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.

Power laws and crossovers in off-critical surface-directed spinodal decomposition.

Abstract: We study the dynamics of phase separation in binary mixtures near a surface with a preferential attraction for one of the components of the mixture. We obtain detailed numerical results for a range of mixture compositions. In the case where the minority component is attracted to the surface, wetting layer growth is characterized by a crossover from a surface-potential-dependent growth law to a universal law. We formulate a simple phenomenological model to explain our numerical results.

Geochemistry of the Floodplain Sediments of the Kaveri River, Southern India

Abstract: The floodplain sediments of the Kaveri River, southern India, derived from Archean gneissic and charnockitic source regions, show interbedding of silty (4-4.7 ) and sandy units (1.4-3.7 ). The geochemistry of silty beds is remarkably uniform at a given location and over a lateral distance of nearly 250 km; the sandy beds have more variable chemical compositions, yet are comparable to those of silty beds except for the diluting effect of quartz. Silty sediments retain the geochemical signature of prominently exposed source rocks for almost all elements and provide evidence of tectonic instability in the source region. The floodplain sediments contain all size grades (sand, silt, and clay), which may have resulted in minimizing the biases imposed on suspended and bedload sediments due to sorting. The low Chemical Index of Alteration (CIA), the dominance of unweathered primary minerals, and the minor proportion of smectitic clay all suggest that the region has been subjected to little chemical weathering. This is possible if the region has undergone recent uplift, exposing fresh Archean rock to surface denudation. The formation of fertile farmland along the Kaveri River course and its delta is related to these recent geological processes.

On the Recent Floods in India

Abstract: Floods in the Indian subcontinent have affected habitat, population, economy, etc. Due to the detrimental effects of recent floods on the economy, governance, etc., it is imperative to understand the associated dynamics, manifestations and fallouts for proper policy planning recommendations. The present study endeavours to provide an integrated rationale of meteorological and geomorphological aspects associated with four recent extreme floods in Uttarakhand (2013), Srinagar (2014), Chennai (2015) and Gujarat (2017). It is important to mention here that these floods occurred under different atmospheric circulations and geomorphological setting, and had an entirely different gambit for policy planning and governance. Consolidation of these issues will help policy planners and technologists, in case advance warning system based on these findings can be developed.

Spatiotemporal effects of the causal factors on COVID-19 incidences in the contiguous United States

Abstract: Since December 2019, the world has been witnessing the gigantic effect of an unprecedented global pandemic called Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) - COVID-19. So far, 38,619,674 confirmed cases and 1,093,522 confirmed deaths due to COVID-19 have been reported. In the United States (US), the cases and deaths are recorded as 7,833,851 and 215,199. Several timely researches have discussed the local and global effects of the confounding factors on COVID-19 casualties in the US. However, most of these studies considered little about the time varying associations between and among these factors, which are crucial for understanding the outbreak of the present pandemic. Therefore, this study adopts various relevant approaches, including local and global spatial regression models and machine learning to explore the causal effects of the confounding factors on COVID-19 counts in the contiguous US. Totally five spatial regression models, spatial lag model (SLM), ordinary least square (OLS), spatial error model (SEM), geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR), are performed at the county scale to take into account the scale effects on modelling. For COVID-19 cases, ethnicity, crime, and income factors are found to be the strongest covariates and explain the maximum model variances. For COVID-19 deaths, both (domestic and international) migration and income factors play a crucial role in explaining spatial differences of COVID-19 death counts across counties. The local coefficient of determination (R2) values derived from the GWR and MGWR models are found very high over the Wisconsin-Indiana-Michigan (the Great Lake) region, as well as several parts of Texas, California, Mississippi and Arkansas.