Implications of binary black hole detections on the merger rates of double neutron stars and neutron star-black holes

We show that the inferred merger rate and chirp masses of binary black holes (BBHs) detected by advanced LIGO (aLIGO) can be used to constrain the rate of double neutron star (DNS) and neutron star - black hole (NSBH) mergers in the universe. We explicitly demonstrate this by considering a set of publicly available population synthesis models of \citet{Dominik:2012kk} and show that if all the BBH mergers, GW150914, LVT151012, GW151226, and GW170104, observed by aLIGO arise from isolated binary evolution, the predicted DNS merger rate may be constrained to be $2.3-471.0$~\rate~ and that of NSBH mergers will be constrained to $0.2-48.5$~\rate. The DNS merger rates are not constrained much but the NSBH rates are tightened by a factor of $\sim 4$ as compared to their previous rates. Note that these constrained DNS and NSBH rates are extremely model dependent and are compared to the unconstrained values $2.3-472.5$ \rate~ and $0.2-218$ \rate, respectively, using the same models of \citet{Dominik:2012kk}. These rate estimates may have implications for short Gamma Ray Burst progenitor models assuming they are powered (solely) by DNS or NSBH mergers. While these results are based on a set of open access population synthesis models which may not necessarily be the representative ones, the proposed method is very general and can be applied to any number of models thereby yielding more realistic constraints on the DNS and NSBH merger rates from the inferred BBH merger rate and chirp mass.Comment: 5 pages, no figures, 4 tables, v2: matches published versio

Extraction of Water-body Area from High-resolution Landsat Imagery

Extraction of water bodies from satellite imagery has been broadly explored in the current decade. So many techniques were involved in detecting of the surface water bodies from satellite data. To detect and extracting of surface water body changes in Nagarjuna Sagar Reservoir, Andhra Pradesh from the period 1989 to 2017, were calculated using Landsat-5 TM, and Landsat-8 OLI data. Unsupervised classification and spectral water indexing methods, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Difference Water Index (NDWI), and Modified Normalized Difference Water Index (MNDWI), were used to detect and extraction of the surface water body from satellite data. Instead of all index methods, the MNDWI was performed better results. The Reservoir water area was extracted using spectral water indexing methods (NDVI, NDWI, MNDWI, and NDMI) in 1989, 1997, 2007, and 2017. The shoreline shrunk in the twenty-eight-year duration of images. The Reservoir Nagarjuna Sagar lost nearly around one-fourth of its surface water area compared to 1989. However, the Reservoir has a critical position in recent years due to changes in surface water and getting higher mud and sand. Maximum water surface area of the Reservoir will lose if such decreasing tendency follows continuously

A Comparison of Maslow’s Theory of Hierarchy of Needs with the Pancha Kosha Theory of Upanishads

There is a growing recognition that reductionist and mechanistic worldview that we hold of human motivation needs to be revised and transformed. Many attempts have been made in this direction including a more humanistic approach. In the Western world, Maslow‟s needs hierarchy was a first such attempt.However, not many attempts have been made to expand further the concept of self-actualization proposed by Maslow. On the other hand, there was a criticism of his approach as not being rooted in research and the real world. In this article, the authors attempt to explore the similarities between the needs hierarchy as proposed by Maslow (1943, 1954) and the model of „Pancha Kosha‟ or„five sheaths‟ theory as presented in the Taittriya Upanishad &nbsp

Computational binding mechanism of Mycobacterium tuberculosis UDP-NAG enolpyruvyl transferase (MurA) with inhibitors fosfomycin, cyclic disulfide analog RWJ-3981, pyrazolopyrimidine analog RWJ-110192, purine analog RWJ-140998, 5-sulfonoxy-anthranilic aci

