The scientific consensus has for long been that residential segregation is best conceived of as a multidimensional phenomenon that can exist on several geographical scales (Massey & Denton, 1988; Lee et al., 2008; Reardon & O’Sullivan, 2004; Reardon et al., 2008). Despite this deepened understanding of residential segregation and how to best measure it, theoretical models of segregation processes have tended to disregard the diversity of dimensions and scales of segregation. Moreover, while residential segregation is broadly defined as the spatial separation of people of different social groups (Timberlake & Ignatov, 2014), the frequently used Schelling model is aspatial (Schelling, 1971). In contrast, the lesser-known Sakoda model incorporates a distance-decay effect and is thus explicitly spatial (Sakoda, 1971). The aim of this thesis was to evaluate two theoretical agent-based models of segregation processes—the Schelling- and Sakoda model—by measuring the segregation patterns they generate under different parameter settings across four dimensions and six spatial scales of segregation, ranging from the micro- to the macro-scale. Thus, providing an assessment of the capacity of these models to generate (grow) different forms of residential segregation. Results from simulation experiments showed that the popular Schelling model was limited in its capacity to generate different forms of segregation. In its standard configuration it could generate micro-segregation along two out of four dimensions: Evenness and Exposure. The spatially explicit Sakoda model was able to generate segregation patterns which varied substantially across all scales on the Evenness and Exposure dimensions. In addition, it was able to generate varied patterns of Concentration and Centralization under certain parameter settings. These findings contribute new insights to the possibilities afforded by these two models in modeling processes of residential segregation. If the goal for theoretical models is to generate segregation patterns which vary across all dimensions and scales of residential segregation, then the standard configuration of the Schelling model is not enough. This thesis suggest that the Sakoda model is a promising candidate for this purpose. In addition, this thesis shows the importance of using a comprehensive measurement framework in theoretical modeling of segregation processes.

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Superaggregates are clusters formed by diverse aggregation mechanisms at different scales. They can be found in fluidized nanoparticles and soot formation. An aggregate, with a single aggregation mechanism, can be described by the fractal dimension, df , which is the measure of the distribution and configuration of primary particles into the aggregates. Similarly, a su-peraggregate can be analyzed by the different fractal dimensions that are found at each scale. In a fractal structure aggregate, a self-similarity can be identified at different scales and it has a power law relation between the mass and aggregate size, which can be related to properties like density or light scattering. The fractal dimension, df , can be influenced by aggregation mechanism, particles concentration, temperature, residence time, among other variables. More-over, this parameter can help on the estimation of aggregates’ properties which can help on the design of new processes, analyze health issues and characterize new materials.A multi-dimensional soot aggregate was simulated with the following approach. The first aggregation stage was modeled with a Diffusion Limited cluster-cluster aggregation (DLCA) mechanism, where primary clusters with a fractal dimension, df1, close to 1.44 were obtained. Then, the second aggregation stage was specified by Ballistic Aggregation (BA) mechanism, where the primary clusters generated in the first stage were used to form a superaggregate. All the models were validated with reported data on different experiments and computer models. Using the Ballistic Aggregation (BA) model with primary particles as the building blocks, the fractal dimension, df2, was close to 2.0, which is the expected value reported by literature. However, a decrease on this parameter is appreciated using primary clusters, from a DLCA model, as the building blocks because there is a less compact distribution of primary particles in the superaggregate’s structure.On the second aggregation stage, the fractal dimension, df2, increases when the superaggre-gate size increases, showing an asymptotic behavior to 2.0, which will be developed at higher scales. Partial reorganization was implemented in the Ballistic Aggregation (BA) mechanism where two contact points between primary clusters were achieved for stabilization purposes. This implementation showed a faster increase on the fractal dimension, df2, than without par-tial reorganization. This behavior is the result of a more packed distribution of primary clusters in a short range scales, but it does not affect the scaling behavior of multi-dimensional fractal structures. Moreover, the same results were obtained with different scenarios where the building block sizes were in the range from 200 to 300 and 700 to 800 primary particles.The obtained results demonstrate the importance of fractal dimension, df , for aggregate characterization. This parameter is powerful, universal and accurate since the identification of the different aggregation stages in the superaggregate can increase the accuracy of the estimation of properties, which is crucial in physics and process modeling.

Exclusive breastfeeding and holistic maternity care are strategic to improving maternal and infant health outcomes in Nigeria. This thesis aimed at informing policies and interventions to promote breastfeeding and to improve Nigerian mother’s experiences in antenatal and intrapartum care. The study in this research focused upon psychological dynamics underlying societal culture around maternity and breastfeeding. Using quantitative method, attitudes toward breastfeeding and health orientation were surveyed in 400 Nigerian men and women using the Iowa Infant Feeding Attitude Scale (IIFAS) and the Multidimensional Health Locus of Control Scale (MHLoC). There were more positive attitudes toward breastfeeding in males, participants in the 20-29-year-old age category, and in those who identified as single. Higher internal HLoC was associated with more positive attitudes to breastfeeding and higher EHLoC scores were associated with more negative attitudes to formula feeding. The second study explored the experience of pregnancy and childbirth in Nigerian women. Qualitative interviews with 12 women implied that Nigerian women perceive pregnancy and childbirth as a multidimensional experience comprising physiological and psychological elements and also as risky. Control mechanisms that reflected internal HLoC included choosing multiple antenatal care sources to obtain holistic care, adopting new technology in bridging perceived communication gaps with health care providers and adopting physical and mental strategies in controlling the somatic and sensory changes that accompany pregnancy. Pregnancy and childbirth were viewed through an external HLoC lens as spiritual, and reflected in an entrenched belief in the intervention of deity to mitigate pain and risk associated with childbirth. These results have implications for practice, intervention and policy to promote breastfeeding at the societal level and improve maternity services for the current and next child-bearing generation.

