On December 26th, 2004, 280,000 people lost their lives. A massive earthquake struck Indonesia, triggering a tsunami that affected several countries, including Thailand. The disaster had important implications for health status of Thai citizens, as well as health system planning, and thus underscores the need to study its long-term effect. This dissertation examined the patterns, determinants, and spatial analysis of health service utilization following the tsunami in Thailand. The primary aim was to determine whether tsunami-affected status (personal injury or property loss) and distance to a health facility (public health center or hospital) influenced health service utilization. The study population included Thai citizens (aged 14+), living in the tsunami-affected Thai provinces: Phuket, Phang Nga, Krabi, and Ranong. Study participants were randomly selected from the ‘affected’ and ‘unaffected’ populations. One and two years after the tsunami, participants were interviewed in-person on demographic and socio-economic factors, disaster impact, health status, and health service utilization. Five types of health services were examined: outpatient services, inpatient services, home visits, medications, and informal (unpaid) care. Distance to a health facility was calculated using Geographic Information System’s Network Analyst. The Grossman model of the demand for health care and a distance decay concept provided the foundation for this study. A propensity score method and a two-part model were used to examine the study objectives. There were 1,889 participants. One year after the tsunami, individuals affected by property loss were more likely to use medications than unaffected participants. Two years after the tsunami, individuals with personal injury were more likely to use outpatient services, medications, and informal care than unaffected participants. Distance to a health facility was associated with the use of medications and informal care. The results confirmed the long-term effect of a tsunami. This dissertation may assist the decision- and policy-makers in the identification of those most likely to use health services and in the request of health resources to the affected areas. The patterns, determinants, and spatial analysis of health service utilization found in this study may not be specific to a tsunami and may provide insights on post-disaster contexts of other natural disasters.

This thesis presents a novel Dynamic Gesture Language Recognition (DGLR) system for human-robot and inter-robot communication. We developed and implemented an experimental setup consisting of a humanoid robot/android able to recognize and execute in real time all the arm gestures of the Dynamic Gesture Language (DGL) in similar way as humans do. Our DGLR system comprises two main subsystems: an image processing (IP) module and a linguistic recognition system (LRS) module. The IP module enables recognizing individual DGL gestures. In this module, we use the bag-of-features (BOFs) and a local part model approach for dynamic gesture recognition from images. Dynamic gesture classification is conducted using the BOFs and nonlinear support-vector-machine (SVM) methods. The multiscale local part model preserves the temporal context. The IP module was tested using two databases, one consisting of images of a human performing a series of dynamic arm gestures under different environmental conditions and a second database consisting of images of an android performing the same series of arm gestures. The linguistic recognition system (LRS) module uses a novel formal grammar approach to accept DGL-wise valid sequences of dynamic gestures and reject invalid ones. LRS consists of two subsystems: one using a Linear Formal Grammar (LFG) to derive the valid sequence of dynamic gestures and another using a Stochastic Linear Formal Grammar (SLFG) to occasionally recover gestures that were unrecognized by the IP module. Experimental results have shown that the DGLR system had a slightly better overall performance when recognizing gestures made by a human subject (98.92% recognition rate) than those made by the android (97.42% recognition rate).

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Esta tese visa propor extensões dos modelos de regressão quantílica bayesianos, considerando dados de proporção com inflação de zeros, e também dados censurados no zero. Inicialmente, é sugerida uma análise de observações influentes, a partir da representação por mistura localização-escala da distribuição Laplace assimétrica, em que as distribuições a posteriori das variáveis latentes são comparadas com o intuito de identificar possíveis observações aberrantes. Em seguida, é proposto um modelo de duas partes para analisar dados de proporção com inflação de zeros ou uns, estudando os quantis condicionais e a probabilidade da variável resposta ser igual a zero. Além disso, são propostos modelos de regressão quantílica bayesiana para dados contínuos com um componente discreto no zero, em que parte dessas observações é suposta censurada. Esses modelos podem ser considerados mais completos na análise desse tipo de dados, uma vez que a probabilidade de censura é verificada para cada quantil de interesse. E por último, é considerada uma aplicação desses modelos com correlação espacial, para estudar os dados da eleição presidencial no Brasil em 2014. Nesse caso, os modelos de regressão quantílica são capazes de incorporar essa informação espacial a partir do processo Laplace assimétrico. Para todos os modelos propostos foi desenvolvido um pacote do software R, que está exemplificado no apêndice. / This thesis aims to propose extensions of Bayesian quantile regression models, considering proportion data with zero inflation, and also censored data at zero. Initially, it is suggested an analysis of influential observations, based on the location-scale mixture representation of the asymmetric Laplace distribution, where the posterior distribution of the latent variables are compared with the goal of identifying possible outlying observations. Next, a two-part model is proposed to analyze proportion data with zero or one inflation, studying the conditional quantile and the probability of the response variable being equal to zero. Following, Bayesian quantile regression models are proposed for continuous data with a discrete component at zero, where part of these observations are assumed censored. These models may be considered more complete in the analysis of this type of data, as the censoring probability varies with the quantiles of interest. For last, it is considered an application of these models with spacial correlation, in order to study the data about the last presidential election in Brazil in 2014. In this example, the quantile regression models are able to incorporate spatial dependence with the asymmetric Laplace process. For all the proposed models it was developed a R package, which is exemplified in the appendix.

