Study on Tea Consumption Behavior During the Covid-19 Pandemic and the Analysis of the Antioxidant Activity and Bioactive Compounds of Flavoring Ingredients of Tea

Tea is one of the most popular and oldest beverages available in many varieties and the use of different flavoring ingredients is becoming more common. The present study aimed to examine tea consumption behavior during the COVID-19 pandemic and analyzed the bioactive compounds of tea flavoring ingredients. At first, a cross-sectional study was carried out with 140 randomly selected participants to determine tea consumption patterns and data was collected through face-to-face interviews. Then 2,2-diphenyl-1-picrylhydrazyl (DPPH) test, the Folin-Ciocalteu technique, and the quercetin method were used to assess antioxidant activity, total phenolic content (TPC), and total flavonoid content (TFC) of tea flavoring ingredients. The study found that 57.86% of the participants increased their tea consumption during the COVID-19 pandemic, whereas 22.80% increased their tea consumption by at least one more cup per day. It was also found that ginger was the most popular (29.5%) among fifteen tea flavoring agents. By analyzing tea flavoring ingredients, the maximum antioxidant activity found in cinnamon was 87%, and lemon leaves had the lowest, which was 60%. On a dry weight basis, the TPC of the tea flavoring components ranged from 36.52 mg GAE/g for cloves to 9.62 mg GAE/g for ginger. The maximum TFC was also found in clove with 13.68 mg QE/g, and moringa was the second highest with 12.26 mg GAE/g. The antioxidant activity of flavoring compounds has a significant correlation (p<0.05) with TPC and TFC. Overall, tea consumption behavior with tea flavoring ingredients increased during the COVID-19 pandemic situation. Tea with flavoring ingredients may be one of the best dietary sources of antioxidants, TPC, and TFC which are important for strengthening the immune system and controlling different physiological and metabolic disorders. © 2023 The Author(s). Published by Enviro Research Publishers.

COVID-19 Identification From Lung CT Scans in a Low-Resource Setting Using a Regularized 3D Convolutional Neural Network

Right at the end of 2019, the world saw an outbreak of a new type of SARS (severe acute respiratory syndrome) disease, SARS-Cov-2, or COVID-19. Even in 2022, around 1 million people worldwide are getting infected with the virus every day. To date, more than 6 million people have died as a result of the virus. To tackle the pandemic, the first step is to successfully detect the virus among the mass population. The most popular method is the RT-PCR test, which, unfortunately, is not always conclusive. The physicians thus suggest lung CT tests for the patients for clinical relevance. But the problem with lung CT scans for the detection of coronavirus is that the COVID-19 infected scan is very similar to community-affected pneumonia (CAP) infected scan, and the results in many cases get wrongly interpreted. In addition, the virus is always mutating into different strains, and the severity and infection pattern slightly change with each mutation. Because of this rapid mutation, a large and balanced dataset of lung CT scans is not always available. In this work, we systematically evaluate the accuracy of a deep 3D convolutional neural network (CNN) on a small-scale and highly imbalanced dataset of lung CT scans (the SPGC COVID 2021 dataset). Our experiments show that it can outperform previous state-of-the-art 3D CNN models with proper regularization, an appropriate number of dense layers, and a weighted loss function. Our research, therefore, suggests an effective solution for identifying COVID-19 in lung CT scans using deep learning for small and highly imbalanced datasets. © 2022 IEEE.

Predicting Aramco's IPO Long-Term Performance During COVID Times

This paper aims to assess whether the outbreak of the highly contagious pandemic had an impact on the share prices of recently listed Aramco in light of the Fads hypothesis using the methods of neural network and ARIMA. The IPO of Aramco, the world's largest oil company, was a much-hyped affair. Given the relevant importance of the company, it was expected that Aramco's share prices would not underperform in the long run. But the analysis indicates the opposite. The study uses two time periods using the announcement of the pandemic by the World Health Organization as the threshold date to see the impact of the pandemic on Aramco's share prices. The forecasting results validate the Fads hypothesis implying that Aramco's share prices would have underperformed in the long run, even in the absence of a pandemic outbreak. Finally, the study cautions investors against the hype created by IPOs. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Lightweight CNN Architecture for Chest X-ray Images Analysis to Facilitate Early Stage Detection of Lung Diseases

