Results Based Financing (RBF) models are results oriented, linking performance indicators to incentives to motivate health workers to deliver quality care in anticipation of rewards attached to service delivery. The study sought to explore nurses’ views and experiences regarding the implementation of RBF in Zimbabwe with the aim of recommending measures to strengthen the programme. The researcher used a qualitative, exploratory and descriptive design in this study. The population of this study comprised 21 nurses. Non-probability purposive sampling was used to select professional nurses involved in implementing RBF in Mrewa District, Mashonaland East Province, Zimbabwe. Data were collected through focus group discussions using an interview guide. Three (3) focus group discussions were conducted during this study, following a pilot study consisting of six (6) conveniently sampled nurses in Mashonaland East Province. Interviews were tape recorded and transcribed verbatim. Permission to proceed with this study was granted by the Ministry of Health and Child Care and the University of South Africa. Measures to ensure credibility, dependability, conformability and transferability were followed. Data were analysed using Creswell’s data analysis steps. Data were transcribed and thematically analysed, and emerging patterns were noted. The researcher examined these categories closely and compared them for similarities and differences, identifying the most frequent or significant codes in order to develop the main categories. These were summarised in narrative form. Four themes emerged from data: interpretation of RBF; role of nurses in the implementation of RBF; evaluation of RBF; and strengthening implementation of RBF. The study revealed various interpretations of RBF that converged to definitions of RBF in literature. Nurses viewed themselves as key and important players in the successful implementation of RBF. The successes and challenges of RBF were presented. Several measures that could strengthen the implementation of donor funds were highlighted, including subsidisation of low catchment health facilities, inclusion of district hospitals on the RBF programme, increasing financial autonomy of health facilities and the review of procurement guidelines. The study assumed that these measures will enhance nurses’ work experience in donor funded health care delivery, and improve health outcomes. / Health Studies / M.P.H.

Made available in DSpace on 2014-06-12T15:58:28Z (GMT). No. of bitstreams: 2 arquivo4572_1.pdf: 1149011 bytes, checksum: 92aae8f6f9b5145bfcecb94d96dbbc0b (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2004 / Instituto Federal de Educação, Ciência e Tecnologia de Pernambuco / Um nariz artificial é um sistema modular composto de duas partes principais: um sistema sensor, formado de elementos que detectam odores e um sistema de reconhecimento de padrões que classifica os odores detectados. Redes neurais artificiais têm sido utilizadas como sistema de reconhecimento de padrões para narizes artificiais e vêm apresentando resultados promissores. Desde os anos 80, pesquisas para criação de narizes artificiais, que permitam detectar e classificar odores, vapores e gases automaticamente, têm tido avanços significativos. Esses equipamentos podem ser utilizados no monitoramento ambiental para controlar a qualidade do ar, na área de saúde para realizar diagnóstico de doenças e nas indústrias de alimentos para o controle de qualidade e o monitoramento de processos de produção. Esta dissertação investiga a utilização de quatro técnicas diferentes de redes neurais para criação de sistemas de reconhecimento de padrões em narizes artificiais. O trabalho está dividido em quatro partes principais: (1) introdução aos narizes artificiais, (2) redes neurais artificiais para sistema de reconhecimento de padrões, (3) métodos para medir o desempenho de sistemas de reconhecimento de padrões e comparar os resultados e (4) estudo de caso. Os dados utilizados para o estudo de caso, foram obtidos por um protótipo de nariz artificial composto por um arranjo de oito sensores de polímeros condutores, expostos a nove tipos diferentes de aguarrás. Foram adotadas as técnicas Multi-Layer Perceptron (MLP), Radial Base Function (RBF), Probabilistic Neural Network (PNN) e Time Delay Neural Network (TDNN) para criar os sistemas de reconhecimento de padrões. A técnica PNN foi investigada em detalhes, por dois motivos principais: esta técnica é indicada para realização de tarefas de classificação e seu treinamento é feito em apenas um passo, o que torna a etapa de criação dessas redes muito rápida. Os resultados foram comparados através dos valores dos erros médios de classificação utilizando o método estatístico de Teste de Hipóteses. As redes PNN correspondem a uma nova abordagem para criação de sistemas de reconhecimento de padrões de odor. Estas redes tiveram um erro médio de classificação de 1.1574% no conjunto de teste. Este foi o menor erro obtido entre todos os sistemas criados, entretanto mesmo com o menor erro médio de classificação, os testes de hipóteses mostraram que os classificadores criados com PNN não eram melhores do que os classificadores criados com a arquitetura RBF, que obtiveram um erro médio de classificação de 1.3889%. A grande vantagem de criar classificadores com a arquitetura PNN foi o pequeno tempo de treinamento dos mesmos, chegando a ser quase imediato. Porém a quantidade de nodos na camada escondida foi muito grande, o que pode ser um problema, caso o sistema criado deva ser utilizado em equipamentos com poucos recursos computacionais. Outra vantagem de criar classificadores com redes PNN é relativa à quantidade reduzida de parâmetros que devem ser analisados, neste caso apenas o parâmetro relativo à largura da função Gaussiana precisou ser investigado

