15 research outputs found

    A retrospective descriptive study of the characteristics of deliberate self-poisoning patients with single or repeat presentations to an Australian emergency medicine network in a one year period

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    Background - A proportion of deliberate self-poisoning (DSP) patients present repeatedly to the emergency department (ED). Understanding the characteristics of frequent DSP patients and their presentation is a first step to implementing interventions that are designed to prevent repeated self-poisoning. Methods - All DSP presentations to three networked Australian ED’s were retrospectively identified from the ED electronic medical record and hospital scanned medical records for 2011. Demographics, types of drugs ingested, emergency department length of stay and disposition for the repeat DSP presenters were extracted and compared to those who presented once with DSP in a one year period. Logistic regression was used to analyse repeat versus single DSP data. Results - The study determined 755 single presenters and 93 repeat DSP presenters. The repeat presenters contributed to 321 DSP presentations. They were more likely to be unemployed (61.0% versus 39.9%, p = 0.008) and have a psychiatric illness compared to single presenters (36.6% versus 15.5%, p < 0.001). Repeat presenters were less likely to receive a toxicology consultation (11.5% versus 27.3%, p < 0.001) and were more likely to abscond from the ED (7.5% versus 3.4%, p = 0.004). Repeat presenters were more likely to ingest paracetamol and antipsychotics than single presenters. The defined daily dose for the most common antipsychotic ingested, quetiapine, was less in the repeat presenter group (median 1.9 [IQR: 1.3-3.5]) compared with the single presenter group (4 [1.4-9.5]), (OR 0.85, 95% CI 0.74-0.99). Conclusion - Patients who present repeatedly to the ED with DSP have pre-existing disadvantages, with increased likelihood of being unemployed and having a mental illness. These patients are also more likely to have health service inequities given the greater likelihood to abscond from the ED and lower likelihood of receiving toxicology consultation for their DSP. Early recognition of repeat DSP patients in the ED may facilitate the development of individualised care plans with the aim to reduce repeat episodes of self-poisoning and subsequent risk of successful suicide

    Geometry and field theory in multi-fractional spacetime

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    We construct a theory of fields living on continuous geometries with fractional Hausdorff and spectral dimensions, focussing on a flat background analogous to Minkowski spacetime. After reviewing the properties of fractional spaces with fixed dimension, presented in a companion paper, we generalize to a multi-fractional scenario inspired by multi-fractal geometry, where the dimension changes with the scale. This is related to the renormalization group properties of fractional field theories, illustrated by the example of a scalar field. Depending on the symmetries of the Lagrangian, one can define two models. In one of them, the effective dimension flows from 2 in the ultraviolet (UV) and geometry constrains the infrared limit to be four-dimensional. At the UV critical value, the model is rendered power-counting renormalizable. However, this is not the most fundamental regime. Compelling arguments of fractal geometry require an extension of the fractional action measure to complex order. In doing so, we obtain a hierarchy of scales characterizing different geometric regimes. At very small scales, discrete symmetries emerge and the notion of a continuous spacetime begins to blur, until one reaches a fundamental scale and an ultra-microscopic fractal structure. This fine hierarchy of geometries has implications for non-commutative theories and discrete quantum gravity. In the latter case, the present model can be viewed as a top-down realization of a quantum-discrete to classical-continuum transition.Comment: 1+82 pages, 1 figure, 2 tables. v2-3: discussions clarified and improved (especially section 4.5), typos corrected, references added; v4: further typos correcte

    Multiphase Flow in Porous Media

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    Network Traffic Modeling using a Multifractal Wavelet Model

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    Conference PaperIn this paper, we describe a new multiscale model for characterizing positive-valued and long-range dependent data. The model uses the Haar wavelet transform and puts a constraint on the wavelet coefficients to guarantee positivity, which results in a swift O(N) algorithm to synthesize N-point data sets. We elucidate our model's ability to capture the covariance structure of real data, study its multifractal properties, and derive a scheme for matching it to real data observations. We demonstrate the model's utility by applying it to network traffic synthesis. The flexibility and accuracy of the model and fitting procedure result in a close match to the real data statistics (variance-time plots) and queuing behavior

    Analysis of the Blasius’ Formula and the Navier–Stokes Fractional Equation

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    Fractional Brownian motion and data traffic modeling: The other end of the spectrum

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    Journal PaperWe analyze the fractal behavior of the high frequency part of the Fourier spectrum of fBm using multifractal analysis and show that it is not consistent with what is measured on real traffic traces. We propose two extensions of fBm which come closer to actual traffic traces multifractal properties

    Local Effective Hölder Exponent Estimation on the Wavelet Transform Maxima Tree

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    We present a robust method of estimating an effective H\"older exponent locally at an arbitrary resolution. The method is motivated by the multiplicative cascade paradigm, and implemented on the hierarchy of singularities revealed with the wavelet transform modulus maxima tree. In addition, we illustrate the possibility of the direct estimation of the scaling spectrum of the effective H\"older exponent, and we link it to the established partition functions based multifractal formalism. We motivate both the local and the global multifractal analysis by showing examples of computer generated and real life time series
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