Shiv Nadar University

About: Shiv Nadar University is a education organization based out in Dadri, Uttar Pradesh, India. It is known for research contribution in the topics: Population & Graphene. The organization has 1015 authors who have published 1924 publications receiving 18420 citations.

Multi-scale modeling and simulation of natural fiber reinforced composites (Bio-composites)

Abstract: This work presents the numerical analysis of natural fiber reinforced composites (from renewable sources), to evaluate the mechanical behavior of Bio-composites and elucidated the role of micro-mechanical analytical models (Rule of Mixtures and Halpin-Tsai models). Specifically, this study is carried out to perform the Multi-scale modeling and simulation of Biocomposite that constitute of sisal fiber reinforcement and Epoxidized Soybean-Oil (ESO) based matrix. In general, it is difficult to predict the effective properties of natural fiber reinforced composites due to its heterogeneous properties. The Representative Volume Element (RVE) model is capable to estimate the effective properties of Bio-composites. Therefore, RVE model is designed and analyzed based on micro-mechanics by taking the individual properties of fiber and matrix as an input. As a result, the effective properties of Bio-composite can be obtained. Subsequently, a 3-D model of Bio-composite laminated plate behavior is analyzed based on macro-mechanics by taking the effective properties of Bio-composite obtained from micro-mechanical analysis. The obtained effective properties of Bio-composite are validated with the theoretical (Rule of Mixtures) results. The micro-mechanical analysis is carried out using the Digimat (Multi-scale modeling and simulation tool) and macro-mechanical analysis is performed using Ansys software.

Use of Dimeric Excited States of the Donors in D4-A Systems for Accessing White Light Emission, Afterglow, and Invisible Security Ink

Abstract: Pure organic white light emitters with an afterglow (AG) feature have unique advantages and various potential applications. However, the studies of single-component organic white light emitters wit...

Mutual information based stock networks and portfolio selection for intraday traders using high frequency data: An Indian market case study.

Abstract: In this paper, we explore the problem of establishing a network among the stocks of a market at high frequency level and give an application to program trading. Our work uses high frequency data from the National Stock Exchange, India, for the year 2014. To begin, we analyse the spectrum of the correlation matrix to establish the presence of linear relations amongst the stock returns. A comparison of correlations with pairwise mutual information shows the further existence of non-linear relations which are not captured by correlation. We also see that the non-linear relations are more pronounced at the high frequency level in comparison to the daily returns used in earlier work. We provide two applications of this approach. First, we construct minimal spanning trees for the stock network based on mutual information and study their topology. The year 2014 saw the conduct of general elections in India and the data allows us to explore their impact on aspects of the network, such as the scale-free property and sectorial clusters. Second, having established the presence of non-linear relations, we would like to be able to exploit them. Previous authors have suggested that peripheral stocks in the network would make good proxies for the Markowitz portfolio but with a much smaller number of stocks. We show that peripheral stocks selected using mutual information perform significantly better than ones selected using correlation.

Adaptive phase estimation through a genetic algorithm

Abstract: The authors propose and experimentally demonstrate an adaptive phase estimation protocol using a genetic algorithm, showing convergence to the true parameter value by using very limited data.