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This paper presents the experiences and perspectives of two Yorkville University faculty members teaching quantitative and non-quantitative courses to BBA students remotely and online during the COVID-19 pandemic. The authors discuss new issues faced while teaching online during the crisis. Most universities have shifted their existing courses to the online remote mode of delivery without making any changes to the course design. This study examines teaching differences for quantitative and non-quantitative courses online with a view to make recommendations based on our teaching experiences for transitioning such courses to remote synchronous delivery online. This paper also explores new methods that have been applied during online teaching while conducting different assessments (e.g., quizzes and exams). The authors share their challenges and issues based on two specific courses - Statistics for Business and Introduction to Marketing, which are typical examples of quantitative and non-quantitative courses. The paper suggests teaching approaches and how to conduct assessments online for these types of courses. These recommendations invite further discussion and research into online teaching. © Springer Nature Switzerland AG 2021. All rights reserved.
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The Indian government had been making efforts to foster an innovative business culture by incorporating design thinking and innovation in b-school curricula. Substantial investments had also helped aspiring entrepreneurs pursue their ambitions. One such beneficiary of these initiatives was Preheal Innovations Private Limited, which aimed to build an online platform catering to the healthcare, beauty, and wellness needs of customers throughout India. Mr. Vikrant, founder and CEO of Preheal, had built a robust network of contacts in the healthcare sector after 20 years of experience and this, combined with the company's unique business model catering to customers in both urban (tier-2 and tier-3 cities) and rural areas in India, played a key role in attracting the initial start-up team. However, efforts were interrupted due to COVID-19, which lead to the departure of team members. Post lockdown, Mr. Vikrant had to decide how to relaunch the new venture with either full or partial staffing, in a changed business environment. © 2022 NeilsonJournals Publishing.
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Introduction Children with cancer are immunocompromised due to the disease per se or anticancer therapy. Children are believed to be at a lower risk of severe coronavirus disease 2019 (COVID-19) disease.Objective This study analyzed the outcome of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in children with cancer.Materials and Methods A retrospective analysis was performed on patients (<= 14 years) with cancer attending the pediatric oncology services of our institute who tested positive for the SARS-CoV-2 infection and those who had COVID-19 disease between August 2020 and May 2021. Real-time reverse transcriptase-polymerase chain reaction performed on the nasopharyngeal swab identified the SARS-CoV-2 infection. The primary endpoints were clinical recovery, interruption of cancer treatment, and associated morbidity and mortality.Results Sixty-six (5.7%) of 1,146 tests were positive for the SARS-CoV-2 infection. Fifty-two (79%) and 14 (21%) patients had hematolymphoid and solid malignancies. Thirty-two (48.5%) patients were asymptomatic. A mild-moderate, severe, or critical disease was observed in 75% (18/24), 12.5% (3/24), and 12.5% (3/24) of the symptomatic patients. The "all-cause" mortality was 7.6% (5/66), with only one (1.5%) death attributable to COVID-19. Two (3%) patients required ventilation. Two (3%) patients had a delay in cancer diagnosis secondary to COVID-19 infection. Thirty-eight (57.6%) had a disruption in anticancer treatment.Conclusion Children with cancer do not appear to be at an increased risk of severe illness due to SARS-CoV-2 infection. Our findings substantiate continuing the delivery of nonintensive anticancer treatment unless sick. However, SARS-CoV-2 infection interrupted anticancer therapy in a considerable proportion of children.