Semantic interoperability (SI) of a product model refers to automatic exchange of meaning associated with the product data, among applications/domains throughout the product development cycle. In the product development cycle, several applications (engineering design, industrial design, manufacturing, supply chain, marketing, maintenance etc.) and different engineering domains (mechanical, electrical, electronic etc.) come into play making the ability to exchange product data with semantics very significant. With product development happening in multiple locations with multiple tools/systems, SI between these systems/domains becomes important. The thesis presents a feature-based framework for shape model to address these SI issues when exchanging shape models. Problem of exchanging semantics associated with shape model to support the product lifecycle has been identified and explained. Different types of semantic interoperability issues pertaining to the shape model have been identified and classified. Features in a shape model can be associated with volume addition/subtraction to/from base-solid, deformation/modification of base-sheet/base surface, forming of material of constant thickness. The DIFF model has been extended to represent, classify and extract Free-Form Surface Features (FFSFs) and deformation features in a part model. FFSFs refer to features that modify a free-form surface. Deformation features are created in constant thickness part models, for example, deformation of material (as in sheet-metal parts) or forming of material (as in injection molded parts with constant thickness), also referred to as constant thickness features. Volumetric features covered in the DIFF model have been extended to classify and represent volumetric features based on relative variations of cross-section and PathCurve. Shape feature ontology is described based on unified feature taxonomy with definitions and labels of features as defined in the extended DIFF model. Features definitions are used as intermediate and unambiguous representation for shape features. The feature ontology is used to capture semantics of shape features. The proposed ontology enables reasoning to handle semantic equivalences between feature labels, and is used to map shape features from a source to target applications. Reasoning framework for identification of semantically equivalent feature labels and representations for the feature being exchanged across multiple applications is presented and discussed. This reasoning framework is used to associate multiple construction paths for a feature and associate applicable meanings from the ontology. Interface is provided to select feature label for a target application from the list of labels which are semantically equivalent for the feature being exchanged/mapped. Parameters for the selected feature label can be mapped from the DIFF representation; the feature can then be represented/constructed in the target application using the feature label and mapped parameters. This work shows that product model with feature information (feature labels and representations), as understood by the target application, can be exchanged and maintained in such a way that multiple applications can use the product information as their understandable labels and representations. Finally, the thesis concludes by summarizing the main contributions and outlining the scope for future work.

Esta tese visa propor extensões dos modelos de regressão quantílica bayesianos, considerando dados de proporção com inflação de zeros, e também dados censurados no zero. Inicialmente, é sugerida uma análise de observações influentes, a partir da representação por mistura localização-escala da distribuição Laplace assimétrica, em que as distribuições a posteriori das variáveis latentes são comparadas com o intuito de identificar possíveis observações aberrantes. Em seguida, é proposto um modelo de duas partes para analisar dados de proporção com inflação de zeros ou uns, estudando os quantis condicionais e a probabilidade da variável resposta ser igual a zero. Além disso, são propostos modelos de regressão quantílica bayesiana para dados contínuos com um componente discreto no zero, em que parte dessas observações é suposta censurada. Esses modelos podem ser considerados mais completos na análise desse tipo de dados, uma vez que a probabilidade de censura é verificada para cada quantil de interesse. E por último, é considerada uma aplicação desses modelos com correlação espacial, para estudar os dados da eleição presidencial no Brasil em 2014. Nesse caso, os modelos de regressão quantílica são capazes de incorporar essa informação espacial a partir do processo Laplace assimétrico. Para todos os modelos propostos foi desenvolvido um pacote do software R, que está exemplificado no apêndice. / This thesis aims to propose extensions of Bayesian quantile regression models, considering proportion data with zero inflation, and also censored data at zero. Initially, it is suggested an analysis of influential observations, based on the location-scale mixture representation of the asymmetric Laplace distribution, where the posterior distribution of the latent variables are compared with the goal of identifying possible outlying observations. Next, a two-part model is proposed to analyze proportion data with zero or one inflation, studying the conditional quantile and the probability of the response variable being equal to zero. Following, Bayesian quantile regression models are proposed for continuous data with a discrete component at zero, where part of these observations are assumed censored. These models may be considered more complete in the analysis of this type of data, as the censoring probability varies with the quantiles of interest. For last, it is considered an application of these models with spacial correlation, in order to study the data about the last presidential election in Brazil in 2014. In this example, the quantile regression models are able to incorporate spatial dependence with the asymmetric Laplace process. For all the proposed models it was developed a R package, which is exemplified in the appendix.

Indiana University-Purdue University Indianapolis (IUPUI) / Accurate estimation of alcohol price elasticity is important for policy analysis – e.g.., determining optimal taxes and projecting revenues generated from proposed tax changes. Several approaches to specifying and estimating the price elasticity of demand for alcohol can be found in the literature. There are two keys to policy-relevant specification and estimation of alcohol price elasticity. First, the underlying demand model should take account of alcohol consumption decisions at the extensive margin – i.e., individuals' decisions to drink or not – because the price of alcohol may impact the drinking initiation decision and one's decision to drink is likely to be structurally different from how much they drink if they decide to do so (the intensive margin). Secondly, the modeling of alcohol demand elasticity should yield both theoretical and empirical results that are causally interpretable. The elasticity estimates obtained from the existing two-part model takes into account the extensive margin, but are not causally interpretable. The elasticity estimates obtained using aggregate-level models, however, are causally interpretable, but do not explicitly take into account the extensive margin. There currently exists no specification and estimation method for alcohol price elasticity that both accommodates the extensive margin and is causally interpretable. I explore additional sources of bias in the extant approaches to elasticity specification and estimation: 1) the use of logged (vs. nominal) alcohol prices; and 2) implementation of unnecessarily restrictive assumptions underlying the conventional two-part model. I propose a new approach to elasticity specification and estimation that covers the two key requirements for policy relevance and remedies all such biases. I find evidence of substantial divergence between the new and extant methods using both simulated and the real data. Such differences are profound when placed in the context of alcohol tax revenue generation.