42 research outputs found

    Border Insecurity: Reading Transnational Environments in Jim Lynch’s Border Songs

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    This article applies an eco-critical approach to contemporary American fiction about the Canada-US border, examining Jim Lynch’s portrayal of the British Columbia-Washington borderlands in his 2009 novel Border Songs. It argues that studying transnational environmental actors in border texts—in this case, marijuana, human migrants, and migratory birds—helps illuminate the contingency of political boundaries, problems of scale, and discourses of risk and security in cross-border regions after 9/11. Further, it suggests that widening the analysis of trans-border activity to include environmental phenomena productively troubles concepts of nature and regional belonging in an era of climate change and economic globalization. Cet article propose une lecture écocritique de la fiction étatsunienne contemporaine portant sur la frontière entre le Canada et les États-Unis, en étudiant le portrait donné par Jim Lynch de la région frontalière entre la Colombie-Britannique et Washington dans son roman Border Songs, paru en 2009. L’article soutient que l’étude, dans les textes sur la frontière, des acteurs environnementaux transnationaux – dans ce cas-ci, la marijuana, les migrants humains et les oiseaux migratoires – jette un jour nouveau sur la contingence des limites territoriales politiques, des problèmes d’échelle et des discours sur le risque et la sécurité des régions transfrontalières après les évènements du 11 septembre 2001. Il suggère également qu’en élargissant l’analyse de l’activité transfrontalière pour y inclure les phénomènes environnementaux, on brouille de façon productive les concepts de nature et d’appartenance régionale d’une époque marquée par les changements climatiques et la mondialisation de l’économie

    Factors Associated with Revision Surgery after Internal Fixation of Hip Fractures

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    Background: Femoral neck fractures are associated with high rates of revision surgery after management with internal fixation. Using data from the Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trial evaluating methods of internal fixation in patients with femoral neck fractures, we investigated associations between baseline and surgical factors and the need for revision surgery to promote healing, relieve pain, treat infection or improve function over 24 months postsurgery. Additionally, we investigated factors associated with (1) hardware removal and (2) implant exchange from cancellous screws (CS) or sliding hip screw (SHS) to total hip arthroplasty, hemiarthroplasty, or another internal fixation device. Methods: We identified 15 potential factors a priori that may be associated with revision surgery, 7 with hardware removal, and 14 with implant exchange. We used multivariable Cox proportional hazards analyses in our investigation. Results: Factors associated with increased risk of revision surgery included: female sex, [hazard ratio (HR) 1.79, 95% confidence interval (CI) 1.25-2.50; P = 0.001], higher body mass index (fo

    Model-SelectionforNon-ParametricFunction Approximation in Continuous ControlProblems: A Case Study in aSmartEnergy System

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    Abstract. This paper investigates the application of value-function-based reinforcementlearningtoasmartenergycontrolsystem,specificallythetaskofcontrollingan HVACsystemtominimize energywhile satisfyingresidents’comfort requirements. In theory, value-function-based reinforcement learning methods can solve control problems such as this one optimally. However, since choosing anappropriate parametric representationof the value function turnsout tobe difficult,wedevelopanalternativemethod,whichresultsinapracticalalgorithm for value function approximation in continuous state-spaces. To avoid the need to carefully design a parametric representation for the value function, we use a smooth non-parametric function approximator, specifically Locally Weighted LinearRegression(LWR).LWRisusedwithinFittedValueIteration(FVI),which hasmetwithseveralpracticalsuccesses.However,forefficiencyreasons,LWRis usedwithalimitedsample-size,whichleadstopoorperformancewithoutcareful tuningofLWR’sparameters.Wethereforedevelopanefficientmeta-learningprocedure that performs online model-selection and tunes LWR’s parameters based ontheBellmanerror.Ouralgorithmisfullyimplementedandtestedinarealistic simulationoftheHVACcontroldomain,andresultsinsignificantenergysavings.
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