Modernizing the Sikkim University Library: Transformation from Traditional to Modern Library Services and Facilities.

Information and Communication Technology has transformed the whole gamut of Library and Information Centers and its adaptability in libraries are inevitable. Although the primary function of a library remains the same, i.e. to acquire, organize and provide access to information to the users, the ways the tasks have been carried out are changing tremendously. This continuous development has demanded the libraries to modernize. With this perspective, the paper has attempted to highlight the transformation of Sikkim University Library of North East India. The paper will focus on modern services and facilities and provision of different E-resources in the library. It will also highlight the infrastructure of Sikkim University Library

Soil moisture modeling and scaling using passive microwave remote sensing

Soil moisture in the shallow subsurface is a primary hydrologic state governing land-atmosphere interaction at various scales. The primary objectives of this study are to model soil moisture in the root zone in a distributed manner and determine scaling properties of surface soil moisture using passive microwave remote sensing. The study was divided into two parts. For the first study, a root zone soil moisture assessment tool (SMAT) was developed in the ArcGIS platform by fully integrating a one-dimensional vadose zone hydrology model (HYDRUS-ET) with an ensemble Kalman filter (EnKF) data assimilation capability. The tool was tested with dataset from the Southern Great Plain 1997 (SGP97) hydrology remote sensing experiment. Results demonstrated that SMAT displayed a reasonable capability to generate soil moisture distribution at the desired resolution at various depths of the root zone in Little Washita watershed during the SGP97 hydrology remote sensing experiment. To improve the model performance, several outstanding issues need to be addressed in the future by: including "effective" hydraulic parameters across spatial scales; implementing subsurface soil properties data bases using direct and indirect methods; incorporating appropriate hydrologic processes across spatial scales; accounting uncertainties in forcing data; and preserving interactions for spatially correlated pixels. The second study focused on spatial scaling properties of the Polarimetric Scanning Radiometer (PSR)-based remotely sensed surface soil moisture fields in a region with high row crop agriculture. A wavelet based multi-resolution technique was used to decompose the soil moisture fields into larger-scale average soil moisture fields and fluctuations in horizontal, diagonal and vertical directions at various resolutions. The specific objective was to relate soil moisture variability at the scale of the PSR footprint (800 m X 800 m) to larger scale average soil moisture field variability. We also investigated the scaling characteristics of fluctuation fields among various resolutions. The spatial structure of soil moisture exhibited linearity in the log-log dependency of the variance versus scale-factor, up to a scale factor of -2.6 (6100 m X 6100 m) irrespective of wet and dry conditions, whereas dry fields reflect nonlinear (multi-scaling) behavior at larger scale-factors

Modeling and application of soil moisture at varying spatial scales with parameter scaling

The dissertation focuses on characterization of subpixel variability within a satellite-based remotely sensed coarse-scale soil moisture footprint. The underlying heterogeneity of coarse-scale soil moisture footprint is masked by the area-integrated properties within the sensor footprint. Therefore, the soil moisture values derived from these measurements are an area average. The variability in soil moisture within the footprint is introduced by inherent spatial variability present in rainfall, and geophysical parameters (vegetation, topography, and soil). The geophysical parameters/variables typically interact in a complex fashion to make soil moisture evolution and dependent processes highly variable, and also, introduce nonlinearity across spatio-temporal scales. To study the variability and scaling characteristics of soil moisture, a quasi-distributed Soil-Vegetation-Atmosphere-Transfer (SVAT) modeling framework is developed to simulate the hydrological dynamics, i.e., the fluxes and the state variables within the satellite-based soil moisture footprint. The modeling framework is successfully tested and implemented in different hydroclimatic regions during the research. New multiscale data assimilation and Markov Chain Monte Carlo (MCMC) techniques in conjunction with the SVAT modeling framework are developed to quantify subpixel variability and assess multiscale soil moisture fields within the coarse-scale satellite footprint. Reasonable results demonstrate the potential to use these techniques to validate multiscale soil moisture data from future satellite mission e.g., Soil Moisture Active Passive (SMAP) mission of NASA. The results also highlight the physical controls of geophysical parameters on the soil moisture fields for various hydroclimatic regions. New algorithm that uses SVAT modeling framework is also proposed and its application demonstrated, to derive the stochastic soil hydraulic properties (i.e., saturated hydraulic conductivity) and surface features (i.e., surface roughness and volume scattering) related to radar remote sensing of soil moisture

See-saw fermion masses in an SO(10) GUT

In this work we study an SO(10) GUT model with minimum Higgs representations belonging only to the 210 and 16 dimensional representations of SO(10). We add a singlet fermion S in addition to the usual 16 dimensional representation containing quarks and leptons. There are no Higgs bi-doublets and so charged fermion masses come from one-loop corrections. Consequently all the fermion masses, Dirac and Majorana, are of the see-saw type. We minimize the Higgs potential and show how the left-right symmetry is broken in our model where it is assumed that a D-parity odd Higgs field gets a vacuum expectation value at the grand unification scale. From the renormalization group equations we infer that in our model unification happens at 10^{15} GeV and left-right symmetry can be extended up to some values just above 10^{11} GeV. The Yukawa sector of our model is completely different from most of the standard grand unified theories and we explicitly show how the Yukawa sector will look like in the different phases and briefly comment on the running of the top quark mass. We end with a brief analysis of lepton number asymmetry generated from the interactions in our model.Comment: 30 pages, 10 figure

L-Band Vegetation optical depth and effective scattering albedo estimation from SMAP

Over land the vegetation canopy affects the microwave brightness temperature by emission, scattering and attenuation of surface soil emission. Attenuation, as represented by vegetation optical depth (VOD), is a potentially useful ecological indicator. The NASA Soil Moisture Active Passive (SMAP) mission carries significant potential for VOD estimates because of its radio frequency interference mitigation efforts and because the L-band signal penetrates deeper into the vegetation canopy than the higher frequency bands used for many previous VOD retrievals. In this study, we apply the multi-temporal dual-channel retrieval algorithm (MT-DCA) to derive global VOD, soil moisture, and effective scattering albedo estimates from SMAP Backus-Gilbert enhanced brightness temperatures posted on a 9 km grid and with three day revisit time. SMAP VOD values from the MT-DCA follow expected global distributions and are shown to be highly correlated with canopy height. They are also broadly similar in magnitude (though not always in seasonal amplitude) to European Space Agency Soil Moisture and Ocean Salinity (SMOS) VOD. The SMOS VOD values are based on angular brightness temperature information while the SMAP measurements are at a constant incidence angle, requiring an alternate approach to VOD retrieval presented in this study. Globally, albedo values tend to be high over regions with heterogeneous land cover types. The estimated effective scattering albedo values are generally higher than those used in previous soil moisture estimation algorithms and linked to biome classifications. MT-DCA retrievals of soil moisture show only small random differences with soil moisture retrievals from the Baseline SMAP algorithm, which uses a prior estimate of VOD based on land cover and optical data. However, significant biases exist between the two datasets. The soil moisture biases follow the pattern of differences between the MT-DCA retrieved and Baseline-assigned VOD values