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Educational technological tools are now an integral part of the education industry. Various platforms used for educational purposes were analyzed to find the perception of the learner;however, the major analyzing trends revolve around Zoom, Google meet, Google Classroom, and Institutional LMS, overlooking the evaluation of the perception of Teachly: an Ed-tech application developed by Harvard Kennedy School. The objective of this study is to determine the perception of students at Stamford University (n = 36) who enrolled and completed a semester at Teachly using descriptive statistics. For precision, a slider scale was used to collect data using the Google form in a semi-structured questionnaire. The data were then analyzed using the mean and standard deviation to find the central tendency and the measure of variability. The analysis confirms that the student has a positive perception towards using Teachly covering Walgito's three components of perception, and it also points out some limitations identified by the student which hampers its future implementation. © 2023, North American Business Press. All rights reserved.
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The COVID-19 pandemic has disrupted education systems worldwide, and as we navigate the post-pandemic period, schools have been predicted to face diverse challenges. Specially, private schools in rural areas of developing countries often operate on small budgets and rely heavily on student fees to sustain their operations. Their challenges are supposed to be bigger. This study aimed to explore the subjective experiences of 14 entrepreneurs-cum-principals (ECPs) from 14 private kindergarten schools in rural Bangladesh, in terms of the post-pandemic school challenges they faced and strategies to overcome them. The study utilised a qualitative approach employing phenomenological inquiry within an interpretivist paradigm. Data were collected through participant observation notes, school documents, and semi-structured interviews. The data were analysed following Auerbach and Silverstein's coding methods, resulting in themes emerging regarding the challenges the ECPs faced and the strategies they adopted to overcome them. The findings are discussed, and recommendations are made. © 2023 ASPE.
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The COVID-19 pandemic not only imposed severe health risks but also raised major challenges to the economy, due to widespread and severe measures to control the spread of the disease. Food value chains were disrupted by restrictions of the movement of people and commodities, which had significant impacts on the livelihoods of smallholder farmers. The purpose of this research is to determine the impact of the COVID-19 pandemic on Bangladeshi vegetable farmers. A total of 320 vegetable farmers were selected from the North-West region of Bangladesh. Both quantitative and qualitative data were collected through a digital survey method. Analysis revealed that around 3-5% of the marketed surplus of the farms was reduced during the pandemic due to the disturbances. The majority of the farm households reported that there was a significant reduction in their family income and, as a consequence, around 38% of farm households had cut down on their food consumption during the pandemic. The farmers were found to follow different strategies to cope with the difficulties and respond to government initiatives to mitigate such impacts. Despite all the restrictions and risks, extension services were still available to help the farmers. On the basis of the findings, this study suggests the importance of collaborative participation of the relevant bodies to decrease the effects of COVID-19 on farm households by employing all available mechanisms and focusing more on identifying effective coping strategies that can be supported in the event of future shocks, for more sustainable and resilient food systems.
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This study investigates engagement activities higher education institutions have been providing to develop a learning culture as well as entrepreneurship skills for undergraduate entrepreneurship education learners in Australia. This research is intended to explore changes and adjustments made in the curriculum of undergraduate entrepreneurship education programmes in selected higher education institutions in Australia due to uncertainties caused by COVID-19. We focused on six Australian universities offering undergraduate entrepreneurship programmes, which were purposefully chosen. Data and information were gathered from the universities' websites, documents available from the same source, the universities' structure of engagement activities, and their curriculum. Previous literature was referred to for models already proposed and executed. By considering the COVID-19 crisis as well as similar types of future uncertainties, the study has identified the necessity of implementing open innovation and experiential learning models in a blended environment and having strong IT infrastructure for sustainable industry-university collaboration to facilitate a learning culture and develop entrepreneurship skills in undergraduate entrepreneurship education learners in Australia. © 2023 by the authors.