Lung-related diseases are one of the significant causes of death among infants and children. However, the mortality rate can be reduced by the detection of lung abnormality at an early stage. Traditionally, radiologists identify irregularities by interpreting chest x-ray images which is time-consuming. Therefore, researchers have proposed many automated systems for diagnosing pneumonia and other lung-related diseases. Due to the remarkable performance of Convolutional Neural Networks(CNN) in image classification, it has gained immense popularity in chest x-ray image analysis. Most of the research has utilized famous pre-trained Imagenet models for more accurate analysis of Chest X-ray images. However, the problem with these architectures is that they have many parameters that increase the training time, which makes the detection process lengthy. This paper introduces a lightweight, compact, and well-tuned CNN architecture with far fewer parameters than the pre-trained model to analyze two of the most common lung diseases, pneumonia and Covid-19. We have evaluated our model on two benchmark datasets. Experimental results show that our lightweight CNN model has far fewer hyperparameters than other state-of-the-art models but achieves similar results. We have achieved an accuracy of 90.38% on the kermany dataset and 96.90% on the Covid-19 Radiography dataset. © 2022 IEEE.

COVID-19 in city and municipal area: socio-economic analysis in Khulna, Bangladesh

Purpose: In Bangladesh (a middle-income and densely populated country) where socio-economic factors act badly on human activities during COVID-19. This research mainly focused on observing the socio-economic aspects of the Pandemic on human life between city and municipal areas. Design/methodology/approach: This research relied on Khulna City Corporation (KCC) and Paikgacha Municipality of Bangladesh. A random sampling technique was adopted for choosing 622 stakeholders (318 in the city and 304 in the municipal area). Here, the socio-economic factors have been fixed based on the literature review and expert opinion. This study explored two mainstream social and economic issues affected by the Pandemic. Several statistical tests were performed to find the relationship among Socio-economic factors. Findings: The study shows that the Pandemic caused great harm to city areas rather than municipal areas. The city is faced with tremendous pressure on the economic aspect as well. Besides, the pandemic affects savings, education sectors, food habits and other factors in both areas. The trip distribution also differs between the study areas and the mobility pattern shows that people migrated to rural areas from city space during the Pandemic. Originality/value: This research will assist in focusing on a micro-level perspective in the future to analyze socio-economic changes. Moreover, it can help to point out the administrative prospects in the future. © 2023, Emerald Publishing Limited.

Dietary food antioxidants and their radical scavenging activity: A review

Food antioxidants can prevent or/and delay free radical formation which is responsible for oxidative stress. Nowadays, natural remedy becomes the highest concern in many countries, as well as discouraging the intake of synthetic counterparts to avoid the burden of side effects on human health. Regular intake of dietary antioxidants could help to improve the fitness of the body, and subsequently make the body more competitive in its fight against diseases through enhanced immune response. The present review thus summarised recent knowledge on the dietary source of antioxidants, and also mechanism of action and functionalities on human health benefits. Due to the proven ability to restore mitochondrial function and cellular redox balance, food antioxidants also have great potential as natural therapies against COVID-19. However, the numbers of trials are still limited. There must be more tests with the hope that these compounds will mitigate the COVID-19 and similar outbreaks in the future © All Rights Reserved

Sustainable and efficient E-learning internet of things system through blockchain technology

E-Learning Education systems are gaining attention day-to-day because of their inclusive pertinence in the distance education system. Due to COVID-19, the online learning education system has become very popular. Most probably, all education systems have been using the IoT-based E-Learning system to continue the students' education without hindrance during the COVID lockdown. Several E-Learning IoT schemes are explored that reflect privacy and security, but still, there is no detailed scheme;hence, it needs a sustainable, secure E-Learning IoT system. The characteristics and prospects of the Internet of Things are discussed in this article. By analyzing the various functions and capabilities of the Internet of Things, this article aims to provide an overview of the various advantages and challenges of using the platform for e-learning. This paper proposed the E-Learning IoT architecture with Blockchain technology, with layers of different IoT and Blockchain concepts to secure the online education system. Also, the block diagram of the proposed architecture demonstrates how students can securely access or interact with the online learning system through Blockchain technology. By implementing the proposed e-learning IoT architecture, universities and colleges can improve their distance learning programs and increase efficiency without affecting their academic activities. Finally, the study found that e-learning positively impacts students' learning experience and overall quality of education. It also exhibited a significant positive impact on their flexibility and academic productivity. © The Author(s) 2023.