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According to Chinese health officials, almost 250 million people in China may have caught Covid-19 in the first 20 days of December. Due to the Covid-19 pandemic and its global spread, there is a significant impact on our health system and economy, causing many deaths and slowing down worldwide economic progress. The recent pandemic continues to challenge the health systems worldwide, including a life that realizes a massive increase in various medical resource demands and leads to a critical shortage of medical equipment. Therefore, physical and virtual analysis of day-to-day death, recovery cases, and new cases by accurately providing the training data are needed to predict threats before they are outspread. Machine learning algorithms in a real-life situation help the existing cases and predict the future instances of Covid-19. Providing accurate training data to the learning algorithm and mapping between the input and output class labels minimizes the prediction error. Polynomials are usually used in statistical analysis. Furthermore, using this statistical information, the prediction of upcoming cases is more straightforward using those same algorithms. These prediction models combine many features to predict the risk of infection being developed. With the help of prediction models, many areas can be strengthened beforehand to cut down risks and maintain the health of the citizens. Many predictions before the second wave of Covid-19 were realized to be accurate, and if we had worked on it, we would have decreased the fatality rate in India. In particular, nine standard forecasting models, such as linear regression (LR), polynomial regression (PR), support vector machine (SVM), Holt's linear, Holt-Winters, autoregressive (AR), moving average (MA), seasonal autoregressive integrated moving average (SARIMA), and autoregressive combined moving average (ARIMA), are used to forecast the alarming factors of Covid-19. The models make three predictions: the number of new cases, deaths, and recoveries over the next 10 days. To identify the principal features of the dataset, we first grouped different types of cases as per the date and plotted the distribution of active and closed cases. We calculated various valuable stats like mortality and recovery rates, growth factor, and doubling rate. Our results show that the ARIMA model gives the best possible outcomes on the dataset we used with the most minor root mean squared error of 23.24, followed by the SARIMA model, which offers somewhat close results to the AR model. It provides a root mean square error (RMSE) of 25.37. Holt's linear model does not have any considerable difference with a root mean square error of 27.36. Holt's linear model has a value very close to the moving average (MA) model, which results in the root mean square of 27.43. This research, like others, is also not free from any shortcomings. We used the 2019 datasets, which missed some features due to which models like Facebook Prophet did not predict results up to the mark;so we excluded those results in our outcomes. Also, the python package for the Prophet is a little non-functional to work on massive Covid-19 datasets appropriately. The period is better, where there is a need for more robust features in the datasets to support our framework. © 2023 River Publishers.
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The global COVID-19 emergency disrupted educational systems and created internship crises for universities and students. In response, one west coast social work program's practicum education department developed virtual, interactive labs that addressed the Grand Challenges (GCs), increased interdisciplinary collaboration, and ensured the continuation of student practicum. The Experiential Learning Labs (ELLs) created an organic collective of academics, students, and practitioners who invested in student learning while advancing their pedagogy. Additionally, the ELLs aligned with the GCs by engaging with students virtually (Eradicate Social Isolation) and adapting practicum education standards to meet expectations (Create Social Responses to a Changing Environment). Finally, this conceptual article describes how skills were built and strategies identified for faculty members and students to increase interdisciplinary collaboration.
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This study aimed to analyze the effect of eight weeks of neurofeedback training to increase the stress tolerance level of swimmers the current researchers examined six all-India inter-university male swimmers. For the pre-test and post-test data on stress tolerance of swimmers, the current author used the computer-based, pre-designed "Determination Test" on the Schuhfried "Vienna Test System", and the alpha and beta training was provided by the pre-designed Med-Life biofeedback/neurofeedback system. Swimmers go-through under rigorous training cycle andat the time of the race, and to reduce that stress, the current author conducted this study on swimmers and found the difference in the mean of the pre-test, i.e., 27.67, while the post-test mean was 59.5;hence, the value of the t-statistic(5.338) is also significant to its corresponding p-value, i.e., 0.003, which is less than 0.05, which shows the significant difference between the pre-test and post-test, thus leading the current authors conclude that there was a significant improvement in the swimmers' perception of their stress tolerance level and, it shows that the 21 tutelage sessions were effective in increasing the swimmers' stress tolerance level, which can help the swimmers maintain an optimal state at the time of the race.Copyright © 2023, Anka Publishers. All rights reserved.
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Background: Vaccines are considered as the one of the main pillars in halting and ending the presently on-going coronavirus disease (COVID-19 disease) pandemic which has spread globally since it was first detected in Wuhan, China in December 2019. In the absence of specific therapy, infection prevention practices and mass vaccination remains the mainstay in controlling the disease. Objectives: Objective of the study was to assess COVID-19 vaccination status, socio-demographic and clinical profile among healthcare workers diagnosed with COVID-19. Methodology: A cross-sectional survey from 1st March 2021 to 30th June 2021 among healthcare workers who were diagnosed with COVID-19 in a tertiary care institute of Uttarakhand, India was conducted, and universal sampling was used. Institutional Ethics Committee approved this study. Results: Total 662 healthcare workers were diagnosed with COVID-19. 429 (64.8%) of these COVID-19 diagnosed healthcare workers had received either single (129,30%) or both dose (300,70%) of COVID-19 vaccine while remaining 233 (35.2%) belonged to non-vaccinated group. History of exposure to COVID-19 positive patients was higher in vaccinated (66.4%) than in non-vaccinated group (55%) (p = 0.004). Hospitalisation was found to be higher among non-vaccinated (5.6%) than vaccinated group (2.3%) (p = 0.029). Conclusions: This study concludes that being vaccinated against COVID-19 disease provides protection against severe infection and reduces the need for hospitalization.
