Navigating the aftermath of the pandemic: exploring school challenges and changes in Bangladesh

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.

Face Masks Use and Face Perception: Social Judgments and Emotion Recognition

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.

COVID-19 Prediction based on Infected Cases and Deaths of Bangladesh using Deep Transfer Learning

The severely infectious virus known as "COVID-19" has wreaked havoc on the planet, trapping to keep the disease from spreading, while billions of people are staying inside. Every experts and professionals in many disciplines are working tirelessly to create a vaccine and preventative techniques to help the globe overcome this difficult crisis. In Bangladesh, the number of persons infected with Coronavirus is particularly alarming. A accurate prognosis of the epidemic, on the other hand, may aid in the management of this contagious illness until a remedy is discovered. This study aims to forecast impending COVID-19 exposed instances and fatalities using a time series dataset utilizing proposed deep transfer learning model where encoder-decoder CNN-LSTM along with deep CNN pretrained models such as: ResNet-50, DenseNet-201, MobileNet-V2, and Inception-ResNet-V2 performed. We also predict the regular exposed instances and fatalities throughout the following 180 days in data visualization segment using AIC and BIC selection criteria. The suggested paradigms are also used to anticipate Bangladesh's daily confirmed cases and daily which is evaluated by error based on three performance criteria. We discovered that ResNet-50 performs better among others for predicting infected case and deaths owing to COVID-19 in Bangladesh in terms of MAPE, MAE and RMSE evaluations.

Modeling Socio-Economic Consequences of COVID-19: An Evidence From Bibliometric Analysis

The COVID-19 pandemic has pushed the world towards a digital era and affected the environment significantly. The present study uses a bibliometric approach to provide a comprehensive overview based on existing literature related to COVID-19 and E-learning and its environmental consequences, particularly from the year 2020-2022. In addition to the terrible impact of the pandemic on the world, environmental advantages have also been noticed. The findings show that the use of E-learning reduces the consumption of paper and prevents the cutting of trees which makes the environment more sustainable. The optimum use of technology leads to the conservation of the environment. Second, E-learning ensures developing and less developing countries to provide education at lower levels or remote areas of the society. The findings also suggest that governments and educational institutions should upgrade technology and digital tools in order to enhance E-learning education. Additionally, reviewing 1807 published articles extracted from the SCOPUS database, enrich literature related to COVID-19, E-learning, and the environment. This study also represents a graphical visualization of the bibliometric analysis using VOSviewer and R studio software. A coupling map and three-field plot also have been used for directions for future research.

Remote Work and Changes in Organizational HR Practices During Corona Pandemic: A Study from Bangladesh

This study aims to offer insightful knowledge on organizational members’ real-life experience of working in a ‘new normal’ environment and explores changes in organizational HR practices and the future of work culture during this pandemic. Applying the qualitative methodology through implementing an in-depth interview technique, this study revealed subjective insights on pandemic impacts within diverse organizations and their coping strategies, that is, remote work practices and technological adaptations. The study found out that HR functionalities powered by different online tools and remote work or flexible roster duties are ensuring employee betterment and organizational productivity at the same time. Pandemic countermeasure oriented or transformed HR practices like online training and e-recruitment are keeping the workforce steady in this distressing time, but the ‘new normal’ lifestyle and evolved work environment, practices are putting much stress on and changing the dimension of work policies like employee well-being, compensation, leave, and so on, through isolation, quarantine and strict health guideline type issues. © 2022 Management Development Institute.

Numerical approach to design the graphene-based multilayered surface plasmon resonance biosensor for the rapid detection of the novel coronavirus

