Effect of Covid-19 lockdown on traffic and the crash rate of national highway: Evidence from Bangladesh

This study aims to evaluate the impacts of the COVID-19 lockdown on traffic volume of national highways connecting Dhaka with other divisional cities considering pre, during and post-lockdown periods during COVID-19. Bangladesh Government imposed countrywide lockdown at different steps in different timeline, based on the dissemination rate of COVID-19 virus. As a part of controlling measures, the first lockdown was imposed on March 2020 and vehicular movement on highways connecting capital Dhaka with other divisional cities got banned. Thus, the vehicular traffic contributing to Dhaka using different highways got lessened over period. Before imposing every movement ban, people migrated and left cities. Considering all these scenarios, traffic volume has been studied for the eight National Highways (N1-N8). Along with this, the change in road crash rate over these periods has also been studied. Although it seems that, with the reduction of vehicular movements on road the crash rate would also be lessened, but the observed scenario is opposite. For example, on N1 from March 9 to March 25, 2019, the crash number was 3 and the fatality rate was 4, however in 2020, the numbers were 4 and 18. Moreover, the crash number on N5 was 6 during the shutdown period from March 26 to May 29, 2020, and it was 5 in 2019. The fatality rates were the same in both times, indicating that the travel restrictions did not reduce the number of crashes. The main causes of these collisions during the lockdown were mostly irresponsible driving and high speeds due to comparatively low traffic volume. On the other hand, the crash number on N7 was 17 after shutdown from 30 May 2020 to 28 November 2020, and it was 15 in 2019. It appears that, because passenger vehicle movement was restricted for a long period, vehicular mobility was exegeted, resulting in a rise in ADT values on national highways, as well as an increase in crash counts. Each year, many unexpected crashes occur on these national highways due to uncontrolled driving, overtaking, and high speeds. The study findings can help policy makers to understand the factors behind roadway crashes on the highways during the COVID-19 period. It would eventually govern reliable, efficient roadway system ensuring mobility with safety. © 2023 Author(s).

Framework for Undergraduate Entrepreneurship Education in Australia: Preliminary Exploration

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.

Communicating health and risk information among senior citizens in Bangladesh

The coronavirus disease-2019 (COVID-19) pandemic has affected human being in multiple ways all over the world. Senior citizens are more likely to get sick from COVID-19 compared to other age groups. Little is known about ways to deliver the messages to adult people to get the best outcome. We conducted a direct telephone/mobile phone-based cross-sectional survey among individuals who were aged >60 years old in Bangladesh. Verbal consent was taken and the snowball sampling method was used to reach the participants. This study showed that the most common sources of information about COVID-19 were family members, relatives, friends, and electronic media. Hence, 36.8% participants perceived that the media massages about COVID-19 were difficult to understand. Meanwhile, 53% participants could not understand the meanings news and information as those were presented using unknown terminologies. From the findings it can be concluded that the media messages should be communicated in a way that are context-specific and understandable, especially using more convenient terminology for better understanding for all levels of people. © 2023, Intelektual Pustaka Media Utama. All rights reserved.

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.

Deep Viewing for Covid-19 Detection from X-Ray Using CNN Based Architecture

The Covid-19 coronavirus has turned into a serious, life-threatening disease that is prevalent worldwide as it is most likely to infect. An automated protocol system is a compelling idea to stop the spread of covid19. This article aims at a deep learning model supported by a convolutional neural network (CNN) to facilitate automatic diagnosis from chest X-rays. A collection of 2875 covid19 images and 10293 X-ray pictures to recognize covid19 counts is being used as the data set for the drafting. From the experimental results, it can be seen that the proposed structure achieves 96% specificity, 97% AUC 96% accuracy, 96% sensitivity, and 96% F1-score. Therefore, the results of the proposed system will help clinicians and researchers discover COVID-19 patients and facilitate the treatment of COVID-19 patients.

