Deadly Coronavirus Research Articles

EFL Students’ Perception Towards Online Learning: What to Consider?

The outbreak of the deadly coronavirus at the end of 2019 (Covid-19) has pushed many education institutions to do a radical transformation by moving all forms of their services, particularly teaching from traditional face-to-face mode to completely online foreign language teaching. This research aims to investigate Indonesian students’ perception of online learning and the three most challenging problems they encountered when enrolling in it under the context of English as Foreign Language students. Based on a review of the literature on online learning and students’ perception, a questionnaire was constructed and distributed to 57 students that had enrolled in an online course via google form along with a question asking the three most challenging problems they encountered during the course. Analysis of the responses demonstrated that most of the students had a positive perception of online learning. The three most challenging problems the students encountered were signal (internet connection), money (financial ability), and comprehension (comprehending the course) respectively. The result indicates that online learning can be a reliable solution for the teaching and learning process during a pandemic. However, in applying it, the campus must consider an alternative solution for the students who live in the bad internet connection area. Supporting them with subsidies, or free internet packages can help them to keep studying during a hard time. Lastly, teachers must work hard to ensure that their teachings are understandable so that their students will not get lost during the learning process.

Online Learning as an Alternative Learning Modality in Ecuador’s Education Institutions Amidst Crises and Outbreaks: A SWOT Analysis

The primary objective of this paper is to analyse the weaknesses, threats, strengths, and opportunities (SWOT matrix analysis) of online teaching and learning in educational institutions (primary, secondary, and higher education) in Ecuador during the COVID-19 pandemic. A qualitative study of the descriptive documentary type was conducted, where the information obtained from virtual education in times of crisis and outbreaks was collected and analysed. It was found that educational centers at different levels of schooling still continued to use traditional teaching methods and processes subject to old procedures as a basis for their incursion into virtuality, although other establishments began to handle combined modalities such as blended and hybrid online (e-learning) modes of learning. In conclusion, the deadly coronavirus disease (SARS-COV-2) abruptly halted much human activity worldwide. This forced hesitant educational systems that still used traditional face-to-face teaching methods in classrooms to move and change to online learning in many regions, regardless of the socioeconomic level and demographics of the students. Aside from determining how to deal with the problems and challenges of virtual education during epidemics and natural disasters, it is clear that the number of technology-driven educational start-ups has increased significantly.

COVID-19 Emergency Remote Teaching: The Perceptions, Challenges And Coping Strategies Of Higher Education Students In Ghana.

Emergency Remote Teaching (ERT) is a mode of teaching usually triggered by disasters and pandemics such as the deadly Coronavirus. This study used a self-reported questionnaire to investigate student perceptions, challenges, and coping strategies during ERT at a public university in Ghana. Using a randomly selected sample of 80 students, the study established the relationships between student perceptions and coping strategies, perceptions, and challenges. Out of the 80 respondents, 76 questionnaires were duly completed and were analysed using partial least square structural equation modelling (PLS-SEM). The bootstrapping results indicate that student perceptions of ERT have a significantly positive relationship with their coping strategies, and the challenges have a significant negative relationship with coping strategies. However, student perceptions did not significantly correlate with students’ challenges. Results recommend that the university put appropriate measures in place to reduce the challenges associated with ERT, which will improve students’ perceptions since the pandemic is still raging with a series of waves. Challenges such as lack of access to digital devices and high-speed internet should also be addressed urgently.

Islam and human dignity: the plights of Almajiri street children during the COVID-19 pandemic in Nigeria

This study examines the plight of Almajiri children during the COVID-19 pandemic in Nigeria within the context of religion, child rights, and human rights. Under the cover of being placed under an Islamic scholar for purposes of learning the Koran, the future of the Almajirai children has been mortgaged by the absence of proper care and denial of their rights. As a check to community spreading of the deadly coronavirus, the Almajiri children, notorious for street begging and largely found in Northern Nigeria were billed to be evacuated to their respective home states. Rather than moving these children to their home states in northern Nigeria, the majority of them were taken to the southern part of the country thus leading to intergroup disharmony and suspicion. Worried by the above, the study thus interrogates the nexus between the forceful removal and infringement of the rights of children, the challenge of the COVID-19 pandemic, questionable parenting, and failed governance. The rancor generated from evacuating these children to other parts of Nigeria raises the question of what has gone wrong with parenting and leadership. Sources for writing this paper have been derived from newspapers, journals, and online sources using the descriptive method of analysis.

