Internet of Things (IoT) based Health Monitor Device

Health is the centre of human enlightenment. Due to the recent Covid outbreak and several environmental pollutions, checking one's vitals regularly has become a necessity. Ours is an IoT-based device that measures a user's heart rate, blood oxygen level and body temperature. The device is compact and portable, making it easy for users to wear. The readings are measured and shown on an OLED display with the help of sensors. The data is also available on the cloud. A webpage and a mobile application were developed to view the data from the cloud. Individual graphs of the vitals with time are available on the mobile application. This can be used for progress measurement and statistical analyses. Authorized personnel can access the patient's vitals. This creates a scope for Tele-medication in rural and underdeveloped regions. Besides, one can also view his/her vitals for personal health routine. © 2022 IEEE.

Res-CovNet: an internet of medical health things driven COVID-19 framework using transfer learning.

Major countries are globally facing difficult situations due to this pandemic disease, COVID-19. There are high chances of getting false positives and false negatives identifying the COVID-19 symptoms through existing medical practices such as PCR (polymerase chain reaction) and RT-PCR (reverse transcription-polymerase chain reaction). It might lead to a community spread of the disease. The alternative of these tests can be CT (Computer Tomography) imaging or X-rays of the lungs to identify the patient with COVID-19 symptoms more accurately. Furthermore, by using feasible and usable technology to automate the identification of COVID-19, the facilities can be improved. This notion became the basic framework, Res-CovNet, of the implemented methodology, a hybrid methodology to bring different platforms into a single platform. This basic framework is incorporated into IoMT based framework, a web-based service to identify and classify various forms of pneumonia or COVID-19 utilizing chest X-ray images. For the front end, the.NET framework along with C# language was utilized, MongoDB was utilized for the storage aspect, Res-CovNet was utilized for the processing aspect. Deep learning combined with the notion forms a comprehensive implementation of the framework, Res-CovNet, to classify the COVID-19 affected patients from pneumonia-affected patients as both lung imaging looks similar to the naked eye. The implemented framework, Res-CovNet, developed with the technique, transfer learning in which ResNet-50 used as a pre-trained model and then extended with classification layers. The work implemented using the data of X-ray images collected from the various trustable sources that include cases such as normal, bacterial pneumonia, viral pneumonia, and COVID-19, with the overall size of the data is about 5856. The accuracy of the model implemented is about 98.4% in identifying COVID-19 against the normal cases. The accuracy of the model is about 96.2% in the case of identifying COVID-19 against all other cases, as mentioned.

VRFlex: Towards the Design of a Virtual Reality Hyflex Class Model

Social distancing requirements urged by the COVID-19 pandemic along with high transportation cost reduced inperson meetings significantly in recent times. In consequence, many people are seeking for virtual reality (VR) to feel a similar experiences of visiting and enjoying places that are unaccessible. VR has immense success in domains, such as automotive industry, healthcare, tourism, entertainment, sports etc. It is observed that traditional online synchronous and asynchronous class structure is not quite effective in engaging students in class participation and discussion. Therefore, we introduce a novel VRbased class structure that will simulate the classroom environment for students participating a class virtually. We equipped the classroom with several internet of things (IoT) devices that collects information from the classroom, analyze those information, and determine some interesting information to display for the students participating the class virtually. We design a classroom prototype and validate the prototype with simulation. The result of the simulation shows that such a VR-based classroom model is feasible and can introduce in classrooms. © 2023 IEEE.

The Markov Bernoulli Lomax with Applications Censored and COVID-19 Drought Mortality Rate Data

In this article, we present a Markov Bernoulli Lomax (MB-L) model, which is obtained by a countable mixture of Markov Bernoulli and Lomax distributions, with decreasing and unimodal hazard rate function (HRF). The new model contains Marshall- Olkin Lomax and Lomax distributions as a special case. The mathematical properties, as behavior of probability density function (PDF), HRF, rth moments, moment generating function (MGF) and minimum (maximum) Markov-Bernoulli Geometric (MBG) stable are studied. Moreover, the estimates of the model parameters by maximum likelihood are obtained. The maximum likelihood estimation (MLE), bias and mean squared error (MSE) of MB-L parameters are inspected by simulation study. Finally, a MB-L distribution was fitted to the randomly censored and COVID-19 (complete) data. © 2023 by the authors.