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This study combines an integrated transport, land-use, and energy (iTLE) modeling system with traffic microsimulation model and emission simulator for a holistic analysis of COVID-19 pandemic related changes in traffic flows and emissions. An activity-based travel demand model within iTLE informs pandemic traffic operation scenarios for traffic microsimulation modeling. Link-based simulation outputs inform a finer-grained emission estimation process within a MOtor Vehicle Emission Simulator. Results suggest that the overall network performance improves during lockdown as average delays and queue time decrease by 42.04% and 5.9% respectively compared to pre-COVID condition. Emission results reveal that GHG emissions significantly decrease (64%) in lockdown while it starts increasing gradually in post-pandemic period. Link-based emission analysis indicates that major arterial streets achieve a significant reduction in air pollutant emission. The findings of this study will help transportation planners, engineers, and policymakers to devise effective policies for the improvement of transport operations and emissions.
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The effects of the coronavirus disease 2019 (COVID-19) are not only limited to health, they also impinge on the social life and economy of communities around the globe. Challenges faced by developing countries such as Bangladesh were multi-factorial and its rural population was highly vulnerable in this situation due to their cultural and sociodemographic context. Preventive behavioral changes were considered the best way to fight against the virus in absence of specific treatment and vaccines. This study has tried to explain preventive health practices during the COVID-19 pandemic, and aimed to explore the causal relationships of its major determinants through structural equation modeling (SEM) based on reasoned action approach (RAA). This cross-sectional study was conducted in 2020 among 810 rural Bangladeshi respondents aged 18-55 years. Around half of the respondents showed poor knowledge, motivation and practice regarding COVID-19 and its prevention. Along with socio-demographic factors, information, attitude, motivation, and intention of the people were found to be associated with the adoption of preventive health practices. The causal model of the COVID-19 prevention behaviors was assessed and justified through SEM. The model fits well with the empirical data (GFI=0.94, CFI=0.97, NFI=0.97, RMSEA=0.05, SRMR=0.04). Intention significantly influenced COVID-19 prevention behavior directly, showing the highest effect (β=0.89, p<0.001). Attitude (β=0.83, p<0.001) and motivation (β=0.15, p<0.001) also showed significant direct effects on intention. All the predictors together explained 79.6% of the variance for COVID-19 preventive behaviors. Adequate knowledge, a positive attitude, proper motivation, and positive intention can encourage rural adults to adopt healthy behaviors against COVID-19. The theoretical model of the study effectively explained COVID-19 preventive behaviors rationally and provided a roadmap for policy-makers to formulate strategies to combat COVID-19 and any future similar pandemic. © 2023, Mahidol University - ASEAN Institute for Health Development. All rights reserved.
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Objectives: COVID19 pandemic has caused significant health and economic burden globally. Compared with high-income nations, prevalence of COVID19 infections and mortality has been lower in GCC countries, but it was higher than MENA region average. There is limited evidence in the literature on pattern and factors associated with COVID19 infections and deaths, especially for six GCC countries. The study aims to investigate this trend and associations. Method(s): We used world-o-meter online global database for COVID19 infections and deaths, and other databases to capture country-level socio-economic, demographic, and interventional factors linked with COVID19. Trends in monthly COVID19 data were reported via graphs and a negative binomial regression was estimated to determine the association between factors and monthly COVID19 infections and deaths per million population during March 2020 to October 2021. Result(s): An increasing trend observed in monthly COVID19 cases and deaths up to month 8, followed by a drop and then further increasing trend from month 12 to month 18. For COVID19 infections, negative binomial regression estimates incidence rate ratio (IRR) for 'stringency index' as 1.04 (p<0.001), GDP per capita, IRR=0.99 (p<0.001), CVD death rate, IRR= 0.99 (p<0.001), diabetes prevalence, IRR= 2.26 (p=0.001), hospital beds per 1,000 population, IRR= 0.002 (p=0.010) and containment health index, IRR= 0.88 (p=0.037). These factors also appeared to be statistically significantly associated with monthly COVID19 deaths per million population. Conclusion(s): The study contributes to current evidence-base on factors which are potentially associated with COVID19 infections and mortality in six GCC nations. Healthcare policy makers in the region can lessen their COVID19 related health burden by taking appropriate preventing and mitigating measures in relation to factors that have significant associations with the infection and severe disease.Copyright © 2023
