No description available.

Peu d’études ont évalué les caractéristiques des parcs pouvant encourager l’activité physique spécifiquement chez les jeunes. Cette étude vise à estimer la fiabilité d’un outil d’observation des parcs orienté vers les jeunes, à identifier les domaines conceptuels des parcs capturés par cet outil à l’aide d’une opérationnalisation du modèle conceptuel des parcs et de l’activité physique et à identifier différents types de parcs. Un total de 576 parcs ont été évalués en utilisant un outil d’évaluation des parcs. La fiabilité intra-juges et la fiabilité inter-juges de cet outil ont été estimées. Une analyse exploratoire par composantes principales (ACP) a été effectuée en utilisant une rotation orthogonale varimax et les variables étaient retenues si elles saturaient à ≥0.3 sur une composante. Une analyse par grappes (AG) à l’aide de la méthode de Ward a ensuite été réalisée en utilisant les composantes principales et une mesure de l’aire des parcs. L’outil était généralement fiable et l’ACP a permis d'identifier dix composantes principales qui expliquaient 60% de la variance totale. L’AG a donné un résultat de neuf grappes qui expliquaient 40% de la variance totale. Les méthodes de l’ACP et l’AG sont donc faisables avec des données de parcs. Les résultats ont été interprétés en utilisant l’opérationnalisation du modèle conceptuel. / Few studies have characterized park features that may be appealing for youth physical activity (PA). This study assesses the reliability of a youth-oriented direct-observation park assessment tool; identifies park domains captured by the tool using an operationalized conceptual model of parks and PA, and identifies distinct park types. 576 parks were audited using a park observation tool; intra- and inter-rater reliability were estimated. Exploratory principal component analysis (PCA) was conducted and variables were retained if they loaded at 0.3 or higher. A cluster analysis (CA) was conducted using the principal components and park area. The tool was found to be reliable and PCA yielded ten principal components explaining 60% of the total variance. The CA yielded a nine-cluster outcome explaining 40% of the total variance. PCA and CA were found to be feasible methods to use with park data. The operationalization of the conceptual model helped interpret these results.

High levels of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) in edible fish in the Baltic Sea have raised health concerns in the Baltic region and the rest of Europe. Thus, there are urgent needs to characterize sources in order to formulate effective mitigation strategies. The aim of this thesis is to contribute to a better understanding of past and present sources of PCDD/Fs in the Baltic Sea environment by exploring chemical fingerprints in sediments, air, and biota. The spatial and temporal patterns of PCDD/F distributions in the Baltic Sea during the 20th century were studied in Swedish coastal and offshore sediment cores. The results showed that PCDD/F levels peaked in 1975 (± 7 years) in coastal and 1991 (± 5 years) in offshore areas. The time trends of PCDD/Fs in the sediment cores also showed that environmental half-lives of these pollutants have been shorter in coastal than in offshore areas (15 ± 5 and 29 ± 14 years, respectively). Consequently, there have been remarkable recoveries in coastal areas, but slower recovery in offshore areas with 81 ± 12% and 38 ± 11% reductions from peak levels, respectively. Source-to-receptor multivariate modeling by Positive Matrix Factorization (PMF) showed that six types of PCDD/F sources are and have been important for the Baltic Sea environment: PCDD/Fs related to i) atmospheric background, ii) thermal processes, iii) manufacture and use of tetra-chlorophenol (TCP) and iv) penta-chlorophenol (PCP), v) industrial use of elementary chlo- rine and the chloralkali-process (Chl), and vi) hexa-CDD sources. The results showed that diffuse sources (i and ii) have consistently contributed >80% of the total amounts in the Southern Baltic Sea. In the Northern Baltic Sea, where the biota is most heavily contaminated, impacts of local sources (TCP, PCP and Chl) have been higher, contributing ca. 50% of total amounts. Among the six sources, only Thermal and chlorophenols (ii-iv) have had major impacts on biota. The impact of thermal sources has, however, been declining as shown from source apportioned time-trend data of PCDD/Fs in Baltic herring. In contrast, impacts of chlorophenol-associated sources generally increased, remained at steady-state or slowly decreased during 1990-2010, suggesting that these sources have substantially contributed to the persistently high levels of PCDD/Fs in Baltic biota. Atmospheric sources of PCDD/Fs for the Baltic region (Northern Europe) were also investigated, and specifically whether the inclusion of parallel measurements of metals in the analysis of air would help back-tracking sources. PCDD/Fs and metals in high-volume air samples from a rural field station near the shore of the central Baltic Sea were measured. The study focused on the winter season and air from the S and E sectors, as these samples showed elevated levels of PCDD/Fs, particularly PCDFs. Several metals were found to correlate significantly with the PCDFs. The wide range of candidate metals as source markers for PCDD/F emissions, and the lack of an up-to-date extensive compilation of source characteristics for metal emission from vari- ous sources, limited the use of the metals as source markers. The study was not able to pin-point primary PCDD/F sources for Baltic air, but it demonstrated a new promising approach for source tracing of air emissions. The best leads for back-tracking primary sources of atmospheric PCDD/Fs in Baltic air were seasonal trends and PCDD/F congener patterns, pointing at non-industrial related thermal sources related to heating. The non-localized natures of the sources raise challenges for managing the emissions and thus societal efforts are required to better control atmospheric emissions of PCDD/Fs. / EcoChange / BalticPOPs