Online University Teaching During COVID-19: The Malaysian Experience

Malaysia, like the rest of the world, was hard hit by SARS-CoV-2, also known as COVID-19. After the first COVID-19 case was detected in Malaysia (on January 25, 2020) and traced back to three Chinese nationals, the country was put under Movement Control Order (MCO), a partial lockdown, initially for two weeks, on 18th March. Among MCO consequences: close major economic sectors and educational institutions. Public universities, which began a new semester under a Ministry of Higher Education ruling, switched to online teaching and learning. This chapter chronicles public university experiences with online teaching and learning during the COVID-19 period. A brief background captures the measures taken by the government;how these steps affected university education is appraised next;and finally, the steps taken by the universities to activate online teaching and learning. What challenges cropped up and how to deal with them are acknowledged before drawing conclusions from the online teaching and learning experiences of Malaysian universities. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

Hatirjheel water-taxi service improvements considering riders pre-and-post-trip mode choice characteristics

Dhaka, the capital of Bangladesh, is one of the most congested megacities in the world today. It's predominantly road-based transportation systems have been failing to meet the dwellers mobility needs. Furthermore, the road congestion results in a significant GDP loss every year. The government has been undertaken (and continuing to undertake) numerous transportation systems and services improvement projects such as Metro Rail, BRT, flyovers and U-loop projects along major corridors. Furthermore, being a sustainable (environmentally friendly) mode, a Water-Taxi (WT) service was introduced in the Hatirjheel lake in December 2016 that currently serves major residential, educational, and business/commercial destinations including Rampura/Badda, Badda-Gulshan-1 link road, and Tejgoan/Kawran-Bazar areas through multiple routes. Previous studies reported that the demand for HatirJheel WT service outperforms its service capacity despite noted service weaknesses. This study assesses the WT service characteristics [i.e., WT station characteristics including their surrounding areas and adjacent bus stops, frequency/headway, travel (waiting and running) time, vessel capacity and ridership level as well as access (pre-trip) and egress (post-trip) modes of the riders]. Field data were collected and questionnaire-based surveys were conducted in late 2019 and early 2020 (just before the COVID-19 lockdown). The survey data reveals that a large percentage of WT riders are exchange (transfer) passengers from buses and para-transits. It is also found that WT operates on irregular headways causing long waiting times for riders. The physical separation between bus stops and WT stations, accessing issues (i.e., walking time and safe crossing of roads) with the WT stations as well as long waiting time at stations significantly deteriorate the WT service performance. Taking into consideration of the existing issues and challenges that may jeopardize the implementation of the proposed improvements, a SWOT analysis is also performed. This study recommends that schedule based reliable WT service with a desirable frequency and posted time-table should be introduced to minimize the waiting times at stations. The future study should focus on developing a model for analyzing the effectiveness of WT and bus system integration (i.e., physical and service - schedule and fare - integration between buses and WTs). If desired, WT and bus system integration would improve the service performance of both modes while attracting additional riders and ultimately supporting sustainable development (i.e., alleviating congestion on local roadways and improving air quality etc.). © 2023 Author(s).

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.

The emergence of digital learning in higher education: a lesson from the COVID-19 pandemic

Purpose: The coronavirus disease 2019 (COVID-19) has a significant influence on many aspects of life, including education. As a result, the education system in emerging nations such as Bangladesh needs a rapid transition from conventional to technology-based distance learning. This study looks at the current state of higher education and how well online courses that use technology work. Design/methodology/approach: This study used a structural equation model (SEM) to analyze the 392 student records taken from several universities in Bangladesh. Findings: This research showed that students are more likely to use a digital higher education system if faculty are willing, students are eager and the economy is stable. Students who have had a bad experience with digital learning should know that a virtual evaluation system is needed. The willingness of students to use technology also plays a significant role in whether or not the students will take online classes. The research shows that combining traditional classroom and online learning is the best way to create a long-term learning system. Originality/value: The model suggested in this study has a big effect, and Bangladesh policymakers should consider this model when planning a new kind of technology-based education. © 2023, Emerald Publishing Limited.

