Chandrasekaran Nagarajan, Indira A. and Ramasubramaniam M.
This study aims to analyse the structure of the Indian vaccine supply chain (SC) during the Covid-19 crisis and explore the underlying challenges at each stage in the network. It…
Abstract
Purpose
This study aims to analyse the structure of the Indian vaccine supply chain (SC) during the Covid-19 crisis and explore the underlying challenges at each stage in the network. It also brings out the difference in performance of various constituent states.
Design/methodology/approach
This study relied on both primary and secondary data for the analyses. For the primary data, the study gathered experts’ opinions to validate the authors’ inferences. For the secondary data, it relies on government data provided in websites.
Findings
Based on the quartile analysis and cluster analysis of the secondary data, the authors find that the constituent states responded differently during the first and second waves. This was due to the differences in SC characteristics attributed to varied demographics and administrative efficiency.
Research limitations/implications
This paper’s analyses is primarily limited to secondary information and inferences are based on them. The study has important implications for implementing the large-scale vaccination drives by government and constituent states for better coordination and last-mile delivery.
Originality/value
The contribution is unique in studying the performance of constituent states using statistical techniques, with secondary data from authentic sources. It is also unique in combining this observation with validation from experts.
Details
Keywords
Esra Zeynep Yıldız and Oktay Pamuk
The conversion of fabric into a garment involves many interactions such as the selection of suitable sewing thread, optimization of sewing parameters, ease of conversion of fabric…
Abstract
Purpose
The conversion of fabric into a garment involves many interactions such as the selection of suitable sewing thread, optimization of sewing parameters, ease of conversion of fabric into the garment and actual performance of the sewn fabric during wear of the garment. The adjustment of all sewing parameters is necessary to ensure quality. The purpose of this paper is to define the parameters that affect seam quality comprehensively.
Design/methodology/approach
This study primarily focuses on the studies dealing with the effect of various parameters on-seam quality in detail. A systematic literature review was conducted.
Findings
The interactions between parameters may lead to different results than the effect of a single parameter. In addition, changing some parameters may have a positive effect on one element of seam quality while having a negative effect on another. For this reason, it is very important to properly select the parameters according to the specific end use of the garment products and also to consider the interactions.
Originality/value
The knowledge of various factors that affect seam quality will be helpful for manufacturers to improve production performance and to be able to produce high-quality seam.
Details
Keywords
Rucha Wadapurkar, Sanket Bapat, Rupali Mahajan and Renu Vyas
Ovarian cancer (OC) is the most common type of gynecologic cancer in the world with a high rate of mortality. Due to manifestation of generic symptoms and absence of specific…
Abstract
Purpose
Ovarian cancer (OC) is the most common type of gynecologic cancer in the world with a high rate of mortality. Due to manifestation of generic symptoms and absence of specific biomarkers, OC is usually diagnosed at a late stage. Machine learning models can be employed to predict driver genes implicated in causative mutations.
Design/methodology/approach
In the present study, a comprehensive next generation sequencing (NGS) analysis of whole exome sequences of 47 OC patients was carried out to identify clinically significant mutations. Nine functional features of 708 mutations identified were input into a machine learning classification model by employing the eXtreme Gradient Boosting (XGBoost) classifier method for prediction of OC driver genes.
Findings
The XGBoost classifier model yielded a classification accuracy of 0.946, which was superior to that obtained by other classifiers such as decision tree, Naive Bayes, random forest and support vector machine. Further, an interaction network was generated to identify and establish correlations with cancer-associated pathways and gene ontology data.
Originality/value
The final results revealed 12 putative candidate cancer driver genes, namely LAMA3, LAMC3, COL6A1, COL5A1, COL2A1, UGT1A1, BDNF, ANK1, WNT10A, FZD4, PLEKHG5 and CYP2C9, that may have implications in clinical diagnosis.