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.

Multilevel programming in Cu/NiO y /NiO x /Pt unipolar resistive switching devices.

Abstract: The application of a NiO y /NiO x bilayer in resistive switching (RS) devices with x > y was studied for its ability to achieve reliable multilevel cell (MLC) characteristics. A sharp change in resistance brought about by sweeping the voltage, along with an improved on/off ratio (>103) and endurance (104) were achieved in the bilayer structure as compared to the single NiO x layer devices. Moreover, it was found that nonvolatile and stable resistance levels, especially the multiple low-resistance states of Cu/NiO y /NiO x /Pt memory devices, could be controlled by varying the compliance current. All the multilevel resistance states of the Cu/NiO y /NiO x /Pt bilayer devices were stable for up to 500 consecutive dc switching cycles, as compared to the Cu/NiO x /Pt single layer devices. The temperature-dependent variation of the high and low resistance states of both the bilayer and single layer devices was further investigated to elucidate the charge conduction mechanism. Finally, based on a detailed analysis of the experimental results, comparisons of the possible models for RS in bilayer and single layer memory devices have also been discussed.

Hyperspectral remote sensing in precision agriculture: present status, challenges, and future trends

Abstract: The rapid development of remote sensing has made it possible to study environmental processes and changes in agriculture and also to provide important assistance in relevant practices, even operationally. This chapter describes the latest developments in remote sensing for precision agriculture with particular emphasis placed on the use of hyperspectral sensors. This chapter provides practical information regarding the identification of research challenges, limitations, and advantages of different platforms and sensors for precision agriculture. Hyperspectral remote sensing (HRS) is more effective as compared to multispectral remote sensing because it records radiation in narrow contiguous spectral channels reflected from any feature or target. More accurate spectral information retrieved using HRS can be combined with other techniques to retrieve useful information for precision agriculture. The chapter includes information about HRS sensors and also includes a discussion on the advancement and challenges of spaceborne satellites faced during agriculture monitoring. It concludes with summarizing the hurdles faced during agriculture research using hyperspectral data discussing possible pathways in which relevant research should be directed.

Use of Hyperion for Mangrove Forest Carbon Stock Assessment in Bhitarkanika Forest Reserve: A Contribution Towards Blue Carbon Initiative

Abstract: Mangrove forest coastal ecosystems contain significant amount of carbon stocks and contribute to approximately 15% of the total carbon sequestered in ocean sediments. The present study aims at exploring the ability of Earth Observation EO-1 Hyperion hyperspectral sensor in estimating aboveground carbon stocks in mangrove forests. Bhitarkanika mangrove forest has been used as case study, where field measurements of the biomass and carbon were acquired simultaneously with the satellite data. The spatial distribution of most dominant mangrove species was identified using the Spectral Angle Mapper (SAM) classifier, which was implemented using the spectral profiles extracted from the hyperspectral data. SAM performed well, identifying the total area that each of the major species covers (overall kappa = 0.81). From the hyperspectral images, the NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) were applied to assess the carbon stocks of the various species using machine learning (Linear, Polynomial, Logarithmic, Radial Basis Function (RBF), and Sigmoidal Function) models. NDVI and EVI is generated using covariance matrix based band selection algorithm. All the five machine learning models were tested between the carbon measured in the field sampling and the carbon estimated by the vegetation indices NDVI and EVI was satisfactory (Pearson correlation coefficient, R, of 86.98% for EVI and of 84.1% for NDVI), with the RBF model showing the best results in comparison to other models. As such, the aboveground carbon stocks for species-wise mangrove for the study area was estimated. Our study findings confirm that hyperspectral images such as those from Hyperion can be used to perform species-wise mangrove analysis and assess the carbon stocks with satisfactory accuracy.

Comparative transcriptome analysis of mammary epithelial cells at different stages of lactation reveals wide differences in gene expression and pathways regulating milk synthesis between Jersey and Kashmiri cattle.

Abstract: Jersey and Kashmiri cattle are important dairy breeds that contribute significantly to the total milk production of the Indian northern state of Jammu and Kashmir. The Kashmiri cattle germplasm has been extensively diluted through crossbreeding with Jersey cattle with the goal of enhancing its milk production ability. However, crossbred animals are prone to diseases resulting to unsustainable milk production. This study aimed to provide a comprehensive transcriptome profile of mammary gland epithelial cells at different stages of lactation and to find key differences in genes and pathways regulating milk traits between Jersey and Kashmiri cattle. Mammary epithelial cells (MEC) isolated from milk obtained from six lactating cows (three Jersey and three Kashmiri cattle) on day 15 (D15), D90 and D250 in milk, representing early, mid and late lactation, respectively were used. RNA isolated from MEC was subjected to next-generation RNA sequencing and bioinformatics processing. Casein and whey protein genes were found to be highly expressed throughout the lactation stages in both breeds. Largest differences in differentially expressed genes (DEG) were between D15 vs D90 (1,805 genes) in Kashmiri cattle and, D15 vs D250 (3,392 genes) in Jersey cattle. A total of 1,103, 1,356 and 1,397 genes were differentially expressed between Kashmiri and Jersey cattle on D15, D90 and D250, respectively. Antioxidant genes like RPLPO and RPS28 were highly expressed in Kashmiri cattle. Differentially expressed genes in both Kashmiri and Jersey were enriched for multicellular organismal process, receptor activity, catalytic activity, signal transducer activity, macromolecular complex and developmental process gene ontology terms. Whereas, biological regulation, endopeptidase activity and response to stimulus were enriched in Kashmiri cattle and, reproduction and immune system process were enriched in Jersey cattle. Most of the pathways responsible for regulation of milk production like JAK-STAT, p38 MAPK pathway, PI3 kinase pathway were enriched by DEG in Jersey cattle only. Although Kashmiri has poor milk production efficiency, the present study suggests possible physicochemical and antioxidant properties of Kashmiri cattle milk that needs to be further explored.