In this article, a graphene-based multilayered surface plasmon resonance (SPR) biosensor of (BK7/WS2/Au/BaTiO3/Graphene) is proposed for the rapid detection of the novel coronavirus (COVID-19). The proposed SPR biosensor is designed based on the angular interrogation attenuated total reflection (ATR) method for rapid detection of the COVID-19 virus. The sensor's surface plasmon polaritons (SPPs) and the sensing region refractive index (RI) are changed, owing to the interaction of various concentrated ligand-analytes. The specific ligand is mechanized with the proposed sensor surface and the target analyte that has flowed onto the sensing surface. The proposed sensor is capable of detecting the COVID-19 virus rapidly in two different ligand-analytes environments, such as: (i) the virus spike receptor-binding domain (RBD) as an analyte and monoclonal antibodies (mAbs) as a probe ligand, and (ii) the monoclonal antibodies (IgG or IgM) as an analyte and the virus spike RBD as a probe ligand. Due to the binding of the target ligand-analytes, the concentration level of the sensing region is incremented. As the increment in the concentration level, the RI of the sensing medium increases, therefore the change in RI causes the shift in the SPR angle resulting in the output reflectance intensity. The performance of the multilayered SPR sensor is analyzed numerically using the finite element method (FEM) method. Numerically, the proposed sensor provides the maximum angular shift sensitivity at 230.77 deg/refractive index unit (RIU), detection accuracy (DA) at 0.161 deg(-1), and the figure of merits (FOM) is at 37.22 RIU-1. In addition, with each additional graphene layer number (L), the proposed sensor exhibits the angular shift sensitivity increment (1 + 0.7L) times. The novelty of the proposed multilayer (BK7/WS2/Au/BaTiO3/Graphene) sensor is highly angular sensitivity, and capable of detecting the COVID-19 virus rapidly without a false-positive report. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Image data augmentation for improving performance of deep learning-based model in pathological lung segmentation

Accurate segmentation of lung fields from chest Xray (CXR) images is very important for subsequent analysis of many pulmonary diseases. Deep Neural Networks (DNN)-based methods have achieved remarkable progress in many image related tasks. However, their performance depends highly on the distribution of training and test samples, and they perform well if both training and test samples are from the same distribution. For example, DNN-based lung segmentation methods perform well on segmentation of healthy lung or lung with mild disease, however their performance is poor on lungs with severe abnormalities. Pulmonary opacification, which blurs the lung boundary, is one of the main reasons. A solution to this problem is data augmentation to increase the pool of training images, however despite the great success of traditional data augmentation techniques for natural images, they are not very effective for medical images. To simulate CXR images with opacification and low contrast, we present a novel image data augmentation technique in this study. To generate an augmented image, we first generate a random area inside the lung and then blur the area with a gaussian filter. Then, low contrast is simulated by adjusting the contrast and brightness. To evaluate the utility of the proposed augmentation technique, we applied it to images with different pulmonary diseases such as tuberculosis, pneumoconiosis and covid-19 from three public datasets as well as a private dataset and compared its effect on segmentation performance with traditional data augmentation techniques. Results suggest that the proposed technique outperforms traditional data augmentation techniques for all datasets on lung segmentation, in terms of Dice Coefficient (DC) and Jaccard Index (JI). Extensive experiments on multiple datasets validate the effectiveness of the proposed data augmentation technique.

Applications of geospatial analyses in health research among homeless people: A systematic scoping review of available evidence

Background: Unequal housing access resulted in more than 150 million homeless people worldwide, with mil-lions more expected to be added every year due to the ongoing climate-related crises. Homeless population has a counterproductive effect on the social, psychological integration efforts by the community and exposure to other severe health-related issues. Geographic Information Systems (GIS) have long been applied in urban planning and policy, housing and homelessness, and health-related research. Methods: We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method to systematically review 24 articles collected from multiple databases (n = 10) that focused on health-related issues among homeless people and used geospatial analysis techniques in their research. Results: Our findings indicated a geographic clustering of case study locations- 26 out of the 31 case study sites are from the USA and Canada. Studies used spatial analysis techniques to identify hotspots, clusters and patterns of patient location and population distribution. Studies also reported relationships among the location of homeless shelters and substance use, discarded needles, different infectious and non-infectious disease clusters. Conclusion: Most studies were restricted in analyzing and visualizing the patterns and disease clusters;however, geospatial analyses techniques are useful and offer diverse techniques for a more sophisticated understanding of the spatial characteristics of the health issues among homeless people. Better integration of GIS in health research among the homeless would help formulate sensible policies to counter health inequities among this vulnerable population group.

Antibody Response to SARS-CoV-2 Infection in Non Vaccinated Health Care Personnel