An IoT based Smart Robot that Aids in the Prevention of COVID19 Spread

The Internet of Things revolution is transforming current healthcare practices by combining technological, economic, and social aspects. Since December 2019, the global spread of COVID19 has influenced the global economy. The COVID19 epidemic has forced governments all around the world to implement lockdowns to prevent viral infections. Wearing a face mask in a public location, according to survey results, greatly minimizes the risk of infection. The suggested robotics design includes an IoT solution for facemask detection, body temperature detection, an automatic dispenser for hand sanitizing, and a social distance monitoring system that can be used in any public space as a single IoT solution. Our goal was to use IoT-enabled technology to help prevent the spread of COVID19, with encouraging results and a future Smart Robot that Aids in COVID19 Prevention. Arduino NANO, MCU unit, ultrasonic sensor, IR sensor, temperature sensor, and buzzer are all part of our suggested implementation system. Our system's processing components, the Arduino UNO and MCU modules are all employed to process and output data. Countries with large populations, such as India and Bangladesh, as well as any other developing country, will benefit from using our cost-effective, trustworthy, and portable smart robots to effectively reduce COVID-19 viral transmission. © 2022 IEEE.

Fintech, Pandemic, and the Islamic Financial System: Innovative Financial Services and Its Shariah Compliance

The present study aims to examine the role of Islamic financial system in the recovery of post-COVID-19 pandemic and possible role to be played by the disruptive innovation called Islamic Fintech. The study takes a discourse analysis route to examine the disruptions created by the pandemic on the overall global Islamic economy. Islamic financial system has long established its credentials as the most resilient and sustainable financial system during the global financial crisis of 2008 and the current pandemic provides another opportunity to reestablish its position in the financial sphere and emerge as the main contender to the conventional financial system. The disruptions created by the pandemic have spared no one and created havoc in every sector of the global economy. Islamic financial system has certain ethical and social financial services such as Zakat, Qardh-Al-Hasan, Awaqaf, sadaqa, and Islamic microfinance with wide-ranging social reach aimed at the poor and vulnerable sections of the society. The study provides an overview of the Fintech-based Islamic financial services that can be used to provide efficient, reliable, cost-effective, and innovative financial services to its customers during and after the COVID-19 pandemic. The findings of the study are expected to help the Islamic financial institutions, governments, regulators, and policymakers efficient use of Fintech in solving the problems created by the pandemic. The study is also expected to contribute to creating a more sustainable and resilient alternative to the conventional financial system. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

A bioinformatics approach to characterize a hypothetical protein Q6S8D9_SARS of SARS-CoV

Characterization as well as prediction of the secondary and tertiary structure of hypothetical proteins from their amino acid sequences uploaded in databases by in silico approach are the critical issues in computational biology. Severe acute respiratory syndrome-associated coronavirus (SARS-CoV), which is responsible for pneumonia alike diseases, possesses a wide range of proteins of which many are still uncharacterized. The current study was conducted to reveal the physicochemical characteristics and structures of an uncharacterized protein Q6S8D9_SARS of SARS-CoV. Following the common flowchart of characterizing a hypothetical protein, several sophisticated computerized tools e.g., ExPASy Protparam, CD Search, SOPMA, PSIPRED, HHpred, etc. were employed to discover the functions and structures of Q6S8D9_SARS. After delineating the secondary and tertiary structures of the protein, some quality evaluating tools e.g., PROCHECK, ProSA-web etc. were performed to assess the structures and later the active site was identified also by CASTp v.3.0. The protein contains more negatively charged residues than positively charged residues and a high aliphatic index value which make the protein more stable. The 2D and 3D structures modeled by several bioinformatics tools ensured that the proteins had domain in it which indicated it was functional protein having the ability to trouble host antiviral inflammatory cytokine and interferon production pathways. Moreover, active site was found in the protein where ligand could bind. The study was aimed to unveil the features and structures of an uncharacterized protein of SARS-CoV which can be a therapeutic target for development of vaccines against the virus. Further research are needed to accomplish the task.