Made available in DSpace on 2014-06-12T16:00:25Z (GMT). No. of bitstreams: 2 arquivo6557_1.pdf: 4867823 bytes, checksum: 64578a5cde42f460f0745045ec1bb555 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2007 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / Neste trabalho apresentamos um novo modelo neural para segmentação de imagens, baseado nos Mapas Auto-organizáveis SOM (Mapa Auto-organizável - Self-organizing Map) e GWR (Crescer Quando Requerido - Grow When Required) chamado de LARFSOM (Mapa Auto-organizável com Campo Receptivo Adaptativo Local - Local Adaptive Receptive Field Self-organizing Map). As características principais do modelo são: número adaptativo de nodos, topologia variável, inserção de novos nodos baseada em uma medida de similaridade dos protótipos existentes em relação ao padrão de entrada aferida por meio de campo receptivo, remoção de nodos com informações não significativas ao final do treinamento, rápida convergência e baixo custo de processamento para o treinamento. A rede LARFSOM é capaz de segmentar imagens por cor ou por borda: a primeira, é feita através do agrupamento de informações ocorrido no treinamento da rede LAFRSOM seguido de um processo de quantização de cores; já a segunda, ocorre pelo acréscimo de dois nodos RBF (Função de Base Radial - Radial Basis Function) à rede LARFSOM, criando um modelo de dois estágios chamado LARFSOM-RBF. Adicionalmente, o modelo é capaz de salvar em um formato variante do BMP indexado tanto a rede treinada como as informações espaciais dos pixels da imagem. Acrescido de compactação tipo ZIP o arquivo a ser salvo torna-se bem reduzido. Comparações com outros modelos neurais como o SOM, FS-SOM (Mapa Auto-organizável Sensível à Freqüência - Frequency Sensitive Self-organizing Map) e GNG (Gás Neural Crescente - Growing Neural Gas) são feitas mediante segmentação de imagens do mundo real com diferentes níveis de complexidade. Técnicas de processamento de imagens e o formato JPEG são usados para fins de comparação. Os resultados mostram que a rede LARFSOM atinge maior variação de cores da paleta e melhor distribuição espacial 3D RGB das cores selecionadas que os demais modelos. A qualidade das imagens geradas também figura entre os melhores resultados obtidos