Worldwide, tuberculosis (TB) remains the most frequent and important infectious disease causing morbidity and death. One-third of the world's population is infected with Mycobacterium tuberculosis (Mtb), the etiologic agent of TB. In this context, TB is in the top three, with malaria and HIV being the leading causes of death from a single infectious agent, and about two million deaths are attributable to TB annually. The bacterial enzyme MurA catalyzes the transfer of enolpyruvate from phosphoenolpyruvate (PEP) to uridine diphospho-N-acetylglucosamine (UNAG), which is the first committed step of bacterial cell wall biosynthesis. In this work, 3D structural model of Mtb-MurA enzyme has been developed, for the first time, by homology modeling and molecular dynamics simulation techniques. The model provided clear insight in its structure features, i.e. substrate binding pocket, and common docking site. Multiple sequence alignment and 3D structure model provided the putative substrate binding pocket of Mtb-MurA with respect to E.coli MurA. This analysis was helpful in identifying the binding sites and molecular function of the MurA homologue. Molecular docking study was performed on this 3D structural model, using different classes of inhibitors like fosfomycin, cyclic disulfide analog RWJ-3981, pyrazolopyrimidine analog RWJ-110192, purine analog RWJ-140998, 5-sulfonoxy-anthranilic acid derivatives T6361, T6362 and the results showed that the 5-sulfonoxyanthranilic acid derivatives is showed best interaction compared with other inhibitor, taking in to this we also design a new efficient analogs of T6361 and T6362 which are showed even better interaction with Mtb-MurA than the parental5-sulfonoxy-anthranilic acid derivatives. Further the comparative molecular electrostatic potential and cavity depth analysis of Mtb-MurA suggested several important differences in its substrate and inhibitor binding pocket. Such differences could be exploited in the future for designing of a more specific inhibitor for Mtb-MurA enzym

Reflectivity Parameter Extraction from RADAR Images Using Back Propagation Algorithm

Pattern recognition has been acknowledged as one of the promising research areas and it has drawn the awareness among many researchers since its existence at the beginning of the nineties. Multilayer Neural networks are used in pattern Recognition and classification based on the features derived from the input patterns. The Reflectivity information extracted from the Doppler Weather Radar (DWR) image helps in identifying the convective cloud type which has a strong relation to the precipitation rate. The reflectivity information is rooted in the DWR image with the help of colors and color bar is provided to distinguish among different reflectivity information. Artificial Neural network predicts the color based on the maximum likelihood estimation problem. This paper presents a best possible backpropagation algorithm for color identification in DWR images by comparing various backpropagation algorithms such as LevenbergMarquardt, Conjugate gradient, and Resilient back propagation etc.,. Pattern recognition using Neural networks presents better results compared to standard distance measures. It is observed that Levenberg-Marquardt backpropagation algorithm yields a regression value of 99% approximately and accuracy of 98

Comparison of self-medication practice for dysmenorrhoea in medical, nursing and dental students

Background: Dysmenorrhea is common in adolescent and young adult females and is responsible for impaired daily activities and significant absenteeism from college among female students. The self-treatment strategy varies among the students. Hence, the present study was done to analyse and compare the self-medication practice for dysmenorrhoea among medical, nursing and dental students.Methods: This was a cross-sectional study conducted among 188 female students with dysmenorrhoea in M. S. Ramaiah College Campus, Bangalore which included 62 medical, 63 nursing and 63 dental students. Data was collected with prevalidated questionnaire related to various aspects like demographic data, severity and duration of dysmenorrhoea and pattern of management in the three groups. Data collected was analysed using SPSS version 20.Results: The mean age of female students with dysmenorrhoea was 19.12±0.87 years. 28% students perceived hormonal changes as causative factor for dysmenorrhoea. About 92 (48.9%) were on self-medication and 46 (24.5%) of students used home remedies for dysmenorrhoea. Among 92 students drugscommonly used for self-medication were mefenemic acid+dicyclomine  (67.4%) followed by paracetamol (20.7%), ibuprofen (5.4%), dicyclomine (4.3%), and diclofenac (2.2%). NSAIDS such as mefenamic acid, paracetamol, ibuprofen, diclofenac were used commonly by students in the three groups.Conclusions: Dysmenorrhoea is a common cause for self-medication among young females. Self-medication practice for dysmenorrhoea was seen more in medical students where as non-pharmacological remedies in nursing and dental female students. NSAIDS like mefenamic acid and paracetamol are the mainstay of self-medication for dysmenorrhoea

