The Sub-Saharan Africa (SSA) region is faced with limited medical personnel and healthcare services to address the many healthcare problems of the region. Poor health indicators reflect the overall decline in socio-economic development. Shortages of access to health services in the region is further complicated by the concentration of health services in urban areas, the region’s multiple medical problems (over 70% of HIV/AIDS cases in the world); and the brain drain phenomenon – it is estimated one-third of African physicians emigrate to North America and Europe. The result is that the SSA region is left with about 10 physicians, and 20 beds, per 100,000 patients. Telemedicine has been found to offer socio-economic benefits, reduce costs, and improve access to healthcare service providers by patients, but previous attempts to move various information technologies from developers in the industrial world to the developing world have failed because of a clear neglect of infrastructural and cultural factors that influence such transfers. The objective of this study is to address key factors that challenge the introduction of telemedicine technology into the health sector in SSA in particular, and by extension, other developing countries with similar socio-economic structures. This research offers a distinctive perspective, focusing on visually-based clinical applications in the SSA region, and considerable attention to the national infrastructure and cultural impact of telemedicine transfer (social and value) outcomes. Two research models and its associated hypotheses are proposed and empirically tested using quantitative data collected from SSA physicians and other health professionals. The study also contributes to the ongoing debate on the potential of telemedicine in improving access and reducing costs. This research can help to understand the socio-economic impact of telemedicine outcomes in a comprehensive way. The finding from the survey shows the rapid advances in telemedicine technology specifically, visual clinical applications may become an essential healthcare tool in the near future within SSA countries.

Syfte: Syftet är att försöka urskilja samband mellan hushålls socioekonomiska faktorer och efterfrågan på specifika bostadsattribut. Vår frågeställning är: Finns det samband mellan vilka specifika attribut som efterfrågas hos bostadsrätter och hushålls socioekonomiska faktorer? Vår förhoppning är att åtminstone få en indikation på vad olika typer av hushåll efterfrågar.Metod: Denna studie baseras på en databas bestående av bostadsrättsförsäljningar som skett i Gävle under år 2008 samt socioekonomisk information om de hushåll som förvärvat dessa bostadsrätter. För att estimera hushålls efterfrågan på ett specifikt bostadsrättsattribut har en tvåstegsmetod använts. I ett första steg avslöjas de underförstådda marginalpriserna av bostadsrätters egenskaper med hjälp av den hedoniska metoden. Dessa marginalpriser används i ett andra steg där efterfrågeekvationer för enskilda bostadsrättsattribut estimeras. Beräkningarna utförs i statistikprogrammet EViews.Resultat & slutsats: Resultatet visar att vissa bostadsrättsattribut kan sammankopplas med hushålls socioekonomiska faktorer. Vidare visar resultatet tydliga men mycket svaga mönster vad gäller efterfrågan på marknaden, trots att prisstrukturen på samma marknad är oerhört utmärkande. Vi kan konstatera att prissättningen på bostadsrätters attribut är väldigt tydlig medan konsumtionen av samma attribut är otydlig.Förslag till fortsatt forskning: Intressant vore att jämföra studier av denna typ med hyresmarknadens hyressättningsmodell och se om den utgår från samma värdering av bostadens attribut som det visat sig att hushåll efterfrågar. Ytterligare ett förslag är att skatta en faktisk boendekostnad för samtliga hushåll i datamaterialet och använda disponibel inkomst istället för taxerad förvärvsinkomst i efterfrågefunktionen.Uppsatsens bidrag: Studien har visat hur man kan estimera och finna samband mellan bostadsattribut och hushålls socioekonomiska faktorer. Därutöver har problemet med endogenitet behandlats genom instrumentvariabler och en uppdelning av datamaterialet i fyra delområden. / Aim: The purpose of this study is to try to distinguish a relationship between household socio-economic factors and the demand for specific housing attributes. Our question is: Is there a connection between the specific attributes requested by households and its socio-economic factors? Our hope is to at least get an indication of what different types of households demand.Method: This study is based on information of tenant-owner flats sales made in Gävle in 2008 as well as socio-economic information of those households who bought these flats. In order to estimate demand for a specific housing attribute a two-step method is used. In a first step the implicit marginal prices of housing attributes are revealed by the hedonic method. These marginal rates are used in a second step to reveal the specific household demand for individual housing attributes. The calculations are made in the statistical program EViews.Result & Conclusions: The result shows that some housing attributes can be linked with household socio-economic factors. The result shows a clear but very weak pattern of demand in the market, despite that the price structure in the same market is extremely remarkable. We note that the prices of housing attributes are very clear while the consumption of the same attributes is unclear.Suggestions for future research: It would be interesting to compare the results of this type of study with rental markets rent-model and see if it is based on the same valuation of the dwelling attributes that household’s demand. Another proposal is to estimate the actual housing costs for all of the households in the data and use disposable income rather than actual income in the demand function.Contribution of the thesis: The study has revealed how to estimate and identify household demand for specific housing attributes. In addition, the problem of endogeneity has been treated with instrument variables and a separation of the data set into four submarkets.

