Opportunities and Challenges in Sustainable Development and Governance in South Asia: Case Study of Bhutan

The future of the post-COVID, climate "red code” world, hugely depends on good governance and a transition to low carbon. World leaders have repeatedly stated a unified goal of establishing a carbon–neutral society by mid-century. Analysis shows that South Asia's strong economic expansion has paved the way toward sustainable development, yet the region still has many unsustainable practices, except for Bhutan. As the first-only carbon-negative country globally, it is vital to extensively study, learn, and optimize Bhutan's best practices to improve global climate practices. Bhutan's three G model (gross domestic product—GDP, greenhouse gasses—GHG, gross national happiness—GNH) expands development metrics beyond GDP to people's happiness and environmental well-being. This study demonstrates how adapting practices from Bhutan, which have been molded by local experiences, problems, and opportunities, would effectively bolster green climate practices in the South Asian region. © 2023, The Author(s).

Exploring the Role of Technology Application in Tourism Events, Festivals and Fairs in the United Arab Emirates: Strategies in the Post Pandemic Period

This chapter aims to explore the role of technology application in tourism events, festivals, and fairs in the The United Arab Emirates (UAE) during the post-pandemic period of COVID-19. The chapter specifically focuses on various technical Apps based on the latest technology that may affect tourism events, festivals, and fairs. Existing literature lacks the ubiquitous role of technology Apps in sustainable tourism development in collaboration with tourism festivals, events, and fairs. The study identifies how tourists are affected by technology application, revealing in particular an increased tourism development and how tourists are continually enthralled by and attracted to tourism festivals, events, and fairs due to the advancement of the latest technology application in tourism. In this chapter, the perspective of the UAE is brought into the discussion. The chapter reveals that technology application in tourism festivals, events, and fairs can ensure sustainable tourism development in the UAE, especially in the post-pandemic period of COVID-19. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.

Generalized Linear Regression Model to Determine the Threshold Effects of Climate Variables on Dengue Fever: A Case Study on Bangladesh

One of the leading causes of the increase in the intensity of dengue fever transmission is thought to be climate change. Examining panel data from January 2000 to December 2021, this study discovered the nonlinear relationship between climate variables and dengue fever cases in Bangladesh. To determine this relationship, in this study, the monthly total rainfall in different years has been divided into two thresholds: (90 to 360 mm) and (<90 or >360 mm), and the daily average temperature in different months of the different years has been divided into four thresholds: (16degreeC to <=20degreeC), (>20degreeC to <=25degreeC), (>25degreeC to <=28degreeC), and (>28degreeC to <=30degreeC). Then, quasi-Poisson and zero-inflated Poisson regression models were applied to assess the relationship. This study found a positive correlation between temperature and dengue incidence and furthermore discovered that, among those four average temperature thresholds, the total number of dengue cases is maximum if the average temperature falls into the threshold (>28degreeC to <=30degreeC) and minimum if the average temperature falls into the threshold (16degreeC to <=20degreeC). This study also discovered that between the two thresholds of monthly total rainfall, the risk of a dengue fever outbreak is approximately two times higher when the monthly total rainfall falls into the thresholds (90 mm to 360 mm) compared to the other threshold. This study concluded that dengue fever incidence rates would be significantly more affected by climate change in regions with warmer temperatures. The number of dengue cases rises rapidly when the temperature rises in the context of moderate to low rainfall. This study highlights the significance of establishing potential temperature and rainfall thresholds for using risk prediction and public health programs to prevent and control dengue fever.Copyright © 2023 Shamima Hossain.

Technology Application in the Tourism and Hospitality Industry of the Middle East Asia

The use of technology has arguably benefited the tourism and hospitality industry of the Middle East. Tourists, on the other side, are also privileged for having easier access to scheduling their trips and finding all of the details they need to schedule the perfect trip with the ubiquitous help from the internet. They can also instantly find the necessary information about any chosen destination by browsing the internet. Theoretically, general technology-enhanced tourism and hospitality are relatively well investigated by researchers, meaning that;investigating the effects of technology-based tourism in the Middle East in the challenging pandemic time can be useful. Thus, this chapter is focused on discussing the advancements of the technology-based tourism and hospitality industry in the Middle East, highlighting the COVID-19 and the post-COVID-19 pandemic period. Current scholarly literature on technology-based tourism in the Middle East is brought into the discussion to generate insightful findings for the tourism policy-makers and relevant stakeholders in the Middle East. Results outline the opportunities and challenges of technology-based tourism in the Middle East with theoretical analysis. Although the chapter has limited discussion on a few Middle Eastern countries, it discovers valuable comprehension for the travelers and tourism policy-makers. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.