A Comprehensive Review on Potential and Prospects of Traditional Medicinal Plants for Management of Coronavirus Disease (COVID-19)

Coronavirus disease (COVID-19) has emerged as the most devastating global health crisis since 1918, the era of the influenza pandemic. This outbreak was triggered by the coronavirus SARSCoV-2 that measures less than a millionth of an inch but has sent chills all across the globe. The obnoxious disease first became an epidemic in late December 2019 in Wuhan, China, that aggressively spread to the entire world, causing WHO to announce it as a global pandemic. Several features of the disease were uniformly found worldwide, including the consequential symptoms - difficult breathing or shortness of breath, acute hypoxemic respiratory failure, extreme level of fatigue, chest pain, and /or multi-organ failure leading to death. As of August 2022, the present prevalence of the disease is with a daily positivity rate of 2.71 per cent. An estimated 6 million deaths have been recorded worldwide. With no promising vaccines since its outbreak, and no control over this holocaust even after robust global mass vaccination efforts (vaccine boosters), people turned to indigenous methods, especially medicinal plants, as a lookout for immunization against the disease. According to the latest developments, herbal plants like Ocimum and Azadirachta have acted as promising plants. This review article highlights how few traditional medicinal plants can help create (on a mass scale) vaccines against the deadly coronavirus. We have identified ten significant plants based on the Indian traditional knowledge system and summarized their role in combating the disease. The study also focuses on the modifications of the active constituent from these plants and the underlying processes which can be coaxed together to find a better, reliable cure for the pandemic.

Nanomaterials as Emerging Therapeutics for the Deadly Coronavirus

Since the first reports of a contagious pneumonia such as the disease in China in December 2019, the world has witnessed a situation that very few people have ever seen in their lifetimes [...]

COVID – 19 Social Distancing Kit

Over the past year and a half, the globe has been facing the deadly Coronavirus disease (COVID-19), which is caused by the SARS-CoV-2 virus. COVID-19 can affect anyone at any age and cause serious illness or death. World Health Organization (WHO) recommends staying a minimum of one meter away from others to reduce transmission to stop and hinder transmission, which is known as social distancing. The government’s directives or its guidelines are not followed by the people, which is unfortunate. Proximity sensors and ultrasonic sensors are used to measure distances between users and other people, so that an audio alert is generated if the distance is less than a predetermined limit. The Mobile Application displays the user’s body temperature measured via a temperature sensor through Bluetooth.

World Coronavirus Death Regions & Why

Data analysis of the world’s coronavirus deaths and their locations was conducted from April 2020 through November 2021. The Worldometer Coronavirus was used exclusively for this analysis. It was decided to focus on deaths rather than cases as the deaths per case varied widely across the world from as low as 0.5% to as high as 10+%. Although it was expected that there would be variations in accounting from country to country, expectation was also that deaths were definitive. Although the actual death numbers were meaningful, what was far more important was the when, where, and why the deaths occurred in specific locations. The concept of locations initially was considered countries, but was altered to mean regions of the world as this analysis proceeded. What became apparent as this analysis continued was that the deadly coronavirus regions had common attributes of importance. The most important identified include (a) geography and topography, (b) population density and urban population, (c) seasonal meteorology meaning world tropical jet streams and temperature inversions created, (d) major sources of pollution emissions.

PERCEPTION OF PARENTS ON ONLINE LEARNING DURING COVID 19 – A SURVEY STUDY

2020 is a hard time for the global community around the globe. This COVID-19 Pandemic has shut down the entire world and has resulted in a tremendous crisis. It is the virtual world that has come to the rescue. It has not only enormous consequences but having a devastating impact on global education. Everyone shifting to the digital arena in the education sector. Online learning has dramatically changed the learning process, replacing traditional face-to-face interaction and gaining ground around the globe. Online learning at home requires children to rely on intrinsic motivation and self-directed learning. Parents play a fundamental role in supporting children’s learning. The study explores the perception of parents of online learning during deadly Corona Virus Disease-19. For this purpose, a survey has been organized using a five-point Likert scale to collect data from parents through WhatsApp, one of the social media application software. This study has been conducted by taking a sample of 52 parents of children who are studying from pre-nursery to fifth class. The data has been analyzed with the help of Google forms qualitatively. As a response of parents due to the COVID-19 crisis, this study indicated that the majority of parents are capable to support their child’s learning at home but some parents considered that homeschooling is a burden for their child. Nearly one-third of them reported that their child becomes techno-savvy through online learning but the concentration level of the child has reduced. Half of them (50%) considered that they help their child in his/her homework to reduce academic stress. Also, the findings lay bare that an equal number of respondents reveal that online learning make their children independent but they also have views that the purpose of online learning is just to collect fees from parents. Nearly less than half of the parents receive all the information from the child’s teacher to support online learning.