A Comparative Evaluation between Convolutional Neural Networks and Vision Transformers for COVID-19 Detection

Early illness detection enables medical professionals to deliver the best care and increases the likelihood of a full recovery. In this work, we show that computer-aided design (CAD) systems are capable of using chest X-ray (CXR) medical imaging modalities for the identification of respiratory system disorders. At present, the COVID-19 pandemic is the most well-known illness. We propose a system based on explainable artificial intelligence to detect COVID-19 from CXR images by using several cutting-edge convolutional neural network (CNN) models, as well as the Vision of Transformer (ViT) models. The proposed system also visualizes the infected areas of the CXR images. This gives doctors and other medical professionals a second option for supporting their decision. The proposed system uses some preprocessing of the images, which includes the segmentation of the region of interest using a UNet model and rotation augmentation. CNN employs pixel arrays, while ViT divides the image into visual tokens;therefore, one of the objectives is to compare their performance in COVID-19 detection. In the experiments, a publicly available dataset (COVID-QU-Ex) is used. The experimental results show that the performances of the CNN-based models and the ViT-based models are comparable. The best accuracy was 99.82%, obtained by the EfficientNetB7 (CNN-based) model, followed by the SegFormer (ViT-based). In addition, the segmentation and augmentation enhanced the performance. © 2023 by the authors.

Sentiment Analysis of COVID-19 Vaccination in Bangla Language with Code-Mixed Text from Social Media

The vaccination program which helps avert pandemics is facing new hurdles, including the emergence of hazardous new virus strains and public distrust. Analyzing the sentiment expressed in social media interactions related to vaccines may aid the health authority in implementing public safety procedures and guide the government in developing appropriate policies. The purpose of this research is to identify the public sentiments toward the COVID-19 vaccination in Bangladesh from social media comments. Comments posted on social media platforms often mix formal and informal language known as code-mixed text and do not adhere to any particular grammatical standards. In addition, the Bangla language lacks computational models and annotated resources for sentiment analysis. To overcome this, we created CoVaxBD, a Bangla-English code-mixed and sentiment-annotated corpus of Facebook comments. This paper also proposes a model for sentiment analysis based on the multilingual BERT. It achieves a validation accuracy of around 97.3 % and a precision score of approximately 97.4%. © 2022 IEEE.

Occupational Health of Frontline Workers during the COVID-19 Pandemic: Is Health and Well-Being an Issue in Bangladesh?

The coronavirus outbreak has significantly affected the health and well-being of several people around the world. In a similar vein, Bangladeshi medical professionals have also been affected by several severe physical and mental health complications resulting from their frequent contact with COVID-19 patients. This exposes them to a greater risk of infection with the lethal virus, which can substantially impact their job performance. Therefore, this research aims to investigate the manner in which the COVID-19 pandemic affects the occupational health and safety of medical employees. The researchers deployed a descriptive qualitative technique to investigate the complexities of the COVID-19 crisis amongst medical practitioners. Employing purposeful sampling and in-depth interview techniques, the researchers collected data from a total of 32 healthcare professionals and investigated their state of occupational health, their exposure to stress and trauma, and the effects of stress and trauma on their livelihood, health and well-being. The data revealed the occupational health of healthcare workers as being fragile, resulting to stress and trauma, and eventually, a depressed state of mind. To address this issue, relevant government and non-governmental organizations should concentrate on reducing COVID-19-related risks and repercussions in hospital settings. In addition, policymakers, social workers, public health practitioners and psychologists must work together to ensure that healthcare workers are healthy and safe at work. © 2023, Editura Lumen. All rights reserved.

