E-print Articles
https://repository.iitgn.ac.in/handle/123456789/615
Sun, 21 Apr 2019 20:32:09 GMT2019-04-21T20:32:09ZTime and phase resolved optical spectra of potentially hazardous asteroid 2014 JO25
https://repository.iitgn.ac.in/handle/123456789/4339
Time and phase resolved optical spectra of potentially hazardous asteroid 2014 JO25
Venkataramani, Kumar; Ganesh, Shashikiran; Rai, Archita; Husárik, Marek; Baliyan, K. S.; Joshi, U.C.
The asteroid 2014 JO25, considered to be "potentially hazardous" by the Minor Planet Center, was spectroscopically followed during its close-Earth encounter on 19th and 20th of April 2017. The spectra of the asteroid were taken with the low resolution spectrograph (LISA), mounted on the 1.2-m telescope at the Mount Abu Infrared Observatory, India. Coming from a region close to the Hungaria population of asteroids, this asteroid follows a comet-like orbit with a relatively high inclination and large eccentricity. Hence, we carried out optical spectroscopic observations of the asteroid to look for comet-like molecular emissions or outbursts. However, the asteroid showed a featureless spectrum, devoid of any comet-like features. The asteroid's light curve was analyzed using V band magnitudes derived from the spectra and the most likely solution for the rotation of the asteroid was obtained. The absolute magnitude H and the slope parameter G were determined for the asteroid in V filter band using the IAU accepted standard two parameter H-G model. A peculiar, rarely found result from these observations is its phase bluing trend. The relative B-V color index seems to decrease with increasing phase angle, which indicates a phase bluing trend. Such trends have seldom been reported in literature. However, phase reddening in asteroids is very common. The asymmetry parameter g and the single scattering albedo w were estimated for the asteroid by fitting the Hapke phase function to the observed data. The asteroid shows relatively large value for the single scattering albedo and a highly back scattering surface.
Fri, 01 Mar 2019 00:00:00 GMThttps://repository.iitgn.ac.in/handle/123456789/43392019-03-01T00:00:00ZA subset selection based approach to finding important structure of complex networks
https://repository.iitgn.ac.in/handle/123456789/4338
A subset selection based approach to finding important structure of complex networks
Tripathi, Richa; Reza, Amit
Most of the real world networks such as the internet network, collaboration networks, brain networks, citation networks, powerline and airline networks are very large and to study their structure, and dynamics one often requires working with large connectivity (adjacency) matrices. However, it is almost always true that a few or sometimes most of the nodes and their connections are not very crucial for network functioning or that the network is robust to a failure of certain nodes and their connections to the rest of the network. In the present work, we aim to extract the size reduced representation of complex networks such that new representation has the most relevant network nodes and connections and retains its spectral properties. To achieve this, we use the Subset Selection (SS) procedure. The SS method, in general, is used to retrieve maximum information from a matrix in terms of its most informative columns. The retrieved matrix, typically known as subset has columns of an original matrix that have the least linear dependency. We present the application of SS procedure to many adjacency matrices of real-world networks and model network types to extract their subset. The subset owing to its small size can play a crucial role in analyzing spectral properties of large complex networks where space and time complexity of analyzing full adjacency matrices are too expensive. The adjacency matrix constructed from the obtained subset has a smaller size and represents the most important network structure. We observed that the subset network which is almost half the size of the original network has better information flow efficiency than the original network.
Fri, 01 Mar 2019 00:00:00 GMThttps://repository.iitgn.ac.in/handle/123456789/43382019-03-01T00:00:00ZUnderstanding photon sphere and black hole shadow in dynamically evolving spacetimes
https://repository.iitgn.ac.in/handle/123456789/4304
Understanding photon sphere and black hole shadow in dynamically evolving spacetimes
Mishra, Akash K.; Chakraborty, Sumanta; Sarkar, Sudipta
We study rheology, microstructure, and response to an applied electric field (E) in suspensions of fumed alumina (Al2O3) nanoparticles in a nematic liquid crystal (NLC) made of N-(4-methoxybenzylidene)-4-butylaniline (MBBA). Fumed Al2O3/MBBA suspensions exhibit flowability with nanoparticle volume fraction (ϕ) = 0.001 and 0.007, and become solid-like gels at a nanoparticle ϕ = 0.014 and beyond. The dynamic rheology of gel-like suspensions follows the soft glass rheology (SGR) model. The effective noise temperature remains close to 1 for these Al2O3/MBBA suspensions, which serves as an indication of the presence of glassy dynamics. Further, the optical microscopy and the differential scanning calorimetry (DSC) reveal that the incorporation of fumed We have derived the differential equation governing the evolution of the photon sphere for dynamical black hole spacetimes with or without spherical symmetry. Numerical solution of the same depicting evolution of the photon sphere has been presented for Vaidya, Reissner-Nordström-Vaidya and de-Sitter Vaidya spacetimes. It has been pointed out that evolution of the photon sphere depends crucially on the validity of the null energy condition by the in-falling matter and may present an observational window to even test it through black hole shadow. We have also presented the evolution of the photon sphere for slowly rotating Kerr-Vaidya spacetime and associated structure of black hole shadow. Finally, the effective graviton metric for Einstein-Gauss-Bonnet gravity has been presented, and the graviton sphere has been contrasted with the photon sphere in this context.
Fri, 01 Mar 2019 00:00:00 GMThttps://repository.iitgn.ac.in/handle/123456789/43042019-03-01T00:00:00ZPrediction of the disease controllability in a complex network using machine learning algorithms
https://repository.iitgn.ac.in/handle/123456789/4288
Prediction of the disease controllability in a complex network using machine learning algorithms
Tripathi, Richa; Reza, Amit; Garg, Dinesh
The application of machine learning (ML) techniques spans a vast spectrum of applications ranging from speech, face and character recognition to medical diagnosis to anomaly detection in data and the general classification, prediction and regression problems. In the present work, we demonstrate the application of regression-based state-of-art machine learning techniques to a prediction of disease controllability on complex networks. The complex network models determine the space for distribution of a population of individuals and their interactions with each other. There is numerous epidemic spreading models such as SI (Susceptible-Infected), SIR (Susceptible-Infected-Recovered), SEIR (Susceptible-Exposed-Infected-Recovered), etc., that govern the disease spreading dynamics over time and the stationary state of the disease. We simulate the disease spreading dynamics on a large number of complex networks examples of standard model networks, and determine the basic reproduction number (R 0 ) for each case. R_0 is a metric that determines whether the disease-free epidemic or an endemic state is asymptotically stable. In other words, it determines whether an infectious disease can spread in the population or will die out in the long run and hence indicates the disease controllability on a population. We aim to predict this quantity (R 0 ), based on the importance of complex networks structural properties using the regression techniques of ML, irrespective of the network type. The prediction is possible because of two facts (a) The structure of complex networks plays an essential role in the spreading processes on networks. (b) Availability of non-linear regression techniques with excellent accuracy for prediction of a quantity even for a data which is highly non-linear.
Fri, 01 Feb 2019 00:00:00 GMThttps://repository.iitgn.ac.in/handle/123456789/42882019-02-01T00:00:00Z