The Spaces Between Numbers: Getting International Data on Higher Education Straight

Argues that the participation and attainment data used in international comparisons do not reflect the performance objectives of higher education systems. Suggests economic and demographic frameworks for interpreting data and changes in data collection

Representation and generation of plans using graph spectra

Numerical comparison of spaces with one another is often achieved with set scalar measures such as global and local integration, connectivity, etc., which capture a particular quality of the space but therefore lose much of the detail of its overall structure. More detailed methods such as graph edit distance are difficult to calculate, particularly for large plans. This paper proposes the use of the graph spectrum, or the ordered eigenvalues of a graph adjacency matrix, as a means to characterise the space as a whole. The result is a vector of high dimensionality that can be easily measured against others for detailed comparison. Several graph types are investigated, including boundary and axial representations, as are several methods for deriving the spectral vector. The effectiveness of these is evaluated using a genetic algorithm optimisation to generate plans to match a given spectrum, and evolution is seen to produce plans similar to the initial targets, even in very large search spaces. Results indicate that boundary graphs alone can capture the gross topological qualities of a space, but axial graphs are needed to indicate local relationships. Methods of scaling the spectra are investigated in relation to both global local changes to plan arrangement. For all graph types, the spectra were seen to capture local patterns of spatial arrangement even as global size is varied

Representation and generation of plans using graph spectra

Numerical comparison of spaces with one another is often achieved with set scalar measures such as global and local integration, connectivity, etc., which capture a particular quality of the space but therefore lose much of the detail of its overall structure. More detailed methods such as graph edit distance are difficult to calculate, particularly for large plans. This paper proposes the use of the graph spectrum, or the ordered eigenvalues of a graph adjacency matrix, as a means to characterise the space as a whole. The result is a vector of high dimensionality that can be easily measured against others for detailed comparison. Several graph types are investigated, including boundary and axial representations, as are several methods for deriving the spectral vector. The effectiveness of these is evaluated using a genetic algorithm optimisation to generate plans to match a given spectrum, and evolution is seen to produce plans similar to the initial targets, even in very large search spaces. Results indicate that boundary graphs alone can capture the gross topological qualities of a space, but axial graphs are needed to indicate local relationships. Methods of scaling the spectra are investigated in relation to both global local changes to plan arrangement. For all graph types, the spectra were seen to capture local patterns of spatial arrangement even as global size is varied

Methodological issues in national-comparative research on cultural tastes : the case of cultural capital in the UK and Finland

Drawing on two projects which develop the methodological model of Bourdieu’s Distinction in the UK and Finland, this paper explores the issues raised by the use of multiple correspondence analysis (MCA) and mixed methods in comparative work on cultural tastes. By identifying the problems in the construction of two comparable yet nationally relevant research instruments, the paper considers the importance of the similarities and differences in the meaning of items in different national spaces for Bourdieu-inspired comparative analysis. The paper also reports on the evident similarities between the two constructed spaces and draws on the dialogue between quantitative and qualitative methods enabled by MCA in examining what different positions in social space appear to mean in these countries country. It concludes by suggesting that, whilst Bourdieu’s model provides a robust set of methods for exploring relations between taste and class within nations, used appropriately, it can also provide particular insight to the comparison between national fields

Overall buckling of lightweight stiffened panels using an adapted orthotropic plate method

The ultimate longitudinal bending strength of thin plated steel structures such as box girder bridges and ship hulls can be determined using an incremental–iterative procedure known as the Smith progressive collapse method. The Smith method first calculates the response of stiffened panel sub-structures in the girder and then integrates over the cross section of interest to calculate a moment–curvature response curve. A suitable technique to determine the strength behaviour of stiffened panels within the Smith method is therefore of critical importance. A fundamental assumption of the established progressive collapse method is that the buckling and collapse behaviour of the compressed panels within the girder occurs between adjacent transverse frames. However, interframe buckling may not always be the dominant collapse mode, especially for lightweight stiffened panels such as are found in naval ships and aluminium high speed craft. In these cases overall failure modes, where the buckling mode extends over several frame spaces, may dominate the buckling and collapse response. To account for this possibility, an adaptation to large deflection orthotropic plate theory is presented. The adapted orthotropic method is able to calculate panel stress–strain response curves accounting for both interframe and overall collapse. The method is validated with equivalent nonlinear finite element analyses for a range of regular stiffened panel geometries. It is shown how the adapted orthotropic method is implemented into an extended progressive collapse method, which enhances the capability for determining the ultimate strength of a lightweight stiffened box girder

Feature-based time-series analysis

This work presents an introduction to feature-based time-series analysis. The time series as a data type is first described, along with an overview of the interdisciplinary time-series analysis literature. I then summarize the range of feature-based representations for time series that have been developed to aid interpretable insights into time-series structure. Particular emphasis is given to emerging research that facilitates wide comparison of feature-based representations that allow us to understand the properties of a time-series dataset that make it suited to a particular feature-based representation or analysis algorithm. The future of time-series analysis is likely to embrace approaches that exploit machine learning methods to partially automate human learning to aid understanding of the complex dynamical patterns in the time series we measure from the world.Comment: 28 pages, 9 figure