Acute Kidney Injury after Covid 19 Infection in Chronic Kidney Disease Patients of Bangladesh: Retrospective Study

Introduction: COVID 19 pandemic has caused unprecedented devastation worldwide. Spectrum of Covid 19 illness is wide and variable. Risk of mortality is increased in chronic kidney disease patients, during coronavirus disease. CKD is an independent risk factor for poor outcome. AKI is also common in COVID-19 patients who are hospitalized. This study was undertaken to see the outcome of Covid-19 infection in CKD patients. Method(s): This retrospective observational study was carried out in the Kidney Foundation Hospital and Research Institute, Bangladesh from January 2021 to July 2022. One hundred CKD patients who were on regular follow up in the outpatient department and developed COVID-19 as confirmed by reverse transcription polymerase chain reaction (RT-PCR) test underwent chart review after they consented to be part of the study. Their clinical parameters, treatment regiments and laboratory investigations were noted in a data collection sheet. Data was analyzed by Statistical Analysis Software. Result(s): The mean age of the patients was 55.2 years. Of them 43% were female. Diabetes mellitus was the most common comorbidity, seen in 65% of the patients. 24% were CKD stage 4 or 5 prior to the onset of COVID-19, rest were of earlier stage. Hospitalization was required in 65.3% patients;41.1% required oxygen, steroid given in 19.8% patients,8.4% required ICU transfer. 7 patients died, all of respiratory failure. Treatment with antiviral, biologics like Tocilizumab and plasma exchange was not commonly done. AKI developed in 28% of the patients during the course of the illness. Males were more prone to develop AKI (p = 0.23). People with longer duration of symptoms had higher incidence of AKI (p < 0.0001). AKI incidence did not vary according to baseline eGFR (p = 0.16). Among those who developed AKI, 17.9% required temporary dialysis and 7.1% went on to develop end stage kidney disease. Interim outcomes such as hospitalization, oxygen requirement, ICU transfer and death did not vary according to development of AKI. Conclusion(s): People with chronic kidney disease and other comorbid conditions are at higher risk for more serious COVID-19 illness. In our study it has been shown that a significant proportion of CKD patients developed AKI after COVID 19 infection of which a number of patients develop end stage kidney disease and required renal replacement therapy. No conflict of interestCopyright © 2023

Late acceptance hill climbing aided chaotic harmony search for feature selection: An empirical analysis on medical data

In today's era of data-driven digital society, there is a huge demand for optimized solutions that essentially reduce the cost of operation, thereby aiming to increase productivity. Processing a huge amount of data, like the Microarray based gene expression data, using machine learning and data mining algorithms has certain limitations in terms of memory and time requirements. This would be more concerning, when a dataset comes with redundant and non-important information. For example, many report-based medical datasets have several non-informative attributes which mislead the classification algorithms. To this end, researchers have been developing several feature selection algorithms that try to discard the redundant information from the raw datasets before feeding them to machine learning algorithms. Metaheuristic based optimization algorithms provide an excellent option to solve feature selection problems. In this paper, we propose a music-inspired harmony search (HS) algorithm based wrapper feature selection method. At the beginning, we use a chaotic mapping to initialize the population of the HS algorithm in order to better coverage of the search space. Further to complement the inferior exploitation of the HS algorithm, we integrate it with the Late Acceptance Hill Climbing (LAHC) method. Thus the combination of these two algorithms provides a good balance between the exploration and exploitation of the HS algorithm. We evaluate the proposed feature selection method on 15 UCI datasets and the obtained results are found to be better than many state-of-the-art methods both in terms of the classification accuracy and the number of features selected. To evaluate the effectiveness of our algorithm, we utilize a combination of precision, recall, F1 score, fitness value, and execution time as performance indicators. These metrics enable us to obtain a comprehensive assessment of the algorithm's abilities and limitations. We also apply our method on 3 microarray based gene expression datasets used for prediction of cancer to ensure the scalability and robustness as a feature selection method in real-life scenarios. In addition to this, we test our approach using the COVID-19 dataset, and it performs better than several metaheuristic based optimization techniques. © 2023

An overview of COVID-19: An emerging infectious disease

COVID-19, a public health emergency, has led to substantial loss of human lives worldwide and posed an unparalleled global health threat. The condition has wreaked havoc on both the economy and the social system. Pandemics evoke a nationwide focused response and also test the structure and competence of the health system. The pandemic serves as yet another reminder that we must invest in public health, build national capacity to detect diseases early and respond quickly to emerging infections, improve and respect our national institutions, and base policymaking on evidence. This chapter briefly discusses the epidemiology of the emerging infectious disease COVID-19 and the essential components for the health system's preparedness against a public health emergency. © 2023 Elsevier Inc. All rights reserved.