In many practical applications, it is well known that data collected inevitably contain one or more anomalous outliers; that is, observations that are well separated from the majority or bulk of the data, or in some fashion deviate from the general pattern of the data. The occurrence of outliers may be due to misplaced decimal points, recording errors, transmission errors, or equipment failure. These outliers can lead to erroneous parameter estimation and consequently affect the correctness and accuracy of the model inference. In order to solve these problems, three robust fuzzy neural networks (FNNs) will be proposed in this dissertation. This provides alternative learning machines when faced with general nonlinear learning problems. Our emphasis will be put particularly on the robustness of these learning machines against outliers. Though we consider only FNNs in this study, the extension of our approach to other neural networks, such as artificial neural networks and radial basis function networks, is straightforward. In the first part of the dissertation, M-estimators, where M stands for maximum likelihood, frequently used in robust regression for linear parametric regression problems will be generalized to nonparametric Maximum Likelihood Fuzzy Neural Networks (MFNNs) for nonlinear regression problems. Simple weight updating rules based on gradient descent and iteratively reweighted least squares (IRLS) will be derived. In the second part of the dissertation, least trimmed squares estimators, abbreviated as LTS-estimators, frequently used in robust (or resistant) regression for linear parametric regression problems will be generalized to nonparametric least trimmed squares fuzzy neural networks, abbreviated as LTS-FNNs, for nonlinear regression problems. Again, simple weight updating rules based on gradient descent and iteratively reweighted least squares (IRLS) algorithms will be provided. In the last part of the dissertation, by combining the easy interpretability of the parametric models and the flexibility of the nonparametric models, semiparametric fuzzy neural networks (semiparametric FNNs) and semiparametric Wilcoxon fuzzy neural networks (semiparametric WFNNs) will be proposed. The corresponding learning rules are based on the backfitting procedure which is frequently used in semiparametric regression.

In this study, the method of deciding the water level of the initial tsunami is proposed by using spatial-temporal focusing (Coalescence) theory and waveform inversion reciprocal with Green function. Tsunami and earthquake are so closely bonded that the current tsunami numerical model is dependent on the parameters of the fault and the initial tsunami water level by calculating the theory of half flexibility. But in fact, it is not easy to have the parameters of seabed fault so that the initial tsunami water level is very hard to get a accurate value. On the other hand, although the parameters of fault can be speculated by seismic waves, because ground is uneven medium, therefore, it is still a lot of improvement to get the parameters of fault by using seismic waves. For the tsunami simulation, if you have the value of the initial tsunami water level, the fault parameters can be estimated.Since the propagation of tsunami in the ocean is a linear behavior, the propagating process is affected by the topography of the ocean and the nonlinear effect so minimal that it is to satisfy the linear shallow water equations and the requirement of reversibility;However, in fact, the values of the water level measured by the tide stations on the coast are influenced by the shoaling effect so that the reversibility of linear system can not be directly applied to Coastal areas.Therefore, the overall Inversion procedure on this study consists of two parts; the first one is that the usage of variable coefficient Korteweg-de Vries (vKdV) equation and the Coalescence theory inverses the data gathered by the Coastal tide stations to the water level data where the depth is more than 50m on the linear region, and compares the above results with the stimulation and confirms the accuracy of the inversed waveform;The second one is that according to the reversibility of the linear system the use of least squares and least squares QR- decomposition (LSQR) method reproduce the initial tsunami wave source that compares with the initial tsunami wave source by stimulating and has a very good conformity. The seismic parameters can be easily decided by the above results.