Design & Simulation of Radio Frequency Power Amplifiers for High Efficiency and with out affecting Linearity

The purpose of this work is to study and to observe the two important characteristics of Radio Frequency Power Amplifiers, i.e. efficiency and linearity. To provide maximum efficiency, A standard design Procedure is developed and simulated by using Micro Wave Office (Antenna Design Software). Then the Class F Amplifier is designed to Increase the efficiency and simulated by using AWR MWO. The structure of Doherty Amplifier is introduced to enhance the efficiency without affecting and maintaining the Linearity. The design deals with the Auxiliary device and this device simplifies the circuit. It is able to provide the back-off efficiency and at the same time maintains the linearity

Speaker Identification and Spoken word Recognition in Noisy Environment using Different Techniques

In this work, an attempt is made to design ASR systems through software/computer programs which would perform Speaker Identification, Spoken word recognition and combination of both speaker identification and Spoken word recognition in general noisy environment. Automatic Speech Recognition system is designed for Limited vocabulary of Telugu language words/control commands. The experiments are conducted to find the better combination of feature extraction technique and classifier model that will perform well in general noisy environment (Home/Office environment where noise is around 15-35 dB). A recently proposed features extraction technique Gammatone frequency coefficients which is reported as the best fit to the human auditory system is chosen for the experiments along with the more common feature extraction techniques MFCC and PLP as part of Front end process (i.e. speech features extraction). Two different Artificial Neural Network classifiers Learning Vector Quantization (LVQ) neural networks and Radial Basis Function (RBF) neural networks along with Hidden Markov Models (HMMs) are chosen for the experiments as part of Back end process (i.e. training/modeling the ASRs). The performance of different ASR systems that are designed by utilizing the 9 different combinations (3 feature extraction techniques and 3 classifier models) are analyzed in terms of spoken word recognition and speaker identification accuracy success rate, design time of ASRs, and recognition / identification response time .The testing speech samples are recorded in general noisy conditions i.e.in the existence of air conditioning noise, fan noise, computer key board noise and far away cross talk noise. ASR systems designed and analyzed programmatically in MATLAB 2013(a) Environment

Testing the multipole structure and conservative dynamics of compact binaries using gravitational wave observations: The spinning case

In an earlier work [S. Kastha et al., PRD {\bf 98}, 124033 (2018)], we developed the {\it parametrized multipolar gravitational wave phasing formula} to test general relativity, for the non-spinning compact binaries in quasi-circular orbit. In this paper, we extend the method and include the important effect of spins in the inspiral dynamics. Furthermore, we consider parametric scaling of PN coefficients of the conserved energy for the compact binary, resulting in the parametrized phasing formula for non-precessing spinning compact binaries in quasi-circular orbit. We also compute the projected accuracies with which the second and third generation ground-based gravitational wave detector networks as well as the planned space-based detector LISA will be able to measure the multipole deformation parameters and the binding energy parameters. Based on different source configurations, we find that a network of third-generation detectors would have comparable ability to that of LISA in constraining the conservative and dissipative dynamics of the compact binary systems. This parametrized multipolar waveform would be extremely useful not only in deriving the first upper limits on any deviations of the multipole and the binding energy coefficients from general relativity using the gravitational wave detections, but also for science case studies of next generation gravitational wave detectors.Comment: 16 pages, 8 figures, Mathematica readable supplemental material file for all the inputs to calculate the parametrized waveform is with the sourc