Impact Prediction of Online Education During COVID-19 Using Machine Learning: A Case Study

The transition from traditional to online education is challenging and has many obstacles in various situations. Due to the Covid-19 situation, we use digital blended education from the traditional system. However, in some cases, it can harm our student's academic performance. In this research, we aim to identify the factors that impact the student's academic performance in online education. On the other hand, this study also finds the student Cumulative Grade Point Average (CGPA) fluctuation using machine learning classifiers. To achieve this, we survey to gather data perspective of Bangladesh private university, and this data allows us to analyze and classify using machine learning techniques such as Logistic Regression (LR), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Decision Tree (DT), and Random Forest (RF). This study finds Random Forest (RF) outperforms the other state-of-art classifiers. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Forecasting domestic waste generation during successive COVID-19 lockdowns by Bidirectional LSTM super learner neural network

Accurate prediction of domestic waste generation is a challenging task for municipalities to implement sustainable waste management strategies. In the present study, domestic waste generation in the Kingdom of Bahrain, representing a Small Island Developing State (SIDS) case study, has been investigated during successive COVID-19 lockdowns due to the pandemic in 2020. Temporal trends of daily domestic waste generation between 2019 and 2020 and their statistical analyses exhibited remarkable variations highlighting the impact of consecutive COVID-19 lockdowns on domestic waste generation. Machine learning has great potential for predicting solid waste generation rates, but only a few studies utilized deep learning approaches. The state-of-the-art Bidirectional Long Short-Term Memory (BiLSTM) network model as a deep learning method is applied to forecast daily domestic waste data in 2020. Bayesian optimization algorithm (BOA) was hybridized with BiLSTM to generate a super learner approach. The performance of the BOA-BiLSTM super learner model was further compared with the statistical ARIMA model. Performance indicators of the developed models using ARIMA and BiLSTM showed that the latter yielded superior performance for short-term forecasts of domestic waste generation. The MAE, RMSE, MAPE, and R2 were 47.38, 60.73, 256.43, and 0.46, respectively, for the ARIMA model, compared to 3.67, 12.57, 0.24, and 0.96, respectively, for the BiLSTM model. Additionally, the relative errors for the BiLSTM model were lower than those of the ARIMA model. This study highlights that the BiLSTM can be a reliable forecasting tool for solid waste management policymakers during public health emergencies. © 2022 Elsevier B.V.

Exploring the Relationship Between Student Engagement and Authentic Learning During COVID-19 : Moderating Role of Lack of Attention via WeChat

Social networking sites (SNSs) such as WeChat or Facebook can facilitate university students in learning, especially during a deadly epidemic period such as COYID-19. Student engagement is a challenging task for educators in internet-enabled technology-enhanced learning platforms. This research attempts to identify the relationship between student engagement and authentic learning during COYID-19 through the theory of planned behavior (TPB) as a theoretical base. Quantitative data were collected (n = 285) using an online survey technique with the students from a recognized university in China. All six proposed hypotheses, including a moderating and two mediating variables, were found to be supported. The findings indicated that constructs such as affective engagement (AE) and social engagement (SE) are significant predictors of social interaction (SI) that may lead to accomplish authentic learning task (ALTask). Further, lack of attention (LAN) was found to significantly moderate social interaction and authentic learning tasks during COYID-19.

COVID-19 impact on travel and work habits of office workers in Bangladesh

The SARS-CoV-2 virus has brought unprecedented change to the world. Distancing measures make people find an alternative way to interact with others and fulfill their duty. It is acknowledged that the epidemic has dramatically impacted people's work schedules, which in turn has changed how they travel. Till now very few studies were conducted on this new phenomenon. The purpose of this study is to ascertain how COVID-19 has affected the work schedules and travel habits of office workers in Bangladesh and show the comparative scenario before and during the pandemic. The study is based on primary data. Respondents are surveyed through Google Forms. With the response of 342 respondents, primary data were processed and analyzed. Descriptive analyses were conducted to carry out the output. Inferential analysis was applied somewhere to scrutinize the result. The study reveals that there are significant changes in work patterns and travel patterns of office workers in Bangladesh due to COVID-19. People have shifted from offline to online activities. Travel time and trip frequency per week have been reduced greatly. The usage of the bus has reduced rapidly. Instead, people have started to walk or use a rickshaw, and bicycles. In many cases, offices have provided vehicles. The degree of these changes varies among different socioeconomic groups of people. This study is a useful resource for new policy-making insights and could inspire subsequent research.

