833 resultados para Packing problems


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In order to reduce environmental impacts and achieve sustainability, it is important to balance the interactions between the built and natural environment. The construction industry is becoming more aware of ecological concerns and the importance that biodiversity and maintenance ecosystem services has for sustainability. Bats constitute an important component of urban biodiversity and several species in the UK are highly dependent on buildings, making them particularly vulnerable to anthropogenic and environmental changes. Many buildings suitable for use as bat roosts often require re-roofing as they age and traditional bituminous roofing felts are frequently being replaced with breathable roofing membranes (BRMs). In the UK new building regulations and modern materials may substantially reduce the viability of existing roosts, yet at thesame time building regulations require that materials be fit for purpose. Reports suggest that both bats and BRMs may experience problems when the two interact. Such information makes it important to understand how house dwelling bats and BRMs may be affected. This paper considers the possible ways in which bats and BRMs may interact, how this could affect existing bat roosts within buildings and the implications for BRM service life predictions and warranties. Keywords –Breathable Roofing Membranes, Bats in Buildings, Material Deterioration, Sustainability, Conservation, Biodiversit

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In The Conduct of Inquiry in International Relations, Patrick Jackson situates methodologies in International Relations in relation to their underlying philosophical assumptions. One of his aims is to map International Relations debates in a way that ‘capture[s] current controversies’ (p. 40). This ambition is overstated: whilst Jackson’s typology is useful as a clarificatory tool, (re)classifying existing scholarship in International Relations is more problematic. One problem with Jackson’s approach is that he tends to run together the philosophical assumptions which decisively differentiate his methodologies (by stipulating a distinctive warrant for knowledge claims) and the explanatory strategies that are employed to generate such knowledge claims, suggesting that the latter are entailed by the former. In fact, the explanatory strategies which Jackson associates with each methodology reflect conventional practice in International Relations just as much as they reflect philosophical assumptions. This makes it more difficult to identify each methodology at work than Jackson implies. I illustrate this point through a critical analysis of Jackson’s controversial reclassification of Waltz as an analyticist, showing that whilst Jackson’s typology helps to expose inconsistencies in Waltz’s approach, it does not fully support the proposed reclassification. The conventional aspect of methodologies in International Relations also raises questions about the limits of Jackson’s ‘engaged pluralism’.

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We derive energy-norm a posteriori error bounds, using gradient recovery (ZZ) estimators to control the spatial error, for fully discrete schemes for the linear heat equation. This appears to be the �rst completely rigorous derivation of ZZ estimators for fully discrete schemes for evolution problems, without any restrictive assumption on the timestep size. An essential tool for the analysis is the elliptic reconstruction technique.Our theoretical results are backed with extensive numerical experimentation aimed at (a) testing the practical sharpness and asymptotic behaviour of the error estimator against the error, and (b) deriving an adaptive method based on our estimators. An extra novelty provided is an implementation of a coarsening error "preindicator", with a complete implementation guide in ALBERTA in the appendix.

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We present a Galerkin method with piecewise polynomial continuous elements for fully nonlinear elliptic equations. A key tool is the discretization proposed in Lakkis and Pryer, 2011, allowing us to work directly on the strong form of a linear PDE. An added benefit to making use of this discretization method is that a recovered (finite element) Hessian is a byproduct of the solution process. We build on the linear method and ultimately construct two different methodologies for the solution of second order fully nonlinear PDEs. Benchmark numerical results illustrate the convergence properties of the scheme for some test problems as well as the Monge–Amp`ere equation and the Pucci equation.

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We propose a numerical method to approximate the solution of second order elliptic problems in nonvariational form. The method is of Galerkin type using conforming finite elements and applied directly to the nonvariational (nondivergence) form of a second order linear elliptic problem. The key tools are an appropriate concept of “finite element Hessian” and a Schur complement approach to solving the resulting linear algebra problem. The method is illustrated with computational experiments on three linear and one quasi-linear PDE, all in nonvariational form.

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In the present paper we study the approximation of functions with bounded mixed derivatives by sparse tensor product polynomials in positive order tensor product Sobolev spaces. We introduce a new sparse polynomial approximation operator which exhibits optimal convergence properties in L2 and tensorized View the MathML source simultaneously on a standard k-dimensional cube. In the special case k=2 the suggested approximation operator is also optimal in L2 and tensorized H1 (without essential boundary conditions). This allows to construct an optimal sparse p-version FEM with sparse piecewise continuous polynomial splines, reducing the number of unknowns from O(p2), needed for the full tensor product computation, to View the MathML source, required for the suggested sparse technique, preserving the same optimal convergence rate in terms of p. We apply this result to an elliptic differential equation and an elliptic integral equation with random loading and compute the covariances of the solutions with View the MathML source unknowns. Several numerical examples support the theoretical estimates.

