ABSTRACT
With the rapid growth of internet technology, particularly during the COVID-19 pandemic, a major portion of consumers have intended to do online shopping. Also the use of eco-friendly products is essential in today's environment and human health. Thus this paper investigates the consumers' purchasing behaviour towards substitutable non-green and eco-friendly products in a dual-channel (offline and online channels) supply chain system. The manufacturer offers a novel combination of promotions such as a return policy in the online channel and a warranty policy in the offline channel depending on the position and situation of consumers. Here, the environmental burden is reduced by considering remanufacturing/refurbishing used products during the warranty period. Therefore, consumers' demand depends on price, eco-friendliness level, warranty and return agreements. The entire problem is modelled under centralised and decentralised decision-making scenarios. Finally, the profit maximisation problem is formulated and solved in the game theory framework. A series of sensitivity analyses of various parameters is conducted numerically to validate the problem. It is observed that, due to the instant return facility in the online channel, online demand is higher than the offline with a higher warranty period. © 2023 Informa UK Limited, trading as Taylor & Francis Group.
ABSTRACT
The concentration of reactive trace gases in the atmosphere affects the human health differently. This study presents the changes of aerosol and reactive gases load in the atmosphere from the recent past with the help of Copernicus Atmosphere Monitoring Service (CAMS) data in Indian domain. The EAC4 (ECMWF Atmospheric Composition Reanalysis 4) data sets were used to examine spatially the load of ambient trace gases (NO2, O3, SO2 & CO) and aerosol present in the atmosphere as aerosol optical depth(AOD). The four weekly phases of the study are for April, 2020 (01-07, 08-14, 15-21 & 22-30). It has been observed during the above said phases that the concentration of aerosols, chemically reactive gases and greenhouse gases shows appreciable reduction up to ~60-70 % from CAMS Long Period Average (LPA) 17 years (2003-2019) data over the entire Indian sub-continent, except few pockets of Central (Durg, Indore, Bilaspuretc.) and South West (Kolhapur, Gujaratetc.) India. These slightly higher values in 2nd and 3rd week of April-2020 are due to pre-monsoon dust storm activity and well captured in vertical air flow Omega at 850/NCAR reanalysis. Concentrations of reactive gases from 12 different Central Pollution Control Board (CPCB) stations of India with CAMS, LPA data of April-2019 & 2020 has been compared and show that aerosol load in terms of PM-2.5 & PM-10 is appreciably drop down (60-70 %) over IGP and 25-30 % in other parts of India. The concentration of other reactive gases (NO2, SO2 & CO) with actual data from the month of April, 2019 &2020 also decreases ~ 32 %, 7 %, 17 % over IGP and 16 %, 8 %, 9 % in other parts of India respectively. The concentration of Ozone shows slightly positive behaviour over IGP and negative at other parts of India. This study is further brought out a message for future that we should use the natural resources judiciously as their long term exposure can cause severe health problems and a psychological burden or stress globally during this COVID-19 spread period. © 2022, India Meteorological Department. All rights reserved.
ABSTRACT
Objective: Pharmacovigilance Program of India is a robust program extending from government hospitals to non-government hospital for implementation of policy of safe and rational use of drugs and early signal generation for adverse effects of drugs. Department of Pharmacology, Institute of Medical Sciences, Banaras Hindu University is part of this program since 2004. Retrospective analysis of adverse drug reaction (ADR) reported to the adverse drug monitoring center at tertiary Care Hospital. Methods: The study site was Sir Sundar Lal Hospital, Institute of Medical Sciences Banaras Hindu University, Varanasi. The study was performed after the approval of the Institutional Ethics Committee, letter number: Dean/2020/EC/2153. It was a retrospective observational study. Data collected through VigiFlow software in standard IPC Pharmacovigilance Program of India prescribed suspected ADR form, from March 2020 to June 2021 were analyzed. Causality assessment was done using a World Health Organization Uppsala Monitoring Center scale. Results: In the present study, the percentage of male patients affected is 58% and 42% female patient got suffered from adverse drug effects. About 64% of adverse effect are in possible category followed by probable, that is, 36%. The majority of adverse effects are due to antimicrobials, that is, Cephalosporins and Antitubercular group of drugs. About 20.1% adverse events show gastrointestinal symptoms. In the present study, we also observed that 5.17% adverse effects are due to hydroxychloroquine account for gastritis, headache, lethargy, and vomiting which were prescribed as prophylactic drug for COVID-19. Conclusion: Medicine information OPD in every medical college is the need of the hour to increase awareness regarding adverse events. It is important to spread importance of reporting adverse events by spontaneous reporting under Pharmacovigilance Program of India to detect rare and unusual side effects.
ABSTRACT
Pandemic COVID-19 has been giving the impact that large to the entire community in Indonesia because the pandemic of this, many activities are obstructed, and regulations protocol Health implemented by the government make its people must adjust themselves to abide by the rules that exist. Likewise, in the education sector, this pandemic make learning should be done in online. Learning process would be not as same when face-to-face and each university also has a method of learning that is different to improve the quality of learning that is given to the students. Higher education as learning environment also wanted to know the level of effectiveness of learning online are given and the attitude/behavior of students during follow online learning procedure. This study aims to determine the effect that significant to the effectiveness of the learning of students who do study via online (E-Learning). The sample was determined using the questionnaire collection method and the results of the questionnaire were collected as research data. Then the data is processed and tested (by testing the validity, reliability, etc.). The results show that internal factors have significant correlation to the effectiveness of e-learning. © 2021 IEEE.
ABSTRACT
Coronavirus (Covid-19) is spreading fast, infecting people through contact in various forms including droplets from sneezing and coughing. Therefore, the detection of infected subjects in an early, quick and cheap manner is urgent. Currently available tests are scarce and limited to people in danger of serious illness. The application of deep learning to chest X-ray images for Covid-19 detection is an attractive approach. However, this technology usually relies on the availability of large labelled datasets, a requirement hard to meet in the context of a virus outbreak. To overcome this challenge, a semi-supervised deep learning model using both labelled and unlabelled data is proposed. We develop and test a semi-supervised deep learning framework based on the Mix Match architecture to classify chest X-rays into Covid-19, pneumonia and healthy cases. The presented approach was calibrated using two publicly available datasets. The results show an accuracy increase of around 15% under low labelled / unlabelled data ratio. This indicates that our semi-supervised framework can help improve performance levels towards Covid-19 detection when the amount of high-quality labelled data is scarce. Also, we introduce a semi-supervised deep learning boost coefficient which is meant to ease the scalability of our approach and performance comparison. © 2020 IEEE