Forecasting domestic waste generation during successive COVID - 19 lockdowns by Bidirectional LSTM super learner neural network

Accurate prediction of domestic waste generation is a challenging task for municipalities to implement sustainable waste management strategies. In the present study, domestic waste generation in the Kingdom of Bahrain, representing a Small Island Developing State (SIDS) case study, has been investigated during successive COVID-19 lockdowns due to the pandemic in 2020. Temporal trends of daily domestic waste generation between 2019 and 2020 and their statistical analyses exhibited remarkable variations highlighting the impact of consecutive COVID-19 lockdowns on domestic waste generation. Machine learning has great potential for predicting solid waste generation rates, but only a few studies utilized deep learning approaches. The state-of-the-art Bidirectional Long Short-Term Memory (BiLSTM) network model as a deep learning method is applied to forecast daily domestic waste data in 2020. Bayesian optimization algorithm (BOA) was hybridized with BiLSTM to generate a super learner approach. The performance of the BOA-BiLSTM super learner model was further compared with the statistical ARIMA model. Performance indicators of the developed models using ARIMA and BiLSTM showed that the latter yielded superior performance for short-term forecasts of domestic waste generation. The MAE, RMSE, MAPE, and R2 were 47.38, 60.73, 256.43, and 0.46, respectively, for the ARIMA model, compared to 3.67, 12.57, 0.24, and 0.96, respectively, for the BiLSTM model. Additionally, the relative errors for the BiLSTM model were lower than those of the ARIMA model. This study highlights that the BiLSTM can be a reliable forecasting tool for solid waste management policymakers during public health emergencies.

Variants of the COVID Virus before the Onset of Omicron

COVID-19 belongs to a group of Coronavirus diseases that SARS-CoV-2 causes. The virus spreads from one person to another via the respiratory droplets from an infected individual produced when such an individual coughs, talks, or sneezes. The symptoms of the diseases range from mild to severe, and individuals at age extremities, that are, very old (from 65 years of age), are highly exposed to severe complications. The symptoms manifest from the second day, fourteen days after exposure to the virus. COVID strains keep on changing as a result of mutations in the viral genomic composition. Different variants of COVID-19 exist;these variants vary in severity, as reported by the World Health Organization. There are thousands of variants of COVID in the world;the virus mutates all the time, making the changes inconsequential. Some of the mutations make the virus more infectious, and some mutated viral strains tend to be dominant. Variants of concern include those that have the most potentially concerning changes. India is among the nations where the virus strains have been reported to dominate and spread to other nations. The virus is claiming the lives of many individuals, with every strain spreading from one country to another. The article will address the research review on types of COVID variants and COVID-19 epidemiology in the world. Copyright © 2022, Institute of Medico-legal Publication. All rights reserved.

Correlation between user activity at different rooftop typologies in residential buildings in a compact urban context

The scarcity of public open space has compelled urbanites to use residential rooftops as an interaction space. In Dhaka, rooftops are used for various social and recreational purposes which has extensively increased due to COVID-19 restrictions. During this period, few rooftops are used frequently while few are less occupied. Hence, the study identifies different variables that impact rooftop activities and finds correlations between them using the Pearson correlation coefficient. The study further shows the direction for accelerating the use of rooftops as an interaction space in residential buildings. Highlights for public administration, management and planning: Rooftops represent important places of public life in Dhaka. The variables that affect the social and recreational activities of residential rooftop space are analysed. Statistically significant correlations were found between rooftop occupied by various services and number of activities, floor dampness and number of activities, the rooftop occupied by various services and a comfort zone with a pleasant view, parapet height and number of activities, and parapet height and safety. The paper sets recommendations for designing and managing rooftop spaces. © 2022 Sharif Tousif Hossain et al., published by Sciendo.