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This study aims at exploring the underlying determinants influencing students' continuance intention to use an e-Learning platform during the COVID-19 pandemic. Based on the technology acceptance model and expectation-confirmation model, the study investigated the role of contextual (i.e., social isolation), psychological (academic year loss and cyberchondria), and student support-related (government and institutional supports) determinants on students' continuance intention to use an e-Learning platform during the pandemic. The study collected data from 440 respondents and analyzed those with Structural Equation Modeling. The findings showed that an e-Learning continuance intention during the pandemic is affected by usefulness, ease of use, attitudes, and intention to use the e-Learning platform;while the behavioral intention is influenced by usefulness, ease of use, attitudes, contextual, psychological, and student support-related determinants;and attitudes are impacted by usefulness and ease of use. Moreover, usefulness is predicted by confirmation of expectation;e-satisfaction is forecasted by usefulness and confirmation of expectation;whereas, cyberchondria is influenced by social isolation;fear of academic year loss is influenced by cyberchondria. Finally, intention to use mediated the impact of usefulness, ease of use, attitudes, contextual, psychological, and student support-related determinants on continuance intention. The study contributes to e-Learning literature incorporating contextual, psychological, and student support-related determinants into the technology acceptance model and expectation-confirmation model, which guide policymakers to understand how all levels of students can be brought into the e-Learning platforms that eventually help to eliminate digital discrimination barrier in the academia during any emergency. The policymakers must be careful in designing eLearning platforms since students' e-learning continuance intention may vary due to unprecedented crises, such as COVID-19.
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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.
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Background: Cystic fibrosis (CF) is a chronic, multi-system disease that can greatly affect quality of life, so it is important for people with CF to be closely evaluated. Routine care includes measurement of basic vital signs, which allows providers to assess respiratory, cardiovascular, and nutritional status, all of which are aspects people with CF at high risk of decompensation because of the disease's pathophysiology [1]. Providing patients with home devices can improve access to vital sign monitoring, which in turn can expand the scope of telehealth and bring attention to daily changes in a patient's overall health [2]. We predict that providing patients with medical devices to monitor vitals will benefit their overall health and wellbeing. Method(s): Medical device kits were offered to patients coming for their routine in-person visits at VCU Health Mayland Medical Center. Each kit contained a tape measure, pulse oximeter, thermometer, blood pressure apparatus, and weight scale. Before receiving the kit, patients who agreed to participate in the study filled out a pre-distribution survey that was modeled after the Centers for Disease Control and Prevention Health- Related Quality of Life-14. If patients did not know how to use a device, health care staff instructed them on its use. Twoweeks after they received the kit, patients were emailed a post-distribution survey that assessed the usefulness of each medical device. Result(s): Seventeen of 18 patients (94.4%) agreed to participate in the study. From the pre-distribution survey, 11.8% of patients frequently monitored their vitals;94.1% of those believed that using the devices would help improve the maintenance of their health, and 82.3% were aware of normal values for blood pressure, pulse, oxygen level, and body temperature and how to measure height and weight. All six of the 17 (35.3%) patients who responded to the post-distribution survey stated that the devices had worked as intended and that they did not find the devices too time consuming. Of the five devices that patients received, most patients found the pulse oximeter and blood pressure apparatus to be useful (100%), followed by the weight machine (75%), thermometer (50%), and tape measure (0%). Conclusion(s): Although most patients agreed that