Clinical Characteristics of Deceased with COVID-19 Infection: Data from the Largest COVID-19 Dedicated Hospital in Bangladesh

The death t toll of the coronavirus disease 2019 (COVID-19) has been considerable. Several risk factors have been linked to mortality due to COVID-19 in hospitals. This study aimed to describe the clinical characteristics of patients who either died from COVID-19 at Dhaka Medical College Hospital in Bangladesh. In this retrospective study, we reviewed the hospital records of patients who died or recovered and tested positive for COVID-19 from May 3 to August 31, 2020. All patients who died during the study period were included in the analysis. A comparison group of patients who survived COVID-19 at the same hospital during the same period was systematically sampled. All available information was retrieved from the records, including demographic, clinical, and laboratory variables. Of the 3115 patients with confirmed COVID-19 during the study period, 282 died.The mean age of patients who died was higher than that of those who survived (56.7 vs 52.6 years). Approximately three-fourths of deceased patients were male. History of smoking (risk ratio 2.3;95% confidence interval: 1.6-3.4), comorbidities (risk ratio: 1.5;95% confidence interal:1.1-2.1), chronic kidney disease (risk ratio: 3.2;95% confidence interval: 1.7-6.25), and ischemic heart disease (risk ratio:1.8;95% confidence interval: 1.1-2.9) were higher among the deceased than among those who survived. Mean C-reactive protein and D-dimer levels [mean (interquartile range), 34 (21-56) vs. 24 (12-48);and D-dimer [1.43 (1-2.4) vs. 0.8 (0.44-1.55)] were higher among those who died than among those who recovered. Older age, male sex, rural residence, history of smoking, and chronic kidney disease were found to be important predictors of mortality. Early hospitalization should be considered for patients with COVID-19 who are older, male, and have chronic kidney disease. Rapid referral to tertiary care facilities is necessary for high-risk patients in rural settings.Copyright © 2023 Hoque MM.

Temporal dynamics and fatality of SARS-CoV-2 variants in Bangladesh.

Background and Aims: Since the beginning of the SARS-CoV-2 pandemic, multiple new variants have emerged posing an increased risk to global public health. This study aimed to investigate SARS-CoV-2 variants, their temporal dynamics, infection rate (IFR) and case fatality rate (CFR) in Bangladesh by analyzing the published genomes. Methods: We retrieved 6610 complete whole genome sequences of the SARS-CoV-2 from the GISAID (Global Initiative on Sharing all Influenza Data) platform from March 2020 to October 2022, and performed different in-silico bioinformatics analyses. The clade and Pango lineages were assigned by using Nextclade v2.8.1. SARS-CoV-2 infections and fatality data were collected from the Institute of Epidemiology Disease Control and Research (IEDCR), Bangladesh. The average IFR was calculated from the monthly COVID-19 cases and population size while average CFR was calculated from the number of monthly deaths and number of confirmed COVID-19 cases. Results: SARS-CoV-2 first emerged in Bangladesh on March 3, 2020 and created three pandemic waves so far. The phylogenetic analysis revealed multiple introductions of SARS-CoV-2 variant(s) into Bangladesh with at least 22 Nextstrain clades and 107 Pangolin lineages with respect to the SARS-CoV-2 reference genome of Wuhan/Hu-1/2019. The Delta variant was detected as the most predominant (48.06%) variant followed by Omicron (27.88%), Beta (7.65%), Alpha (1.56%), Eta (0.33%) and Gamma (0.03%) variant. The overall IFR and CFR from circulating variants were 13.59% and 1.45%, respectively. A time-dependent monthly analysis showed significant variations in the IFR (p = 0.012, Kruskal-Wallis test) and CFR (p = 0.032, Kruskal-Wallis test) throughout the study period. We found the highest IFR (14.35%) in 2020 while Delta (20A) and Beta (20H) variants were circulating in Bangladesh. Remarkably, the highest CFR (1.91%) from SARS-CoV-2 variants was recorded in 2021. Conclusion: Our findings highlight the importance of genomic surveillance for careful monitoring of variants of concern emergence to interpret correctly their relative IFR and CFR, and thus, for implementation of strengthened public health and social measures to control the spread of the virus. Furthermore, the results of the present study may provide important context for sequence-based inference in SARS-CoV-2 variant(s) evolution and clinical epidemiology beyond Bangladesh.