COVID-19 Pandemic Data Modeling in Pakistan Using Time-Series SIR.

Pakistan is currently facing the fourth wave of the deadly coronavirus, which was first reported in Wuhan, China, in December 2019. This work utilizes the epidemiological models to analyze Pakistan's COVID-19 data. The basic susceptible, infected, and recovered (SIR) model is studied assuming Bayesian and time-series SIR (tSIR) approaches. Many studies have been conducted from different perspectives, but to the best of our knowledge, no study is available using the SIR models for Pakistan. The coronavirus incubation period has been set to 14 days across the globe; however, this study noticed that the assumption of 14 days is not suitable for Pakistan's data. Furthermore, on the basis of R0, we infer that COVID-19 is not a pandemic in Pakistan, as it was in other nations, such as the United States, India, Brazil, and Italy, among others. We attribute this to the best strategy adopted by the Government of Pakistan to minimize the burden of COVID-19 cases in Pakistani hospitals. It is also noticed that the posterior-based SIR (pSIR) model with uniform prior toR0 and Poisson distribution (of log-likelihood) provides better results as compared to other distributions. From time-series SIR (tSIR), we observed that the value of the reporting rate (ρ) is less than 1 which means that cases are underreported.

Exploring the Deep-Learning Techniques in Detecting the Presence of Coronavirus in the Chest X-Ray Images: A Comprehensive Review.

The deadly coronavirus (COVID-19) is one of the dangerous diseases affecting the entire world and is fastly spreading disease. This spread can be reduced by detecting and quarantining the patients at an earlier stage. The most common diagnostic tool for detecting the coronavirus is the Reverse transcription-polymerase chain reaction (RT-PCR) test which is time-consuming and also needs more equipment and manpower. Furthermore, many countries had a deficit of RTPCR kits. This is why it is exceptionally very crucial to develop artificial intelligence (AI) techniques to detect the outbreak of coronavirus. This motivated many researchers to involve deep-learning methods using X-ray images for more decisive analysis. Thus, this paper outlines many papers that used traditional and pre-trained deep learning methods that are newly developed to reduce the spread of COVID-19 disease. Specifically, advanced deep learning methods play a critical role in extracting the features from the chest X-ray images. These features are then used to classify whether the patient is affected with coronavirus or not. Besides, this paper shows that deep learning techniques have probable applications in the medical field.

Identifying and analysis of key flexible sustainable supply chain management strategies toward overcoming the post-COVID-19 impacts

PurposeThe emerging markets are facing a lot of risks and disruptions across their supply chains (SCs) due to the deadly coronavirus disease 2019 (COVID-19) pandemic. To mitigate the significant post-COVID-19 consequences, organizations should modify their existing strategies and focus more on the key flexible sustainable SC (SSC) strategies. Still now, a limited number of studies have highlighted about the flexible strategies what firms should adopt to reduce the rampant effects in the context of emerging markets.Design/methodology/approachThis study presents an integrated approach including Delphi method, Bayesian, and the Best-Worst-Method (BWM) to identify, assess and evaluate the importance of the key flexible SSC strategies for the footwear industry in the emerging market context.FindingsThe results found the manufacturing flexibility through automation integration as the most important flexible SSC strategy to improve the flexibility and sustainability of modern SCs. Also, developing omni-channel distribution and retailing strategies and increasing the level of preparedness by using artificial intelligent are crucial strategies for overcoming the post-COVID-19 impacts.Originality/valueThe novelty of this research is that the research connects a link among flexible strategies, SCs sustainability, and the impacts of the COVID-19 pandemic. Moreover, the research proposes a novel and intelligent framework based on Delphi and Bayesian-BWM to identify and analyze the key flexible SSC strategies to build up sustainable and robust SCs which can withstand in the post-COVID-19 world.