monitoring their vital signs at home would help maintain or enhance their health (94.1%), before this study, only two (11.8%) indicated that they regularly self-measured their vital signs. Overall, patients received being provided home devices was overall positively, with the pulse oximeter and blood pressure apparatus being the most popular. Reasons included ease of access and ability to self-triage and determine the urgency of seeing a health care provider if feeling unwell. The results of this study highlight not only patient desires to be more involved with their health, but also the importance of continuing to find ways to optimize remote monitoring during this COVID era.Copyright © 2022, European Cystic Fibrosis Society. All rights reserved
ABSTRACT
Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus and dexamethasone is a glucocorticoid widely used for its treatment. Dexamethasone is not used in non-severe cases due to its immunosuppressant action. So, considering this, Estrogen and Estetrol were tested for the treatment of COVID-19 as they all possess a common steroid ring and dislike dexamethasone, they are immunoenhancer. Virtual screening of test ligands was performed through molecular docking, MM-GBSA, simulations, in silico ADMET and drug-likeness prediction to identify their potential to inhibit the effects of SARS-CoV-2. Results showed that test ligands possess drug-like properties and they are safe as drug candidates. The protein-ligand interaction study revealed that they bind with the amino acid residues at the active site of the target proteins and the test ligands possess better binding potential than Dexamethasone. With protein Mpro, Estetrol and Estrogen showed docking score of -7.240 and -5.491 kcal/mol, and with protein ACE2, Estetrol and Estrogen showed docking score of -5.269 and -4.732 kcal/mol, respectively. Further, MD Simulation was carried out and most of the interactions of molecular docking are preserved during simulation. The prominent interactions that our test ligands showed during MD Simulation are similar to drugs that possess in vitro anticovid activity as shown in recent studies. Hence, our test ligands possessed potential for anticovid activity and they should be further tested through in vitro and in vivo studies for their activity against COVID-19.Communicated by Ramaswamy H. Sarma.
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This paper examines the efficiency and asymmetric multifractal features of NFTs, DeFi, cryptocurrencies, and traditional assets using Asymmetric Multifractal Cross-Correlations Analysis covering the period from November 2017 to February 2022. Considering the full sample with a significant variation among asset classes, the study reveals DeFi-DigiByte is the most efficient while the cryptocurrency-Tether is the least efficient. However, S&P 500 showed high efficiency before COVID-19, and DeFi-Enjin Coin advanced as the most efficient asset during COVID-19. The volatility dynamics of NFTs, DeFi, and cryptocurrencies follow strong nonlinear cross-correlations, but evidence of weaker nonlinearity exists in traditional assets. Additionally, the sensitivity to smaller events in bull markets is high for NFTs and DeFi. The findings have significant implications for portfolio diversification when an investor's portfolio set includes traditional assets and cryptocurrency and relatively new blockchain-based assets like NFTs and DeFi. © 2023 The Authors
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With COVID-19, significant life events can alter how individuals perceive and employ transportation systems. The COVID-19 pandemic has disrupted people's lives for a considerable time and may impact how people see travel and use transportation services. Due to the COVID-19 pandemics' severe physiological and psychological effects and ongoing financial difficulties, critical personnel must continue traveling for necessary tasks. The main aim of this study was to explore the use of taxi services after the Covid 19 pandemic perceived by travelers and commuters. To analyze the factors that influenced how people behave while using taxis for necessary travel during the COVID-19 restrictions imposed in Makkah, Madinah, Riyadh, and other Saudi Arabian cities. Between October 30 and December 15, 2021, 524 Saudi travelers participated in the online questionnaire assessment. Respondents' attitudes, perceptions, and attentiveness regarding taxi services after the lockdown were measured using a categorical scale. Statistical analysis was performed using the IBM SPSS-20 version and the Chi-Square, Phi, and Cramer's V tests to analyze were applied. The results of this study revealed how the COVID-19 outbreak caused some people to rethink their travel. This allows behavior-change approaches to target motives, challenges, and attitudes about changing travel options. © 2023, World Scientific and Engineering Academy and Society. All rights reserved.