As a result of the increase in accessibility of computational resources and the increase in the power of the computers during the last two decades, designers are able to create computer models to simulate the behavior of a complex products. To address global competitiveness, companies are forced to optimize their designs and products. Optimizing the design needs several runs of computationally expensive simulation models. Therefore, using metamodels as an efficient and sufficiently accurate approximate of the simulation model is necessary. Radial basis functions (RBF) is one of the several metamodeling methods that can be found in the literature. The established approach is to add a bias to RBF in order to obtain a robust performance. The a posteriori bias is considered to be unknown at the beginning and it is defined by imposing extra orthogonality constraints. In this thesis, a new approach in constructing RBF with the bias to be set a priori by using the normal equation is proposed. The performance of the suggested approach is compared to the classic RBF with a posteriori bias. Another comprehensive comparison study by including several modeling criteria, such as problem dimension, sampling technique and size of samples is conducted. The studies demonstrate that the suggested approach with a priori bias is in general as good as the performance of RBF with a posteriori bias. Using the a priori RBF, it is clear that the global response is modeled with the bias and that the details are captured with radial basis functions. Multi-objective optimization and the approaches used in solving such problems are briefly described in this thesis. One of the methods that proved to be efficient in solving multi-objective optimization problems (MOOP) is the strength Pareto evolutionary algorithm (SPEA2). Multi-objective optimization of a disc brake system of a heavy truck by using SPEA2 and RBF with a priori bias is performed. As a result, the possibility to reduce the weight of the system without extensive compromise in other objectives is found. Multi-objective optimization of material model parameters of an adhesive layer with the aim of improving the results of a previous study is implemented. The result of the original study is improved and a clear insight into the nature of the problem is revealed.

Radial basis function methods exhibit several very attractive properties such as a high order convergence of the approximated solution and flexibility to the domain geometry. However the method in its classical formulation becomes impractical for problems with relatively large numbers of degrees of freedom due to the ill-conditioning and dense structure of coefficient matrix. To overcome the latter issue we employ a localisation technique, namely a partition of unity method, while the former issue was previously addressed by several authors and was of less concern in this thesis. In this thesis we develop radial basis function partition of unity methods for partial differential equations arising in financial mathematics and glaciology. In the applications of financial mathematics we focus on pricing multi-asset equity and credit derivatives whose models involve several stochastic factors. We demonstrate that localised radial basis function methods are very effective and well-suited for financial applications thanks to the high order approximation properties that allow for the reduction of storage and computational requirements, which is crucial in multi-dimensional problems to cope with the curse of dimensionality. In the glaciology application we in the first place make use of the meshfree nature of the methods and their flexibility with respect to the irregular geometries of ice sheets and glaciers. Also, we exploit the fact that radial basis function methods are stated in strong form, which is advantageous for approximating velocity fields of non-Newtonian viscous liquids such as ice, since it allows to avoid a full coefficient matrix reassembly within the nonlinear iteration. In addition to the applied problems we develop a least squares radial basis function partition of unity method that is robust with respect to the node layout. The method allows for scaling to problem sizes of a few hundred thousand nodes without encountering the issue of large condition numbers of the coefficient matrix. This property is enabled by the possibility to control the coefficient matrix condition number by the rate of oversampling and the mode of refinement.

This thesis deals with determination of effect of wing flexibility on load distribution. FSI analysis using modal superposition was used for determination of effect of wing flexibility. Analysis was verified by analytic calculation. Differences of load distribution, between rigid and flexible wing, was determined for the selected flight regimes. Change of the bending moment was up to 3,9 %. Thesis shows importance of including effect of wing flexibility for sailplane design.

In this report the regular finite differences method (FDM) and a least-squares radial basis function-generated finite differences method (RBF-FD-LS) is used to solve the two-dimensional incompressible Navier-Stokes equations for the lid driven cavity problem. The Navier-Stokes equations is solved using stream function-vorticity formulation. The purpose of the report is to compare FDM and RBF-FD-LS with respect to accuracy and computational cost. Both methods were implemented in MATLAB and the problem was solved for Reynolds numbers equal to 100, 400 and 1000. In the report we present the solutions obtained as well as the results from the comparison. The results are discussed and conclusions are drawn. We came to the conclusion that RBF-FD-LS is more accurate when the stepsize of the grids used is held constant, while RBF-FD-LS costs more than FDM for similar accuracy.

This work is describing basic methods of face recognition. The methods PCA, LDA, ICA, trace tranfsorm, elastic bunch graph map, genetic algorithm and neural network are described. In practical part, the PCA, PCA + RBF neural network and genetic algorithms are implemented. The RBF neural network is used in the way of clasificator and genetic algorithm is used for RBF NN training in one case and for selecting eigenvectors from PCA method in the other case. This method, PCA + GA, called EPCA, outperform other methods tested in this work on the ORL testing database.

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