Due to the inherently hierarchical nature of many natural phenomena, data collected rests in nested entities. As an example, students are nested in schools, school are nested in districts, districts are nested in counties, and counties are nested within states. Multilevel models provide a statistical framework for investigating and drawing conclusions regarding the influence of factors at differing hierarchical levels of analysis. The work in this paper serves as an introduction to multilevel models and their comparison to Ordinary Least Squares (OLS) regression. We overview three basic model structures: variable intercept model, variable slope model, and hierarchical linear model and illustrate each model with an example of student data. Then, we contrast the three multilevel models with the OLS model and present a method for producing confidence intervals for the regression coefficients. / text

Applied statisticians are often faced with the problem of dealing with high dimensional data sets when attempting to describe the variability of a single set of variables, or trying to predict the variation of one set of variables from another. In this study, two data reduction methods are described: Redundancy Analysis and Partial Least Squares. A hybrid approach developed by Bougeard et al., (2007) and called Continuum Redundancy-Partial Least Squares, is described. All three methods are extended to the frequency domain in order to allow the lower dimensional subspace used to describe the variability to change with frequency. To illustrate and compare the three methods, and their frequency dependent generalizations, an idealized coupled atmosphere-ocean model is introduced in state space form. This model provides explicit expressions for the covariance and cross spectral matrices required by the various methods; this allows the strengths and weaknesses of the methods to be identified.

Sparse approximation is a Bayesian inference program with a wide number of signal processing applications, such as Compressed Sensing recovery used in medical imaging. Previous sparse coding implementations relied on digital algorithms whose power consumption and performance scale poorly with problem size, rendering them unsuitable for portable applications, and a bottleneck in high speed applications. A novel analog architecture, implementing the Locally Competitive Algorithm (LCA), was designed and programmed onto a Field Programmable Analog Arrays (FPAAs), using floating gate transistors to set the analog parameters. A network of 6 coefficients was demonstrated to converge to similar values as a digital sparse approximation algorithm, but with better power and performance scaling. A rate encoded spiking algorithm was then developed, which was shown to converge to similar values as the LCA. A second novel architecture was designed and programmed on an FPAA implementing the spiking version of the LCA with integrate and fire neurons. A network of 18 neurons converged on similar values as a digital sparse approximation algorithm, with even better performance and power efficiency than the non-spiking network. Novel algorithms were created to increase floating gate programming speed by more than two orders of magnitude, and reduce programming error from device mismatch. A new FPAA chip was designed and tested which allowed for rapid interfacing and additional improvements in accuracy. Finally, a neuromorphic chip was designed, containing 400 integrate and fire neurons, and capable of converging on a sparse approximation solution in 10 microseconds, over 1000 times faster than the best digital solution.

In this paper theories regarding the effect of ideology and government type on public budget deficits are put to a test. Following the least likely-method I construct panel data on Swedish municipalities between 1998 and 2014. The data includes four political variables: the local government’s ideology, majority rule, coalition rule and number of parties in the coalition, and several control variables. The results using a fixed effects-model show that there are no effects of neither ideology, majority rule, coalition rule nor the coalition’s number of parties on Swedish municipalities’ budget deficits.