Background: Antibodies (Abs) are produced by B cells after infection with the SARS/COVID-19 virus. The presence of neutralizing antibody is an indicator of protective immunity for most viral infections. But, we still don’t know how long and how effectively this immune protection will cover. Objectives: This study aimed to estimate the antibodies level in PCR-confirmed COVID-19 subjects in non vaccinated healthcare personnel. Methods: SARS-CoV-2 specific total Abs (IgG and IgM), IgG of nucleocapsid (N) protein and spike (S) protein levels were estimated using two clinically validated and widely used serological assays, detecting antibodies against the Total Antibody, nucleocapsid(N) and spike(S) proteins. Results: A total 130 subjects with PCR-confirmed SARSCoV- 2 infection were included in this study and all subjects were symptomatic and blood samples were collected between 3 to 24 weeks. Of all participants, about 52% were female and mean age was 43.2 years. The study found that the Total Abs, IgG of N protein and neutralizing Abs of S protein were developed 100%, 74.6% and 93.8% respectively. The study also found that the IgG titers of the N protein peaked at about 19 weeks after onset and decreased thereafter. The study also found that the neutralizing Abs of S protein were gradually increasing in the second phase of (9wks-19wks) weeks and in the third phase of (19wks -24wks) weeks after disease onset than compared to the first phase of weeks (3wks- 9wks) and it was significant (p<0.001). Conclusion: The study concluded that the antibodies, total Abs, IgG titer of N protein and neutralizing Abs of S protein were developed 100%, 74.6% and 93.8% respectively. The study also observed that IgG of N protein was decreasing within 19-24 weeks and neutralizing Abs of S protein peaked at 19-24 weeks after the onset of disease. [ 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.)

Design and Implementation of an IoT-Based Healthcare Monitoring System

According to ways-to-die website, over 150,000 people die every day. And the most common cause of death, i.e., about 20% of all deaths, is heart diseases. So, the most crucial contribution from our side to lower this percentage can be to monitor the cardiac values as much as possible. There are conventional methods to measure patients' health and condition, but they are laborious;have possibilities of errors;and nocturnal monitoring has as well been very difficult. Moreover, since 2019, COVID19 has caused more than five million deaths all around the world, as stated by WHO. And it made the physical presence of doctors and caretakers almost impossible. So, we have designed an up-to-date IoT-based project that continuously monitors the patient's body temperature, heart-rate and oxygen saturation level;keep the data readings in display before the patient and in the screen of the doctor's mobile;and it also provides a non-touching handsanitizing system. The proposed design integrates NodeMCU, DS18B20 Temperature sensor, Max30100 Pulse-oximeter, and other required materials in a small box. The readings are as accurate as the conventional medical equipments while it just takes less than a minute of time to perform the whole procedure. The developed project has outperformed the conventional method by providing a safer, less complex, cost effective and faster service. © 2022 IEEE.

Physician's Perception about the Use of PPE, COVID-19 Prevention and Management in Bangladesh

This study aims to explore physician's perceptions about the use of Personal Protective Equipment (PPE), COVID prevention, and management during the COVID pandemic since knowledge on these might explain the reason behind infection and death of physicians in Bangladesh at an unexpected rate. This cross-sectional study was conducted based on an online questionnaire on 346 physicians (n=346) by the Department of Gastroenterology of Mymensingh Medical College Hospital, Bangladesh from 15th July 2020 to 14th September 2020. Physicians of different health care facilities across Bangladesh were invited to take part. Knowledge on specific points of the questionnaire was evaluated, scored, and compared between different groups by Independent sample t-test. Mean knowledge score between the respondents working up to 8 hours and beyond 8 hours per day was 17.28+/-1.28, 16.90+/-1.40 respectively (p=0.03). Mean knowledge score observed between graduate and post-graduate physicians and work experience of 5 years and beyond 5 years were 17.26+/-1.36 vs. 17.16+/-1.27;(p=0.40), 16.87+/-1.75 vs. 17.27+/-1.21;(p=0.11) respectively. Physician's safety should be first concern that is highlighted through proper use of PPE and prevention of COVID. Patient management skills would be better if physicians are trained well on infection prevention and control which in turn will reduce infection and death of physicians.

Early rehabilitation can prevent long COVID pulmonary complications

The long-term sequelae of coronavirus disease 2019 (COVID-19) are only now beginning to be defined, but it is already known that the disease can have direct and indirect impacts mainly on the cardiorespiratory system. The aim of the narrative review is to derive concepts for the treatment based on the experience gained from the early rehabilitation in the treatment of patients with COVID-19, and to prevent long COVID respiratory complications in connection with currently available sources and experiences. An online literature search was conducted June 2020 to January 2021 using Medline, PubMed, Google scholar and manual search to retrieve meta-analyses, systematic reviews, randomized trials, guidelines, recommendations, state of the art, and other peer-reviewed studies investigating the relationship between COVID-19 and early Rehabilitation/mobilization or exercises. Thirty-four articles met the established criteria and the main findings were summarized and described, including indication, contraindication and recommendation for early rehabilitation and exercises prescription. after a detailed observation this review study can predict that long COVID pulmonary complications can be prevented in worth of early rehabilitation.