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To bridge the gaps between traditional mesoscale modelling and microscale modelling, the National Center for Atmospheric Research, in collaboration with other agencies and research groups, has developed an integrated urban modelling system coupled to the weather research and forecasting (WRF) model as a community tool to address urban environmental issues. The core of this WRF/urban modelling system consists of the following: (1) three methods with different degrees of freedom to parameterize urban surface processes, ranging from a simple bulk parameterization to a sophisticated multi-layer urban canopy model with an indoor–outdoor exchange sub-model that directly interacts with the atmospheric boundary layer, (2) coupling to fine-scale computational fluid dynamic Reynolds-averaged Navier–Stokes and Large-Eddy simulation models for transport and dispersion (T&D) applications, (3) procedures to incorporate high-resolution urban land use, building morphology, and anthropogenic heating data using the National Urban Database and Access Portal Tool (NUDAPT), and (4) an urbanized high-resolution land data assimilation system. This paper provides an overview of this modelling system; addresses the daunting challenges of initializing the coupled WRF/urban model and of specifying the potentially vast number of parameters required to execute the WRF/urban model; explores the model sensitivity to these urban parameters; and evaluates the ability of WRF/urban to capture urban heat islands, complex boundary-layer structures aloft, and urban plume T&D for several major metropolitan regions. Recent applications of this modelling system illustrate its promising utility, as a regional climate-modelling tool, to investigate impacts of future urbanization on regional meteorological conditions and on air quality under future climate change scenarios. Copyright © 2010 Royal Meteorological Society

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As the calibration and evaluation of flood inundation models are a prerequisite for their successful application, there is a clear need to ensure that the performance measures that quantify how well models match the available observations are fit for purpose. This paper evaluates the binary pattern performance measures that are frequently used to compare flood inundation models with observations of flood extent. This evaluation considers whether these measures are able to calibrate and evaluate model predictions in a credible and consistent way, i.e. identifying the underlying model behaviour for a number of different purposes such as comparing models of floods of different magnitudes or on different catchments. Through theoretical examples, it is shown that the binary pattern measures are not consistent for floods of different sizes, such that for the same vertical error in water level, a model of a flood of large magnitude appears to perform better than a model of a smaller magnitude flood. Further, the commonly used Critical Success Index (usually referred to as F<2 >) is biased in favour of overprediction of the flood extent, and is also biased towards correctly predicting areas of the domain with smaller topographic gradients. Consequently, it is recommended that future studies consider carefully the implications of reporting conclusions using these performance measures. Additionally, future research should consider whether a more robust and consistent analysis could be achieved by using elevation comparison methods instead.

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This contribution proposes a novel probability density function (PDF) estimation based over-sampling (PDFOS) approach for two-class imbalanced classification problems. The classical Parzen-window kernel function is adopted to estimate the PDF of the positive class. Then according to the estimated PDF, synthetic instances are generated as the additional training data. The essential concept is to re-balance the class distribution of the original imbalanced data set under the principle that synthetic data sample follows the same statistical properties. Based on the over-sampled training data, the radial basis function (RBF) classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier’s structure and the parameters of RBF kernels are determined using a particle swarm optimisation algorithm based on the criterion of minimising the leave-one-out misclassification rate. The effectiveness of the proposed PDFOS approach is demonstrated by the empirical study on several imbalanced data sets.

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This paper investigates urban canopy layers (UCL) ventilation under neutral atmospheric condition with the same building area density (λp=0.25) and frontal area density (λf=0.25) but various urban sizes, building height variations, overall urban forms and wind directions. Turbulent airflows are first predicted by CFD simulations with standard k-ε model evaluated by wind tunnel data. Then air change rates per hour (ACH) and canopy purging flow rate (PFR) are numerically analyzed to quantify the rate of air exchange and the net ventilation capacity induced by mean flows and turbulence. With a parallel approaching wind (θ=0o), the velocity ratio first decreases in the adjustment region, followed by the fully-developed region where the flow reaches a balance. Although the flow quantities macroscopically keep constant, however ACH decreases and overall UCL ventilation becomes worse if urban size rises from 390m to 5km. Theoretically if urban size is infinite, ACH may reach a minimum value depending on local roof ventilation, and it rises from 1.7 to 7.5 if the standard deviation of building height variations increases (0% to 83.3%). Overall UCL ventilation capacity (PFR) with a square overall urban form (Lx=Ly=390m) is better as θ=0o than oblique winds (θ=15o, 30o, 45o), and it exceeds that of a staggered urban form under all wind directions (θ=0o to 45o), but is less than that of a rectangular urban form (Lx=570m, Ly=270m) under most wind directions (θ=30o to 90o). Further investigations are still required to quantify the net ventilation efficiency induced by mean flows and turbulence.