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京都大学 / 新制・課程博士 / 博士(工学) / 甲第24896号 / 工博第5176号 / 新制||工||1988(附属図書館) / 京都大学大学院工学研究科都市社会工学専攻 / (主査)准教授 PIPATPONGSA Thirapong, 教授 肥後 陽介, 教授 岸田 潔, 教授 安原 英明 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

De plus en plus souvent, les études médicales utilisent simultanément de multiples modalités d'acquisition d'image, produisant ainsi des données multidimensionnelles comportant beaucoup d'information supplémentaire dont l'interprétation et le traitement deviennent délicat. Par exemple, les études sur l'ischémie cérébrale se basant sur la combinaison de plusieurs images IRM, provenant de différentes séquences d'acquisition, pour prédire l'évolution de la zone nécrosée, donnent de bien meilleurs résultats que celles basées sur une seule image. Ces approches nécessitent cependant l'utilisation d'algorithmes plus complexes pour réaliser les opérations de filtrage, segmentation et de clustering. Une approche robuste pour répondre à ces problèmes de traitements de données multidimensionnelles est le Mean Shift qui est basé sur l'analyse de l'espace des caractéristiques et l'estimation non-paramétrique par noyau de la densité de probabilité. Dans cette thèse, nous étudions les paramètres qui influencent les résultats du Mean Shift et nous cherchons à optimiser leur choix. Nous examinons notamment l'effet du bruit et du flou dans l'espace des caractéristiques et comment le Mean Shift doit être paramétrés pour être optimal pour le débruitage et la réduction du flou. Le grand succès du Mean Shift est principalement du au réglage intuitif de ces paramètres de la méthode. Ils représentent l'échelle à laquelle le Mean Shift analyse chacune des caractéristiques. En se basant sur la méthode du Plug In (PI) monodimensionnel, fréquemment utilisé pour le filtrage Mean Shift et permettant, dans le cadre de l'estimation non-paramétrique par noyau, d'approximer le paramètre d'échelle optimal, nous proposons l'utilisation du PI multidimensionnel pour le filtrage Mean Shift. Nous évaluons l'intérêt des matrices d'échelle diagonales et pleines calculées à partir des règles du PI sur des images de synthèses et naturelles. Enfin, nous proposons une méthode de segmentation automatique et volumique combinant le filtrage Mean Shift et la croissance de région ainsi qu'une optimisation basée sur les cartes de probabilité. Cette approche est d'abord étudiée sur des images IRM synthétisées. Des tests sur des données réelles issues d'études sur l'ischémie cérébrale chez le rats et l'humain sont aussi conduits pour déterminer l'efficacité de l'approche à prédire l'évolution de la zone de pénombre plusieurs jours après l'accident vasculaire et ce, à partir des IRM réalisées peu de temps après la survenue de cet accident. Par rapport aux segmentations manuelles réalisées des experts médicaux plusieurs jours après l'accident, les résultats obtenus par notre approche sont mitigés. Alors qu'une segmentation parfaite conduirait à un coefficient DICE de 1, le coefficient est de 0.8 pour l'étude chez le rat et de 0.53 pour l'étude sur l'homme. Toujours en utilisant le coefficient DICE, nous déterminons la combinaison de d'images IRM conduisant à la meilleure prédiction. / Medical studies increasingly use multi-modality imaging, producing multidimensional data that bring additional information that are also challenging to process and interpret. As an example, for predicting salvageable tissue, ischemic studies in which combinations of different multiple MRI imaging modalities (DWI, PWI) are used produced more conclusive results than studies made using a single modality. However, the multi-modality approach necessitates the use of more advanced algorithms to perform otherwise regular image processing tasks such as filtering, segmentation and clustering. A robust method for addressing the problems associated with processing data obtained from multi-modality imaging is Mean Shift which is based on feature space analysis and on non-parametric kernel density estimation and can be used for multi-dimensional filtering, segmentation and clustering. In this thesis, we sought to optimize the mean shift process by analyzing the factors that influence it and optimizing its parameters. We examine the effect of noise in processing the feature space and how Mean Shift can be tuned for optimal de-noising and also to reduce blurring. The large success of Mean Shift is mainly due to the intuitive tuning of bandwidth parameters which describe the scale at which features are analyzed. Based on univariate Plug-In (PI) bandwidth selectors of kernel density estimation, we propose the bandwidth matrix estimation method based on multi-variate PI for Mean Shift filtering. We study the interest of using diagonal and full bandwidth matrix with experiment on synthesized and natural images. We propose a new and automatic volume-based segmentation framework which combines Mean Shift filtering and Region Growing segmentation as well as Probability Map optimization. The framework is developed using synthesized MRI images as test data and yielded a perfect segmentation with DICE similarity measurement values reaching the highest value of 1. Testing is then extended to real MRI data obtained from animals and patients with the aim of predicting the evolution of the ischemic penumbra several days following the onset of ischemia using only information obtained from the very first scan. The results obtained are an average DICE of 0.8 for the animal MRI image scans and 0.53 for the patients MRI image scans; the reference images for both cases are manually segmented by a team of expert medical staff. In addition, the most relevant combination of parameters for the MRI modalities is determined.