Clinico-epidemiological Characteristics of Acute Covid-19 Patients in a Tertiary Care Hospital of Dhaka, Bangladesh

Background: The COVID-19 pandemic is a catastrophe enormously affecting the whole world including Bangladesh. This disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) began in Wuhan, China, in December 2019, and since then has been spreading globally. Objectives: To find out the sociodemographic, clinical characteristics and in-hospital outcome of patients of acute COVID-19 confirmed cases in a tertiary care hospital. Methodology: It was a hospital based observational study. Consecutive samples were taken from RT-PCR positive cases (ranging from mild to severe form) admitted in COVID-19 unit of Shaheed Suhrawardy Medical College Hospital, Dhaka during the period of July 1st to August 31st 2020. Socio-demographic and clinical data were collected by using a pretested structured questionnaire. The severity of the COVID- cases was assessed based on the WHO interim guideline. Analysis was done with SPSS (Statistical Package for Social Science) version-23. Results: Total 77 cases were found valid for study according to inclusion and exclusion criteria. Male patients were predominant which was 72.7%. Mean age was found 53.32±13.48 years. The mean age was significantly higher (58.82±11.74 years) in severe group. Fever, shortness of breath (SOB), cough and body ache were the most common presenting symptoms. Body ache was significantly higher in mild & moderate group than severe group. SOB and sore throat were significantly higher in severe group (53.5% vs 79.4%) and (9.3% vs 26.5%) respectively. Diabetes 29(37.7%), hypertension 18(23.4%), CKD 16(20.8%) and hypothyroidism 16(20.8%) were most frequent co-morbidities among the patients. Diabetes, CKD, hypothyroidism and COPD were significantly higher in severe group than mild & moderate group. The mean neutrophil lymphocyte ratio (NLR) was found 1.3±0.3 in mild & moderate group and 2.3±1.0 in severe group. Mean C-reactive protein was found 6.2±2.3 mg/L in mild & moderate group and 8.5±3.9 mg/L in severe group. The mean sodium was found 143.6±7.9 mEq/L in mild & moderate group and 136.5±9.8 mEq/L in severe group. The mean potassium was found 4.7±0.7 mEq/ L in mild & moderate group and 3.9±1.2 mEq/L in severe group. Where X-ray chest (CXR) could reveal abnormalities only in 8 cases (10.4%);HRCT-chest was able to find out abnormalities in 56 cases (72.7%). Abnormal HRCT chest was found in 56 patients among them 48(87.3%) showed normal finding on chest x-ray. The difference was statistically significant between two groups. In HRCT chest abnormalities ground glass opacities (GGOs) was the most frequent observation in 33(58.93%) patients. GGOs was found 12(50.0%) in mild & moderate group and 21(65.63%) in severe group. Major complications were pneumonia 39(50.6%) and severe pneumonia 28(36.4%) respectively. Pneumonia was significant higher in mild & moderate group than severe group (76.7% vs 17.6%). Severe pneumonia was observed 27(79.4%) in severe group. The above-mentioned parameters were statistically significant (p<0.05) between two groups. 2 patients (2.6%) died among them both were in severe group and both were male. Conclusion: Male sex and middle-aged population are mostly affected by the SARS-CoV-2. SOB and sore throat were significantly higher in severe group. Where facilities available strongly suspected individuals may go for HRCTchest. High NLR and CRP as well as lower value of sodium and potassium are good predictors for the severe or critical form of disease spectrum. [ FROM AUTHOR] Copyright of Journal of Bangladesh College of Physicians & Surgeons is the property of Bangladesh College of Physicians & Surgeons 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 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 . (Copyright applies to all s.)

Evaluation of dedicated COVID-19 hospitals in the pandemic response in Iraq: pandemic preparation within a recovering healthcare infrastructure.

The purpose of this study is to evaluate Iraq's health facility preparedness for the surge of hospitalised cases associated with the ongoing COVID-19 pandemic. In this article, we review pandemic preparedness at both general and tertiary hospitals throughout all districts of Iraq. COVID-19 pandemic preparedness, for the purpose of this review, is defined as: (1) staff to patient ratio, (2) personal protective equipment (PPE) to staff ratio, (3) infection control measures training and compliance and (4) laboratory and surveillance capacity. Despite the designation of facilities as COVID-19 referral hospitals, we did not find any increased preparedness with regard to staffing and PPE allocation. COVID-19 designated hospital reported an increased mean number of respiratory therapists as well as sufficient intensive care unit staff, but this did not reach significant levels. Non-COVID-19 facilities tended to have higher mean numbers of registered nurses, cleaning staff and laboratory staff, whereas the COVID-19 facilities were allocated additional N-95 masks (554.54 vs 147.76), gowns (226.72 vs 104.14) and boot coverings (170.48 vs 86.8) per 10 staff, but none of these differences were statistically significant. Though COVID-19 facilities were able to make increased requisitions for PPE supplies, all facility types reported unfulfilled requisitions, which is more likely a reflection of global storage rather than Iraq's preparedness for the pandemic. Incorporating future pandemic preparedness into health system strengthening efforts across facilities, including supplies, staffing and training acquisition, retention and training, are critical to Iraq's future success in mitigating the ongoing impact of the ongoing COVID-19 pandemic.

