Government of India

About: Government of India is a government organization based out in New Delhi, India. It is known for research contribution in the topics: Population & Government. The organization has 2945 authors who have published 2999 publications receiving 44942 citations. The organization is also known as: Union Government & Central Government.

Impact of biomass burning on aerosol properties over tropical urban region of Hyderabad, India

Abstract: [1] Biomass burning is identified as a major source of atmospheric pollution giving rise to the release of large quantities of gaseous emissions and particulate matter. The present study aims at analyzing the impacts of biomass burning on aerosol loading over urban area of Hyderabad, India using synchronous measurements of Aerosol Optical Depth (AOD), solar irradiance in different wavelength bands, aerosol particle size distribution measurements and black carbon (BC) aerosol mass concentration. Temporal variation of AOD and aerosol index (AI) correlated with occurrence of forest fires as derived from DMSP-OLS and MODIS satellite data. BC values showed good correlation with total aerosol number density and showed patterns correlating with wind direction. Radiative forcing estimated from synchronous measurements of AOD and ground reaching broadband solar irradiance suggested −12.5W/m 2 reduction per 0.1 increase in AOD. Diffuse to direct ratio of solar irradiance showed variations correlating with changes in aerosol optical depth.

Comparative analysis of the quantum FinFET and trigate FinFET based on modeling and simulation

Abstract: A comparative analysis of the trigate fin-shaped field-effect transistor (FinFET) and quantum FinFET (QFinFET) is carried out by using density gradient quantization models in the Synopsys three-dimensional (3-D) technology computer-aided design (TCAD) platform. The gate dielectric stack comprising 0.5 nm SiO2 (k = 3.9) and 2 nm HfO2 (k = 22) contributes to an effective oxide thickness of 0.86 nm and is kept constant throughout the study. The results demonstrate that the QFinFET can overcome the limitations of current FinFET devices when scaling down to the atomic level. An analytical model including quantum-mechanical effects for evaluation of the drain current of the FinFET is established and validated using the TCAD software. The degradation in the drive current with downscaling of the fin thickness for the trigate FinFET and the increase in the drive current for the QFinFET are presented. The results are improved by taking into account different channel lengths and body thicknesses to estimate the drain current–gate voltage and gate capacitance–gate voltage characteristics for both the trigate FinFET and QFinFET. The drain-induced barrier lowering and subthreshold swing are also analyzed for the trigate FinFET and QFinFET at different technology nodes, revealing excellent characteristics. It is clearly established that the QFinFET can overcome the limitations faced by current FinFET devices when scaling the silicon down to the atomic level and may represent the next generation of FinFET devices.

Effects of precipitation, temperature and topographic parameters on evergreen vegetation greenery in the Western Ghats, India

Abstract: Bi-weekly National Oceanic and Atmospheric Administration-advanced very high-resolution radiometer (NOAA-AVHRR) satellite data covering a fourteen-year time period (1990–2003) were used to examine spatial patterns in the normalized difference vegetation index (NDVI) and their relationships with environmental variables covering tropical evergreen forests of the Western Ghats, India. NDVI values and corresponding environmental variables were extracted from 23 different forested sites using the NOAA-AVHRR global inventory monitoring and modelling studies (GIMMS) dataset. We specifically used the partial least square (PLS) multivariate regression technique that combines features from principal component analysis and multiple regression to link spatial patterns in NDVI with the environmental variables. PLS regression analysis suggested the two-component model to be the best model, explaining nearly 71% of the variance in the NDVI datasets with relatively good R2 value of 0.78 and a predicted R2 value of 0.74. The most important positive predictors for NDVI included Riva's continentality index, precipitation indicators summed over different quarters, average precipitation and elevation. Also, the results from PLS regression clearly suggested that bio-climatic indicators that relied only on precipitation parameters had much more positive influence than indicators that combined both temperature and precipitation together. These results highlight the climatic controls of vegetation vigor in evergreen forests and have implications for monitoring bio-spheric activity, developing prognostic phenology models and deriving land cover maps in the Western Ghats region of India. Copyright © 2008 Royal Meteorological Society