953 resultados para declarative, procedural, and reflective (DPR) model


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The use of image processing techniques to assess the performance of airport landing lighting using images of it collected from an aircraft-mounted camera is documented. In order to assess the performance of the lighting, it is necessary to uniquely identify each luminaire within an image and then track the luminaires through the entire sequence and store the relevant information for each luminaire, that is, the total number of pixels that each luminaire covers and the total grey level of these pixels. This pixel grey level can then be used for performance assessment. The authors propose a robust model-based (MB) featurematching technique by which the performance is assessed. The development of this matching technique is the key to the automated performance assessment of airport lighting. The MB matching technique utilises projective geometry in addition to accurate template of the 3D model of a landing-lighting system. The template is projected onto the image data and an optimum match found, using nonlinear least-squares optimisation. The MB matching software is compared with standard feature extraction and tracking techniques known within the community, these being the Kanade–Lucus–Tomasi (KLT) and scaleinvariant feature transform (SIFT) techniques. The new MB matching technique compares favourably with the SIFT and KLT feature-tracking alternatives. As such, it provides a solid foundation to achieve the central aim of this research which is to automatically assess the performance of airport lighting.

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The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.

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This paper describes the development of neural model-based control strategies for the optimisation of an industrial aluminium substrate disk grinding process. The grindstone removal rate varies considerably over a stone life and is a highly nonlinear function of process variables. Using historical grindstone performance data, a NARX-based neural network model is developed. This model is then used to implement a direct inverse controller and an internal model controller based on the process settings and previous removal rates. Preliminary plant investigations show that thickness defects can be reduced by 50% or more, compared to other schemes employed. (c) 2004 Elsevier Ltd. All rights reserved.

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The effects of linear scaling of the atomic charges of a reference potential on the structure, dynamics, and energetics of the ionic liquid 1,3-dimethylimidazolium chloride are investigated. Diffusion coefficients that span over four orders of magnitude are observed between the original model and a scaled model in which the ionic charges are +/- 0.5 e. While the three-dimensional structure of the liquid is less affected, the partial radial distribution functions change markedly-with the positive result that for ionic charges of +/- 0.7 e, an excellent agreement is observed with ab initio molecular dynamics data. Cohesive energy densities calculated from these partial-charge models are also in better agreement with those calculated from the ab initio data. We postulate that ionic-liquid models in which the ionic charges are assumed to be +/- 1 e overestimate the intermolecular attractions between ions, which results in overstructuring, slow dynamics, and increased cohesive energy densities. The use of scaled-charge sets may be of benefit in the simulation of these systems-especially when looking at properties beyond liquid structure-thus providing on alternative to computationally expensive polarisable force fields.

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Background: Interleukin-17A (IL-17A) is the founding member of a novel family of inflammatory cytokines that plays a critical role in the pathogenesis of many autoimmune diseases, including multiple sclerosis (MS) and its animal model, experimental autoimmune encephalomyelitis (EAE). IL-17A signals through its receptor, IL-17RA, which is expressed in many peripheral tissues; however, expression of IL-17RA in the central nervous system (CNS) and its role in CNS inflammation are not well understood. Methods: EAE was induced in C57Bl/6 mice by immunization with myelin oligodendroglial glycoprotein. IL-17RA expression in the CNS was compared between control and EAE mice using RT-PCR, in situ hybridization, and immunohistochemistry. Cell-type specific expression was examined in isolated astrocytic and microglial cell cultures. Cytokine and chemokine production was measured in IL-17A treated cultures to evaluate the functional status of IL-17RA. Results: Here we report increased IL-17RA expression in the CNS of mice with EAE, and constitutive expression of functional IL-17RA in mouse CNS tissue. Specifically, astrocytes and microglia express IL-17RA in vitro, and IL-17A treatment induces biological responses in these cells, including significant upregulation of MCP-1, MCP-5, MIP-2 and KC chemokine secretion. Exogenous IL-17A does not significantly alter the expression of IL-17RA in glial cells, suggesting that upregulation of chemokines by glial cells is due to IL-17A signaling through constitutively expressed IL-17RA. Conclusion: IL-17RA expression is significantly increased in the CNS of mice with EAE compared to healthy mice, suggesting that IL-17RA signaling in glial cells can play an important role in autoimmune inflammation of the CNS and may be a potential pathway to target for therapeutic interventions. © 2009 Sarma et al; licensee BioMed Central Ltd.