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Background: COVID-19 is a deadly viral infection that kills many people throughout the globe. The goal of this study was to find out how people in Pakistan felt about the COVID-19 vaccine.Method: Convenience and respondent-driven sampling method was used to conduct an online survey with 15 closed-and open-ended questions to a sample of 330 participants. The proportion of people who had a positive attitude towards vaccination vs. those who had a negative attitude towards vaccination was revealed by the closed-ended questions. The open-ended questions elicited qualitative data on why peo-ple accepted or rejected the vaccination.Results: 62.9% of the total number of respondents, male 1.97 times more likely (OR: 1.97, CI: 1.08-3.58) than female, 80% younger than 50 years, higher age groups, 71.3% married, 69.3% of the working population intended to get vaccinated with COVID-19 vaccine. People who held pro-vaccine health beliefs, had knowledge of, access to the COVID-19 vaccine, were employed, or under government pressure to get vaccinated, or visited public vaccination location, reported a positive attitude towards vaccination. People with safety concerns, social pressure of not getting vaccinated, low levels of awareness, trust and belonging to communities with anti-vaccination beliefs were likely to have negative attitudes towards COVID-19 vaccine.Conclusion: This study helps to identify the attitudes of people and has implications for COVID-19 immunization efforts in Pakistan for various population segments.
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Recently, the widespread and extremely fatal disease known as the coronavirus spread throughout the entire world. China's Wuhan city served as its first hub for its spread. The COVID-19 outbreak has briefly disrupted our daily routines by affecting worldwide trade and travel. Precautions include hand washing, using hand sanitizer, keeping a safe distance, and most importantly wearing a mask. However, putting on a mask that prevents to some extent airborne droplet transmission will be helpful as a precautionary measure in this pandemic. In the near future, many public service providers will ask the customers to wear masks correctly to avail of their services. However, ensuring that everyone wears a face mask is a difficult chore. Many techniques such as Machine Learning, Deep learning models like CNN, RNN, MobileNet etc. are available to solve this problem. This paper presents a simplified approach using MobileNet-V2 for Face Mask Detection. The model is developed by utilizing TensorFlow, Keras, OpenCV, and Scikit-Learn. The face mask detection model's objective is to identify people's faces and determine whether they are wearing masks at the time they are recorded in the image. An alert will sound if there is a desecration on the scene or in public areas. The challenge with the model is to detect the face mask during motion of a person. Precision, recall, F1-score, support, and accuracy are used to evaluate the system's performance and show its practical pertinency. The system operates with a 99.9% F1 score. The currently developed model will be used in conjunction with embedded camera infrastructure which may then be used to a variety of verticals, including schools, universities, public spaces, airport terminals/gates, etc. © 2023 IEEE.
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This article seeks to systematically identify and model antecedents of entrepreneurial bootstrapping and bricolage to determine and interpret the relationships and hierarchy between them. Entrepreneurial bootstrapping and bricolage are key dynamic capabilities that help entrepreneurs access, accumulate and enhance resources to adapt to scarce business environments. The article employs a modified total interpretive structural modelling analysis to determine hierarchical inter-relationships between the antecedents and a Matrice d' Impacts Croises Multiplication Applique An Classment analysis to understand their driving and dependence powers. The results highlight that founder characteristics and human capital are placed at the lower levels, making them critical driving elements of the model along with environmental hostility and resource constraints. Entrepreneurial orientation, slack, external financial capital and entrepreneurial frugality are dependent variables, with social capital as a linkage variable. This study will guide entrepreneurs trying to implement resourcefulness behaviours to respond to the coronavirus disease-2019 crisis by prioritising driving antecedents to impact the dependent factors further. © 2023 Entrepreneurship Development Institute of India.
