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.

Effects of coil misalignment in a four coil implantable wireless power transfer system

Abstract: Wireless power transfer (WPT) is the technology which enables transfer of electric power without wires. It is based on the principle of electromagnetic induction. There are two main modes of WPT, inductive and resonance based coupling. In either of these methods, for the highest possible efficiency to be achieved, the transmitter and receiver coil should be aligned perfectly with each other, but in real world WPT applications, there is always some amount of misalignment between the transmitting and receiving coils. Therefore, it is important to study the effects of coil misalignment to identify and make the necessary design changes to maintain the efficiency of power transfer. This paper presents a simulation analysis on the effect of coil misalignments in a resonance coupled four coil WPT system and the effects of lateral misalignment have also been analyzed and documented.

Targeted repression of Plasmodium apicortin by host microRNA impairs malaria parasite growth and invasion

Abstract: Mature human erythrocytes contain a rich pool of microRNAs (miRNAs), which result from differentiation of the erythrocytes during the course of haematopoiesis. Recent studies have described the effect of erythrocytic miRNAs on the invasion and growth of the malaria parasite Plasmodium falciparum during the asexual blood stage of its life cycle. In this work, we have identified two erythrocytic miRNAs, miR-150-3p and miR-197-5p, that show favourable in silico hybridization with Plasmodium apicortin, a protein with putative microtubule-stabilizing properties. Co-expression of P. falciparum apicortin and these two miRNAs in a cell line model resulted in downregulation of apicortin at both the RNA and protein level. To create a disease model of erythrocytes containing miRNAs, chemically synthesized mimics of miR-150-3p and miR-197-5p were loaded into erythrocytes and subsequently used for invasion by the parasite. Growth of the parasite was hindered in miRNA-loaded erythrocytes, followed by impaired invasion; micronemal secretion was also reduced, especially in the case of miR-197-5p. Apicortin expression was found to be reduced in miRNA-loaded erythrocytes. To interpret the effect of downregulation of apicortin on parasite invasion to host erythrocytes, we investigated the secretion of the invasion-related microneme protein apical membrane antigen 1 (AMA1). AMA1 secretion was found to be reduced in miRNA-treated parasites. Overall, this study identifies apicortin as a novel target within the malaria parasite and establishes miR-197-5p as its miRNA inhibitor. This miRNA represents an unconventional nucleotide-based therapeutic and provides a new host factor-inspired strategy for the design of antimalarial molecular medicine.This article has an associated First Person interview with the first author of the paper.

On the utility of power spectral techniques with feature selection techniques for effective mental task classification in noninvasive BCI.

Abstract: In this paper classification of mental task-root Brain-Computer Interfaces (BCI) is being investigated, as those are a dominant area of investigations in BCI and are of utmost interest as these systems can be augmented life of people having severe disabilities. The BCI model's performance is primarily dependent on the size of the feature vector, which is obtained through multiple channels. In the case of mental task classification, the availability of training samples to features are minimal. Very often, feature selection is used to increase the ratio for the mental task classification by getting rid of irrelevant and superfluous features. This paper proposes an approach to select relevant and non-redundant spectral features for the mental task classification. This can be done by using four very known multivariate feature selection methods viz, Bhattacharya's Distance, Ratio of Scatter Matrices, Linear Regression and Minimum Redundancy & Maximum Relevance. This work also deals with a comparative analysis of multivariate and univariate feature selection for mental task classification. After applying the above-stated method, the findings demonstrate substantial improvements in the performance of the learning model for mental task classification. Moreover, the efficacy of the proposed approach is endorsed by carrying out a robust ranking algorithm and Friedman's statistical test for finding the best combinations and comparing different combinations of power spectral density and feature selection methods.

Moving Beyond Market Research: Demystifying Smartphone User Behavior in India

Abstract: Large-scale mobile data studies can reveal valuable insights into user behavior, which in turn can assist system designers to create better user experiences. After a careful review of existing mobile data literature, we found that there have been no large-scale studies to understand smartphone usage behavior in India -- the second-largest and fastest growing smartphone market in the world. With the goal of understanding various facets of smartphone usage in India, we conducted a mixed-method longitudinal data collection study through an Android app released on Google Play. Our app was installed by 215 users, and logged 11.9 million data points from them over a period of 8 months. We analyzed this rich dataset along the lines of four broad facets of smartphone behavior -- how users use different apps, interact with notihcations, react to different contexts, and charge their smartphones -- to paint a holistic picture of smartphone usage behavior of Indian users. This quantitative analysis was complemented by a survey with 55 users and semi-structured interviews with 26 users to deeply understand their smartphone usage behavior. While our first-of-its-kind study uncovered many interesting facts about Indian smartphone users, we also found striking differences in usage behavior compared to past studies in other geographical contexts. We observed that Indian users spend significant time with their smartphones after midnight, continuously check notifications without attending to them and are extremely conscious about their smartphones’ battery. Perhaps the most dramatic finding is the nature of mobile consumerism of Indian users as shown by our results. Taken together, these and the rest of our findings demonstrate the unique characteristics that are shaping the smartphone usage behavior of Indian users.