Blockchain in supply chain management: a review, bibliometric, and network analysis.

Blockchain is a distributed ledger technology that has attracted both practitioners and academics attention in recent years. Several conceptual and few empirical studies have been published focusing on addressing current issues and recommending the future research directions of supply chain management. To identify how blockchain can contribute to supply chain management, this paper conducts a systematic review through bibliometric and network analysis. We determined the key authors, significant studies, and the collaboration patterns that were not considered by the previous publications on this angel of supply chain management. Using citation and co-citation analysis, key supply chain areas that blockchain could contribute are pinpointed as supply chain management, finance, logistics, and security. Furthermore, it revealed that Internet of Things (IoT) and smart contracts are the leading emerging technologies in this field. The results of highly cited and co-cited articles demonstrate that blockchain could enhance transparency, traceability, efficiency, and information security in supply chain management. The analysis also revealed that empirical research is scarce in this field. Therefore, implementing blockchain in the real-world supply chain is a considerable future research opportunity.

A Novel Clustering Methodology Based on Modularity Optimisation for Detecting Authorship Affinities in Shakespearean Era Plays.

In this study we propose a novel, unsupervised clustering methodology for analyzing large datasets. This new, efficient methodology converts the general clustering problem into the community detection problem in graph by using the Jensen-Shannon distance, a dissimilarity measure originating in Information Theory. Moreover, we use graph theoretic concepts for the generation and analysis of proximity graphs. Our methodology is based on a newly proposed memetic algorithm (iMA-Net) for discovering clusters of data elements by maximizing the modularity function in proximity graphs of literary works. To test the effectiveness of this general methodology, we apply it to a text corpus dataset, which contains frequencies of approximately 55,114 unique words across all 168 written in the Shakespearean era (16th and 17th centuries), to analyze and detect clusters of similar plays. Experimental results and comparison with state-of-the-art clustering methods demonstrate the remarkable performance of our new method for identifying high quality clusters which reflect the commonalities in the literary style of the plays.