Demystifying and Analysing Metaverse Towards Education 4.0

The word Metaverse has influenced many sectors such as healthcare, education, retail and manufacturing and few more industries are there which will be impacted by 2026 as per the research conducted by Gartner. The word 'Metaverse' especially in education sector came into existence after the COVID-19 epidemic when the humanity were forced to think about the new methodology of educating and teaching. This ecosphere is the combination of technologies which enables multimodal interactions with artificial environment, electronic library and people such as Virtual Reality (VR) and Augmented Reality (AR). It is believed that metaverse will improve collaboration, training process will be enhanced and most importantly it will create a happier workplace. This is only the reason that many corporate giants like Nvidia, facebook, apple, epic Games and companies has shifted towards this pedagogical ecosystem. This technology has the potential which enables absolute incorporating user conversation in actual-time and compelling interactivity with digital artifact. In this paper, we are addressing metaverse in education along with a detailed framework of metaverse in education. It includes a comparative study of conventional education, online education and metaverse education based on parameters like place of learning, resources used, teaching methodology, learning experience, learning target and learning assessment. Competency based education, energize student and positive attitude towards learning. The various challenges of the metaverse in educational sector are also debated. This paper will help the researcher's fraternity to get a deeper insight along with a clear perception of this ecosystem in education. © 2023 IEEE.

Accidental finding of COVID-associated mucormycosis (CAM) in a patient presenting as toothache: A case report and review of literature.

Mucormycosis is an angio-invasive rapidly progressing fungal infection, usually reported in immunocompromised individuals. We present a case of COVID-associated mucormycosis in a patient with a presenting symptom of toothache in the maxilla with a possible mild case of COVID-19. Abstract: Coronavirus-associated mucormycosis (CAM) had reached epidemic status, especially during the second wave of COVID-19. It was especially prevalent in India with a large mortality rate. Mucormycosis, particularly the rhinocerebral type is seen to be greatly associated with COVID-19, especially in patients with altered immunity. Uncontrolled diabetes, chronic kidney disease, immunocompromised patients, malignant hematological diseases, etc. are the major risk factors of CAM, precipitated by the injudicious use of corticosteroids for the treatment of COVID-19. CAM may often present in the maxillofacial region which warrants that dental clinicians be aware of the clinical presentation, diagnostic guidelines, and appropriate management measures for the disease. This report is one such case of CAM involving the posterior maxilla in a middle-aged individual with mild COVID-19 symptoms.

The reshaping of the future by the Covid 19 pandemic: an assessment of the impact on population and resources

The WHO declared Covid 19 as a pandemic on the eleventh of March, 2020. This led to individuals, governments, institutions and businesses asking what impact this pandemic would have on the future. What imprint would this outbreak leave on human civilisation? Pandemics can alter the course of history. Pandemics impact people, governments, policies and economies. The pandemic has broken out at a time of significant demographic transition. 2020 was the first year in documented human history where the global population of people over the age of 60 is more than the population of children younger than 5 years of age. The richer countries have high concentrations of aging populations. Historically, pandemics have had significant impacts on cities and urban areas. Public health institutions, garbage collection, sanitation, scientific drainage and hospitals all developed to varying extents in urban responses to epidemics. The covid 19 pandemic has also brought about changes. In 2019, the United Nations reported that there had been a 33 percent increase in the population of migrants across the world. The international migrant population was put at 270 million. The previous forecast was for this population level to be attained in 2050. But the pandemic has slowed the growth of migration. The impact of the pandemic on energy markets was immediate and cataclysmic. Large parts of the global economy were forced to close down. The demand for petroleum fell by 25 percent in the United States. The demand for public transport fell by 70 percent in San Francisco, 60 percent in London and 80 percent in Italy and France between March and May 2020. Pandemics and changes in climate are inextricably linked. As humans encroach further into the wild, the United Nations expects more animal viruses to infect and affect humans. 75 percent of all emerging infectious diseases originate in animals. 60 percent of viruses infecting humans come from wildlife and livestock. Zoonotic epidemics are triggered by flooding, climate variability and other extreme weather events linked to climate change. Climate change has also expanded the span of geographies susceptible to zoonoses. Even though this pandemic has brought to the fore these dangers, steps to effectively tackle climate change and to implement practices in agriculture that are more sustainable have halted. The global food system is responsible for fulfilling the nutrition requirements of 80 percent of the world's population. This system has been greatly disturbed by the pandemic. 4 shocks account for this great disturbance: 1. The movement of agricultural goods has been disturbed by restrictions on transport. 2. Supply chains have been seriously damaged by borders being sealed and bans on exports. 3. Overall production has been reduced because of major disruptions in the supply of agricultural raw material, labor and services. 4. Food purchasing power has reduced dramatically because of job losses, especially among the socioeconomically disadvantaged sections of society.Copyright © 2020 Ubiquity Press. All rights reserved.