Design of a low cost Ultraviolet Disinfection unit to minimize the cross-contamination of COVID-19 in transport

In this work, a cost-effective disinfection system for Coronavirus Disease of 2019 (COVID-19) is proposed to be used inside public transport. The disinfection system is twofold, firstly containing a tower unit where UV-C (Ultraviolet type-C) lamps are positioned in parallel, in such a way that, 360-degree space is covered, and secondly a power unit that incorporates robotics and electrical parts. The UVC unit is a separate and movable tower that can be placed anywhere inside a vehicle horizontally or vertically. UV lamps in the tower have a 254 nm wavelength with a total power of 180 Watt. The system can provide a dose of it 16.9 mj/cm2 within 26.83 seconds if the distance of the targeted surface inside a vehicle from the UVC light source is 1.5 meters. Various distances from the UV source to the targeted surface inside the vehicle are chosen and calculated the required corresponding times to achieve the required dose to inactivate all viral concentrations. The developed disinfection system not only minimizes the growth of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by performing robotic features ensuring human detection auto turn off but also utilizes minimum labor work which is vital in the current Covid-19 pandemic. © 2022 IEEE.

MODELING SARS-CoV-2 SPREAD WITH DYNAMIC ISOLATION

Background: The SARS-CoV-2 pandemic is spreading with a greater intensity across the globe. The synchrony of public health interventions and epidemic waves signify the importance of evaluation of the underline interventions. Method: We developed a mathematical model to present the transmission dynamics of SARSCoV-2 and to analyze the impact of key nonpharmaceutical interventions such as isolation and screening program on the disease outcomes to the people of New Jersey, USA. We introduced a dynamic isolation of susceptible population with a constant (imposed) and infection oriented interventions. Epidemiological and demographic data are used to estimate the model parameters. The baseline case was explored further to showcase several critical and predictive scenarios. Results and analysis: The model simulations are in good agreement with the infection data for the period of 5 March 2020 to 31 January 2021. Dynamic isolation and screening program are found to be potential measures that can alter the course of epidemic. A 7% increase in isolation rate may result in a 31% reduction of epidemic peak whereas a 3 times increase in screening rate may reduce the epidemic peak by 35%. The model predicts that nearly 9.7% to 12% of the total population of New Jersey may become infected within the middle of July 2021 along with 24.6 to 27.3 thousand cumulative deaths. Within a wide spectrum of probable scenarios, there is a possibility of third wave. Conclusion: Our findings could be informative to the public health community to contain the pandemic in the case of economy reopening under a limited or no vaccine coverage. Additional epidemic waves can be avoided by appropriate screening and isolation plans. © 2022 Mathematics in Applied Sciences and Engineering. All rights reserved.

Covid-19 Classification Using HOG-SVM and Deep Learning Models

COVID-19 is measured as the biggest hazardous and fast infectious grief for the human body which has a severe impact on lives, health, and the community all over the world. It is still spreading throughout the world with different variants which is silently killing many lives globally. Thus, earlier diagnosis and accurate detection of COVID-19 cases are essential to protect global lives. Diagnosis COVID-19 through chest X-ray images is one of the best solutions to detect the virus in the infected person properly and quickly at a low cost. Encouraged by the existing research, in this paper, we proposed a hybrid model to classify the Covid cases and non-Covid cases with chest X-ray images based on feature extraction, machine learning and deep learning techniques. Two feature extractors, Histogram Oriented Gradient (HOG) and CNN (MobileNetV2, Sequential, ResNet152V2) are used to train the model. For the classification, we utilized two approaches: Support Vector Machine (SVM) for machine learning and CNN (MobileNetV2, Sequential, ResNet152V2) classifiers for deep learning. The experimental result analysis shows that the Sequential model and the ResNet152V2 model achieve 100% and 82.6% accuracy respectively which is satisfactory. On the other hand, the HOG-SVM method successfully detects all the test images correctly which provides the best result with 100% accuracy, specificity, and responsiveness over a limited public dataset. © 2022 IEEE.