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Since inception of Corona Virus, 47.6 Cr. individuals got infected and 61L deaths occurred. Still it's going on and spreading across the world. Many health workers, researchers, experts, scientists are making efforts to slow down its pace & putting efforts in evaluating the techniques to detect it. For this, it is highly required to understand the virus & its versions. It is a part of SARS - Severe acute respiratory syndrome. To detect COVID, there are numerous ways but using Chest X-beams we are able to reduce the detection time and cost. To evaluate the Chest X-beams we need radiologists. So here, we develop a model to identify COVID X-beam in comparison to Normal X-beam. These days DL algo's are producing best results in classification. A pre-trained CNN models using large datasets is to preferred for image classification. Firstly our models need to be trained and then tested to recognize the images of X-beams of one of the either case. Logically we have to locate the best CNN model for diagnosis. © 2022 IEEE.
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COVID-19 due to SARS-CoV-2 is a global pandemic that presents a serious challenge from many angles for healthcare professionals. The virus causes a potentially fatal disease that is easily transmitted among patients and caregivers, hence specific dead body care is required for such patients. Our study was conducted to identify knowledge, attitude, and practice regarding COVID-19 dead body care among hospital nursing personnel. A cross sectional survey-based study was performed involving 282 nurses who worked in COVID-19 units during data collection from July 2020 to September 2020. The online structured questionnaire was based on world health organization guidelines, institutional infection control protocols, and course material regarding emerging respiratory diseases including COVID-19. We found that work experience in the COVID-19 unit had a significant impact on knowledge and practice regarding COVID-19 dead body care. Similarly, we observed that training improved the knowledge and practice of nursing personnel regarding dead body care. Good knowledge, attitude, and practice were observed in experienced and trained nurses (p-value <0.005). No significant changes were observed with age, gender, and education qualification. Overall knowledge, attitude, and practice regarding COVID-19 dead body care were moderate to good. Adequate training among nurses should prevent the transmission of disease due to occupational exposure.Copyright © 2023, Science Publications. All rights reserved.
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COVID-19 pandemic has emerged as prime health challenge of 21st century forcing policy makers, health experts and governance institutions world over to revisit and re-invigorate public health policies through inter-institutional collaborations. Subsequent global lockdowns caused unprecedented shock to world economies, downslide of socio-economic development, concern for public safely, emphasis on augmentation of health infrastructure, capacity building of health care providers and development of effective Corona Virus containment strategies. Health institutions world over are grappling to control spread of the infection through Symptomatic Target Testing, Cluster Testing and Phased Vaccination. Multiple vaccines have been developed with varied efficacy, cost concerns and involvement of logistic issues;leading to vaccine-multilateralism and re-emphasis on universalization of public health policies under Sustainable Development Goals (SDGs) mechanism. This paper aims to assess impact of this grievous pandemic on public health sector of Jammu & Kashmir, explore challenges faced by public health institutions, analyze effectiveness of government interventions and suggest measures for revival of public health care services in the region.Copyright © 2021 JK Practitioner. All rights reserved.
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Background: Clinical outcome and parenchymal lung abnormalities (PLA) data from hospitalized patients with COVID 19 pneumonia are limited. Objective(s): (1) Understand and compare the patterns of PLA on high resolution computed tomographic (HRCT) at admission, 4-8 weeks post-admission in all patients and 10-12 weeks post-admission in a subgroup of patients (2) follow up their general health status on phone 6 months post admissionMethods: Prospective, observational study of consecutive adult patients hospitalized with RT-PCR confirmed COVID-19 pneumonia in a tertiary centre, India. Clinical data and HRCT image patterns and distribution of PLA at admission, 4-8 weeks in all patients and at 10-12 weeks in a subgroup of patients were analysed using a novel, composite radiological score (CRS). Surviving patients were followed up telephonically 6 months later. Finding(s): Of 179 patients, HRCT features were ground glass opacity (144, 80.4%), consolidation (23, 12.8%) and reticulation (7, 4%) at admission. 74% demonstrated resolving PLA with 14% showing complete resolution at 4-8 weeks. Fine reticulations were seen in 12% at 8 weeks and 20% in a subgroup of 44 patients who had persisting symptoms at 10-12 weeks. CRS correlated well with clinical severity and recovery (p=0.003). At 6 months, 144 responded to the phone follow up, reported no functional impairment and had returned to their pre-COVID health status. Conclusion(s): PLA resolved in 88% at 8 weeks and all the 144 patients who were followed up at 6 months reported return to pre-COVID 19 health status. This is quite reassuring amidst concerns of 'long COVID'.