Monitoring of the Social Distance between Passengers in Real-time through Video Analytics and Deep Learning in Railway Stations for Developing the Highest Efficiency

Near the end of December 2019, the globe was hit with a major crisis, which is nothing but the coronavirus-based pandemic. The authorities at the train station should also keep in mind the need to limit the spread of the covid virus in the event of a global pandemic. When it comes to controlling the COVID-19 epidemic, public transportation facilities like train stations play a pivotal role because of the proximity of so many people who may be exposed to the virus. Using common place CCTV cameras and deep learning with simple online and real-time (DeepSORT) methods, this study develops social distance monitoring using a YOLOv4 identification of a Surveillance Object Model. Based on experiments conducted with a minicomputer equipped with an Intel 11th Gen Intel(R) Core(TM) i3-1115G4 at 3.00GHz, 2995 Mhz, two Core(s), four Logical processor, four gigabytes of random-access memory (RAM), this paper makes use of CCTV surveillance, which was put into practice at the Guindy railway station, Chennai, Tamilnadu in India in order to detect the violation of social distancing. © 2022 IEEE.

IoT-Based Safe College Management System

The lifestyle of the people has changed completely after the COVID-19 pandemic. The virus has also undergone various mutation and causing scare to human existence. Hence, it is important to monitor the health conditions of the faculty members and students before letting them into the college premises. So, we have introduced a system which implements 3 stage screening process. First, it checks whether the visitor is fully vaccinated or not. Then during the second phase, the health condition of the people is monitored. Finally, people without mask are also prevented from entering into college campus. In addition to it, the system also dispenses the hand sanitizing liquid to the visitors. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Estimating social distance in public places for COVID-19 protocol using region CNN

The coronavirus disease has spread throughout the world and its fear has made people to be more cautious in public places. Since precautionary measures are the only reliable protocol to defend ourselves, social distancing is the only best approach to defend against the pandemic situation. The reproduction number i.e. R0 factor of COVID-19, can be slowed down only through the physical distancing norms. This research proposes a deep learning approach for maintaining the social distance by tracking and detecting the people present indoor and outdoor scenarios. Surveillance video is taken as the input and applied into you only look once (YOLO) V3 algorithm. The persons in the video are identified based on the segmentation algorithm present within the framework and then using Euclidean distance the image is evaluated. The bounding box algorithm helps to segregate the humans based on the minimum distance threshold. The proposed method is evaluated for images with peoples in the market, availing essential commodities and students entry inside a campus. Our proposed region-based convolutional neural network (RCNN) algorithm gives a better accuracy over the traditional models and hence the service can be implemented in general for places where social distancing is mandatory. © 2023 Institute of Advanced Engineering and Science. All rights reserved.