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This paper presents a computational approach designed to construct and query a literature-based knowledge graph for predicting novel drug therapeutics. The main objective is to offer a platform that discovers drug combinations from FDA-approved drugs and accelerates their investigations by domain scientists. Specifically, the paper introduced the following algorithms: (1) an algorithm for constructing the knowledge graph from drug, gene, and disease mentions in the biomedical literature;(2) an algorithm for vetting the knowledge graph from drug combinations that may pose a risk of drug interaction;(3) and two querying algorithms for searching the knowledge graph by a single drug or a combination of drugs. The resulting knowledge graph had 844 drugs, 306 gene/protein features, and 19 disease mentions. The original number of drug combinations generated was 2,001. We queried the knowledge graph to eliminate noise generated from chemicals that are not drugs. This step resulted in 614 drug combinations. When vetting the knowledge graph to eliminate the potentially risky drug combinations, it resulted in predicting 200 combinations. Our domain expert manually eliminated extra 54 combinations which left only 146 combination candidates. Our three-layered knowledge graph, empowered by our algorithms, offered a tool that predicted drug combination therapeutics for scientists who can further investigate from the viewpoint of drug targets and side effects. © 2022 IEEE.
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The COVID-19 pandemic places additional constraints on hospitals and medical services. Understanding the period for support requirements for COVID-19 infected admitted to hospitals is critical for resource distribution planning in hospitals, particularly in resource-reserved settings. Machine Learning techniques are being used to approximate a patient's duration of stay in the hospital. This research uses Decision Tree, Random Forest and K-Nearest Neighbors, Voting classifiers, and Stacking classifiers to predict patients' length of stay in the hospital. Due to the imbalance in the dataset, Adaptive Synthetic (ADASYN) was used to resolve the issue, and the permutation feature importance method was employed to find the feature importance scores in identifying important features during the models' development process. The proposed "ADASEML” has shown superior performance to the earlier works, with an accuracy of 80%, precision of 78%, and recall of 80%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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An outbreak of the severe acute respiratory syndrome corona virus (SARS-CoV-2) made face masks use a norm in individuals' daily lives. The information individuals obtained with face perception is potentially affected by regular face mask use. This study investigated the effects of face masks, ethnicities, and sex on the social judgments including sex, age, trustworthiness, facial attractiveness, and approachability. Later, the effects of face masks, ethnicities, and sex, and facial expressions of happy, neutral, and sad faces on valence and arousal were studied. Likert-type scales and Self-Assessment Manikin were used in an online experiment by Psychopy to capture face perception. Only sex influences sex score in an apparent manner, and unmasked faces appear as more attractive. Face masks and ethnicities do not seem to have effects on sex, age, attractiveness, trustworthiness, and approachability. Faces with different expressions influence the scoring in valence and arousal scale. The results of the present study may be informative for the current pandemic for people to have fruitful social engagements. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Technology has played a vital part in improving quality of life, especially in healthcare. Artificial intelligence (AI) and the Internet of Things (IoT) are extensively employed to link accessible medical resources and deliver dependable and effective intelligent healthcare. Body wearable devices have garnered attention as powerful devices for healthcare applications, leading to various commercially available devices for multiple purposes, including individual healthcare, activity alerts, and fitness. The paper aims to cover all the advancements made in the wearable Medical Internet of Things (IoMT) for healthcare systems, which have been scrutinized from the perceptions of their efficacy in detecting, preventing, and monitoring diseases in healthcare. The latest healthcare issues are also included, such as COVID-19 and monkeypox. This paper thoroughly discusses all the directions proposed by the researchers to improve healthcare through wearable devices and artificial intelligence. The approaches adopted by the researchers to improve the overall accuracy, efficiency, and security of the healthcare system are discussed in detail. This paper also highlights all the constraints and opportunities of developing AI enabled IoT-based healthcare systems. © 2023 by the authors.