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Background: Since Coronavirus (COVID-19) is increasing its influence from China and spreading its reservoir to neighboring areas and other nations, expanded national and foreign efforts are being made to control this epidemic. Method(s): This review incorporated the information depicting the effect of COVID-19 on different industrial sectors. Result(s): According to the World Health Organization, the outbreak was first identified in the Chinese city of Wuhan in December 2019 and has affected more than 17660523 people (confirmed cases) worldwide, and more than 680894 people have died. In addition to its alarming impact on human health, the novel strain of COVID-19 has dramatically slowed down not just the Chinese economy but also the world economy. The increased uncertainty has led to financial market volatility. Conclusion(s): Some firm decisions and policies must be framed out to stabilize the world economy so that threatening socio-economic impact cannot be sustained for a longer period of time for the welfare of humankind.Copyright © 2021 Bentham Science Publishers.
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Background Heart failure (HF) is the leading cause of readmissions among Medicare beneficiaries. The Hospital Readmissions Reduction Program (HRRP) passed under the Patient Protection and Affordable Care Act began assessing financial penalties in October 2012 for hospitals with higher-than-expected readmissions for acute myocardial infarction, heart failure and pneumonia among fee-for-service Medicare beneficiaries. Excess HF readmissions have been a dominant driver of HRRP penalties. Methods We obtained data on 30-day readmissions, observation stay rates and mortality rates from January 2006 to December 2021 from the CMS website. Mean, SD and temporal trends were analyzed for intervals before HRRP penalty implementation (January 2006 to September 2012) and after (October 2012 to December 2021). Results The 30-day HF readmission rate was 24.52% [0.48] before HRRP implementation and decreased to 22.35% [0.44] between October 2012 to December 2021, p<0.001. Observation stay rates increased from 1.14% [0.30] to 2.13% [0.23], p<0.001. Risk-adjusted mortality rates increased from 10.56% [0.44] to 11.25% [0.36], p<0.001. Temporal trend analysis showed mortality peaked after HRRP enactment but declined to pre-HRRP levels until an increase during the COVID-19 pandemic. Conclusion HRRP penalties led to reduced 30-day HF readmissions but had the unintended consequence of increased observation stays. Mortality peaked following HRRP penalty implementation and then decreased until 2020. [Formula presented]Copyright © 2023 American College of Cardiology Foundation
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All eukaryotic cells have a system in place called the ubiquitin-dependent proteolysis system to control protein degradation;nevertheless, any flaws in this system can initiate numerous fatal diseases, including cancer, metabolic problems, neurological disorders and diseases. These health complications interlink with faults in ubiquitin-dependent proteolysis. Ubiquitin assists as a post-translational targeting signal for altering the structure, localization of other proteins, features and functioning styles of the cells and tissues. The ubiquitin ligase standardizes the specific nature of the ubiquitination features and cellular response. The ubiquitin ligase is a critical element of the enzymatic cascade that regulates the part of the multipubiquitin chain to the target or labile protein. Consequently, the attachment of the ubiquitin topology is crucial for regulating healthy growth, differentiation, and protection of cells from damage by xenobiotics, infections, mutations, and environmental stresses. Protein degradation is adopted by the cells as a route to enduringly deactivate proteins. The 26S proteasome is responsible for ATP-dependent protein failure in the cytoplasm and nuclei of eukaryotes. Most proteins are covalently associated with a multi-ubiquitin chain and engage the 26S proteasome. In the testes, the ubiquitin ligases E1, E2, E3, and UBC4 are dynamic. Here, prompt and large protein alterations are essential for a cell to respond to its environment, and a complex web of interrelated events, including control over synthesis, localization, and degradation. The regulator of the cell cycle, receptor processing, growth management, and stress response are all subject to intracellular proteolysis. This chapter focuses on (I) the significant contribution of ubiquitination in the cellular signaling pathways that contract with these external influences;(II) the mechanisms of ubiquitination-deubiquitination that offer the system its high level of selectivity, (III) the role of ubiquitin-dependent degradation in initiating diseases in humans and forthcoming clinical claims developed to employ the cell's built-in proteolytic machinery to cure diseases;(IV) to examine imaginable clinical practices fashioned to exploit the body's own proteolytic machinery to cure the diseases, and analyze the effectiveness of vaccinations, antibodies, and other possible therapies that aim to block SARS-CoV-2 entrance pathways. Lastly, the authors include the most important unanswered queries pertaining to this crucial route. © 2023 Nova Science Publishers, Inc.