Smart Contract-Based Secure Decentralized Smart Healthcare System

Social distancing has been imposed to prevent substantial transmission of the COVID-19 outbreak, which is presently a global public health issue. Medical healthcare providers rely on telemedicine to monitor their patients, particularly those with chronic conditions. However, telemedicine faces many implementation-related risks, including data breaches, access restrictions within the medical community, inaccurate diagnosis, fraud, etc. The authors propose a transparent, tamper-proof, distributed, decentralized smart healthcare system (DSHS) that uses blockchain-based smart contracts. The authors use an immutable modified Merkel tree structure to hold the transaction for viewing contracts on a public blockchain, updating patient health records (PHR), and exchanging PHR to all entities. It is verified by a performance evaluation based on the Ethereum platform. The simulation results show that the proposed system outperforms existing approaches by enhancing transparency, boosting efficiency, and reducing average latency in the system. The proposed system improves the functionality of the SHS environment.

Vocal Biomarker Based COVID-19 Detection Using DNN and Transfer Learning ResNet50

The aim of this study is to automate the detection of COVID-19 patients by analysing the acoustic information embedded in cough samples. COVID-19 is a respiratory disease having cough acoustics as a common symptom and indicator. The primary focus is classification of generated deep features from analytical and mathematical representation of cough acoustics using signal processing techniques Mel-frequency cepstral coefficients (MFCCs) and Mel-spectrogram. MFCCs provides feature vector representation of cough signal and is used as an input for deep neural network (DNN) to generate deep features. Transfer Learning ResNet-50 based Convolutional Neural Network (CNN) model is used to generate deep features from image representation of cough in the form of Mel Spectrogram. Dataset labelling is done with two categories of COVID-19 and Non-COVID-19 classes. Among them, we have used 70% of the dataset for training and 30% for testing purposes. The deep features generated from MFCCs and Mel Spectrograms are concatenated along with a feature value output from a DNN having Metadata as input. The final concatenated feature vector is sent for Softmax based classification. By completing the whole process, we obtained the training AUC (Area Under Curve) (ROC) 95.39%, validation AUC as 88.19% and testing AUC as 88.76%. The analysis of final AUC with epoch curve shows constant increase in training AUC and convergence of validation and testing AUC at certain value representing model training as perfectly fit and no overfitting-underfitting problem. © 2022 IEEE.

Vocal Biomarker Based COVID-19 Detection Using Convolutional and Deep Neural Networks

The aim of this study is to automate the detection of COVID-19 patients by analysing the acoustic information embedded in cough speech sounds. COVID-19 mainly affects the peoples respiratory system. Then accordingly respiratory-related speech sounds have the potential to hold important information to detect the cause. The principal aim of the Coswara dataset is to analyse the spectrogram representation of cough sound samples and investigate whether COVID-19 patients are improve the frequency content of produced sound signals. Coronavirus effects the whole respiratory system and consequently respiratory-related signals such as cough can possibly contain potential data for the distinctive presence of COVID-19 patients. The Coswara dataset is recorded with labeled categorized into cough sounds with final labeling into Covid-19 positive and Non-Covid classes. Among dataset, we have considered 70% dataset for training and 30% for testing purpose. The Mel Frequency Cepstral Coefficients (MFCCs) are used to extract characteristics of cough sound features from cough speech samples. These features are then used as learning input for machines to label using the self-designed Convolutional Neural Network (CNN) model. After completing the whole process, we got the training AUC (Area Under Curve) for (ROC) 98.84%, validation AUC as 88.23% and testing AUC as 87.09%. © 2022 IEEE.