Scientometric Analysis of COVID-19 Scholars Publication using Machine Learning

The global health crisis that started in December 2019 resulted in an outbreak of coronavirus named COVID-19. Scientists worldwide are working to demystify the transmission and pathogenic mechanisms of the deadly coronavirus. The World Health Organization has declared COVID-19 a pandemic in March 2020, which makes it essential to track and analyse the research state of COVID-19 for guidance on further research. This research was conducted using scientometric analysis, knowledge-mapping analysis, COVID-19 studies and journal classifications. The publications used in this study include over 3000 COVID-19 papers made available to the public from 1 January 2018 to 15 April 2021 in the PubMed databases. In this study, it was discovered that the rapid reaction of researchers worldwide resulted in a fast growth trend between 2019 and 2021 in the number of publications related to COVID-19. It was discovered that the largest number of studies is in the United States of America, which is one of the countries most affected by a pandemic. The method adopted for this study involved the use of documents such as Case Reports (CAT), Journal Article (JAT), letter (LTR), EAT, and Editorial (EDT). This is followed by the classification of COVID-19 related publications that were retrieved from PubMed between 2019 and 2021 using machine learning (ML) models such as Naïve Bayes (NB), Bayesian Generalized Linear Model (BGL), Heteroscedastic Discriminant Analysis (HDA) and Multivariate Adaptive Regression Spline (MAR). Simulation results show that the classification accuracy of MAR is better than that of other ML models used in this study. The sensitivity of the MAR is within the range of 100%. This shows that MAR performs better than NB, BGL and HDA. MAR performs better with an overall accuracy of 89.62%. Our results show a high degree of strong collaboration in coronavirus research and the exchange of knowledge in the global scientific community.

Towards Sustainable Learning during Suspension of Universities: Investigating Students Manners of Studying during Covid-19 in Pakistan

The global wave of Covid 19 has affected the entire world economically, socially andadministratively. The entire educational institutions got suspended and shifted towards online learning during the outbreak of the deadly coronavirus. The previous researchers have examined the technological barriers faced by the students while attending online classes. This research focuses on investigating student manners' of studying while the suspension of universities during the wake of Covid 19 in Pakistan. Through analyzing the original data obtained from private and public university students of Islamabad during Covid 19, this study establishes the learning behavior of students which has been significantly affected due to their learning environment, intrinsic and extrinsic motivation, availability of learning resources, availability of learning aids and their socioeconomic status obstructing their access to online education due to lack of financial support. It has been examined that the learning hours spent by the students during Covid 19 have been significantly reduced. It strongly depicts a lack of motivation on the part of parents and teachers and an inability to interact with classmates to boost their learning.

Forensic Analysis of COVID-19 Data from 198 Countries Two Years after the Pandemic Outbreak

The availability of accurate information has proved fundamental to managing health crises. This research examined pandemic data provided by 198 countries worldwide two years after the outbreak of the deadly Coronavirus in Wuhan, China. We compiled and reevaluated the consistency of daily COVID-19 infections with Benford’s Law. It is commonly accepted that the distribution of the leading digits of pandemic data should conform to Newcomb-Benford’s expected frequencies. Consistency with the law of leading digits might be an indicator of data reliability. Our analysis shows that most countries have disseminated partially reliable data over 24 months. The United States, Israel, and Spain spread the most consistent COVID-19 data with the law. In line with previous findings, Belarus, Iraq, Iran, Russia, Pakistan, and Chile published questionable epidemic data. Against this trend, 45 percent of countries worldwide appeared to demonstrate significant BL conformity. Our measures of Benfordness were moderately correlated with the Johns Hopkins Global Health Security Index, suggesting that the conformity to Benford’s law may also depend on national health care policies and practices. Our findings might be of particular importance to policymakers and researchers around the world.

Message from the Chief Editor: Service Excellence Attitudes Amidst COVID-19 Pandemic

The deadly coronavirus disease which started in the year 2019 (COVID-19, as named by the World Health Organization) is a grave threat to mankind. This COVID-19 pandemic has caused the loss of many lives worldwide. Henceforth, this omni-present and dangerous virus need to be curbed soonest possible and the entire world needs to do its best with love and care to contain the pandemic. As being said, all stakeholders at different levels need to join hands and work together consistently and continuously to achieve service excellence for sustainability.

Three-dimensional DenseNet self-attention neural network for automatic detection of student's engagement.

