Scientific rationale of Indian AYUSH Ministry advisory for COVID-19 prevention, prophylaxis, and immunomodulation

The current outbreak of COVID-19 is caused by the SARS-CoV-2 virus that has affected > 210 countries. Various steps are taken by different countries to tackle the current war-like health situation. In India, the Ministry of AYUSH released a self-care advisory for immunomodulation measures during the COVID-19 and this review article discusses the detailed scientific rationale associated with this advisory. Authors have spotted and presented in-depth insight of advisory in terms of immunomodulatory, antiviral, antibacterial, co-morbidity associated actions, and their probable mechanism of action. Immunomodulatory actions of advised herbs with no significant adverse drug reaction/toxicity strongly support the extension of advisory for COVID-19 prevention, prophylaxis, mitigations, and rehabilitation capacities. This advisory also emphasized Dhyana (meditation) and Yogasanas as a holistic approach in enhancing immunity, mental health, and quality of life. The present review may open-up new meadows for research and can provide better conceptual leads for future researches in immunomodulation, antiviral-development, psychoneuroimmunology, especially for COVID-19.Copyright © 2021, Institute of Korean Medicine, Kyung Hee University.

The impact of Covid-19 on household poverty: examining impacts and resilience in a 40-year timeframe in rural Rajasthan (India)

To what extent has chronic poverty increased during the pandemic? In July and August 2021, we revisited seven villages of southern Rajasthan (India), where we had studied household poverty dynamics in 2002. We find that in the two decades before the pandemic (2002–2020), people's structural positions improved vastly, chronic poverty fell from nearly half to less than 20% of households. These gains in resilience helped people cope with the pandemic. The majority suffered deep income losses between February 2020 and August 2021, but there is no evidence of any substantive rise in chronic poverty. © 2023 Oxford Department of International Development.

Automated COVID-19 detection using Deep Convolutional Neural Network and Chest X-ray Images

COVID-19 was previously identified as 2019-nCoV, however it was reclassified as severe acute respiratory syndrome coronavirus 2 by the International Committee on Taxonomy of Viruses (ICTV) (SARS-CoV-2). It was first discovered in Wuhan, China's Hubei Province, and has since spread all over the world. The scientific community is working to develop COVID-19 detection technologies that are both quick and accurate. Chest x-ray imaging can aid in the early diagnosis of COVID-19 patients. In COVID-19 individuals, chest x-rays can indicate a variety of lung abnormalities, including lung consolidation, ground-glass opacity, and others. The COVID-19 biomarkers, however, must be identified by qualified and experienced radiologists. Each report must be inspected by the radiologist, which is a time-consuming procedure. The medical infrastructure is currently overburdened due to the huge volume of patients. In this study, we propose automatic COVID-19 identification in chest x-rays using a deep learning technique. COVID-19, pneumonia, and healthy x-rays are included in the dataset for the studies. The proposed model had an average accuracy and sensitivity of 97 percent. The obtained findings demonstrate that the model can compete with existing state-of-the-art models. © 2021 IEEE.

Understanding Supply Chain Visibility Through Experts’ Perspective: A Delphi Based Approach

Visibility in production logistics and across the supply chain has become a key concern for organizations. Its need has been further emphasized due to the current COVID 19 crisis. Organizations find it challenging to prepare the internal logistics and supply chain, and quickly respond to such unexpected events, due to low visibility. Against this backdrop, the paper, which is a work-in-progress, systematically documents different factors influencing supply chain visibility and crucial information that should be collected and shared among supply chain partners for better visibility. A Delphi analysis is being conducted with twenty-six supply chain experts from various globally recognized enterprises with manufacturing units located worldwide. The study starts with a short open-ended questioner to collect a comprehensive list of antecedents, drivers, barriers, effects, and visibility information based on the qualitative response from the experts. The preliminary results from the first round of the Delphi analysis indicate that risk management, environmental sustainability, and supply chain control are some of the key drivers. Lack of IT infrastructure and maturity are some of the barriers, integrated systems, and technology maturity are among the key antecedents and gaining planning capability and better customer service are some of the positive effects of supply chain visibility as per the experts’ opinion. In addition, information related to planning, supplier location, and deviation are among the crucial ones that require the collection and sharing for better supply chain visibility. This research study is among the few that empirically explores factors influencing supply chain visibility and generates new insights as to why the barriers can be difficult to overcome in complex supply chain settings. © 2021, IFIP International Federation for Information Processing.

Requirements on Supply Chain Visibility: A Case on Inbound Logistics

Events such as Covid-19 have revealed the vulnerabilities that companies face due to low visibility. Consequently, companies experience impact on their supply chains in terms of disruptions of material supply, deliveries, productivity and revenue. Thus, the importance of Supply Chain Visibility (SCV) in global and competitive markets with increasing sustainability demands has received widespread recognition. Yet, the literature provides limited understanding of requirements to consider when developing a SCV system. Addressing the gap, this study presents the findings from a case study during the first months of 2021 at a global manufacturing company developing a SCV system to improve their inbound flow. Using a system engineering perspective, this study presents requirements highlighted during early stage for a SCV system. The results indicate the importance of ensuring SCV system requirements to enable data collection, handling and usage for decision making leading to both supply chain sustainability and resilience. This study contributes to the understanding of SCV by presenting and categorizing requirements considered for real-time case at a manufacturing company when developing a SCV system. © 2021, IFIP International Federation for Information Processing.