Comparative Analysis of Security and Privacy Technique for Federated Learning in IOT Based Devices

The field of machine learning has been seen as a major development in the last few years. Many new algorithms and many new methods have been put forward by various researchers in this domain. Before the COVID-19 pandemic, things were done manually but after this situation, the culture of working from home has been started at almost every organization except few a necessary government organizations which include healthcare and other emergency services. Online work involves a lot of data transfer and hence it is demanded new development in machine learning and this learning emerged as one such development. Federated learning enables multiple devices to build a common machine learning model without sharing data which helps in providing better data privacy because training data are not transmitted to a central server. Federated learning is also known as collective learning where we train the algorithms across various devices with the help of decentralized data samples without the involvement of actual data. In this paper, the authors will provide various use cases, as well as a comparative study of various federated learning frameworks. This paper will provide in-depth knowledge as well as future research directions in the field of federated learning. © 2022 IEEE.

Smart Contract-Based Secure Decentralized Smart Healthcare System

Social distancing has been imposed to prevent substantial transmission of the COVID-19 outbreak, which is presently a global public health issue. Medical healthcare providers rely on telemedicine to monitor their patients, particularly those with chronic conditions. However, telemedicine faces many implementation-related risks, including data breaches, access restrictions within the medical community, inaccurate diagnosis, fraud, etc. The authors propose a transparent, tamper-proof, distributed, decentralized smart healthcare system (DSHS) that uses blockchain-based smart contracts. The authors use an immutable modified Merkel tree structure to hold the transaction for viewing contracts on a public blockchain, updating patient health records (PHR), and exchanging PHR to all entities. It is verified by a performance evaluation based on the Ethereum platform. The simulation results show that the proposed system outperforms existing approaches by enhancing transparency, boosting efficiency, and reducing average latency in the system. The proposed system improves the functionality of the SHS environment. © 2022 Taru Publications. All rights reserved.

Estimation of Social Distance for COVID19 Prevention using K-Nearest Neighbor Algorithm through deep learning

Coronavirus disease has a crisis with high spread throughout the world during the COVID19 pandemic period. This disease can be easily spread to a group of people and increase the spread. Since it is a worldly disease and not plenty of vaccines available, social distancing is the only best approach to defend against the pandemic situation. All the affected countries' governments declared locked-down to implement social distancing. This social separation and persons not being in a mass group can slow down the spread of COVID19. It reduces the physical contact between infected persons and normal healthy persons. Almost every health organization tells that to follow social distancing people should maintain at least 6 feet of distance from each other. This research proposes a deep learning approach for social distancing which is developed for tracking and detecting people who are in indoor as well as outdoor scenarios using YOLO V3 video analytic technique. This approach focuses to inspect whether the people are maintaining social distancing in many areas, using surveillance video with measuring the distance in real-time performance. Most of the early studies of detecting social distance monitoring were based on GPS for tracking the movements of people where the signals could be lost. On the other hand, some countries use drones to detect large gatherings of people who cannot have a clear view at night times [10]. In the future, the proposed system can be used fully for detecting threats in the public crowded or it can detect any person affected by critical situations (ie fainting, Cordia arrest) or planting the crops in the forms evenly with a uniform measurement. This proposal could be used in many fields like crowd analysis, autonomous vehicles, and human action recognition and could help the government authorities to redesign the public place layout and take precautionary action in the risk zones. This system analyses the social distancing of people by calculating the distance between people to slow downing the spread of the COVID 19 virus. © 2022 IEEE.

AI Enabled Educational Bot to Improve Learning Outcomes using Bag of Words Algorithm

Educational institutes across India have closed due to COVID-19 pandemic which has jeopardized academic schedules. To maintain their academic activities, several Indian educational institutes have shifted to online learning platforms. However, there are still questions about the effectiveness, design, and readiness of e-learning. In light of this fact, E-learning still tends to be controversial. As a result, it is inevitable to design an application with greater usability. In this paper, a novel application tool is developed and the design is proposed to enhance student knowledge and facilitate their study process, so they can study in comfort at home. The proposed system allows students to receive personalized educational assistance and also allows the students to get instant responses to all their questions throughout the day via a voice-enabled chatbot. It facilitates the connection between students and tutors, as well as the awarding of mind coins and badges based on how well they do, motivating them to learn more. Data analytics is incorporated and usability is measured. The result shows that the proposed system has greater usability resulting in a progressive improvement in the student's performance. © 2022 IEEE.

