Blockchain-Enabled Supply Chain platform for Indian Dairy Industry: Safety and Traceability.

Conventional food supply chains are centralized in nature and possess challenges pertaining to a single point of failure, product irregularities, quality compromises, and loss of data. Numerous cases of food fraud, contamination, and adulteration are daily reported from multiple parts of India, suggesting the absolute need for an upgraded decentralized supply chain model. A country such as India, where its biggest strength is its demographic dividend, cannot afford to malnutrition a large population of its children by allowing them to consume contaminated and adulterated dairy products. In view of the gravity of the situation, we propose a blockchain-enabled supply chain platform for the dairy industry. With respect to the supply chain platform, the dairy products of choice include milk, cheese, and butter. Blockchain is one of the fastest growing technologies having widespread acceptance across multiple industry verticals. Blockchain possesses the power to transform traditional supply chains into decentralized, robust, transparent, tamper proof, and sustainable supply chains. The proposed supply chain platform goes beyond the aspect of food traceability and focuses on maintaining the nutritional values of dairy products, identification of adulteration and contamination in dairy products, the increasing economic viability of running a dairy farm, preventing counterfeit dairy products, and enhancing the revenue of the dairy company. The paper collates the mentioned functionalities into four distinct impact dimensions: social, economic, operations, and sustainability. The proposed blockchain-enabled dairy supply chain platform combines the use of smart contracts, quick response code (QR code) technology, and IoT and has the potential to redefine the dairy supply chains on socio-economic, operational, and sustainability parameters.

Blockchain-Cloud Integration: A Survey.

Over the last couple of years, Blockchain technology has emerged as a game-changer for various industry domains, ranging from FinTech and the supply chain to healthcare and education, thereby enabling them to meet the competitive market demands and end-user requirements. Blockchain technology gained its popularity after the massive success of Bitcoin, of which it constitutes the backbone technology. While blockchain is still emerging and finding its foothold across domains, Cloud computing is comparatively well defined and established. Organizations such as Amazon, IBM, Google, and Microsoft have extensively invested in Cloud and continue to provide a plethora of related services to a wide range of customers. The pay-per-use policy and easy access to resources are some of the biggest advantages of Cloud, but it continues to face challenges like data security, compliance, interoperability, and data management. In this article, we present the advantages of integrating Cloud and blockchain technology along with applications of Blockchain-as-a-Service. The article presents itself with a detailed survey illustrating recent works combining the amalgamation of both technologies. The survey also talks about blockchain-cloud services being offered by existing Cloud Service providers.

A Social Network Analysis Approach to COVID-19 Community Detection Techniques.

Machine learning techniques facilitate efficient analysis of complex networks, and can be used to discover communities. This study aimed use such approaches to raise awareness of the COVID-19. In this regard, social network analysis describes the clustering and classification processes for detecting communities. The background of this paper analyzed the geographical distribution of Tambaram, Chennai, and its public health care units. This study assessed the spatial distribution and presence of spatiotemporal clustering of public health care units in different geographical settings over four months in the Tambaram zone. To partition a homophily synthetic network of 100 nodes into clusters, an empirical evaluation of two search strategies was conducted for all IDs centrality of linkage is same. First, we analyzed the spatial information between the nodes for segmenting the sparse graph of the groups. Bipartite The structure of the sociograms 1-50 and 51-100 was taken into account while segmentation and divide them is based on the clustering coefficient values. The result of the cohesive block yielded 5.86 density values for cluster two, which received a percentage of 74.2. This research objective indicates that sub-communities have better access to influence, which might be leveraged to appropriately share information with the public could be used in the sharing of information accurately with the public.