Use of Antimicrobials among Suspected COVID-19 Patients at Selected 12 Hospitals in Bangladesh: Findings from the First Wave of COVID-19 Pandemic

Background. Antimicrobials are empirically used in COVID-19 patients resulting in inappropriate stewardship and increased antimicrobial resistance. Our objective was to assess antimicrobial use among suspected COVID-19 in-patients while waiting for the COVID-19 test report. Methods. From March to August 2020, we collected data from in-patients of 12 tertiary-level hospitals across Bangladesh. We identified suspected COVID-19 patients;collected information on antimicrobial received within 24 h before and on hospitalization;and tested nasopharyngeal swab for SARS-CoV-2 using rRT-PCR. We used descriptive statistics and a regression model for data analysis. Results. Among 1188 suspected COVID-19 patients, the median age was 34 years (IQR:2-56), 69% were male, 40% had comorbidities, 53% required oxygen, and 1% required ICU or ventilation support after admission. Antibiotics were used in 92% of patients, 47% within 24 h before, and 89% on admission. Patients also received antiviral, mostly favipiravir (1%) and antiparasitic drugs particularly ivermectin (3%). Third-generation cephalosporin use was the highest (708;60%), followed by macrolide (481;40%), and the majority (853;78%) who took antibiotics were SARS-CoV-2 negative. On admission, 77% mild and 94% moderately ill patients received antibiotics. Before admission, 3% patients had two antibiotics, and on admission, 27% received two to four classes of antibiotics at the same time. According to WHO AWaRe classification, the Watch group antibiotics were mostly used before (43%) as well as on admission (80%). Reserve group antibiotic particularly linezolid was used in 1% patients includes mild cases on admission. Antibiotic use on admission was higher among severely ill patients (AOR = 11.7;95%CI:4.5-30.1) and those who received antibiotics within 24 h before hospital admission (AOR = 1.6;95%CI:1.0-2.5). Antimicrobials used among suspected COVID-19 patients and SARS-CoV-2 positive and negative patients 24 h before and on hospital admission at 12 selected hospitals in Bangladesh, March-August 2020 Antimicrobials used on admission among suspected COVID-19 patients according to disease severity at 12 selected hospitals in Bangladesh, March-August 2020 Conclusion. Antimicrobial use was highly prevalent among suspected COVID-19 in-patients in Bangladesh. Initiating treatment with Watch group antibiotics like third-generation cephalosporin and azithromycin among mild to moderately ill patients were common. Promoting antimicrobial stewardship with monitoring is essential to prevent blanket antibiotic use, thereby mitigating antimicrobial resistance.

Depression and Behavioral Changes Associated with Social Media Dependency During COVID-19 Pandemic Among University Students in Bangladesh: A Cross-Sectional Study

Background: With its rapid spread, the coronavirus disease 2019 (COVID-19) pandemic had a detrimental effect on students' psychological well-being, depression, and behavioral changes due to indefinite educational leaves, lockdowns, restricted outdoor activities, and excess use of social media. This study aims to assess the relationship of social media exposure with the psychological well-being, depression, and behavioral changes of Bangladeshi university students. Methods: A web-based cross-sectional survey was carried out on 530 students from June 17 to July 10, 2020, to evaluate psychological well-being, depression, behavioral changes, and social media exposure via self-reported measures. Results: The prevalence of factors were as follows: poor psychological well-being was 24.9%;moderate to severe depression was 56.6%;severe behavioral changes was 32.1%;and of moderate to severe addiction to social media exposure was 38.3%. All factors were positively associated with social media exposure. Multivariate logistic regression showed that the addiction of participants to social media was 7.488 times higher with severe behavioral changes (OR: 7.488;95% CI 4.708-11.909), 2.299 times higher with poor psychological functioning (OR: 2.299;95% CI 1.421-3.721), 30.54 times higher with severe depressed (OR: 30.54;95% CI 15.0-62.177) than that of individuals without such symptoms. Conclusions: The above findings imply that the government needs to pay greater attention to improve the overall situation of Bangladeshi university students.

