982 resultados para NUMERICAL EVALUATION


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A multi-objective design optimization study has been conducted for upstream fuel injection through porous media applied to the first ramp of a two-dimensional scramjet intake. The optimization has been performed by coupling evolutionary algorithms assisted by surrogate modeling and computational fluid dynamics with respect to three design criteria, that is, the maximization of the absolute mixing quantity, total pressure saving, and fuel penetration. A distinct Pareto optimal front has been obtained, highlighting the counteracting behavior of the total pressure against the mixing efficiency and fuel penetration. The injector location and size have been identified as the key design parameters as a result of a sensitivity analysis, with negligible influence of the porous properties in the configurations and conditions considered in the present study. Flowfield visualization has revealed the underlying physics associated with the effects of these dominant parameters on the shock structure and intensity.

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This research was an economic analysis of two novel health education interventions compared to existing practice for reproductive health among young people in northern Vietnam. The research showed that implementing an educational intervention including school-based and health facility-based components was cost effective for males and females. The findings will assist decision makers in efficient allocation of scarce resources for adolescent health promotion in Vietnam and similar socio-economic contexts in Asia.

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Numerical results are presented to investigate the performance of a partly-filled porous heat exchanger for waste heat recovery units. A parametric study was conducted to investigate the effects of inlet velocity and porous block height on the pressure drop of the heat exchanger. The focus of this work is on modelling the interface of a porous and non-porous region. As such, numerical simulation of the problem is conducted along with hot-wire measurements to better understand the physics of the problem. Results from the two sources are then compared to existing theoretical predictions available in the literature which are unable to predict the existence of two separation regions before and after the porous block. More interestingly, a non-uniform interface velocity was observed along the streamwise direction based on both numerical and experimental data.

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A fractional FitzHugh–Nagumo monodomain model with zero Dirichlet boundary conditions is presented, generalising the standard monodomain model that describes the propagation of the electrical potential in heterogeneous cardiac tissue. The model consists of a coupled fractional Riesz space nonlinear reaction-diffusion model and a system of ordinary differential equations, describing the ionic fluxes as a function of the membrane potential. We solve this model by decoupling the space-fractional partial differential equation and the system of ordinary differential equations at each time step. Thus, this means treating the fractional Riesz space nonlinear reaction-diffusion model as if the nonlinear source term is only locally Lipschitz. The fractional Riesz space nonlinear reaction-diffusion model is solved using an implicit numerical method with the shifted Grunwald–Letnikov approximation, and the stability and convergence are discussed in detail in the context of the local Lipschitz property. Some numerical examples are given to show the consistency of our computational approach.

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We present a systematic, practical approach to developing risk prediction systems, suitable for use with large databases of medical information. An important part of this approach is a novel feature selection algorithm which uses the area under the receiver operating characteristic (ROC) curve to measure the expected discriminative power of different sets of predictor variables. We describe this algorithm and use it to select variables to predict risk of a specific adverse pregnancy outcome: failure to progress in labour. Neural network, logistic regression and hierarchical Bayesian risk prediction models are constructed, all of which achieve close to the limit of performance attainable on this prediction task. We show that better prediction performance requires more discriminative clinical information rather than improved modelling techniques. It is also shown that better diagnostic criteria in clinical records would greatly assist the development of systems to predict risk in pregnancy. We present a systematic, practical approach to developing risk prediction systems, suitable for use with large databases of medical information. An important part of this approach is a novel feature selection algorithm which uses the area under the receiver operating characteristic (ROC) curve to measure the expected discriminative power of different sets of predictor variables. We describe this algorithm and use it to select variables to predict risk of a specific adverse pregnancy outcome: failure to progress in labour. Neural network, logistic regression and hierarchical Bayesian risk prediction models are constructed, all of which achieve close to the limit of performance attainable on this prediction task. We show that better prediction performance requires more discriminative clinical information rather than improved modelling techniques. It is also shown that better diagnostic criteria in clinical records would greatly assist the development of systems to predict risk in pregnancy.

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We describe a sequence of experiments investigating the strengths and limitations of Fukushima's neocognitron as a handwritten digit classifier. Using the results of these experiments as a foundation, we propose and evaluate improvements to Fukushima's original network in an effort to obtain higher recognition performance. The neocognitron's performance is shown to be strongly dependent on the choice of selectivity parameters and we present two methods to adjust these variables. Performance of the network under the more effective of the two new selectivity adjustment techniques suggests that the network fails to exploit the features that distinguish different classes of input data. To avoid this shortcoming, the network's final layer cells were replaced by a nonlinear classifier (a multilayer perceptron) to create a hybrid architecture. Tests of Fukushima's original system and the novel systems proposed in this paper suggest that it may be difficult for the neocognitron to achieve the performance of existing digit classifiers due to its reliance upon the supervisor's choice of selectivity parameters and training data. These findings pertain to Fukushima's implementation of the system and should not be seen as diminishing the practical significance of the concept of hierarchical feature extraction embodied in the neocognitron. © 1997 IEEE.

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Objective: To formally evaluate the written discharge advice for people with mild traumatic brain injury (mTBI). Methods: Eleven publications met the inclusion criteria: (1) intended for adults; (2) ≤two A4 pages; (3) published in English; (4) freely accessible; and (5) currently used (or suitable for use) in Australian hospital emergency departments or similar settings. Two independent raters evaluated the content and style of each publication against established standards. The readability of the publication, the diagnostic term(s) contained in it and a modified Patient Literature Usefulness Index (mPLUI) were also evaluated. Results: The mean content score was 19.18 ± 8.53 (maximum = 31) and the mean style score was 6.8 ± 1.34 (maximum = 8). The mean Flesch-Kincaid reading ease score was 66.42 ± 4.3. The mean mPLUI score was 65.86 ± 14.97 (maximum = 100). Higher scores on these metrics indicate more desirable properties. Over 80% of the publications used mixed diagnostic terminology. One publication scored optimally on two of the four metrics and highly on the others. Discussion: The content, style, readability and usefulness of written mTBI discharge advice was highly variable. The provision of written information to patients with mTBI is advised, but this variability in materials highlights the need for evaluation before distribution. Areas are identified to guide the improvement of written mTBI discharge advice.