5 resultados para exception

em Greenwich Academic Literature Archive - UK


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Three hundred participants, including volunteers from an obsessional support group, filled in questionnaires relating to disgust sensitivity, health anxiety, anxiety, fear of death, fear of contamination and obsessionality as part of an investigation into the involvement of disgust sensitivity in types of obsessions. Overall, the data supported the hypothesis that a relationship does exist between disgust sensitivity and the targeted variables. A significant predictive relationship was found between disgust sensitivity and total scores on the obsessive compulsive inventory (OCI; Psychological Assessment 10 (1998) 206) for both frequency and distress of symptomatology. Disgust sensitivity scores were significantly related to health anxiety scores and general anxiety scores and to all the obsessional subscales, with the exception of hoarding. Additionally, multiple regression analyses revealed that disgust sensitivity may be more specifically related to washing compulsions: frequency of washing behaviour was best predicted by disgust sensitivity scores. Washing distress scores were best predicted by health anxiety scores, though disgust sensitivity entered in the second model. It is suggested that further research on the relationship between disgust sensitivity and obsessionality could be helpful in refining the theoretical understanding of obsessions.

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This paper will discuss Computational Fluid Dynamics (CFD) results from an investigation into the accuracy of several turbulence models to predict air cooling for electronic packages and systems. Also new transitional turbulence models will be proposed with emphasis on hybrid techniques that use the k-ε model at an appropriate distance away from the wall and suitable models, with wall functions, near wall regions. A major proportion of heat emitted from electronic packages can be extracted by air cooling. This flow of air throughout an electronic system and the heat extracted is highly dependent on the nature of turbulence present in the flow. The use of CFD for such investigations is fast becoming a powerful and almost essential tool for the design, development and optimization of engineering applications. However turbulence models remain a key issue when tackling such flow phenomena. The reliability of CFD analysis depends heavily on the turbulence model employed together with the wall functions implemented. In order to resolve the abrupt fluctuations experienced by the turbulent energy and other parameters located at near wall regions and shear layers a particularly fine computational mesh is necessary which inevitably increases the computer storage and run-time requirements. The PHYSICA Finite Volume code was used for this investigation. With the exception of the k-ε and k-ω models which are available as standard within PHYSICA, all other turbulence models mentioned were implemented via the source code by the authors. The LVEL, LVEL CAP, Wolfshtein, k-ε, k-ω, SST and kε/kl models are described and compared with experimental data.

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We consider the multilevel paradigm and its potential to aid the solution of combinatorial optimisation problems. The multilevel paradigm is a simple one, which involves recursive coarsening to create a hierarchy of approximations to the original problem. An initial solution is found (sometimes for the original problem, sometimes the coarsest) and then iteratively refined at each level. As a general solution strategy, the multilevel paradigm has been in use for many years and has been applied to many problem areas (most notably in the form of multigrid techniques). However, with the exception of the graph partitioning problem, multilevel techniques have not been widely applied to combinatorial optimisation problems. In this paper we address the issue of multilevel refinement for such problems and, with the aid of examples and results in graph partitioning, graph colouring and the travelling salesman problem, make a case for its use as a metaheuristic. The results provide compelling evidence that, although the multilevel framework cannot be considered as a panacea for combinatorial problems, it can provide an extremely useful addition to the combinatorial optimisation toolkit. We also give a possible explanation for the underlying process and extract some generic guidelines for its future use on other combinatorial problems.

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The multilevel paradigm as applied to combinatorial optimisation problems is a simple one, which at its most basic involves recursive coarsening to create a hierarchy of approximations to the original problem. An initial solution is found, usually at the coarsest level, and then iteratively refined at each level, coarsest to finest, typically by using some kind of heuristic optimisation algorithm (either a problem-specific local search scheme or a metaheuristic). Solution extension (or projection) operators can transfer the solution from one level to another. As a general solution strategy, the multilevel paradigm has been in use for many years and has been applied to many problem areas (for example multigrid techniques can be viewed as a prime example of the paradigm). Overview papers such as [] attest to its efficacy. However, with the exception of the graph partitioning problem, multilevel techniques have not been widely applied to combinatorial problems and in this chapter we discuss recent developments. In this chapter we survey the use of multilevel combinatorial techniques and consider their ability to boost the performance of (meta)heuristic optimisation algorithms.

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Background: Interprofessional education (IPE) introduced at the beginning of pre-registration training for healthcare professionals attempts to prevent the formation of negative interprofessional attitudes which may hamper future interprofessional collaboration. However, the potential for IPE depends, to some extent, on the readiness of healthcare students to learn together. Objectives: To measure changes in readiness for interprofessional learning, professional identification, and amount of contact between students of different professional groups; and to examine the influence of professional group, student characteristics and an IPE course on these scores over time. Design: Annual longitudinal panel questionnaire survey at four time-points of pre-registration students (n = 1683) drawn from eight healthcare groups from three higher education institutions (HEIs) in the UK. Results: The strength of professional identity in all professional groups was high on entry to university but it declined significantly over time for some disciplines. Similarly students’ readiness for interprofessional learning was high at entry but declined significantly over time for all groups, with the exception of nursing students. A small but significant positive relationship between professional identity and readiness for interprofessional learning was maintained over time. There was very minimal contact between students from different disciplines during their professional education programme. Students who reported gaining the least from an IPE course suffered the most dramatic drop in their readiness for interprofessional learning in the following and subsequent years; however, these students also had the lowest expectations of an IPE course on entry to their programme of study. Conclusion: The findings provide support for introducing IPE at the start of the healthcare students’ professional education to capitalise on students’ readiness for interprofessional learning and professional identities, which appear to be well formed from the start. However, this study suggests that students who enter with negative attitudes towards interprofessional learning may gain the least from IPE courses and that an unrewarding experience of such courses may further reinforce their negative attitudes.