Extent and causes of increased domestic violence during the COVID-19 pandemic: community health worker perspectives from Kenya, Bangladesh, and Haiti

Background: Emerging data, media reports, and anecdotal evidence suggest that domestic violence (DV) has increased during the COVID-19 pandemic. However, more detailed data are needed on the magnitude, forms, and causes of DV during COVID-19 in different contexts worldwide. We sought to contribute such evidence from the perspective of community health workers (CHWs) in low-middle income countries in three different regions of the world.

Multi-employer work and personal support workers: Employment trends in the long-term care workforce

Background: In Canadian nursing homes, ∼75-90% of direct resident care is provided by personal support workers (PSWs). However, despite providing the majority of direct care to residents, PSWs require the lowest level of education among healthcare providers to practice in long-term care, they are afforded the least autonomy among their colleagues, and receive the lowest salary in the sector. In addition there is a persistent reliance on part-time, temporary, casual, or shift-work contracts. Furthermore, many PSWs have multiple employers or work more than full-time hours across several facilities to earn a living wage. It is more important now than ever that we take a closer look at the this workforce to consider its structure, workplace arrangements, and how these characteristics directly impact the workforce that our long-term care systems rely on. We conducted a descriptive analysis to better understand the characteristics, including sociodemographic and health, of PSWs who practice multi-employer work. Methods: We recruited PSWs from multiple nursing homes (n=26) across Ontario, Canada. Survey data was collected between February and September 2021. The survey specifically captured the following domains: participant sociodemographics, workplace arrangements, history of COVID-19, vaccination status, health status and health care use. Results: 323 PSWs completed the survey and were included in the analysis. Of these, 22% (n=71) held 2 or more jobs. Of those that held 2 or more jobs, 84% (n=60) self-identifed as female. With respect to immigration status, 58% (n=147) of PSWs who only held one job identified as immigrants, whereas 66% (n=47) of PSWs who held 2 or more jobs were immigrants. Finally, there were no significant differences in self-reported health status. 60% of those with 2 or more jobs reported excellent health, while similarly, 58% of those with 1 job also reported “excellent” when asked about their health status. Conclusions: Our findings indicate there is a trend among this workforce in holding additional jobs, suggesting it is worth exploring why they do so, and what our long-term care systems can do to better support this workforce. As such, follow-up interviews are underway.

Detecting Public Outlook Towards Vaccination Using Machine Learning Approaches: A Systematic Review

Vaccination is an effective measure to prevent the spread of harmful diseases. The prevalence towards vaccine hesitancy, however, has been growing throughout the years and expressed openly in various social media platforms. Research works on automating the detection of public’s opinion towards vaccination in social media has recently gained significant popularity with the rise of the COVID-19 pandemic. This paper presents a systematic review on the machine learning approaches used by researchers to detect the inclination of the public towards vaccination. We analyzed the research work conducted within the past five years and summarized their findings. Our systematic review reveals that Support Vector Machine is the most widely used machine learning technique in identifying public sentiment towards vaccination producing the best performance with an F1-score of 97.3, while Twitter is found to be the most popular platform for extracting source of data. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Supervised Method Pursued For Overall Impact of Online Class During Lockdown in Bangladesh

The purpose behind this survey is to know what is the perspective of teachers and students on online education and to predict the future of online education in Bangladesh by collaborating with their perspective. The participants are students and teachers from the school, college and university of Bangladesh. The data for this research has been collected through a google form where there are two separate sections of the question set both for teachers and students. There are already some existing works on this topic but they only focus on the perspective of the students on online education and they only show what students feel about online learning. They don't give us the data what teachers have to say about online learning and they don't also mention the steps we should take to make online learning system suitable both for teachers and students. As we know to have a proper education environment the cooperation between both students and teachers should be satisfactory. So, by our survey we tried to cover the point of view of both students and teachers and by comparing their viewpoint we will give the decision if this online education will be fruitful and if it's not then what steps we should take to make it fruitful. This research can help both students and teachers to know each other's points of view so they can participate in online learning sessions by cooperating with each other and also can help the education sectors to know what they should do to support all the students and teachers with online education. © 2021 IEEE.