ABSTRACT
This study aims to assess the employee morale (EM) in the hospitality and tourism industry through social media literacy (SML), psychological capital (PsC), and work engagement (WE). The study followed the deductive reasoning approach to examine the hypothesized associations. The structural equation modeling via SmartPLS3 was used to analyse the cross-sectional data which were collected through the convenience sampling method. Results showed that SML and PsC positively influenced WE, and WE predicted EM. WE partially mediates the influence of SML and PsC on EM. The moderating effect of age showed that young adults' social media literacy on WE significantly differs from old adults. However, no significant age difference between young and old is observed between PsC and WE. The study intends to develop a better comprehension of practitioners' insights of SML, PsC that may influence employees' WE and morale who have been working in hospitality and tourism industries. The strengths and limitations of the study are also documented for policymakers, academics, and future researchers. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.
ABSTRACT
The Covid-19 coronavirus has turned into a serious, life-threatening disease that is prevalent worldwide as it is most likely to infect. An automated protocol system is a compelling idea to stop the spread of covid19. This article aims at a deep learning model supported by a convolutional neural network (CNN) to facilitate automatic diagnosis from chest X-rays. A collection of 2875 covid19 images and 10293 X-ray pictures to recognize covid19 counts is being used as the data set for the drafting. From the experimental results, it can be seen that the proposed structure achieves 96% specificity, 97% AUC 96% accuracy, 96% sensitivity, and 96% F1-score. Therefore, the results of the proposed system will help clinicians and researchers discover COVID-19 patients and facilitate the treatment of COVID-19 patients.
ABSTRACT
The importance of Micro, Small, and Medium Enterprises (MSMEs) in economic development and their access to finance post-COVID-19 has been highlighted in this study. The availability of finance is a critical factor for MSMEs to flourish, and they are mostly severely affected by the economic recession. The purpose of this study was to ascertain how the firm-specific factors such as Location, Industry, Size, Age, Ownership, Collaterals, and Business information affect their access to finance in India. This study used a survey to collect primary data from 200 MSMEs in India. Descriptive and logistic regression analysis was used to analyze the data. Results show that firms with collateral, larger and older firms, and private limited firms are less likely to face problems in raising finance, while service firms are more likely to face problems in raising finance. The results from this study will add to the understanding of the financing problems faced by MSMEs in India. The study recommends that firm attributes are important for accessing finance and help policymakers and researchers develop new strategies and policies to support the financing of MSMEs in India. © 2022 Uddin et al.
ABSTRACT
The present world is passing a great pandemic where Bangladesh facing an unavoidable challenge to cope up with COVID-19. The government and other health organizations haveannounced somehealth warnings to prevent COVID-19 and make people aware. The study aims to explore the health warnings status (knowledge and practicesof health warnings) among low socio-economic people in Bangladesh. This cross-sectional study was conducted with 400 participants (low socio-economic people) by using the purposive sampling and semi-structured questionnaire survey over six months. This study illustrates that 60% of respondents noticed they and their family members are very aware and knowledgeable about COVID-19where 36.5% are conscious to make their family aware about corona virus. About 32% of respondents use mask when they go to outside during corona period where 25% use hand sanitizer. It is found that there is a significant association between occupation, age and perception on people’s awareness about COVID-19 at 1% level of significance where (p<0.002), (p<0.001) respectively. Along with, educational qualification is also significantly associated at 10% level of significance where (p<0.08).Most of the respondents have moderate level of knowledge and awareness about COVID-19 health warnings.
ABSTRACT
The Covid-19 coronavirus has turned into a serious, life-threatening disease that is prevalent worldwide as it is most likely to infect. An automated protocol system is a compelling idea to stop the spread of covid19. This article aims at a deep learning model supported by a convolutional neural network (CNN) to facilitate automatic diagnosis from chest X-rays. A collection of 2875 covid19 images and 10293 X-ray pictures to recognize covid19 counts is being used as the data set for the drafting. From the experimental results, it can be seen that the proposed structure achieves 96% specificity, 97% AUC 96% accuracy, 96 % sensitivity, and 96 % F1-score. Therefore, the results of the proposed system will help clinicians and researchers discover COVID-19 patients and facilitate the treatment of COVID-19 patients. © 2021 Institute of Advanced Engineering and Science (IAES).
ABSTRACT
Recently, during the COVID-19 situation, the requirement and importance of tracking patients from a remote location have increased significantly. Most patients now prefer to obtain their doctor's care and check their health status through their mobile phone call, Skype, Facebook Messenger, or other online resources. There is, however, a major concern about the privacy of patients when using online resources. Patients usually choose to keep their information confidential, which should be only accessible to authorized individuals.The most current remote patient monitoring system is organization-centric and patient's privacy and security rely on healthcare providers' mercy. Blockchain technologies have attracted the attention of researchers for designing eHealth applications to provide patients with secure and privacy-preserving health services. Blockchain researchers have recently proposed some models for remote patient monitoring systems. However, most of those researchers have applied public blockchains where health data is available to all participants with the property of data tamper-proof. In this paper, we propose a novel remote patient monitoring model using a decentralized private blockchain to protect patient's privacy and increase the system's efficiency. The private blockchain will be implemented on Hyperledger Fabric where a Patient centric Agents (PCA) manage patient's data and coordinate authorization to form a secure channel to transmit data to the private blockchain. A hybrid consensus by combining Proof of Integrity (PoI) and Proof of Validity (PoV) is used to protect data privacy and integrity when retrieving data from a blockchain-based cloud database. Finally, the Merkle Tree algorithm was used for data processing and authentication when collecting data and uploading it to a cloud database.
ABSTRACT
Background: Cornonavirus disease (COVID-19) has been declared pandemic by the World Health Organization on the 11th March 2020. The knowledge, attitudes and practices of the population towards the COVID-19, play an integral role in determining community’s readiness to engage themselves in government measures including behavioural change in prevention and control of the disease. Objectives: The study was aimed to determine the knowledge levels, attitudes and practices towards the COVID-19 among the Bangladeshi population. Methods: A cross sectional study was conducted among 1549 adult population across Bangladesh including Dhaka city and rural areas during March-April 2020. Data were collected using a structured and pretested questionnaire through online, self-administered and face to face interview. The study instrument consisted of 7 items on socio-demographic characteristics, 12 items on knowledge, 4 items on attitudes and 5 items on practices related to COVID-19. Independent sample t-tests, chi-square tests, one-way analysis of variance (ANOVA) and binary logistic regression were performed to assess the attitudes and practices in relation to knowledge. Results: Of the total 1549 study population, 1249 were interviewed online, 194 were self-administered and 106 were through face to face interview. The lowest level of knowledge prevailed among the above 50 years’ age group regarding the disease, which was higher among female (p=0.03), and more among the respondents having education level below graduation (p=0.000;OR=1.6, χ2=17.6). Of the total respondents, 73.5% having negative attitude towards use of face mask, though 69.8% having the appropriate knowledge on mode of transmission of the virus (p=0.000). Though, 51.6% of the study population, having adequate knowledge, but only 52.1% using face mask (p>0.05) and 51.8% practicing hand washing (p>0.05). More than 70.0% respondents having knowledge on social distancing, but only 50.0% was practicing it. Male respondents had 1.5 times more knowledge about the social distancing than the female counterpart (p=0.000). Conclusion: Public awareness campaign should be enhanced critically focusing the target audience covering the knowledge gaps, motivation for appropriate practices and further improvement of attitudes towards prevention and control of COVID-19 in Bangladesh thus suggested. © 2020 Bangladesh Medical Research Council. All rights reserved.