金融風暴喚起各界改革財務報表未能反映實際虧損的缺失，因此，新版國際財務報導準則第9號及第17號公報應運而生，未來正式接軌後，對於壽險業的財報將產生重大衝擊，更突顯其資產負債管理之重要性，故本研究係採用主成分分析建構極端利率情境，並考量折現率需反映現時狀況下，於資產面分別以攤銷後成本或公允價值衡量、負債面採公允價值評價，欲探討資產負債配置及攤銷後成本比重不同時，利率變動對於壽險公司股東權益波動度之影響，以供壽險業參考。 研究結果發現攤銷後成本比重能夠有效控制股東權益波動度。再者，壽險公司應審慎評估海外投資比例，並配合其壽險商品外幣保單之銷售策略加以布局，同時謹慎考量會計決策，適當選擇攤銷後成本權重，方能有效控制資產負債表之波動。 / The financial crisis has caused wide public concern since it is failed to reflect the actual losses in financial statements. As a result, International Accounting Standards Board (IASB) issued new International Financial Reporting Standards, IFRS 9 and IFRS 17. The surplus of life insurers may fluctuate sharply if assets and liabilities don’t match appropriately under these new IFRS Standards. We follow the international regulation standard by using principal component analysis to generate extreme interest rate shock scenarios. This study examines the volatility of surplus under extreme interest rate shock scenarios for different combinations of liabilities, fair-valued assets, and amortized cost assets. In particular, the assets are measured at amortized cost or fair value, and all liabilities were acquired at fair value approach. In the numerical analysis, we showed that it is one of the most effective methods to control the surplus volatility by adjusting the percentage of amortized cost assets. Furthermore, life insurer should adjust the percentage of foreign investments and insurance policies carefully in order to reduce the fluctuation in shareholders’ equity.

Cette thèse est centrée sur le développement de nouvelles méthodes d'imputation multiples, basées sur des techniques d'analyse factorielle. L'étude des méthodes factorielles, ici en tant que méthodes d'imputation, offre de grandes perspectives en termes de diversité du type de données imputées d'une part, et en termes de dimensions de jeux de données imputés d'autre part. Leur propriété de réduction de la dimension limite en effet le nombre de paramètres estimés.Dans un premier temps, une méthode d'imputation simple par analyse factorielle de données mixtes est détaillée. Ses propriétés sont étudiées, en particulier sa capacité à gérer la diversité des liaisons mises en jeu et à prendre en compte les modalités rares. Sa qualité de prédiction est éprouvée en la comparant à l'imputation par forêts aléatoires.Ensuite, une méthode d'imputation multiple pour des données quantitatives basée sur une approche Bayésienne du modèle d'analyse en composantes principales est proposée. Elle permet d'inférer en présence de données manquantes y compris quand le nombre d'individus est petit devant le nombre de variables, ou quand les corrélations entre variables sont fortes.Enfin, une méthode d'imputation multiple pour des données qualitatives par analyse des correspondances multiples (ACM) est proposée. La variabilité de prédiction des données manquantes est reflétée via un bootstrap non-paramétrique. L'imputation multiple par ACM offre une réponse au problème de l'explosion combinatoire limitant les méthodes concurrentes dès lors que le nombre de variables ou de modalités est élev / This thesis proposes new multiple imputation methods that are based on principal component methods, which were initially used for exploratory analysis and visualisation of continuous, categorical and mixed multidimensional data. The study of principal component methods for imputation, never previously attempted, offers the possibility to deal with many types and sizes of data. This is because the number of estimated parameters is limited due to dimensionality reduction.First, we describe a single imputation method based on factor analysis of mixed data. We study its properties and focus on its ability to handle complex relationships between variables, as well as infrequent categories. Its high prediction quality is highlighted with respect to the state-of-the-art single imputation method based on random forests.Next, a multiple imputation method for continuous data using principal component analysis (PCA) is presented. This is based on a Bayesian treatment of the PCA model. Unlike standard methods based on Gaussian models, it can still be used when the number of variables is larger than the number of individuals and when correlations between variables are strong.Finally, a multiple imputation method for categorical data using multiple correspondence analysis (MCA) is proposed. The variability of prediction of missing values is introduced via a non-parametric bootstrap approach. This helps to tackle the combinatorial issues which arise from the large number of categories and variables. We show that multiple imputation using MCA outperforms the best current methods.

