The World Health Organization (WHO) declared coronavirus (COVID-19) as a global health emergency in January, which has impacted different sectors worldwide. The education sector in India and worldwide is no exception, as the current health crisis and lockdown regulations disrupted the educational learning system. Educational institutions have slowly acclimatized to digital learning platforms to minimize the impact of the pandemic on education. Even the post-graduate students in India have also moved to the virtual mode of learning. Accessible technologies have made it possible for the students to step gradually to flexible learning. Flexible learning is learner-centered, giving independence and encouraging flexibility in the time, mode, evaluations, academic and personal excellence, and study location. AIM: This research aims to analyze management students’ perception towards flexible learning methodology for self-development and active learning in times of the ongoing COVID-19 pandemic in India. METHODOLOGY: A self-administered online structured questionnaire was drafted and sent to 410 management students. The research is based on methods of quantitative analysis from the students pursuing an MBA. The survey data is mainly based on the factors that motivate and enhances management student’s participation in flexible learning. RESULT: A total of 244 management students responded to the questionnaire. The study highlights that five main factors are important for the management students to lead them to an effective way of learning regarding flexibility dimensions: resources, content, delivery, time, and pedagogy.

Aim: To determine the binding affinity (kcal/mol) for various antiviral peptides to target Influenza A virus PB2 protein using Hpepdock software and comparison with the Macrocyclic iHA100 peptide (Reference peptide). Materials and methods: The three-dimensional (3D) coordinates for PB2 protein were retrieved from Protein Data Bank (PDB ID 4P1U). The structures of 16 antiviral peptides were modeled using HPEPDOCK. The Molecular docking analysis of PB2 protein with derived antiviral peptides was performed using Hpepdock software. This software employs an algorithm that generates the output complexes based on the peptide conformations and orientations. Results: Molecular docking analysis revealed that antiviral peptides, P9R Peptide 2, P9R Peptide 3 and P9R Peptide 1, could bind PB2 protein with higher affinity in comparison with the reference Macrocyclic iHA-100 peptide. The antiviral peptides of P9R Peptide 2, P9R Peptide 3 and P9R Peptide 1 with p=0.786, p>0.05 insignificant (-202.728Kcal/mol), p=0.001, p<0.05 significant (-198.255 Kcal/mol), p=0.259, p>0.05 insignificant (-195,788 Kcal/mol) showed better results in comparison to reference Macrocyclic iHA-100 peptide (-154.392 Kcal/ mol). Conclusion: The identified antiviral peptides could more effectively inhibit PB2 protein than the other available peptides on the market to treat Influenza A viral disease.

It is known that is an ambiguous phenomenon in medicine. There is no generally accepted definition of the concept, and approaches to the interpretation of the essence of the category in question are also different, since polypragmasia, being vital for certain diseases, can become extremely dangerous in certain conditions of the human body, as well as negatively affect the health of patients of certain age groups. In particular, elderly and senile patients, often in a state of comorbidity or polymorbidity and taking a significant amount of medications (both prescribed by a doctor and without one), are at risk of a possible deterioration in their health. Accordingly, the identification of problematic aspects of polypragmasia in the development of comorbidity in elderly and senile patients, as well as the identification of ways to reduce the negative impact on the state of health of extended drug therapy is quite relevant in modern conditions.

Aim: Chronic kidney disease (CKD) is among the main 20 reasons for death worldwide and influences around 10% of the world grown-up populace. CKD is an issue that upsets typical kidney work. The main objective of this study aims to find the best-suited algorithm that will give us the most ideal prediction. We will be comparing Novel Decision Tree with Linear Regression to find out which of these can give us the best accuracy. Material and Methods: The study used 322 samples with Novel Decision Tree and Linear Regression is executed with varying training and testing splits for predicting the accuracy for kidney disease prediction with the G-power value of 80% and the kidney datasets were collected from various web sources with recent study findings and threshold 0.05%, confidence interval 95% mean and standard deviation. The performance of the classifiers are evaluated based on their accuracy rate using the chronic kidney disease dataset. Results: The accuracy of predicting kidney disease in Novel Decision Tree (96.66%) and Linear Regression (85.25%) is obtained. There is a statistical 2-tailed significant difference in accuracy for two algorithms is 0.000 (p<0.05) by performing independent samples t-tests. Conclusion: This study concludes that the Prediction of Kidney disease using the Novel Decision Tree (DT) algorithm appears to be significantly better than the Linear Regression(LR) with improved accuracy.

Aim: Prediction of coronary illness utilizing novel Novel k nearest neighbor (KNN) and contrasting its accuracy with decision tree algorithm. Materials and Methods: Two gatherings are proposed for foreseeing the accuracy (%) of coronary illness. To be specific, novel Novel k nearest neighbor and decision tree algorithm. Here we take 20 examples each for assessment and look at. The sample size was calculated using G power with pretest power at 80% and the alpha of 0.05 value. Result: The decision tree gives better accuracy (84.95%) contrasted with the Novel k nearest neighbor accuracy (76.29 %). Along these lines the factual meaning of the decision tree is superior to the novel k nearest neighbor algorithm with significance value of 0.115. Conclusion: From the outcome, it may very well be inferred that the decision tree helps in anticipating the coronary illness with more precision contrasted with the novel k nearest neighbor algorithm.