Design and Development of a low cost Ultraviolet Disinfection system to reduce the cross infection of SARS-CoV-2 in ambulances

In this study, a low-cost Ultraviolet disinfection system is proposed to be used inside ambulances for minimizing the cross-infection of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) during patient transfer. The disinfection system consists of a tower unit that contains the Ultraviolet type C (UVC) light fixture and a control box where the power unit is placed. The UVC tower unit is portable, lightweight, and can be easily placed anywhere inside an ambulance. Two ultraviolet (UV) lamps used in the tower part have 254 nm wavelength with a total power of 180 Watt. The disinfection system can provide a dose of 16.9mj/cm2 within 1.06 seconds and 26.83 seconds if the distance of the targeted surface inside the ambulance from UV sources are 0.3 meters and 1.5 meters respectively. We have chosen various distances from UV source to targeted surface inside an ambulance and calculated the required corresponding times to reach the required dose to inactivate all viral concentrations. The designed disinfection system not only reduces the spread of SARS-CoV-2 by the semi-autonomous way inside ambulances but also requires the least labor efforts which are crucial in the current Covid-19 pandemic. © 2021 IEEE.

A Comparative Study of Particulate Matter Between New Delhi, India and Riyadh, Saudi Arabia During the COVID-19 Lockdown Period

Novel Coronavirus disease (COVID-19), after being identified in late December 2019 in Wuhan city of China, spread very fast and has affected all the countries in the world. The impact of lockdowns on particulate matter during the lockdown period needs attention to explore the correlation between anthropogenic and natural emissions. The current study has demonstrated the changes in fine particulate matter PM2.5, PM10 and their effect on air quality during the lockdown. The air quality before the lockdown was low in New Delhi (India) and Riyadh (Saudi Arabia), among major cities worldwide. The air quality of India is influenced by dust and sand from the desert and surrounding areas. Thus, the current study becomes important to analyse changes in the air quality of the Indian sub-continent as impacted by dust storms from long distances. The result indicated a significant reduction of PM2.5 and PM10 from 93.24 to 37.89 mu g/m(3) and from 176.55 to 98.87 mu g/m(3) during the lockdown period as compared to pre lockdown period, respectively. The study shows that average concentrations of PM10 and PM2.5 have declined by -44% and -59% during the lockdown period in Delhi. The average value of median PM10 was calculated at 33.71 mu g/m(3) for Riyadh, which was lower than that value for New Delhi during the same period. The values of PM10 were different for pre and during the lockdown periods in Riyadh, indicating the considerable influence on air quality, especially the concentration of PM10, from both the natural (sand and dust storms) and the anthropogenic sources during the lockdown periods. However, relatively smaller gains in the improvement of air quality in Riyadh were correlated to the imposition of milder lockdown and the predominance of natural factors over the anthropogenic factors there. The Air Quality Index (AQI) data for Delhi showed the air quality to be 'satisfactory' and in the green category during the lockdown period. This study attempts to better understand the impact of particulate matter on the short- and long-term air quality in Delhi during the lockdown. This study has the scope of being scaled up nationwide, and this might be helpful in formulation air pollution reduction and sustainable management policies in the future.

Maternal and Fetal Outcome of Pregnancy with COVID-19 in A Tertiary Level Hospital, Bangladesh

Introduction: Pregnant women have long been recognized as a vulnerable population during infectious disease pandemics due to physiological changes in the immune, pulmonary, cardiac and coagulation systems. It is essential to acquire knowledge of pregnancy outcomes, potential complications and neonatal health conditions born to an infected mother with COVID-19. Material and methods: This cross-sectional observational study was conducted in Combined Military Hospital (CMH), Jashore from June 2020 to July 2021 among 100 hospitalized laboratory-confirmed COVID-19 positive pregnant women, patients who had clinical symptoms of COVID but RT PCR negative were excluded. The aim of the study was to evaluate the clinical profile and maternal and fetal outcome of pregnancy. Relevant data were recorded in a preformed data collection sheet and analyzed by SPSS version 20. Results: Among 100 COVID-19 positive hospitalized pregnant women, the mean age of participants was 27years (range 19-40 years), Maximum infection rate observed during 12 to 28 weeks of gestation among the participants, 21% got infected at 37 to 40 weeks of gestation and 20% got infected at 32 to 36 weeks. Seventy-four percent patients underwent delivery during the study & 23% of them continued with ongoing pregnancy;67 of the participants underwent LUCS and 7 vaginal deliveries were done, 3% had abortion and IUFD 1%,61% were multipara and 39% were Primipara, associated co-morbidities were subclinical hypothyroidism(15%), pregnancy induced HTN(12%) and GDM(8%);36% participants were asymptomatic and 44% had mild symptoms, rate of LUCS was higher than (90.64%) vaginal delivery. Among the 73 live births, 80.82% were term and 10.18% were preterm of neonates, small for gestational was seen in the case of 20.55% neonates. Testing for SARS-CoV-2 was performed in all neonatal throat swabs and found positive in one case only. Eighty-six percent neonates were well-baby and 9.58% neonates required NICU admission. There were 2 neonatal deaths due to severe prematurity and 2 babies were found to have congenital cardiac anomaly and cleft lip, cleft palate. Though 36% of patients were asymptomatic but 10% were severe and in the critical stage. HDU support needed for 8% of patients and ICU support for 6%. Conclusion: This cross-sectional study supports that pregnant women with COVID-19 infection are at increased risk of adverse pregnancy and birth outcomes and a low risk of congenital transmission. Availability of ICU in critical conditions is needed for better pregnancy outcomes. [ 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.)