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Introduction: Severe acute respiratory syndrome coronavirus 2 associated pneumonia (COVID-19) is a modern pandemic. Recent evidence suggests that kidney is an important target organ in COVID-19. High concentration of Angiotensin converting enzyme receptors in the proximal tubules make them an early target. Proximal tubular dysfunction (PTD) may act as an early predictor of acute kidney injury (AKI), need for renal replacement therapy (RRT), intensive care unit (ICU) transfer, mechanical ventilation, hospital length of stay (LOS) and death. Method(s): This prospective observational study was conducted in the COVID unit, Bangabandhu Sheikh Mujib Medical University. 87 COVID-19 patients without preexisting kidney disease were screened for markers of PTD on admission. Patients having at least 2 of the 4 defining markers of PTD (inappropriate uricosuria, renal phosphate leak, normoglycemic glycosuria and proteinuria) positive on admission were considered to have PTD. 35 patients with PTD and 35 without PTD were followed up throughout their hospital stay and compared. Result(s): 52.9% of the patients had at least 2 of the 4 defining markers of PTD positive on admission. The most prevalent markers were proteinuria (66.7%), followed by hyperuricosuria (42.5%), renal phosphate leak (28.7%) and normoglycemic glycosuria (20.7%). Also, 67% patients had renal sodium leak and 32.2% patients had renal potassium leak. Mean age was 55.7 years. 50% of the patients were diabetic. The PTD group had significantly lower oxygen saturation and higher parenchymal involvement on HRCT chest, CRP and LDH compared to the non PTD group on admission. 32.9% patients developed AKI during their hospital stay. PTD group had higher odds of developing AKI (odds ratio 17.5 for stage 1, 24.8 for stage 2 and 25.5 for stage 3;p<0.0001). The mean duration of hospital stay was 9 days higher in the PTD group (p<0.001). PTD group also had higher odds of transferring to ICU (OR=9.4, p=0.002), need for mechanical ventilation (OR=10.1, p=0.002) and death (OR=10.3, p=0.001). There was complete recovery of PTD in 32.6% and complete renal recovery in 47.8% of patients during their hospital stay. 26.1% of the patients who developed AKI required hemodialysis. 11.4% of all patients died. Conclusion(s): Proximal tubular dysfunction is highly prevalent in COVID-19 patients very early in the disease and may act as a predictor of AKI, ICU transfer, need for mechanical ventilation and death. No conflict of interestCopyright © 2023
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Purpose: Using smart mobile devices, called mobile health (mHealth), facilitates providing health services, speeds up the process and reduces the costs and complications of direct services. Also, mHealth has many capabilities and applications in epidemic and pandemic outbreaks. This study aimed to systematically review the mHealth adoption researches in epidemic/pandemic outbreaks and provide some suggestions for future research for tackling for COVID-19. Design/methodology/approach: The results produced in this study are based on the literature analysis of 36 articles on mHealth adoption. To find the relevant studies;searches were done in PubMed, Google, Web of Science and Scopus by related keywords during 2020–2022. After selecting the studies based on the inclusion and exclusion criteria, data were collected by using PRIZMA methods for systematically reviewing the literature. Findings: Of the 727 retrieved studies, 36 studies related to mHealth services during the pandemic situation were included. This has been performed by collecting data including demographic details, methodological details, limitations and significance of relationships between the constructs from the available articles based on the mHealth services. All studies emphasized the positive effect of mHealth for usage in epidemic/pandemic outbreaks. Research limitations/implications: The main applications of mHealth for epidemic/pandemic outbreaks included public health aspects, data management, educational programs, diagnosis as well as treatment. mHealth is an appropriate method for encountering epidemic/pandemic outbreaks due to its extensive applications. In the pandemic outbreak of COVID-19, mHealth is one of the best choices to use in the patient-physician relationship as Tele-visits, using in fever coach, providing real-time information for healthcare providers, population monitoring and detecting the diseases based on data obtained from different locations. These findings will help the mHealth providers to design their services accordingly. Originality/value: This study contributes to the researchers and academicians by providing relevant information regarding the mHealth during the COVID-19 pandemic. This is the first time initiative to explore the research questions and future research direction for the researchers during the COVID-19 outbreak. Based on this, we present a comprehensive and actionable research agenda and practical implications. © 2023, Emerald Publishing Limited.