44,014 research outputs found

    Thermodynamic Geometry and Topological Einstein-Yang-Mills Black Holes

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    From the perspective of the statistical fluctuation theory, we explore the role of the thermodynamic geometries and vacuum (in)stability properties for the topological Einstein-Yang-Mills black holes. In this paper, from the perspective of the state-space surface and chemical Wienhold surface, we provide the criteria for the local and global statistical stability of an ensemble of topological Einstein-Yang-Mills black holes in arbitrary spacetime dimensions D≥5D\ge 5. Finally, as per the formulations of the thermodynamic geometry, we offer a parametric account of the statistical consequences in both the local and global fluctuation regimes of the topological Einstein-Yang-Mills black holes.Comment: 39 pages, 16 figures. Keywords: Thermodynamic Geometry; Topological Einstein-Yang-Mills Black Holes; Higher Dimensional Gravity; Cosmological Constant. Two typos correcte

    Estimation of the Sensitive Volume for Gravitational-wave Source Populations Using Weighted Monte Carlo Integration

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    The population analysis and estimation of merger rates of compact binaries is one of the important topics in gravitational wave (GW) astronomy. The primary ingredient in these analyses is the population-averaged sensitive volume. Typically, sensitive volume, of a given search to a given simulated source population, is estimated by drawing signals from the population model and adding them to the detector data as injections. Subsequently injections, which are simulated gravitational waveforms, are searched for by the search pipelines and their signal-to-noise ratio (SNR) is determined. Sensitive volume is estimated, by using Monte-Carlo (MC) integration, from the total number of injections added to the data, the number of injections that cross a chosen threshold on SNR and the astrophysical volume in which the injections are placed. So far, only fixed population models have been used in the estimation of the merger rates. However, as the scope of population analysis broaden in terms of the methodologies and source properties considered, due to an increase in the number of observed GW signals, the procedure will need to be repeated multiple times at a large computational cost. In this letter we address the problem by performing a weighted MC integration. We show how a single set of generic injections can be weighted to estimate the sensitive volume for multiple population models; thereby greatly reducing the computational cost. The weights in this MC integral are the ratios of the output probabilities, determined by the population model and standard cosmology, and the injection probability, determined by the distribution function of the generic injections. Unlike analytical/semi-analytical methods, which usually estimate sensitive volume using single detector sensitivity, the method is accurate within statistical errors, comes at no added cost and requires minimal computational resources.Comment: 11 pages, 1 figur

    Understanding the impact of privacy concerns and trust on social networking sites: Analysing user intentions towards willingness to share digital identities

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    Participation in social networking sites (SNS) has dramatically increased in recent years. SNS focus on building online communities of people who share interests and/or activities, or who are interested in exploring the interests and activities of others. This study examines the experiences of SNS users, and explores how the depth of their experience and knowledge of the Internet, trust and privacy concerns impact upon their individual willingness to share information about their own identity with other users on social networking websites. An acceptance model is proposed that incorporates cognitive, as well as affective, attitudes as primary influencing factors on user attitudes and behaviour which, in turn, are driven by underlying beliefs, perceived levels of privacy and trust, attitudinal experiences and knowledge, as well as a willingness to share. The proposed conceptual model for this study is derived from the literature review and Theory of Planned Behaviour. This model explains how people experience different levels of motivation about sharing knowledge and seeking information from other members which, in turn, leads to a divergence in both intentions and behaviours within virtual communities. The model shows excellent measurement properties and establishes two distinct constructs—specifically, the need for perceived levels of privacy, and the need for established levels of trust within SNS. This study is based on quantitative methodology and uses a structural equation model to test the construction of the model and its hypothesis. The data for this study were collected from a Facebook forum, with a sample size of 155 SNS users. The main theoretical contribution of this study is to provide greater understanding and new insights into privacy concerns and trust, in so far as these factors impact upon SNS users‘ willingness to readily share information regarding their digital identities. Secondly, this study will enrich the existing literature regarding the inter-relationship between the extent of SNS users‘ length and depth of experience as Internet users, as this impact upon their willingness to share identity-based information
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