Today, due to the widespread outbreak of the deadly coronavirus, popularly known as COVID-19, the traditional classroom education has been shifted to computer-based learning. Students of various cognitive and psychological abilities participate in the learning process. However, most students are hesitant to provide regular and honest feedback on the comprehensiveness of the course, making it difficult for the instructor to ensure that all students are grasping the information at the same rate. The students’ understanding of the course and their emotional engagement, as indicated via facial expressions, are intertwined. This paper attempts to present a three-dimensional DenseNet self-attention neural network (DenseAttNet) used to identify and evaluate student participation in modern and traditional educational programs. With the Dataset for Affective States in E-Environments (DAiSEE), the proposed DenseAttNet model outperformed all other existing methods, achieving baseline accuracy of 63.59% for engagement classification and 54.27% for boredom classification, respectively. Besides, DenseAttNet trained on all four multi-labels, namely boredom, engagement, confusion, and frustration has registered an accuracy of 81.17%, 94.85%, 90.96%, and 95.85%, respectively. In addition, we performed a regression experiment on DAiSEE and obtained the lowest Mean Square Error (MSE) value of 0.0347. Finally, the proposed approach achieves a competitive MSE of 0.0877 when validated on the Emotion Recognition in the Wild Engagement Prediction (EmotiW-EP) dataset.

Supporting about ‘education’ in elementary School: A review of literature

This research aims to analyze a review of literature about education in elementary school. Aside from historical changes, the study aims to investigate the intricate, overt and covert deliberate manipulations of this behavior for paranoid reasons. It also looks into the terrible treatment they received as a result of the deadly coronavirus pandemic, also known as the research community's term for learning loss. In this study, data gathering strategies such as documentation were employed, as well as data analysis techniques such as data collection, data display, data reduction, data analysis, and data conclusion. Islamic education has three obstacles in the globalization era: cultural globalization challenges, low levels of social capital challenges, and structural and cultural challenges.

Contribution of Host miRNA-223-3p to SARS-CoV-Induced Lung Inflammatory Pathology.

ABSTRACTSevere acute respiratory syndrome coronavirus (SARS-CoV) and the closely related SARS-CoV-2 are emergent highly pathogenic human respiratory viruses causing acute lethal disease associated with lung damage and dysregulated inflammatory responses. SARS-CoV envelope protein (E) is a virulence factor involved in the activation of various inflammatory pathways. Here, we study the contribution of host miRNAs to the virulence mediated by E protein. Small RNAseq analysis of infected mouse lungs identified miRNA-223 as a potential regulator of pulmonary inflammation, since it was significantly increased in SARS-CoV-WT virulent infection compared to the attenuated SARS-CoV-ΔE infection. In vivo inhibition of miRNA-223-3p increased mRNA levels of pro-inflammatory cytokines and NLRP3 inflammasome, suggesting that during lung infection, miRNA-223 might contribute to restrict an excessive inflammatory response. Interestingly, miRNA-223-3p inhibition also increased the levels of the CFTR transporter, which is involved in edema resolution and was significantly downregulated in the lungs of mice infected with the virulent SARS-CoV-WT virus. At the histopathological level, a decrease in the pulmonary edema was observed when miR-223-3p was inhibited, suggesting that miRNA-223-3p was involved in the regulation of the SARS-CoV-induced inflammatory pathology. These results indicate that miRNA-223 participates in the regulation of E protein-mediated inflammatory response during SARS-CoV infection by targeting different host mRNAs involved in the pulmonary inflammation, and identify miRNA-223 as a potential therapeutic target in SARS-CoV infection.

EFFECT OF THE PROLONGED LOCKDOWN ON SCHOOLWORK PARTICIPATION IN THE POST COVID-19 ERA

The extended academic disruption occasioned by the deadly coronavirus epidemic undoubtedly created a lacuna in the global education landscape. Indeed, the situation propelled many students to unacademically-related activities for a more extended period. Thus, a growing insinuation seems to suggest that many students are struggling with academic activities in the post-Covid-19 classroom. Therefore, the purpose of the present study was to explore the correlation between the prolonged Covid-19 lockdown and schoolwork participation. One hundred and ninety-five secondary school students were randomly drawn from public and private secondary schools in Anambra State, Nigeria. The participant completed a self-report measure of the Schoolwork Engagement Inventory (Salmela-Aro &Upadaya, 2012). The result showed that prolonged Covid-19 lockdown positively correlated with schoolwork participation. Thus, it is concluded that the lengthy lockdown is a significant predictor of schoolwork participation. The research implications and conclusion are discussed.