Renal Outcomes in Hospitalized Patients Receiving Hemodialysis After Infection With Acute COVID-19

Background: Kidney injury in acute COVID-19 infection has been associated with decreased survival and prolonged duration of hospitalization irrespective of patient population or severity of illness. Outcomes among hospitalized patients with COVID-19 and kidney injury in terms of recovery of renal function are insufficiently assessed. A good understanding of the same is vital to plan post-discharge renal care, and to estimate the potential burden that COVID-19 confers on the nephrology community. Method(s): In a cohort study, we included patients who received hemodialysis (HD) during hospital stay after infection with COVID-19 at our center following kidney injury during the second wave of the pandemic in New Delhi between June and December, 2021. Participants were excluded if they received dialysis following previously existing chronic renal failure. Participants were followed-up telephonically for a period of six months to assess renal function and need for HD. Recovery of renal function was considered early if serum creatinine improved by 33% of peak value during hospital stay, or late if a 33% reduction in follow-up evaluation was noted over the discharge serum creatinine. Result(s): A total of 62 patients (34 (54.8%) men and 28 (45.2%) women) with a mean age of 51.2 years (+16.3), and mean urea of 181 mg/dl (+95.7) and mean creatinine of 6.9 mg/dl (+3.5) at presentation were included in the cohort. Of these, 31 (50%) had presented with mild, 11 (17.7%) with moderate and 20 (32.3%) with severe disease. Ten (16.1%) succumbed to illness during hospital stay and another 12 (19.3%) patients died during the follow up period. 34 (54.8%) patients were discharged from hospital on HD, and 18 (29%) were not advised HD at discharge. While 30 (75%) of the survivors had indicated early renal recovery at discharge, none had recovered renal function at the end of follow up period. A median decline of 48% and 44% at follow up was noted from the peak and presentation values of creatinine recorded during hospital stay. Conclusion(s): Patients undergoing hemodialysis after hospitalization with acute COVID-19 infection had poor short-term outcome and survivors continued to have renal impairment after six months. It is important to recognize recovery rates and patterns to offer early comprehensive renal care.

Drug Utilization Study in Patients with COVID-19 Associated Mucormycosis in a Tertiary Care Hospital, South India

COVID-19 associated Mucormycosis (CAM) is a serious condition in India considering its unprecedented surge due to COVID-19 and associated high morbidity and mortality. Diabetes and widespread usage of steroids in a background of COVID-19 appear to increase the risk of CAM exponentially. Management includes control of the underlying disease or risk factor, strict glycemic control, surgical debridement of necrotic infected tissue, and specific antifungal therapy. First-line antifungal treatment consists of intravenous liposomal Amphotericin-B (LAB) or deoxycholate Amphotericin-B (AMB) and second-line antifungals are intravenous (IV) or oral Posaconazole (POS) which can also be used as salvage therapy. This study is conducted to observe the drug utilization pattern in CAM patients in tertiary care hospital in South India. A prospective, observational study of 55 patients admitted with CAM are studied in terms of drug usage. POS (98.2%) is the most used drug followed by intravenous AMB (49.1%). POS-Oral are used in 83.6 %, POS-IV in 49.1% and AMB-retrobulbar is used in 12.7% of the patents and average doses are within recommended limits. Anti-bacterial and other antifungal are used in 12.7% and 1.8% of the patients respectively, and 34.5% underwent debridement surgery. Though LAB is the recommended 1st line treatment, AMB and POS are used as a reasonably accepted alternative. Guidelines and recommendations should consider cost, compliance (availability ot oral formulations), safety profile, and accessibility aspects which suit the needs of Low-and middle-income countries (LMIC) especially during an unprecedented event like COVID-19 and CAM. Copyright © 2022, Dr. Yashwant Research Labs Pvt. Ltd.. All rights reserved.

