this work, we collected the world data of COVID-19 cases in terms of confirmed, recovery, active, and death and provided visualization. We have also tried to find the patient’s risk level in terms of high, medium, and low by analyzing the patient’s symptoms and previous health histories such as high blood pressure, cardiac disease, diabetes, kidney issues, and others. We applied the C4.5 machine learning (ML) classifier to the considered dataset after preprocessing for risk assessment. The results obtained from the study indicate that the algorithm helps in achieving 75% accuracy. © Springer Nature Switzerland AG 2021.

From Phytochemicals to Phytomedicines: Potential Roles of Plant-Based Biomolecules in the Covid Era

The current pandemic of the Corona virus disease (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has urged for the invention and implementation of effective drugs and vaccines to mitigate the adversity worldwide. Numerable studies are going on to identify and evaluate the efficacy of several synthetic drugs and vaccines. In this scenario, identification and use of plant-derived biomolecules against SARS-CoV-2 could be highly beneficial. Furthermore, upscale production of such plant metabolites and plant-based vaccines can help in controlling the pandemic. Several previous studies have reported the success of plant-based traditional medicines in immunity enhancement and decreasing viral loads. Thus, in depth researches involving the phytochemicals could reveal their roles and level of efficacy against SARS-CoV-2. Considering the present scenario, this review article presents the perspectives of using the phytochemicals in mitigating SARS-CoV-2, and the possible evolution of these phytochemicals into phytomedicines.