The term mobility is at the same time, cause and effect of urban development and is directly related to the economic, social and environmental aspects of a region. Your qualified plan implies harmonic, efficient and democratically run cities. Whereas parking lots are defined as infrastructure for urban mobility, the research objective was to identify the perceptions of users of municipal services paid parking lots in order to provide subsidies for the construction of the Mobility Plan Territorial Bagé. Therefore, the present study uses the model ECSI (European Customer Satisfaction Index) to measure relationships involving users of the municipal system of rotating Paid parking. ECSI was estimated by PLS-PM method that is characterized by its robustness in the face of structural models with missing data with normality. Data were collected through questionnaires and applied, non-randomly by 401 users Municipal Parking System Revolving in the city of Bagé, Rio Grande do Sul. The results indicate that the system user considers the level of service provided satisfactory (mean 7.65), and this construct most affected by the expectation of users and image of service. The construct with the lowest average was the perceived value (average 6.73). The constructs differ systematically only for income and age profiles, in other words, as age or income increase, the trend is that there is a better evaluation of the different constructs. / O termo mobilidade é, ao mesmo tempo, causa e efeito do desenvolvimento urbano e está diretamente relacionado com os aspectos econômicos, sociais e ambientais de uma região. O seu planejamento qualificado implica cidades harmônicas, eficientes e democraticamente geridas. Considerando que estacionamentos são definidos como infraestrutura de mobilidade urbana, o objetivo da pesquisa foi o de identificar a percepção dos usuários do serviço municipal de estacionamento rotativo a fim de fornecer subsídios para a construção do Plano de Mobilidade Territorial de Bagé. Para tanto, o presente estudo utiliza o modelo ECSI (Índice Europeu de Satisfação do Cliente) na mensuração das relações que envolvem os usuários do sistema municipal de estacionamento rotativo Pago. O ECSI foi estimado pelo método PLS-PM que se caracteriza por sua robustez diante de modelos estruturais compostos por dados com falta de normalidade. Os dados foram coletados mediante questionários aplicados e respondidos, de forma não aleatória, por 401 usuários do sistema municipal de estacionamento rotativo na cidade de Bagé, Rio Grande do Sul. Os resultados obtidos permitem concluir que o usuário do sistema considera o nível de serviço prestado satisfatório (média 7,65), sendo este constructo mais afetado pela expectativa dos usuários e imagem do serviço prestado. O constructo com menor média foi o valor percebido (média 6,73). Os constructos diferem de forma sistemática apenas para os perfis renda e idade, ou seja, à medida que a idade ou a renda aumentam, a tendência é de que exista uma melhor avaliação dos diferentes constructos.

O presente trabalho propõe um procedimento de valoração de marca a partir de uma modelagem por equações estruturais. Este estudo aponta inicialmente um modelo conceitual de valoração de marca baseado na linha comportamental, ou seja, nas percepções dos consumidores e não no valor monetário da marca. Para aplicação do procedimento proposto, foram escolhidas as marcas das operadoras de telefonia celular Vivo e Claro. A partir da modelagem por equações estruturais, ajustou-se o modelo teórico proposto a cada uma das marcas em questão. Com o modelo ajustado foi possível calcular não apenas o valor de marca para cada um dos consumidores, como todas as variáveis que o compõe. A possibilidade de se calcular o valor de marca para cada consumidor é extremamente inovadora, uma vez que nenhum dos modelos revistos na literatura científica aponta esta funcionalidade. Com a base de dados contendo o valor de marca para cada consumidor entrevistado, foi possível efetuar diversas análises estatísticas, que permitiram compreender de forma profunda quais variáveis mais impactam o valor das marcas, ou ainda quais são seus pontos fortes e fracos. Com isto, foi possível desenhar estratégias de marketing específicas para que cada uma das marcas aumente o seu valor. / The present study brings a procedure that measures brand equity using a structural equation modeling. This thesis proposes a conceptual model that measures brand equity based in a behaviorist approach, i.e., based on the consumers perception instead of monetary brand value. To apply this procedure two cellular phone operator brands were chosen: Vivo and Claro, and then, using structural equation modeling, it was possible to adjust the initial theory model to each brand. From the adjusted model it was possible to measure not only brand equity to each consumer, but all variables that are part of it. The possibility to measure brand equity to each consumer is extremely new, once no other models - reviewed in the scientific literature - pointed this feature. Using the database and the brand value to each interviewed consumer it was possible to make statistic analysis that allowed comprehending deeply which variables impact brand equity, or what are their strong and weak points. Then it was possible to make specific marketing strategies to each brand to increase their equity.