Application of the SOBC model to study customers' online purchase intentions in an emerging economy during COVID-19: does gender matter?

Purpose: Online shopping around the world is growing exponentially, especially during the COVID-19 pandemic. This study aims to examine how an online customer's purchasing experience influences his/her buying intention and willingness to believe in fraud news, as well as the ripple impact of satisfaction and trust, with gender as a moderator in an emerging economy during COVID-19. Design/methodology/approach: Based on the underpinning of the stimulus-organism-behavior-consequence (SOBC) theory, the research model was developed, and collected data from 259 respondents using convenience samples technique. Next, the data were analyzed using partial least squares-based structural equation modeling (PLS-SEM), SPSS (Statistical Package for the Social Sciences) and Hayes Process Macro. Findings: The study results confirmed that the online shopping experience (OSE) has positive impact on customers' satisfaction (CS), purchase intention (PI) and customer trust (CT);CS has positive effects on trust toward online shopping and their future product PI;future product PI significantly affects customers' propensity to believe and act on fraud news (PBAFN). The finding also states that gender moderates the relationships of CS to PI, OSE to PI and PI to PBAFN, but doesn't moderate the CT to PI relationship. Originality/value: The study findings will assist policymakers and online vendors to win customers' hearts and minds' through confirming satisfaction, trust and a negative attitude toward fake news, which will lead to customer loyalty and the sustainable development of the industry. Finally, the limitations and future research directions are discussed. © 2023, Md. Rabiul Awal, Md. Shakhawat Hossain, Tahmina Akter Arzin, Md. Imran Sheikh and Md. Enamul Haque.

Information Dissemination During the COVID-19 Pandemic in Bangladesh

The main aim of this study is to identify the role of information dissemination on urban and rural citizens of Bangladesh during the COVID-19 pandemic and the role of misinformation in this process. The study also aimed at finding appropriate counter misinformation strategies regarding COVID-19. An online questionnaire was prepared to collect the viewpoints of the urban and rural citizens of Bangladesh regarding dissemination of information during COVID-19, misinformation regarding COVID-19, and counter misinformation strategies. Along with demographic and general information, a five-point Likert scale was used to measure COVID-19 related misinformation beliefs and how to counter them. Chi square tests were used to determine the association between current residency, information sources, the importance of information dissemination, reactions after getting COVID related information, and evaluative steps after getting information and before disseminating it. Additionally, nonparametric Mann–Whitney U and Kruskal–Wallis tests were conducted to know the significance of difference in respondents' assessment on COVID-19 related misinformation in terms of their demographic characteristics. Cronbach's alpha score was obtained to see the reliability of the questionnaire items. The current study reveals that both urban and rural citizens of Bangladesh are influenced by information dissemination regarding COVID-19 and they have lower level of misinformation belief. The respondents have differences in misinformation belief by different demographic groups. Respondents' educational status, information literacy, sources of getting information, and evaluative steps after getting information have significant differences in misinformation belief. The study also noticed the support of respondents for countering misinformation strategies regarding COVID-19 © Abu Sayed, Md. Ziaul Haque, Md. Rifat Mahmud, 2022

Virtual communities decision model (VCDM): An empirical validation in online social networks (OSNs) adoption among capital market investors

