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Automatic lung infection segmentation in computed tomography (CT) scans can offer great assistance in radiological diagnosis by improving accuracy and reducing time required for diagnosis. The biggest challenges for deep learning (DL) models in segmenting infection region are the high variances in infection characteristics, fuzzy boundaries between infected and normal tissues, and the troubles in getting large number of annotated data for training. To resolve such issues, we propose a Modified U-Net (Mod-UNet) model with minor architectural changes and significant modifications in the training process of vanilla 2D UNet. As part of these modifications, we updated the loss function, optimization function, and regularization methods, added a learning rate scheduler and applied advanced data augmentation techniques. Segmentation results on two Covid-19 Lung CT segmentation datasets show that the performance of Mod-UNet is considerably better than the baseline U-Net. Furthermore, to mitigate the issue of lack of annotated data, the Mod-UNet is used in a semi-supervised framework (Semi-Mod-UNet) which works on a random sampling approach to progressively enlarge the training dataset from a large pool of unannotated CT slices. Exhaustive experiments on the two Covid-19 CT segmentation datasets and on a real lung CT volume show that the Mod-UNet and Semi-Mod-UNet significantly outperform other state-of-theart approaches in automated lung infection segmentation. IEEE
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After COVID, Original equipment manufacturers (OEMs), suppliers, and distributors are seeking to streamline their local, and global supply chains, the issue has gained attention in how they identify, analyze and manage their procurement and supply chain processes with stakeholders. The use of supply chain adoption with green technology has been found to be preferred in international trade, which is forcing the Indian manufacturing, and services sector to realign their CSR (Corporate social responsibility) goals. Interviews have been conducted with a mix of manufacturing and service firms in India to map their sustainable procurement in green supply chain practices. The use of MAXQDA software for conducting thematic analysis to support the inductive theory and trend-building practices to know "what, why, and how” from interview responses. Each sector, manufacturing, and service showed distinct trends that divorce their approach to green SCM, and supply chain strategy for sustainable procurement though they have spread awareness about sustainable procurement with stakeholders, emphasizing economic benefits. Additionally, they pursued jointly to address environmental issues and extend societal benefits as a CSR effort. Variances in approaches in manufacturing are market-linked, time-based, and demand-driven while services firms showed green technology pledges as CSR initiatives along with an environmental emphasis in downstream SCM activities. © 2023 K.J. Somaiya Institute of Management Studies and Research.
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A total of 77 literatures till November 2020 were screened regarding various interventions to treat COVID-19 patients, among which 16 and 15 studies fulfilling predefined exclusion and inclusion criteria were subjected to Pairwise and Network meta-analysis respectively. In Pairwise meta-analysis, the recovery rate of treatment with Lopinavir/Ritonavir versus other antiviral (OR= 0. 0381, CI= 0.0021-0.6870), placebo (OR= 0.6592, CI= 0.4207-1. 0329), Remdesivir (OR= 0.5286, CI= 0.3915-0.7137) and standard care (OR= 0.9787, CI= 0.8523-1.1238) in fixed and random effect model with 95% confidence limit found statistically significant protection than those of all other treatment. In Network meta-analysis, recovery estimates sizes of treatment, in reference with other antivirals 1.0000 (0.9917, 1.0000) shows less risk with treatment Standard care 0.7811 (0.6696, 0.8417), Remdesivir 0.7717 (0.6491, 0.8144), Lopinavir/ Ritonavir 0.7801 (0.6701, 0.8473), Placebo 0.7219 (0.6178, 0.7836).Copyright © 2023, Institute of Medico-legal Publication. All rights reserved.
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The paper presents the development of a virtual simulation lab that provides theoretical content, simulations, models, videos, animations, comparative simulations, simulation benches, and self-check quizzes for e-learning and self-study. Self-assessment evaluates the learning capability and knowledge of students. Virtual lab may play a role to teach students without any assistance of teachers or professors in this coronavirus disease 2019 (COVID-19) pandemic situation. The COVID-19 pandemic situation has barriers between students and universities in delivering lectures in classroom. Virtual simulation labs can change the traditional teaching practices of classroom teaching in the world. The virtual lab is developed due to the limited equipment of experiments and simultaneously assisting students. Web-based educational resources have taken an important place in e-education and self-study through an e-learning platform. Virtual simulation lab is an e-learning platform where students can perform experimentation without any direct involvement on instruments in physical lab. An interactive virtual lab is developed to provide web-based global access to everyone without any authentication. This lab does not require any credentials to login into website. This lab provides multiple options for experimentations of various simulations. This can be accessed through web address http://14.139.245.230/mfvlab/home.php and contains over 950 simulations. Each module contains theory as well as audio embedded videos to reduce time and effort required to understand and analysis of various process parameters. Comparative simulation compares among different materials and processed and different parameters with interactive graphs. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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World Health Organization (WHO) China Country Office told instances of pneumonia of unknown etiology found in Wuhan City, Hubei Province of China, thereafter,on seventh January 2020, Chinese specialists recognized another novel strain of Coronavirus as the causative agent of the sickness. The infection agent has been renamed by WHO as SARS-CoV- 2 and the disease brought about by it as COVID-19. The virus first identified in China has now spread to more than 210 nations/domains, with reports of nearby transmission occurring in more than 160 of these nations/regions. According to WHO there has been an aggregate of millions of confirmed cases and a great many passings because of COVID-19 around the world. This study was conducted to determine the determinants of cases, recovered and death of the covid-19 with some demographic and economical factors in Indian states. The correlation analysis showed that total population, male, female, population above 60, no. of literate, person below poverty and private hospital has significant positive correlation with covid-19 cases, recovery and death whereas population density, public hospital, per capita income, shikhs, Christians, vegetarian- nonvegetarian, tobacco smoker, toddy - country liquor, beer, imported alcohol-wine did not show significance.
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The current pandemic has been declared as a Public Health Emergency of International Concern (PHEIC) by the World Health Organization (WHO) on January 30, 2020. The causative agent of the COVID-19 is Sub-Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2) that causes lung severe disease, is an enveloped RNA virus. The bats being the main zoonotic reservoir of the virus. As of July 9, 2020, the number of cases has around 11,841,326 cases, with 544,739 death cases worldwide. Surveillance of the asymptomatic carriers, the certain lockdown of the public places, quarantine of 14 days of suspected individuals, or other necessary steps have been taken by each country to limit the coronavirus infection. Many scientists have worked on drug repositioning of already available drugs to compete and kill the virus. The following literature review has been conducted from the publicly available information about the evolutionary relationship, genetics, pathogenesis, clinical manifestation, diagnosis, or laboratory tests of the virus. We have also covered the available control or precautionary measures, and treatment against the virus.