IoT-Based Safe College Management System

The lifestyle of the people has changed completely after the COVID-19 pandemic. The virus has also undergone various mutation and causing scare to human existence. Hence, it is important to monitor the health conditions of the faculty members and students before letting them into the college premises. So, we have introduced a system which implements 3 stage screening process. First, it checks whether the visitor is fully vaccinated or not. Then during the second phase, the health condition of the people is monitored. Finally, people without mask are also prevented from entering into college campus. In addition to it, the system also dispenses the hand sanitizing liquid to the visitors. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Does smartphone affect work-life balance, stress and satisfaction among teachers during online education?

The purpose of this paper is to study the impact of smartphone usage on school teachers’ Work-Life Balance (WLB) in the online mode of education during the COVID-19 pandemic. The COVID-19 pandemic insisted teachers adopt online teaching using a smartphone, which facilitates many teachers to qualitatively manage their work and family responsibilities during the transformed work mode. The target population of the current research is private school teachers working in all levels of school education across major cities in India namely Chennai, Hyderabad, Bengaluru, Delhi and Mumbai. Validated structured questionnaires were administered and primary data was gathered from 478 respondents, who were chosen using a convenience sampling technique. The hypothetical relationships among smartphone use, WLB, personal life and job satisfaction are found to be positive, whereas work stress is found to have a significantly negative relationship with WLB, personal life and job satisfaction. Therefore, it is concluded that the pertinent use of smartphones enhances the WLB of school teachers by helping them to meet family and job demands effectively. Copyright © 2022 Inderscience Enterprises Ltd.

Receptor binding domain of SARS-CoV-2 from Wuhan strain to Omicron B.1.1.529 attributes increased affinity to variable structures of human ACE2.

BACKGROUND: COVID-19 is an infectious disease declared as a global pandemic caused by SARS-CoV-2 virus. Genomic changes in the receptor binding domain (RBD) region of SARS-CoV-2 led to an increased, infectivity in humans through interaction with the angiotensin-converting enzyme2 (ACE2) receptor. Simultaneously, the genetic variants in ACE2 provide an opportunity for SARS-CoV-2 infection and severity. We demonstrate the binding efficiencies of RBDs of SARS-CoV-2 strain with ACE2 variants of the human host. METHODOLOGY: A Total of 615 SARS-CoV-2 genomes were retrieved from repository. Eighteen variations were identified contributing to structural changes in RBD that are distributed in 615 isolates. An analyses of 285 single nucleotide variances at the coding region of the ACE2 receptor showed 34 to be pathogenic. Homology models of 34 ACE2 and 18 RBD structures were constructed with 34 and 18 structural variants, respectively. Protein docking of 612 (34 *18) ACE2-RBD complexes showed variable affinities compared to wildtype Wuhan's and other SARS-CoV-2 RBDs, including Omicron B.1.1.529. Finally, molecular dynamic simulation was performed to determine the stability of the complexes. RESULTS: Among 612, the top 3 complexes showing least binding energy were selected. The ACE2 with rs961360700 variant showed the least binding energy (-895.2 Kcal/mol) on binding with the RBD of Phe160Ser variant compared to Wuhan's RBD complex. Interestingly, the binding energy of RBD of Omicron B.1.1.529 with ACE2 (rs961360700) structure showed least binding energy of -1010 Kcal/mol. Additionally, molecular dynamics showed structure stability for all the analysed complexes with the RMSD (0.22-0.26 nm), RMSF (0.11-0.13 nm), and Rg (2.53-2.56 nm). CONCLUSION: In conclusion, our investigation highlights the clinical variants contributing to structural variants in ACE2 receptors that lead to efficient binding of SARS-CoV-2. Therefore, screening of these ACE2 polymorphisms will help detect COVID-19 risk population so as to provide additional care and for safe management.