The COVID-19 epidemic has hastened the growth of Virtual Communities and is affecting virtually every part of work in the public and business sectors. Virtual communities, popular forums for communication and entertainment, increasingly affected the users' decisions. Though many technology adoption models/theories are available, a distinctive model for decision-making in a virtual environment is scarce. This research developed the virtual communities' decision model and empirically tested its performance. This study examined 16 well-established theories/models of information technology, social science, marketing, and behavioral finance and extracted nine constructs from 58 identified constructs considering theoretical cohesiveness along with the three-stage method proposed by Moore and Benbasat. A unified model for virtual communities' decisions (VCDM) is developed and validated using the data collected from individual capital market investors in Bangladesh. The structural equation modeling technique is used to analyze the data. The upshot implies that VCDM performs adequately and explains the maximum variances in intention to decision and investment. VCDM also outperforms the majority of the related theoretical models. The acceptance levels of fit indices and all significant relationships among different constructs are also empirically validated. The moderating effect of the virtual group use experience is also confirmed. Future research can use VCDM in marketing, behavioral finance, ecommerce, information systems and social science context. VCDM thus facilitates a beneficial tool for managers, service providers, and other users to assess the likelihood of effectiveness for decisions in a virtual environment.

A pilot study regarding the consequences of the COVID-19 pandemic on healthcare education in India and the implications

Introduction: The early approaches to prevent the spread of COVID-19 included lockdown and social distancing measures, leading to university closures. These measures forced unparalleled changes to the delivery of healthcare education. Concerns included the preparedness of faculty and students to e-learning as well as the routine availability and funding of equipment and internet bundles. This needed addressing with fully trained healthcare professionals required given rising inappropriate use of antibiotics in India, growing prevalence of non-communicable diseases as well as an increasing number of patients with joint comorbidities. Consequently, there is a need to ascertain the current impact of the pandemic on healthcare student education across India. Materials and Methods: This was a pilot study among 10 purposely selected healthcare educators in both private and public universities. The questionnaire built on published studies. Results: Identified challenges included a lack of familiarity with online education, no bedside teaching, lack of equipment and affordability of internet bundles, poor internet connectivity and postponed examinations. Ways forward included training faculty on e-learning, providing students with loans and other financial support to purchase equipment and internet bundles, establishing COVID-19 prevention protocols and protective equipment, recording lectures and tutorials to make up for lost time and simulated methods to teach clinical aspects. Conclusion: Despite challenges, there was a rapid move to online learning among surveyed universities. Alongside this, courses to address lack of familiarity with e-learning approaches with hybrid teaching approaches here to stay. The next step will be to undertake a wider study and to use the combined findings to provide future guidance.

SS+CEDNet: A Speech Privacy Aware Cough Detection Pipeline by Separating Sources

Cough is one of the most distinguishable symptoms for Influenza-like-illness (ILI) and Severe Acute Respiratory Infection (SARI). Considering the recent worldwide COVID-19 pandemic, many types of research are ongoing all around the world for the accurate detection of cough events. But background speech events make it difficult for the algorithms to detect cough events and the performance of the models drops significantly. At the same time, speech privacy is not preserved in the traditional cough detection models. In this paper, we are proposing a pipeline, named SS+CEDNet, to overcome these problems. The pipeline consists of a Source Separation (SS) and a Cough Event Detection (CED) model. The SS model at first separates the cough and speech sources. Finally, the separated cough source is passed through the CED model to detect cough events. The pipeline not only preserves speech privacy by separating the sources but also shows a better cough detection accuracy. © 2022 IEEE.

The Effect of the COVID-19 Pandemic on Pulmonary Tuberculosis Control in the Selected Upazila Health Complexes of Dhaka Division, Bangladesh.

Despite the enormous disruption of tuberculosis (TB) services reported globally, Bangladesh's impact is not well documented. We aimed to assess the effect of the COVID-19 pandemic on the TB control program in Bangladesh from patients' and service providers' perspectives. We conducted a cross-sectional study from November-December 2021 at six conveniently selected Upazila Health Complexes (UHC) of the Dhaka division, Bangladesh. We conducted face-to-face interviews among 180 pulmonary TB service recipients and all TB service providers working in the selected UHC. We also reviewed TB registries from each UHC. All data were summarized using descriptive statistics tools. We found a 31% reduction in presumptive TB cases during 2021 compared to 2020. Other TB services, such as testing, were reduced by 16-36% during the same period. Service receivers reported a lack of transportation (95%), and a lack of adequate human resources (89%) as critical barriers to receiving and providing TB service, respectively. The findings of our study showed substantial interruption of TB service delivery during the COVID-19 pandemic, threatening the recent progress and pushback from achieving the 2035 End TB targets. Early mitigation of TB service delivery through adopting remote follow-ups using digital health technology and integrating COVID-19 and TB screening is essential for the continuity of essential TB services and achieving global TB targets.