AlNiCo magnets are known for high-temperature stability and superior corrosion resistance and have been widely used for various applications. Reported magnetic energy density ((BH) max) for these magnets is around 10 MGOe. Theoretical calculations show that ((BH) max) of 20 MGOe is achievable which will be helpful in covering the gap between AlNiCo and Rare-Earth Elements (REE) based magnets. An extended family of AlNiCo alloys was studied in this dissertation that consists of eight elements, and hence it is important to determine composition-property relationship between each of the alloying elements and their influence on the bulk properties. In the present research, we proposed a novel approach to efficiently use a set of computational tools based on several concepts of artificial intelligence to address a complex problem of design and optimization of high temperature REE-free magnetic alloys. A multi-dimensional random number generation algorithm was used to generate the initial set of chemical concentrations. These alloys were then examined for phase equilibria and associated magnetic properties as a screening tool to form the initial set of alloy. These alloys were manufactured and tested for desired properties. These properties were fitted with a set of multi-dimensional response surfaces and the most accurate meta-models were chosen for prediction. These properties were simultaneously extremized by utilizing a set of multi-objective optimization algorithm. This provided a set of concentrations of each of the alloying elements for optimized properties. A few of the best predicted Pareto-optimal alloy compositions were then manufactured and tested to evaluate the predicted properties. These alloys were then added to the existing data set and used to improve the accuracy of meta-models. The multi-objective optimizer then used the new meta-models to find a new set of improved Pareto-optimized chemical concentrations. This design cycle was repeated twelve times in this work. Several of these Pareto-optimized alloys outperformed most of the candidate alloys on most of the objectives. Unsupervised learning methods such as Principal Component Analysis (PCA) and Heirarchical Cluster Analysis (HCA) were used to discover various patterns within the dataset. This proves the efficacy of the combined meta-modeling and experimental approach in design optimization of magnetic alloys.