Deep Viewing for Covid-19 Detection from X-Ray Using CNN Based Architecture

The Covid-19 coronavirus has turned into a serious, life-threatening disease that is prevalent worldwide as it is most likely to infect. An automated protocol system is a compelling idea to stop the spread of covid19. This article aims at a deep learning model supported by a convolutional neural network (CNN) to facilitate automatic diagnosis from chest X-rays. A collection of 2875 covid19 images and 10293 X-ray pictures to recognize covid19 counts is being used as the data set for the drafting. From the experimental results, it can be seen that the proposed structure achieves 96% specificity, 97% AUC 96% accuracy, 96 % sensitivity, and 96 % F1-score. Therefore, the results of the proposed system will help clinicians and researchers discover COVID-19 patients and facilitate the treatment of COVID-19 patients. © 2021 Institute of Advanced Engineering and Science (IAES).

Performance of Pharmacy Students in a Communications Course Delivered Online During the COVID-19 Pandemic

Objective. To examine pharmacy student readiness, reception, and performance in a communications course during the COVID-19 pandemic and to compare that with the performance of students who completed the same course in person the previous year. Methods. First-year Doctor of Pharmacy students (2020 cohort) enrolled in a professional communications course completed pre- and post-course surveys regarding their readiness for and changes in perception of online learning. Student learning was assessed using midterm and final examination grades. These grades were then compared with those of students who had completed the same course in person (on campus) the previous year (2019 cohort). Results. Students' preference for face-to-face instruction decreased from the pre-course to the postcourse survey as indicated by responses made using a five-point Likert-scale (difference in means = -1.59;p < .05). Their comfort level with online learning increased (difference in means = +0.38, p < .05) by the end of the course. Students did not perceive any appreciable changes in rapport with the instructor by the end of the study. Course performance of students in the online cohort did not differ significantly from that of the 2019 cohort (p> .05). Conclusion. This study demonstrated that first year PharmD students were already somewhat prepared for online learning when they began a communication course, with further adjustment occurring as the quarter progressed. Remote online learning did not seem to impact pharmacy student learning in this communications course conducted during the COVID-19 crisis.

Assessment of Mortality and Morbidity in Covid patients with Previous Pulmonary Pathology of SKZH/CMH and Covid Isolation Hospital Muzaffarabad, AJK

Aim: To study the severity of symptoms, rates of mortality and morbidity in COVID patients with and without previous pulmonary pathology. Methodology: The cohort study consisted of 244 patients and nearly all the individuals had underlying diseases. Data collection forms included demographic data, medical history, history of exposure to infection, symptoms, signs, laboratory findings, HRCT results, treatment measures especially history of corticosteroid use, and duration of illness. Results: In 244 patients, 180 patients were having the pulmonary pathology and other 64 were having no pulmonary pathology. 77.2% (139/180) of the patients showed severe symptoms in the previous pulmonary pathology while 21.8% (10/64) showed severe symptoms in the group with no pulmonary pathology. 16.1% (29/180) patients died because of COVID and were also having pulmonary pathology. While 10.9% (7/64) patients died in the group having no pulmonary pathology. Conclusion: In this study, 16.1% patients died of COVID with pulmonary pathology. While 10.9% patients died having no pulmonary pathology. 77.2% of the patients showed severe symptoms with previous pulmonary pathology while 21.8% showed severe symptoms with no pulmonary pathology.

