43,285 research outputs found

    Using Different Approaches to Evaluate Individual Social Equity in Transport

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    Inequalities not only exist in the field of economics in relation to income and wealth, but also in other areas, such as the transport sector, where access to and use of different transport modes varies markedly across population groups, and which provides the means to access everyday living activities. A key concern within the transport sector is that inequality has extended beyond the traditional measures of travel, and now covers a wide range of effects relating to social exclusion, freedom, well-being and being able to access reasonable opportunities and resources. In order to address the aforementioned issues, an important question to resolve is what type of methods can be used to measure inequalities in transport most effectively. Therefore, this study aims to apply different approaches, including the Capabilities Approach (CA) and a further six inequality indices, namely the Gini coefficient, the Atkinson index, the Palma ratio, the Pietra ratio, the Schutz coefficient and the Theil index, to the case study using the relatively migrant-rich lower-income neighbourhood of Tuqiao, in Beijing, in order to assess individual transport-related social inequity issues. The findings suggest that the CA is useful in assessing transport-related inequalities where there are significant barriers to the take up of accessibility, for example where there are high levels of disadvantaged groups and disaggregated analysis can be undertaken. The Palma ratio appears to have a larger effect than the Gini coefficient and the other inequality indices when measuring transport-related social inequity. In addition, we also found that most income inequality methods adapted from econometrics may be better suited to measuring transport-related social inequity between different regions, cities or countries, or within the same area, but at different points in time, rather than to measuring a single neighbourhood as a whole. Finally, we argue that to what extent politicians or transport planners can use appropriate management tools to measure transport-related social inequalities may be significant in terms of the progress that can be made in the fight against social inequity in the transport field

    Sequential Adaptive Detection for In-Situ Transmission Electron Microscopy (TEM)

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    We develop new efficient online algorithms for detecting transient sparse signals in TEM video sequences, by adopting the recently developed framework for sequential detection jointly with online convex optimization [1]. We cast the problem as detecting an unknown sparse mean shift of Gaussian observations, and develop adaptive CUSUM and adaptive SSRS procedures, which are based on likelihood ratio statistics with post-change mean vector being online maximum likelihood estimators with â„“1\ell_1. We demonstrate the meritorious performance of our algorithms for TEM imaging using real data

    Long-Range Plasmon Assisted Energy Transfer Between Fluorescent Emitters

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    We demonstrate plasmon assisted energy transfer between fluorophores located at distances up to 7 μ7 \, \mum on the top of a thin silver film. Thanks to the strong confinement and large propagation length of surface plasmon polaritons, the range of the energy transfer is almost two orders of magnitude larger than the values reported in the literature so far. The parameters driving the energy transfer range are thoroughly characterized and are in very good agreement with theoretically expected values.Comment: 5 pages, 4 figures, accepted for publication in Physical Review Letter

    Deviation contribution plots of multivariate statistics

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    As data analytic techniques evolve and the accessibility of process measurements improves, data-driven process monitoring has enjoyed a quick development in both theoretical and application perspectives recently. Although abundant process measurements will facilitate data-driven process monitoring and lead to better monitoring indices, it becomes difficult to identify the underlying variables that are responsible for a fault directly with the monitoring indices as the scope of measured variables is getting broader. To restrain the scope and identify the source of fault, contribution plots are commonly used in fault diagnosis in order to quantify the influence of process variables in presence of fault. Nevertheless, as sophisticated monitoring techniques become more and more complicated, deriving corresponding contribution plots is challenging. The concept of deviation contribution plots is proposed to address this issue. By extending the original definition of contribution for linear processes, the deviation contribution is defined to quantify the contribution of deviations in originally measured variables to the deviation of monitoring indices. The ability of proposed deviation contribution plots to identify influential variables in monitoring algorithms based on nonlinear feature extractions is verified by both numerical simulation and the Tennessee Eastman Process benchmark case study

    Diagnosability of Fuzzy Discrete Event Systems

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    In order to more effectively cope with the real-world problems of vagueness, {\it fuzzy discrete event systems} (FDESs) were proposed recently, and the supervisory control theory of FDESs was developed. In view of the importance of failure diagnosis, in this paper, we present an approach of the failure diagnosis in the framework of FDESs. More specifically: (1) We formalize the definition of diagnosability for FDESs, in which the observable set and failure set of events are {\it fuzzy}, that is, each event has certain degree to be observable and unobservable, and, also, each event may possess different possibility of failure occurring. (2) Through the construction of observability-based diagnosers of FDESs, we investigate its some basic properties. In particular, we present a necessary and sufficient condition for diagnosability of FDESs. (3) Some examples serving to illuminate the applications of the diagnosability of FDESs are described. To conclude, some related issues are raised for further consideration.Comment: 14 pages; revisions have been mad

    Analysis of the Early-time Optical Spectra of SN 2011fe in M101

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    The nearby Type Ia supernova (SN Ia) SN 2011fe in M101 (cz = 241 km s^(–1)) provides a unique opportunity to study the early evolution of a "normal" SN Ia, its compositional structure, and its elusive progenitor system. We present 18 high signal-to-noise spectra of SN 2011fe during its first month beginning 1.2 days post-explosion and with an average cadence of 1.8 days. This gives a clear picture of how various line-forming species are distributed within the outer layers of the ejecta, including that of unburned material (C+O). We follow the evolution of C II absorption features until they diminish near maximum light, showing overlapping regions of burned and unburned material between ejection velocities of 10,000 and 16,000 km s^(–1). This supports the notion that incomplete burning, in addition to progenitor scenarios, is a relevant source of spectroscopic diversity among SNe Ia. The observed evolution of the highly Doppler-shifted O I λ7774 absorption features detected within 5 days post-explosion indicates the presence of O I with expansion velocities from 11,500 to 21,000 km s^(–1). The fact that some O I is present above C II suggests that SN 2011fe may have had an appreciable amount of unburned oxygen within the outer layers of the ejecta
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