Exposure to nature during the COVID-19 pandemic and the associated effect on mental health: a systematic review with meta-analysis

BACKGROUND: The COVID-19 pandemic and measures such as lockdowns to control its transmission generated unique effects on psychological health and well-being. In these circumstances, access to nature and outdoor spaces became a potentially important coping strategy, but the evidence exploring the mental health benefits of nature exposure during different stages of the pandemic is mixed and poorly understood. We systematically synthesised the evidence to examine larger trends in associations between nature exposure and mental health during the COVID-19 pandemic.

The shock of the SARS-CoV-2 pandemic on health professionals’ education: A pilot qualitative study in Malaysia

Various measures were introduced globally to prevent the spread of COVID-19, including travel restrictions, social distancing, and closure of educational establishments. Implementing these measures resulted in appreciable changes to health professions’ education (HPE) with concerns regarding the level of preparedness among faculty members and students towards e-learning, including laboratory, clinical, and other forms of hands-on training. In addition, the affordability of devices and Internet bundles arose, especially among students in low and middle-income countries. A pilot qualitative study was conducted in Malaysia to ascertain critical challenges and how higher learning establishments addressed them. The study was undertaken among 10 purposely selected educators in both public and private universities in Malaysia using an established questionnaire to ascertain critical challenges and responses. The main issues included unfamiliarity with e-learning approaches and inadequate availability of devices and Internet bundles among students. Furthermore, the study revealed a lack of interaction between faculty members and students, concerns with conducting practicals and clinical examinations, and mental distress among faculty members. Measures introduced to address concerns included supporting digital needs through the provision of software, devices, and Internet bundles, enhanced training of educators in e-learning approaches, providing clinical case banks and curated patient data, and establishing clear policies and procedures for classroom and clinical teaching. Psychological support, including stress management, was also provided. The challenges and lessons learned in Malaysia regarding HPE during the pandemic were similar to other countries with hybrid learning here to stay. © 2022 Ambigga Krishnapillai et al. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).

Online Teaching during Covid-19 Pandemic-Perceptions of Undergraduate Medical Students in Uttar Pradesh, India

Background: The suspension of regular face to face teaching during Covid Pandemic lead to emergence of e learning with full swing in every field of education including Medical Colleges. Students perceived this unplanned shift differently. So, this study was planned to explore the perceptions of medical students in a private medical university, UP, India. Method(s): A questionnaire was designed on google form with close ended questions & likert scale questions. Link was shared with medical students who have attended online classes during Covid pandemic. Respondents were 140 medical undergraduates from 1st Professional, regular & supple batch. Responses were analyzed & results obtained Results: Majority of students (76%) used smart phones to attend online classes & Google meet was the preferred platform (45.35%). Duration of online classes preferred by students was 30-45min (54.28%). Usefulness of online teaching by most of the students perceived as passable (Likert scale, LS-2.84). Extent of understanding the topic was not equivalent to face to face (LS-2.9) & internet connectivity posed problems (LS-3.77) that is why most of the students demanded for reconduction of classes after resumption of regular offline classes (LS-3.91) Most of them agreed that medical learning is suffering as practical classes cannot be conducted online (LS-3.98). Most of the students (45%) confessed that self-study was less at homes as compared to their hostels. Both students & their parents were worried about quality of studies & their future performance in exams (LS 4.02 &3.66). Students were not in favour of online teaching in future (LS-2.31) Conclusion(s): Online teaching was well received by medical students but they faced several challenges like sometimes internet connectivity issues, less student teacher interaction, methodology barriers, less development of practical skills. Despite all the hassles, chain of learning did not break due to online teaching. Efforts should be done to address the problems faced by students at individual faculty level as well as Institute level. Copyright © 2022, Dr Yashwant Research Labs Pvt Ltd. All rights reserved.