Impacts of COVID-19 on Small-Scale Dairy Enterprises in an Indian Megacity—Insights from Greater Bengaluru

Natural calamities and pandemics massively affect small-scale entrepreneurs. In this paper, we aim to assess how the COVID-19 pandemic affected small dairy farms in the megacity of Bengaluru, India, where they supply a high share of the milk demand. In 2020 a total of 129 farms were visited before the first lockdown (January to March) and interviewed again after the lockdown had been loosened (August to September). Questions addressed feed supply to dairy cows, milk yield and marketing, and coping strategies for lockdown impacts. Results showed that the share of farmers not feeding concentrates increased from 1% before lockdown to 7% afterward (p < 0.05), and those not offering dry forages increased from 20% to 33% (p < 0.05) due to increasing forage prices. Milk yield dropped in all surveyed farms from 3905 L before to 2861 L after lockdown (p < 0.05) due to the sale of 30% of lactating cows across the farms. Enabling farmers to better cope with shocks through feed storage and by processing their surplus milk into durable products should be prioritised by supporting institutions such as dairy cooperatives. Alternatively, insurance schemes can capacitate farmers to maintain a fresh milk supply to urban consumers in the wake of global challenges. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Bidirectional Convolutional-LSTM based Network for lung segmentation of chest X-ray images

Deep Neural Networks (DNN)-based methods, particularly UNet, are considered as state-of-the-art for many medical imaging tasks. However, despite remarkable progress on segmenting the normal lung, performance of the UNet is unsatisfactory on challenging chest X-ray (CXR) images. This could be due to mainly two limiting factors: (1) skip connections that merge feature maps of similar size from encoding and decoding paths, and (2) loss of spatial information due to repetitive down-sampling operations. To overcome these problems, in this study, we propose a DNN-based new architecture that replaces the skip connections with a bidirectional convolutional-LSTM (BC-LSTM) module that allows exchange of more information between encoder and decoder paths and also capture spatiotemporal information. For further improvement, we add a multiple kernel pooling (MKP) block at the lowest level of UNet to encode more spatial information by different sized pooling operations. To evaluate the performance of our method, we use CXR images with different pulmonary diseases such as tuberculosis, pneumoconiosis, and Covid-19 from four public datasets as well as a private dataset and compare its performance with a standard UNet model. Results suggest that the proposed framework outperforms the UNet for all five datasets on lung segmentation, in terms of two evaluation metrics, namely Dice Coefficient (DC) and Jaccard Index (JI). © 2021 IEEE.

Economical and wearable pulse oximeter using IoT

In this paper, we are presenting economical and wearable pulse oximeter device using sparkfun oximeter sensor and raspberry pi v2 model B. Nowadays whole world is facing COVID-19 pandemic from December 2091 and in corona patient blood oxygen level is goes down due to attack of virus on lungs. In this scenario economical and easy to use pulse oximeter is life saving device. To control pandemic;early detection of patients are important. We have presented easy to develop oximeter in this paper. We have connected sensor to Arduino uno using I2C protocol and using serial communication data is sent do raspberry pi using USB serial communication. We have achieved over 90% accuracy when result are compare with commercial health band. We also did comparative study with other sensor and we found that sparkfun sensor gives more accurate and quick results. Data visualization is implemented using python library. This device can be used as health band or for covid-19 patient monitoring using remotely. © 2021 IEEE.

Impact of SARS-CoV-2 infection on veterinary medical education

SARS-CoV-2 infection is considered an international disaster. The second and third waves of the SARS-CoV-2 pandemic are ongoing. The universities of most countries of the world are closed to prevent the spread of SARS-CoV-2 infection. Many universities of the globe stopped direct classroom teaching, and some started online teaching to minimise the effects of SARS-CoV-2 on education. In this manuscript, an attempt has undertaken to analyse the influence of the SARS-CoV-2 pandemic on global veterinary medical education. We have conducted a literature search in different databases following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines using different keywords to find out peer-reviewed scientific articles about the impact of COVID-19 on veterinary medical education. The literature search generated 17 eligible scientific papers for qualitative analysis of the effect of COVID-19 on veterinary medical education. The COVID-19 pandemic has a severe adverse influence on veterinary medical education. Shifting from direct classroom teaching to online teaching is one of the sweeping impacts. It might be possible to conduct online classes for veterinary medical education. But the supply of electronic devices, motivation to students in self-learning, institutional support etc., are crucial for interactive situated learning of veterinary courses. Research and development of sustainable, worthwhile methods for remote teaching veterinary medical students are essential. Reshaping the veterinary medical education programs using core theory, practical and clinical curricula is crucial for conducting uninterrupted veterinary education programs during current COVID-19 and future pandemics.

