Samarium Ion-Doped Maghemite Nanoparticles: Synthesis,Microstructural, Optical,and Magnetic Characterizations

Journal of Superconductivity and Novel Magnetism - This paper reports the effects of rare-earth Sm3+ ion substitution on structural, optical, electronic, and magnetic responses of maghemite...     

A study on investments in the big data-driven supply chain,performance measures and organisational performance in Indian retail 4.0 context

The use of digital technologies such as ‘internet of things’ and ‘big data analytics’ have transformed the traditional retail supply chains into data-driven retail supply chains referred to as ‘Retail 4.0.’ These big data-driven retail supply chains have the advantage of providing superior products and services and enhance the customers shopping experience. The retailing industry in India is highly competitive and eager to transform into the environment of retail 4.0. The literature on big data in the supply chain has mainly focused on the applications in manufacturing industries and therefore needs to be further investigated on how the big data-driven retail supply chains influence the supply chain performance. Therefore, this study investigates how the retailing 4.0 context in India is influencing the existing supply chain performance measures and what effect it has on the organisational performance. The findings of the study provide valuable insights for retail supply chain practitioners on planning BDA investments. Based on a survey of 380 respondents selected from retail organisations in India, this study uses governance structure as the moderating variable. Implications for managers and future research possibilities are presented.     

Distributed Generation of NASA Earth Science Data Products

The objective of this work is the development of Grid-based approaches through which NASA data centers can become active participants in serving data users by transforming archived data into the specific form needed by the user. This approach involves generating custom data products from data stored in multiple NASA data centers. We describe a prototype developed to explore how Grid technology can facilitate this multi-center product generation. Our initial example of a custom data product is phenomena-based subsetting. This example involves production of a subset of a large collection of data based on the subset's association with some phenomena, such as a mesoscale convective system (severe storm) or a hurricane. We demonstrate that this subsetting can be performed on data located at a single data center or at multiple data centers. We also describe a system that performed customized data product generation using a combination of commodity processors deployed at a NASA data center, Grid technology to access these processors, and data mining software that intelligently selects where to perform processing based on data location and availability of compute resources. This demonstration also suggests that we could create a catalog of phenomena related data at multiple data centers, in which the catalog can contain references to the original data in different locations. The catalog is important to providing other users with efficient access to the data belonging to the identified phenomenon.