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The purpose of the study is to analytically as well as numerically investigate the weight of throughflow on the onset of Casson nanofluid layer in a permeable matrix. This study examines both the marginal and over stable kind of convective movement in the system.

A double-phase model is used for Casson nanofluid, which integrates the impacts of thermophoresis and Brownian wave, whereas for flow in the porous matrix the altered Darcy model is occupied under the statement that nanoparticle flux is disappear on the boundaries. The resultant eigenvalue problem is resolved analytically as well as numerically with the help of Galerkin process with the Casson nanofluid Rayleigh–Darcy number as the eigenvalue.

The findings revealed that the throughflow factor postpones the arrival of convective flow and reduces the extent of convective cells, whereas the Casson factor, the Casson nanoparticle Rayleigh–Darcy number and the reformed diffusivity ratio promote convective motion and also decrease the extent of convective cells.

Controlling the convective movement in heat transfer systems that generate high heat flux is a real mechanical challenge. The proposed framework proved that the use of throughflow is one of the most important ways to control the convective movement in Casson nanofluid. To the best of the authors’ knowledge, no inspection has been established in the literature that studies the outcome of throughflow on the Casson nanofluid convective flow in a porous medium layer. However, the convective flow of Casson nanofluid finds many applications in improving heat transmission and energy efficiency in a range of thermal systems, such as the cooling of heat-generating elements in electronic devices, heat exchangers, pharmaceutical practices and hybrid-powered engines, where throughflow can play a significant role in controlling the convective motion.

The purpose of this paper is to identify, classify and prioritize supply chain risks faced by Indian micro small and medium manufacturing companies and to develop a comprehensive supply chain risk index.

Primary data has been collected from 354 Indian micro small and medium enterprises on the different supply chain risks faced by them. An extensive literature review followed by expert's interview has been carried out in order to finalize the supply chain risks. A hybrid methodology consists of AHP and Fuzzy TOPSIS is applied for the data analysis. A sensitivity analysis has been done to check the robustness and consistency of the results.

Results depict the importance of supply side and financial side risks faced by manufacturing supply chains, thus adding to the ongoing academic debate on the importance of supply chain finance solutions.

Study is limited to the scope of an emerging market. Generalization of results needs more systematic studies around the world in different supply chains.

Supply chain managers can consider the benchmark framed in this study in order to identify the health of their supply chain and to efficiently employ supply chain risk management strategies.

The current study is novel in developing a supply chain risk index using a hybrid AHP-Fuzzy TOPSIS methodology with a comprehensive list of 26 supply chain risks under 5 categories for an MSME supply chain. To the best of the authors’ knowledge, this is the first study incorporating financial risks in the development of a supply chain risk index.

This purpose of this study is to perfrom the analysis of COVID-19 with the help of blood samples. The blood samples used in the study consist of more than 100 features. So to process high dimensional data, feature reduction has been performed by using the genetic algorithm.

In this study, the authors will implement the genetic algorithm for the prediction of COVID-19 from the blood test sample. The sample contains records of around 5,644 patients with 111 attributes. The genetic algorithm such as relief with ant colony optimization algorithm will be used for dimensionality reduction approach.

The implementation of this study is done through python programming language and the performance evaluation of the model is done through various parameters such as accuracy, sensitivity, specificity and area under curve (AUC).

The implemented model has achieved an accuracy of 98.7%, sensitivity of 96.76%, specificity of 98.80% and AUC of 92%. The results have shown that the implemented algorithm has performed better than other states of the art algorithms.

The purpose of this research is to portray the historical evolution of retailing from 1980 to 2020. The study considers India as the domain as it is one of the fastest growing markets, and the retail growth is anticipated to reach more than one trillion dollars within this decade.

This paper captures the historical growth trajectory of retailing from the pre-online era to the online era and highlights how the retail environment has become modernized and sophisticated in the process in India.

The study traces the journey of retail from 1980 to the multi-billion-dollar sector it had become by 2020 in India. Furthermore, the article provides an overview of how the different retail forms and players in the Indian retail sector have been shaping the industry over the last four decades. During this period, there has been a transformational change in the format of Indian retailing. The Indian consumers' shopping mindset moved from physical in the 1980s to online, and now “Phygital” (Physical and Digital) in 2020s has become an omni-channel platform in Indian retail.

This paper aims to present a viewpoint of the evolutions of retailing from the unorganized to the organized form and from the physical to the online form over the last 40 years in the Indian retail sector landscape.