r-LSTM: Time Series Forecasting for COVID-19 Confirmed Cases with LSTM-based Framework

The coronavirus disease 2019 (COVID-19) caused a pandemic outbreak with affecting 213 nations worldwide. Global policymakers are imposing many measures to slow and reduce the rapid growth of the infections. On the other hand, the healthcare system is encountering significant challenges for a massive number of COVID-19 confirmed or suspected individuals seeking treatment. Therefore, estimating the number of confirmed cases is necessary to provide valuable insights into the growth of the outbreak and facilitate policy making process. In this study, we apply ARIMA models as well as LSTM-based recurrent neural network to forecast the daily cumulative confirmed cases. The LSTM architecture generates more precise forecasting by leveraging both short- and long-term temporal dependencies from the pandemic time series data. Due to the stochastic nature in optimization and random initialization of weights in neural network, the LSTM based model produce less reproducible outcome. In this paper, we propose a reproducible-LSTM (r-LSTM) framework that produces a reproducible and robust results leveraging z-score outlier detection method. We performed five round of nested cross validation to show the consistency in evaluating model performance. The experimental results demonstrate that r-LSTM outperformed the ARIMA model producing minimum MAPE, RMSE, and MAE.

Epidemiology, risk, myths, pharmacotherapeutic management and socio-economic burden due to novel covid-19: A recent update

The novel human coronavirus disease (COVID-19) is the major pandemic throughout the globe and its occurrence is due to the presence of severe acute respiratory syndrome coronavirus (SARS-CoV2). That began from Wuhan, Hubei province of China in late 2019 and afterward drastically spread worldwide. It effects around 213 countries and territories around the globe and have reported a total of 8,128,490 confirmed cases of COVID-19. As an unprecedented global pandemic it sweeps the planet and affects each and every human being either physically, mentally or economically. The most common symptoms of COVID-19 are pyrexia, tiredness, and dry cough but in some cases it is asymptomatic. It can be diagnosed by a health care provider based on symptoms and confirmed through laboratory tests. Till date there is not even a single drug or vaccine that can be used for the effective treatment for this disease. The international community is to introduce a global synchronized strength to prevent the outbreak that needs a strong public health response, high level political commitment and sufficient funding. The aim of this review article is to summarise the recent state of awareness, epidemiology and social impact on surrounding due to outbreak of COVID-19 pandemic.

The Effects of Social Media Consumption Among the Internet Users During COVID-19 Lockdown in India: Results from an Online Survey

In previous studies it has been found that spending too much time on social media can have negative effects on social and mental wellbeing of the users. Average time spent on social media increased drastically during covid-19 lockdown in India. The present study thus aims to analyze the direct and indirect effects of extensive social media and social networking services' usage during the lockdown in India. A nationwide online survey was conducted through a ‘Google Forms' questionnaire between 30th June 2020 to 27th July 2020. A total of 818 respondents took part in the study. ‘Social media effect index' was constructed using exploratory factor analysis. Ordinal logistic regression was employed to analyze the effect of social media consumption on social and mental wellbeing of respondents. The average time spent on social networking sites in a day increased from 3.08 hours to 5.17 hours.75% people reported an increased time spent on social media and other services during the lockdown. 60% had reported procrastinating due to extensive SNS usage and had also experienced irregularities in sleep pattern. Respondents from age group ‘21 - 30‘ (OR: 0.22, 95% CI: 0.08 - 0.62) were 78% less likely of having a higher index value compared to ‘>30 years’ age group. During the lockdown there has been a significant increase in social media consumption. The study finds mixed effects of social media consumption during lockdown on users however younger participants reported a negative effect of the consumption on their social and mental well being. © 2020. All Rights Reserved.

Real World Solutions for Smart Cities Transportation to be Pandemic Ready

The COVID-19 is the current widespread health disaster. It has speedily spread all over the world causing a massive impact on the health, environmental, social and economic condition of the total world's population. Enormous actions undertaken worldwide to minimize the expansion of this deadly contagion by testing at a large scale, quarantining the suspected people, upholding lockdowns and restricting social gatherings. The transportation sector has been one amid the leading sufferers of Coronavirus. Airlines, railways, and the public transport sector are badly hit due to this coronavirus outbreak. In this paper, we studied the effect of Coronavirus on the various transport sectors all over the world, taking into consideration the worldwide scenario and India's condition as well. Further, this paper analyses the possible ways and measures regarding how the transport services are dealing with this pandemic. © 2020 IEEE.