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Background: Neuronal loss in multiple sclerosis (MS) and its animal model, experimental autoimmune encephalomyelitis (EAE), correlates with permanent neurological dysfunction. Current MS therapies have limited the ability to prevent neuronal damage. Methods: We examined whether oral therapy with SRT501, a pharmaceutical grade formulation of resveratrol, reduces neuronal loss during relapsing-remitting EAE. Resveratrol activates SIRT1, an NAD-dependent deacetylase that promotes mitochondrial function. Results: Oral SRT501 prevented neuronal loss during optic neuritis, an inflammatory optic nerve lesion in MS and EAE. SRT501 also suppressed neurological dysfunction during EAE remission, and spinal cords from SRT501-treated mice had significantly higher axonal density than vehicle-treated mice. Similar neuroprotection was mediated by SRT1720, another SIRT1-activating compound; and sirtinol, an SIRT1 inhibitor, attenuated SRT501 neuroprotective effects. SIRT1 activators did not prevent inflammation. Conclusions: These studies demonstrate that SRT501 attenuates neuronal damage and neurological dysfunction in EAE by a mechanism involving SIRT1 activation. SIRT1 activators are a potential oral therapy in MS. © 2010 by North American Neuro-Ophthalmology Society.

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The sediment sequence from Hasseldala port in southeastern Sweden provides a unique Lateglacial/early Holocene record that contains five different tephra layers. Three of these have been geochemically identified as the Borrobol Tephra, the Hasseldalen Tephra and the 10-ka Askja Tephra. Twenty-eight high-resolution C-14 measurements have been obtained and three different age models based on Bayesian statistics are employed to provide age estimates for the five different tephra layers. The chrono- and pollen stratigraphic framework supports the stratigraphic position of the Borrobol Tephra as found in Sweden at the very end of the Older Dryas pollen zone and provides the first age estimates for the Askja and Hasseldalen tephras. Our results, however, highlight the limitations that arise in attempting to establish a robust, chronologically independent lacustrine sequence that can be correlated in great detail to ice core or marine records. Radiocarbon samples are prone to error and sedimentation rates in lake basins may vary considerably due to a number of factors. Any type of valid and 'realistic' age model, therefore, has to take these limitations into account and needs to include this information in its prior assumptions. As a result, the age ranges for the specific horizons at Hasseldala port are large and calendar year estimates differ according to the assumptions of the age-model. Not only do these results provide a cautionary note for overdependence on one age-model for the derivation of age estimates for specific horizons, but they also demonstrate that precise correlations to other palaeoarchives to detect leads or lags is problematic. Given the uncertainties associated with establishing age-depth models for sedimentary sequences spanning the Lateglacial period, however, this exercise employing Bayesian probability methods represents the best possible approach and provides the most statistically significant age estimates for the pollen zone boundaries and tephra horizons. Copyright (C) 2006 John Wiley & Sons, Ltd.

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Given the relative lack of research on sustainable development in Northern Ireland, this paper focuses on the tensions between environmental governance and regulation on the one hand, and the ‘post-conflict’ imperative for Northern Ireland to compete and grow as a regional economy without continued British state subvention and subsidisation. The paper outlines how this ‘trade-off’ between ‘environment’ and ‘economy’ is essentially misplaced. It argues that this trade-off can be avoided if there is a shift in focus from an ‘environment versus the economy’ policy position to one in which the ‘triple bottom line’ (social, economic and environmental) of sustainable development becomes the over-arching policy agenda. Sustainable development, unlike either orthodox environmental or economic policy, also connects centrally with the unique ‘post-conflict transformation’ agenda of Northern Ireland. For example, promoting a human rights civic culture, tackling socioeconomic inequality and social exclusion, and building a shared future based on supporting sustainable communities and an innovative model of a ‘green(ing) economy’ goes beyond orthodox economic growth. However, it is clear from the Executive’s Programme for Government, failure to support the creation of an independent Environment Protection Agency, and above all the prioritisation of orthodox economic growth based on foreign direct investment that neither environmental protection nor sustainable development is or will be high on the political or policy agenda in Northern Ireland.

