ABSTRACT
The recent witnessed pandemic COVID-19 caused severe distress in the Global Supply Chains (GSCs). Worldwide lockdowns, job losses, etc. helped in the creation of this problem. We describe the characteristics that distinguish epidemic outbreaks as a distinct supply chain disruption risk category. It is clearly highlighted that there is lack of visibility of disruptions in GLOBAL SUPPLY Chains and delayed industry response to COVID-19. The COVID-19 outbreak has certainly forced firms to re-evaluate their business strategies. The lead time, the speed of epidemic propagation and the upstream and downstream interruption durations in the supply chain are all significant aspects. This research can be used by decision teams to predict the short-term and long-term impacts of supply chain occurrences and to define pandemic supply chain strategies and tactics. This paper discusses the impact of COVID-19, the effect of lockdown and problems in existing technologies. Possible solutions regarding reducing the effect of pandemic and plans to prepare for the future are also depicted. © 2022 The authors and IOS Press. All rights reserved.
ABSTRACT
Corona virus was declared a global pandemic that has affected people worldwide. It is critical to diagnose corona virus-infected individuals to restrict the virus's transmission. Recent research indicates that radiological methods provide valuable information in identifying infection using deep learning algorithms. Deep learning has contributed to large-scale medical data research, providing new ways and chances for diagnostic tools. This research attempted to investigate how the Capsule Networks leverage chest X-ray scans to identify the infected person. We suggest Capsule Networks identify the illness using chest X-ray data. The proposed approach is rapid and robust, classifying scans into COVID-19, No Findings, or any other issue in the lungs. The study can be used as a preliminary diagnosis by medical practitioners, and the study focuses on the COVID-19 class, a minority class in all public data sets accessible, and ensures that no COVID-19 infected individual is identified as Normal. Even with a small dataset, the model provides 96.37% accuracy for COVID-19 and for the non-COVID-19, and on multi-class classification, it provides an accuracy of 95.12%. © 2022 IEEE.
ABSTRACT
The 2nd Sustainable Development Goal (SDG) of the United Nations attempt to eliminate the potential hunger and food insecurity issues by 2030, but in the plight of COVID19 pandemic it has become far more critical and persistent issue globally as well as in India. The nation-wide socio-economic surveys of National Sample Survey Office (NSSO) in India are designed to produce reliable and representative estimates of important food insecurity parameters at state and national level for both rural and urban sectors separately but these surveys cannot be used directly to generate reliable district level estimates. Whereas, efficient and representative disaggregated level estimates for the extent (or incidence) of food insecurity prevalence has direct impact on strategizing effective policy plans and monitoring progress towards eliminating food insecurity. In this backdrop, the paper outlines small area estimation approach to estimate the incidence of food insecurity across the districts of rural Uttar Pradesh in India by linking data from the 2011-12 Household Consumer Expenditure Survey of NSSO, and the 2011 Indian Population Census. A spatial map has been generated showing spatial disparity for the incidence of food insecurity in Uttar Pradesh. These disaggregated level estimates are relevant and purposeful for SDG indicator 2.1.2 - severity of food insecurity. The estimates and map of food insecurity incidences are expected to deliver invaluable information to the policy-analysts and decision-makers. © 2022 - IOS Press. All rights reserved.
ABSTRACT
The presence of human coronavirus HKU1 infection associated with pericardial inflammation is not reported. We are reporting a young woman with systemic lupus erythematosus, who was positive for HKU1 during her pericarditis flare. Diagnostic imaging demonstrated pericardial effusion, edema, and late gadolinium enhancement on cardiac magnetic resonance imaging and echocardiography. She was on multiple anti-inflammatory medications and achieved remission with anakinra. Her management and a brief literature review is also presented.