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The IoT (Internet of Things), a network of interconnected systems and data analytics, which can provides information about the spread of diseases/Virus globally. Typically, IoT is a bridge between machine learning philosophy, real time application such as security system, smart lights, smart speakers and many more. [1.2]. In current situation (pandemic), all over the world, is facing the problem where all are sucked down and looking for solution which can resolve the problem with cost -effective solution that has risen. Researchers are looking forward for the challenges and describing the studies which can overcome with the by IoT. The brief review aimed to significant applications over the COVID-19. © 2022 IEEE.
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In the majority of the affected nations, suicidal behavior against COVID-19 leads to various concerns. This study aimed to analyze determinants affecting suicidal behaviour among university students in Uttarakhand. An online cross-sectional survey of 18-year-old university students in Uttarakhand was conducted between April 2 and May 13, 2022. The questionnaire comprised socio-demographic information, the Suicidal Behaviors' Questionnaire-Revised (SBQ-R) scale, and elements related to the physical and psychological health of COVID-19 (CRPPF). The statistical study included demographic information, basic statistics in terms of frequency and percentage, and logistic regression. In comparison to students with fewer than seven family members, students with more than seven family members were less likely to participate in suicide behaviour (AOR = 2.21;95% CI: 1.79 to 2.67) and vice versa (AOR = 0.81;95% CI: 0.56 to 0.97). According to the study, a substantial majority of students (76.35%) claimed that the lockdown implemented to stop the spread of COVID-19 was extremely upsetting for them and that the pandemic had caused them to miss their graduation (73.90%). Adjusted multivariate logistic regression shows that feelings of a burden on family, (AOR= 1.98, 95% CI: 1.09 to 2.82), distancing from family or friends, (AOR =1.66;95% CI: 1.26 to 2.01), having relationship dilemmas, (AOR= 2.31;95% CI: 1.84 to 2.97), and being anxious during the lockdown, (AOR= 1.84;95% CI: 1.08 to 2.27), are significant factors among participants that are linked to higher risk of engaging in suicidal behaviour. The possibility of university students engaging in suicide behaviour was significantly affected by numerous factors. In addition to defending the students' mental health, the concerned authorities should devise and implement strategies to safeguard the students' physical health. © 2023, Editorial board of Journal of Experimental Biology and Agricultural Sciences. All rights reserved.
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The pandemic Covid-19 is a name coined by WHO on 31st December 2019. This devastating illness was carried on by a new coronavirus known as SARS-COV-2. Most of the research has focused on estimating the total number of cases and mortality rate of COVID-19. Due to this, people across the world were stressed out by observing the growing number of cases every day. As a means of maintaining equilibrium, this paper aims to identify the best way to predict the number of recovered cases of Coronavirus in India. Dataset was divided into two parts: training and testing. The training dataset utilised 70% of the dataset, and the testing dataset utilised 30%. In this paper, we applied 10 machine learning techniques i.e. Random Forest Classifier (RF), Naive Bayes (NB), Quadratic Discriminant Analysis (QDA), Gradient Boosting Classifier (GBM), Linear Discriminant Analysis (LDA), Logistic Regression (LR), K Neighbour Classifier (KNN), Decision Tree Classifier (DT), SVM - Linear and Ada-Boost Classifier in order to predict recovered patients in India. Our study suggests that Random Forest Classifier outperforms other machine learning models for predicting the recovered Coronavirus patients having an accuracy of 0.9632, AUC of 0.9836, Recall of 0.9640, Precision of 0.9680, F1 Score of 0.9617 and Kappa of 0.9558. © 2022 IEEE.