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Introduction

Belfast has been a focus of academic attention for the last forty years with most interest centred on various aspects of ‘the Troubles’. Where there has been interest in the built environment, it has largely been about how the ‘security situation’ impacted directly on architecture and on the design and layout of social housing. This paper seeks to go beyond this to explore how the political- administrative culture of ‘the Troubles’ interacted with ‘normal’ market forces to shape the central area of the city, and to consider the responses of a recently formed activist group, known as the Forum for Alternative Belfast (hereafter referred to as the Forum). The paper is written by three of the directors of the Forum.1 Moreover, the empirical research presented here was undertaken by the Forum as part of a campaign to address issues relating to the design, layout and quality of Belfast’s built environment. In the longstanding tradition of participant observation working within an action-research paradigm, the participants have attempted to offer an account that is evidentially and purposefully selfcritical and reflective. It is of course recognised that while this approach offers many positive attributes, such as phenomenological access through immersion in the project, it also has the potential to bring compromise on research detachment and objectivity.2 To address the latter, the authors have attempted
to avoid polemical argument, and to support claims with primary or secondary research evidence. The authors also acknowledge that action-research has a chequered history; however, they would argue
that their approach is faithful to a concept that sees ‘research’ defined as understanding and ‘action’ defined as seeking change. The Forum’s very purpose is to seek change, but to do this requires evidence, collaboration and demonstration. And in this sense, it is a learning process for all participants, including the research activists, government officials, community organisations and students. The authors also recognise the complexity of factors that affect urban management and change, particularly in a city such as Belfast, which has had to cope with political violence for over thirty years. And they appreciate that in the context of conflict, governance is skewed to cope with political realities. Hamdi reminds us, however, that in practice there is an ‘important dialectic between top-down planning, with its formal and designed laws and structures, and bottom-up selforganizing collectivism—those “quantum and emergent systems” which Jane Jacobs argued long ago give cities their life and order.’3

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Abstract. We explore the distances between home and work for employees at twenty-eight different employment sites across Northern Ireland. Substantively, this is important for better understanding the geography of labour catchments. Methodologically, with data on the distances between place of residence (566 wards) and place of work for some 15 000 workers, and the use of multilevel modelling (MLM), the analysis adds to the evidence derived from other census-based and survey-based studies. Descriptive analysis is supplemented with MLM that simultaneously explores individual, neighbourhood, and site variations in travel-to-work patterns using hierarchical and cross-classified model specifications, including individual and ecological predictor variables (and their cross-level interactions). In doing so we apportion variability to different levels and spatial contexts, and also outline the factors that shape spatial mobility. We find, as expected, that factors such as gender and occupation influence the distance between home and work, and also confirm the importance of neighbourhood characteristics (such as population density observed in ecological analyses at ward level) in shaping individual outcomes, with major differences found between urban and rural locations. Beyond this, the analysis of variability also points to the relative significance of residential location, with less individual variability in travel-to-work distance between workers within wards than within employment sites. We conclude by suggesting that, whilst some general ‘rules’ about the factors that shape labour catchments are possible (eg workers in rural areas and in higher occupations travel further than others), the complex variability between places highlighted by the MLM analysis illustrates the salience of place-specific uniqueness.

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The motivation for this paper is to present procedures for automatically creating idealised finite element models from the 3D CAD solid geometry of a component. The procedures produce an accurate and efficient analysis model with little effort on the part of the user. The technique is applicable to thin walled components with local complex features and automatically creates analysis models where 3D elements representing the complex regions in the component are embedded in an efficient shell mesh representing the mid-faces of the thin sheet regions. As the resulting models contain elements of more than one dimension, they are referred to as mixed dimensional models. Although these models are computationally more expensive than some of the idealisation techniques currently employed in industry, they do allow the structural behaviour of the model to be analysed more accurately, which is essential if appropriate design decisions are to be made. Also, using these procedures, analysis models can be created automatically whereas the current idealisation techniques are mostly manual, have long preparation times, and are based on engineering judgement. In the paper the idealisation approach is first applied to 2D models that are used to approximate axisymmetric components for analysis. For these models 2D elements representing the complex regions are embedded in a 1D mesh representing the midline of the cross section of the thin sheet regions. Also discussed is the coupling, which is necessary to link the elements of different dimensionality together. Analysis results from a 3D mixed dimensional model created using the techniques in this paper are compared to those from a stiffened shell model and a 3D solid model to demonstrate the improved accuracy of the new approach. At the end of the paper a quantitative analysis of the reduction in computational cost due to shell meshing thin sheet regions demonstrates that the reduction in degrees of freedom is proportional to the square of the aspect ratio of the region, and for long slender solids, the reduction can be proportional to the aspect ratio of the region if appropriate meshing algorithms are used.