Assessing disruptive crises of supply chain in firms amidst COVID-19: An application of multi category-SVM using k-means clustering

Supply chains are encountering more uncertain conditions and risks. Disruptions that impede the flow of material through a supply chain that can also result in failure to deliver end goods are a significant category of risks. The consequence of the Covid-19 outbreak has led to shut down production in the supply chain system, resulting in significant impediments for many foreign supply-dependent enterprises. The constraints cause substantial disruptions of the supply chain, production delays, and supplier delays. In recent years, managing supply chain risks has been given more importance to protect supply chains from interruptions by forecasts and prevention. The effects of disruptions on logistics, costs, demand, profits, and inventory levels of the supply chain are analyzed. SVM is one of the most convenient and effective supervised learning algorithms commonly used for classification and regression challenges. This paper presents a modernistic machine learning model, multi-category support vector machines (MC-SVM) algorithm through training on selected samples. In order to abet MC-SVM model to perform well on imbalanced data, k-means clustering algorithm has been proposed to classify clusters of nodes at-disruption, which share similar interruption profiles and can find the relationships between the data object, provide massive information and contribute significantly to accelerating classification and prediction of the SVM model. Data from portfolios of different firms in pharmaceutical industry has been used to train the MC-SVM model which maps the economic performance of a firm to a certain type of supply chain disruption (SCD). The potentiality of this research will privilege better management of the supply chain and thus will permit a network to approach faster response times to the customer, lower costs in all respects of the chain and to the end customer terrific levels of stretch-ability, lower inventories throughout the chain, and diminished the bottleneck effect in the supply chain logistics. © IEOM Society International.

Covid-19 Lockdown and Changes of Air Quality in the Most Polluted City of Bangladesh

Air pollution is now a serious issue all over the world. Especially, people of developing countries are seriously affected by air pollution because, like other pollution, air pollution is not given importance. Due to the covid-19 lockdown, pollution is reduced, and as expected, the air quality of Dhaka city has improved. Daily AQI data was collected for the months of April, May and June (2020) and compared with the last six years of data for these months respectively. It was found that the mean AQI of Dhaka city in April, May, and June lower than the last six years in the same period. The mean AQI decreased 43.52 %, 22.37 %, 9,82 %, 16.38 %, 41.43 %, 34.16 % in April when compared with April 2014-2019 respectively and the mean AQI decreased 33.69 %, 37.97 %, 39.25 %, 36.81 %, 45.59 %, 44.15 % in May when compared with May 2014-2019 respectively. The mean AQI decreased 26.48 %, 11.40 %, 8.28 %, 30.61 %, 36.37 % and increase 3.07 % in June (2020) when it compared with June 2014-2019 respectively. This study includes the statistical examination of air quality before and at the time of covid-19 lockdown in Dhaka city. [ABSTRACT FROM AUTHOR] Copyright of Journal of Scientific Research is the property of Rajshahi University, Faculty of Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

COVID-19 and global clothing retailers' responsibility to vulnerable workers: NGO counter-rhetoric

Purpose This article aims to examine how non-governmental organisations (NGOs)' narratives portray the vulnerability of workers in global clothing supply chains during the COVID-19 crisis. Design/methodology/approach The research analyses the rhetoric in global clothing retailers' and NGOs' counter-rhetoric during the first seven months of 2020. Findings During this period, retailers employed rhetorical strategies to legitimise irresponsible actions (corporate hegemony prevailed), while NGOs embraced forms of counter-rhetoric trying to delegitimise the retailers' logic, stressing the role of neoliberalism in worsening the situation. Originality/value The authors contribute to the literature by providing new insight into the consequences of COVID-19 for retailers' neoliberal practices and the livelihood of workers in global supply chains. Findings of this study extend authors' knowledge about retailers' COVID-19 measures: These have contributed to the plights of workers working for their supply factories in the global South.

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.