Supply Chain Vulnerability and Resilience: A Case of Harvested Shrimp from Kerala, India

Recurrent natural disasters, the impact of economic turbulences, the COVID-19 pandemic and other factors have heightened concerns about constructing resilient systems. Supply chain weaknesses have been demonstrated in the past, encouraging the creation of preventative capabilities to retain competitiveness and respond to changes in both macro and local contexts. Examining the multiple risks presented in the culture and marketing of shrimp, as well as the measures taken by shrimp farmers to overcome the disturbances (producers of the group's most valuable commodity), could provide insight into the current situation and aid in decision-making. Agility, distribution network structure, visibility relating producers to final consumers, communication between supply chain partners, sharing of benefits and uncertainties, global mapping and collaborative actions across supply chain partners were the drivers of supply chain resilience. Uncertainties associated with demand, supply, operational and environmental conditions were identified as supply chain vulnerability factors. The shrimp farmers maintain a balance between dependability measures and risks in the industry and continue the business, knowing that overcoming vulnerability could result in even higher susceptibilities. © 2023 Institute of Rural Management.

Detection and Analysis of Covid-19 and Pneumonia through X-Ray Images Using Transfer Learning

In recent months, the fight against COVID-19 has grown into one of the most actively pursued anti-toxin treatment strategies worldwide. Correct medical reasoning and a swift response are essential to preventing the COVID-19 epidemic from taking an unexpected turn. Corona virus can be detected via RT-PCR, although chest X-ray techniques have been more effective and helpful in detecting the virus's effects. With an increasing number of people being detected with COVID and a larger number of X-rays being taken, it is now viable to use transfer learning to categorise the X-ray results. Covid19, bacterial pneumonia, and normal incident X-ray datasets have been combined to develop an automatic method for detecting the disease. Specifically, the objective of this study is to achieve better image classification results over state-of-the-art models like the Convolutional Neural Network (CNN) that were developed recently. The data sets were collected from freely accessible online medical sources. The results shows that significant biomarkers associated with Covid-19 illness can be identified using a combination of Transfer Learning and X-ray imaging. In our experiments, we found that the best accuracy was achieved using a combination of VGG16, Resnet50, and a Convolutional Layer, with respective values of 96.78, 98.66%, and 96.46 %. X-rays' potential for utility in diagnosis has grown as the failure rates of older, more established analytical methods have grown alarmingly high. Copyright © 2022, Anka Publishers. All rights reserved.