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C17 polyacetylenes are a group of bioactive compounds present in carrots which have recently gained scientific attention due to their cytotoxicity against cancer cells. In common with many bioactive compounds, their levels may be influenced by thermal processes, such as boiling or water immersion. This study investigated the effect of a number of water immersion time/temperature combinations on concentrations of these compounds and attempted to model the changes. Carrot samples were thermally treated by heating in water at temperatures from 50–100 °C and holding times of 2–60 min. Following heating, levels of falcarinol (FaOH), falcarindiol (FaDOH), falcarindiol-3-acetate (FaDOAc) and Hunter colour parameters (L*a*b*) were determined. FaOH, FaDOH, FaDOAc levels were significantly reduced at lower temperatures (50–60 °C). In contrast, samples heated at temperatures from 70–100 °C exhibited higher levels of polyacetylenes (p < 0.05) than did raw unprocessed samples. Regression modelling was used to model the effects of temperature and holding time on the levels of the variables measured. Temperature treatment and holding time were found to significantly affect the polyacetylene content of carrot disks. Predicted models were found to be significant (p < 0.05) with high coefficients of determination (R2).

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Algal blooms caused by cyanobacteria are characterized by two features with different time scales: one is seasonal outbreak and collapse of a bloom and the other is diurnal vertical migration. Our two-component mathematical model can simulate both phenomena, in which the state variables are nutrients and cyanobacteria. The model is a set of one-dimensional reaction-advection-diffusion equations, and temporal changes of these two variables are regulated by the following five factors: (1) annual variation of light intensity, (2) diurnal variation of light intensity, (3) annual variation of water temperature, (4) thermal stratification within a water column and (5) the buoyancy regulation mechanism. The seasonal change of cyanobacteria biomass is mainly controlled by factors, (1), (3) and (4), among which annual variations of light intensity and water temperature directly affect the maximum growth rate of cyanobacteria. The latter also contributes to formation of the thermocline during the summer season. Thermal stratification causes a reduction in vertical diffusion and largely prevents mixing of both nutrients and cyanobacteria between the epilimnion and the hypolimnion. Meanwhile, the other two factors, (2) and (5), play a significant role in diurnal vertical migration of cyanobacteria. A key mechanism of vertical migration is buoyancy regulation due to gas-vesicle synthesis and ballast formation, by which a quick reversal between floating and sinking becomes possible within a water column. The mechanism of bloom formation controlled by these five factors is integrated into the one-dimensional model consisting of two reaction-advection-diffusion equations.

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In small islands, a freshwater lens can develop due to the recharge induced by rain. Magnitude and spatial distribution of this recharge control the elevation of freshwater and the depth of its interface with salt water. Therefore, the study of lens morphology gives useful information on both the recharge and water uptake due to evapotranspiration by vegetation. Electrical resistivity tomography was applied on a small coral reef island, giving relevant information on the lens structure. Variable density groundwater flow models were then applied to simulate freshwater behavior. Cross validation of the geoelectrical model and the groundwater model showed that recharge exceeds water uptake in dunes with little vegetation, allowing the lens to develop. Conversely, in the low-lying and densely vegetated sectors, where water uptake exceeds recharge, the lens cannot develop and seawater intrusion occurs. This combined modeling method constitutes an original approach to evaluate effective groundwater recharge in such environments.
[Comte, J.-C., O. Banton, J.-L. Join, and G. Cabioch (2010), Evaluation of effective groundwater recharge of freshwater lens in small islands by the combined modeling of geoelectrical data and water heads, Water Resour. Res., 46, W06601, doi:10.1029/2009WR008058.]

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The validation of variable-density flow models simulating seawater intrusion in coastal aquifers requires information about concentration distribution in groundwater. Electrical resistivity tomography (ERT) provides relevant data for this purpose. However, inverse modeling is not accurate because of the non-uniqueness of solutions. Such difficulties in evaluating seawater intrusion can be overcome by coupling geophysical data and groundwater modeling. First, the resistivity distribution obtained by inverse geo-electrical modeling is established. Second, a 3-D variable-density flow hydrogeological model is developed. Third, using Archie's Law, the electrical resistivity model deduced from salt concentration is compared to the formerly interpreted electrical model. Finally, aside from that usual comparison-validation, the theoretical geophysical response of concentrations simulated with the groundwater model can be compared to field-measured resistivity data. This constitutes a cross-validation of both the inverse geo-electrical model and the groundwater model.
[Comte, J.-C., and O. Banton (2007), Cross-validation of geo-electrical and hydrogeological models to evaluate seawater intrusion in coastal aquifers, Geophys. Res. Lett., 34, L10402, doi:10.1029/2007GL029981.]