Human Mobility Driven Modeling of an Infectious Disease

In conventional disease models, disease properties are dominant parameters (e.g., infection rate, incubation pe-riod). As seen in the recent literature on infectious diseases, human behavior - particularly mobility - plays a crucial role in spreading diseases. This paper proposes an epidemiological model named SEIRD+m that considers human mobility instead of modeling disease properties alone. SEIRD+m relies on the core deterministic epidemic model SEIR (Susceptible, Exposed, Infected, and Recovered), adds a new compartment D - Dead, and enhances each SEIRD component by human mobility information (such as time, location, and movements) retrieved from cell-phone data collected by SafeGraph. We demonstrate a way to reduce the number of infections and deaths due to COVID-19 by restricting mobility on specific Census Block Groups (CBGs) detected as COVID-19 hotspots. A case study in this paper depicts that a reduction of mobility by 50 % could help reduce the number of infections and deaths in significant percentages in different population groups based on race, income, and age. © 2022 IEEE.

GENETIC VARIATION IN PHOSPHORUS DEFICIENCY TOLERANCE AMONG BANGLADESHI RICE (ORYZA SATIVA L.) CULTIVARS AND INTROGRESSION LINES WITH IR 64 GENETIC BACKGROUND

Phosphorus is vital nutrient for the crop yield, and Breeding rice for tolerant to low phosphorus, efficient in uptake and assimilation is the best way for sustainable production. This study aimed to evaluate Bangladeshi rice cultivars and introgression lines (INLs) under phosphorus deficient soil to understand the genetic variation in deficiency tolerance. A total of 28 rice genotypes from various ecotypes such as Aus, Aman, Boro and Jhum and INLs were collected and grown in pot contained highly phosphorus deficient soil in the rooftop polythene shed house during October 2019 to March 2020. A phosphorus deficiency susceptible variety, IR 64, was used as control, and experiment was conducted following randomized complete block design with two replications. Biomass related traits such as dry weight (DW) and relative dry weight (RDW, %) were analyzed at early vegetative stage. Visual score based on the responses to artificial drought occurred due to absence of water for 5 consecutive days because of government imposed Covid-19 lockdown were also evaluated in a scale of 0 to 4. Plants showed wide variation in the measured traits in both in the phosphorus added normal or phosphorus deficient conditions. Two patterns of responses were observed. One pattern was similar to susceptible control IR 64 and another is highly sensitive to P-deficiency. Cluster analysis resulted four groups (I to IV). Group I consist of four rice varieties including Pathar kuchi, Lal dhan, INL-9, and INL-30, and showed low DW and low tolerances to phosphorus deficiency and artificial drought. Group II contain nine accessions including IR 64, Murali, Kuti Agrani, Kernaicha, and five INLs, and showed higher DW and susceptibility to phosphorus deficiency and artificial drought. Group III had medium DW and highly sensitive to phosphorus deficient condition and the accessions Kali jira and Aus (Awned) were included. Two jhum variety, Renkhoa Dhan and Galongpru, and seven INLs belong to the group IV which showed medium DW but tolerant to phosphorus deficiency and artificial drought compare to other groups. The genetic variations of DW and RDW under phosphorus deficient and artificial drought conditions were clarified among rice varieties in Bangladesh and INLs with IR 64 genetic background, and several varieties and INLs were found as the promising materials for further breeding program. [ABSTRACT FROM AUTHOR] Copyright of JAPS: Journal of Animal & Plant Sciences is the property of Knowledge Bylanes and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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.