Jawaharlal Nehru University

About: Jawaharlal Nehru University is a education organization based out in New Delhi, India. It is known for research contribution in the topics: Population & Politics. The organization has 6082 authors who have published 13455 publications receiving 245407 citations. The organization is also known as: JNU.

An Advanced DV-Hop Localization Algorithm for Wireless Sensor Networks

Abstract: In emerging sensor network applications, localization in wireless sensor network is a recent area of research. Requirement of its applications and availability of resources need feasible localization algorithm with lower cost and higher accuracy. In this paper, we propose an Advanced DV-Hop localization algorithm that reduces the localization error without requiring additional hardware and computational costs. The proposed algorithm uses the hop-size of the anchor (which knows its location) node, from which unknown node measures the distance. In the third step of Advanced DV-Hop algorithm, inherent error in the estimated distance between anchor and unknown node is reduced. To improve the localization accuracy, we use weighted least square algorithm. Furthermore, location of unknown nodes is refined by using extraneous information obtained by solving the equations. By mathematical analysis, we prove that Advanced DV-Hop algorithm has lesser correction factor in the distance between anchor and the unknown node compared with DV-Hop algorithm, improved DV-Hop algorithm (Chen et al. 2008) and improved DV-Hop algorithm (Chen et al. in IEICE Trans Fundam E91-A(8), 2008), which is cause of better location accuracy. Simulation results show that the performance of our proposed algorithm is superior to DV-Hop algorithm and improved DV-Hop algorithms in all considered scenarios.

Design and analysis of a large-scale COVID-19 tweets dataset

Abstract: As of July 17, 2020, more than thirteen million people have been diagnosed with the Novel Coronavirus (COVID-19), and half a million people have already lost their lives due to this infectious disease. The World Health Organization declared the COVID-19 outbreak as a pandemic on March 11, 2020. Since then, social media platforms have experienced an exponential rise in the content related to the pandemic. In the past, Twitter data have been observed to be indispensable in the extraction of situational awareness information relating to any crisis. This paper presents COV19Tweets Dataset (Lamsal 2020a), a large-scale Twitter dataset with more than 310 million COVID-19 specific English language tweets and their sentiment scores. The dataset’s geo version, the GeoCOV19Tweets Dataset (Lamsal 2020b), is also presented. The paper discusses the datasets’ design in detail, and the tweets in both the datasets are analyzed. The datasets are released publicly, anticipating that they would contribute to a better understanding of spatial and temporal dimensions of the public discourse related to the ongoing pandemic. As per the stats, the datasets (Lamsal 2020a, 2020b) have been accessed over 74.5k times, collectively.

Genomic mechanisms of climate adaptation in polyploid bioenergy switchgrass.

Abstract: Long-term climate change and periodic environmental extremes threaten food and fuel security1 and global crop productivity2-4. Although molecular and adaptive breeding strategies can buffer the effects of climatic stress and improve crop resilience5, these approaches require sufficient knowledge of the genes that underlie productivity and adaptation6-knowledge that has been limited to a small number of well-studied model systems. Here we present the assembly and annotation of the large and complex genome of the polyploid bioenergy crop switchgrass (Panicum virgatum). Analysis of biomass and survival among 732 resequenced genotypes, which were grown across 10 common gardens that span 1,800 km of latitude, jointly revealed extensive genomic evidence of climate adaptation. Climate-gene-biomass associations were abundant but varied considerably among deeply diverged gene pools. Furthermore, we found that gene flow accelerated climate adaptation during the postglacial colonization of northern habitats through introgression of alleles from a pre-adapted northern gene pool. The polyploid nature of switchgrass also enhanced adaptive potential through the fractionation of gene function, as there was an increased level of heritable genetic diversity on the nondominant subgenome. In addition to investigating patterns of climate adaptation, the genome resources and gene-trait associations developed here provide breeders with the necessary tools to increase switchgrass yield for the sustainable production of bioenergy.