Movie Theaters in India face a big crisis amid COVID-19, which leads to the shutdown of Theaters for more than five months. In the meantime, Online Streaming Services are expanding their business and revenue like never before. This scenario raises the very prominent questions (1) whether the movie theater business will survive this hard time and get back to normal growth and business once COVID-19 cases will reduce. The situation gets back to ‘old normal’. (2) Will Online Streaming services and Movie Theater business co-exist in the future, as Satellite TV and Movie Theater exist for many decades. Based on data from an empirical survey conducted among regular moviegoers, this paper questions the claims of the industry that the Movie Theater business is in danger due to the emergence of online streaming services. It has been observed that the increase in traffic on these online streaming services is mainly because of the lockdown situation, and once theater reopens and the COVID-19 health crisis went, people will get back to Theaters in a large number. Data from the reopening of Theaters in other countries are also showing the same result.

Papillary thyroid cancer (PTC) is one of the generic types of thyroid cancer and most prevalent form of malignancy among all cancers of the thyroid. It is also one of the few cancers with a rapidly increasing incidence. PTC constitutes approximately 85% of all thyroid cancer cases. PTC is usually contained within the thyroid gland and generally biologically indolent. The present article provides an updated condensed overview of PTC, which focuses mainly on the molecular and biomarker investigations.

A little more than 50 million Indians are reported to have the ill effects of depression, and another 38 million, reported anxiety. Despite this, it is crippling to see that India’s emotional well-being is hardly examined. Besides, with a boom in the service industry, the working environment conditions assume a significant role in the Indian workforce’s mental health. Our research explored job satisfaction with mental health, demographic factors, and organizational stressors of those working in the IT/ITeS industry. This study tries to address the gap in the research done on this theme for the Indian workforce. An advanced cross-sectional study was directed to around 220 people presently working or involved in the IT/ITeS industry utilizing approved instruments like the Minnesota Satisfaction Questionnaire. An individual regression model was utilized to examine each satisfaction factor’s relationship with the individual’s mental health, demographic factors, and organizational stressors. Study discoveries demonstrate that a considerable association is present between job satisfaction and mental health; Age, sex, educational background, marital status did not have a strong association with job satisfaction. In contrast, individual yearly earnings and yearly household earnings had a positive association. This research even discovered that emotional stress is a strong attribute to all the facets of an individual’s job satisfaction. This study may cause to notice a superior comprehension of this relationship and assist organizations with structuring and executing employee assistance programs and different tools to enhance job satisfaction and reduce stress.

The department of transfusion medicine, also known as blood bank, is one of the most important parts of a healthcare facility. It provides whole blood as well as different blood components like packed red blood cells (RBCs), thrombocytes, plasma, and coagulation factors to all necessary patient populations. Almost all major surgeries require it and only 2.5 billion of the 5 billion blood units required by our country each year are available. Because human blood cannot be replaced; the gift of life is given through the optimum use of blood and its components. According to the study, the demand for screened blood is very high, and the world requires blood every two seconds. When a patient is admitted to a hospital’s emergency ward, a blood test is almost always performed first and is usually followed by a blood request. Gunshot wounds and unintentional injuries necessitate immediate medical attention, including blood transfusions to compensate for low blood pressure and significant blood loss. A hospital would be considered incomplete without a state-of-the-art blood bank, which significantly facilitates cardiac procedures. Cross-matching and organizing at least five units of packed RBCs is a scheduled step in cardiac surgery. Simultaneously, one donor is cross-matched and tested for all parameters, including infectious markers, and its blood is drawn first when needed during cardiac surgery. After that, the arranged blood units are supplied for transfusion. This happens because freshly drawn blood contains all of the essential components for coagulation. Burns, hemorrhagic disorders, anemia, leukemia, thalassemia, and hemolytic disorders, as well as accidents, all necessitate the use of blood. Patients are transfused with blood on an exchange basis. During an emergency, blood units are released from the blood bank, and replacement blood is drawn from the patient’s relatives. Because of this system of blood unit replacement, many patients’ relatives are hesitant to donate blood or do not have donors for replacement. This gave rise to one of blood banks’ most unethical practices: bringing in paid donors or professional donors to donate blood for patients in exchange for money. This became a major issue for the entire medical community, resulting in the spreading of diseases such as HIV, HBsAg, HCV, SYPHILIS, and blood with low hemoglobin levels being transfused to patients with no clinical benefits.

Aim : Prediction of heart disease using Novel Random Forest and comparing its accuracy with Naive Bayes algorithm. Materials and methods: Two groups are proposed for predicting the accuracy (%) of heart disease. Namely, the Novel Random Forest and Naive Bayes algorithm. Here we take 20 samples each for evaluation and compared. The sample size was calculated using G power with pretest power at 80% and the alpha of 0.05 value. Result : The Novel Random Forest gives better accuracy (86.40%) compared to the Naive Bayes accuracy (80.08%). Therefore the statistical significance of Novel Random Forest is better than Naive Bayes algorithm. Conclusion: From the result, it can be concluded that Novel Random Forest helps in predicting heart disease with more accuracy compared to Naive Bayes algorithm.