Text is no longer confined to scanned pages and often appears in camera-based images originating from text on real world objects. Unlike the images from conventional flatbed scanners, which have a controlled acquisition environment, camera-based images pose new challenges such as uneven illumination, blur, poor resolution, perspective distortion and 3D deformations that can severely affect the performance of any optical character recognition (OCR) system. Due to the variations in the imaging condition as well as the target document type, traditional OCR systems, designed for scanned images, cannot be directly applied to camera-captured images and a new level of processing needs to be addressed. In this thesis, we study some of the issues commonly encountered in camera-based image analysis and propose novel methods to overcome them. All the methods make use of color connected components. 1. Connected component descriptor for document image mosaicing Document image analysis often requires mosaicing when it is not possible to capture a large document at a reasonable resolution in a single exposure. Such a document is captured in parts and mosaicing stitches them into a single image. Since connected components (CCs) in a document image can easily be extracted regardless of the image rotation, scale and perspective distortion, we design a robust feature named connected component descriptor that is tailored for mosaicing camera-captured document images. The method involves extraction of a circular measurement region around each CC and its description using the angular radial transform (ART). To ensure geometric consistency during feature matching, the ART coefficients of a CC are augmented with those of its 2 nearest neighbors. Our method addresses two critical issues often encountered in correspondence matching: (i) the stability of features and (ii) robustness against false matches due to multiple instances of many characters in a document image. We illustrate the effectiveness of the proposed method on camera-captured document images exhibiting large variations in viewpoint, illumination and scale. 2. Font and background color independent text binarization The first step in an OCR system, after document acquisition, is binarization, which converts a gray-scale/color image into a two-level image -the foreground text and the background. We propose two methods for binarization of color documents whereby the foreground text is output as black and the background as white regardless of the polarity of foreground-background shades. (a) Hierarchical CC Analysis: The method employs an edge-based connected component approach and automatically determines a threshold for each component. It overcomes several limitations of existing locally-adaptive thresholding techniques. Firstly, it can handle documents with multi-colored texts with different background shades. Secondly, the method is applicable to documents having text of widely varying sizes, usually not handled by local binarization methods. Thirdly, the method automatically computes the threshold for binarization and the logic for inverting the output from the image data and does not require any input parameter. However, the method is sensitive to complex backgrounds since it relies on the edge information to identify CCs. It also uses script-specific characteristics to filter out edge components before binarization and currently works well for Roman script only. (b) Contour-based color clustering (COCOCLUST): To overcome the above limitations, we introduce a novel unsupervised color clustering approach that operates on a ‘small’ representative set of color pixels identified using the contour information. Based on the assumption that every character is of a uniform color, we analyze each color layer individually and identify potential text regions for binarization. Experiments on several complex images having large variations in font, size, color, orientation and script illustrate the robustness of the method. 3. Multi-script and multi-oriented text extraction from scene images Scene text understanding normally involves a pre-processing step of text detection and extraction before subjecting the acquired image for character recognition task. The subsequent recognition task is performed only on the detected text regions so as to mitigate the effect of background complexity. We propose a color-based CC labeling for robust text segmentation from natural scene images. Text CCs are identified using a combination of support vector machine and neural network classifiers trained on a set of low-level features derived from the boundary, stroke and gradient information. We develop a semiautomatic annotation toolkit to generate pixel-accurate groundtruth of 100 scenic images containing text in various layout styles and multiple scripts. The overall precision, recall and f-measure obtained on our dataset are 0.8, 0.86 and 0.83, respectively. The proposed method is also compared with others in the literature using the ICDAR 2003 robust reading competition dataset, which, however, has only horizontal English text. The overall precision, recall and f-measure obtained are 0.63, 0.59 and 0.61 respectively, which is comparable to the best performing methods in the ICDAR 2005 text locating competition. A recent method proposed by Epshtein et al. [1] achieves better results but it cannot handle arbitrarily oriented text. Our method, however, works well for generic scene images having arbitrary text orientations. 4. Alignment of curved text lines Conventional OCR systems perform poorly on document images that contain multi-oriented text lines. We propose a technique that first identifies individual text lines by grouping adjacent CCs based on their proximity and regularity. For each identified text string, a B-spline curve is fitted to the centroids of the constituent characters and normal vectors are computed along the fitted curve. Each character is then individually rotated such that the corresponding normal vector is aligned with the vertical axis. The method has been tested on a data set consisting of 50 images with text laid out in various ways namely along arcs, waves, triangles and a combination of these with linearly skewed text lines. It yields 95.9% recognition accuracy on text strings, where, before alignment, state-of-the-art OCRs fail to recognize any text. The CC-based pre-processing algorithms developed are well-suited for processing camera-captured images. We demonstrate the feasibility of the algorithms on the publicly-available ICDAR 2003 robust reading competition dataset and our own database comprising camera-captured document images that contain multiple scripts and arbitrary text layouts.

Methodologies designed to support new product development are receiving increasing interest in recent literature. A significant percentage of new product failure is attributed to a mismatch between designed product features and consumer liking. A variety of methodologies have been proposed and tested for consumer liking or preference prediction, ranging from statistical methodologies e.g. multiple linear regression (MLR) to non-statistical approaches e.g. artificial neural networks (ANN), support vector machines (SVM), and belief rule-based (BRB) systems. BRB has been previously tested for consumer preference prediction and target setting in case studies from the beverages industry. Results have indicated a number of technical and conceptual advantages which BRB holds over the aforementioned alternative approaches. This thesis focuses on presenting further advantages and applications of the BRB methodology for consumer liking prediction. The features and advantages are selected in response to challenges raised by three addressed case studies. The first case study addresses a novel industry for BRB application: the fast moving consumer goods industry, the personal care sector. A series of challenges are tackled. Firstly, stepwise linear regression, principal component analysis and AutoEncoder are tested for predictors’ selection and data reduction. Secondly, an investigation is carried out to analyse the impact of employing complete distributions, instead of averages, for sensory attributes. Moreover, the effect of modelling instrumental measurement error is assessed. The second case study addresses a different product from the personal care sector. A bi-objective prescriptive approach for BRB model structure selection and validation is proposed and tested. Genetic Algorithms and Simulated Annealing are benchmarked against complete enumeration for searching the model structures. A novel criterion based on an adjusted Akaike Information Criterion is designed for identifying the optimal model structure from the Pareto frontier based on two objectives: model complexity and model fit. The third case study introduces yet another novel industry for BRB application: the pastry and confectionary specialties industry. A new prescriptive framework, for rule validation and random training set allocation, is designed and tested. In all case studies, the BRB methodology is compared with the most popular alternative approaches: MLR, ANN, and SVM. The results indicate that BRB outperforms these methodologies both conceptually and in terms of prediction accuracy.

Les amas de galaxies sont l'une des plus grandes structures dans l'univers et jouent le rôle d'hôte de plusieurs phénomènes complexes. Bien qu'il existe beaucoup d'études portant sur leur formation et leur évolution, l'avènement récent de l'apprentissage automatique en astronomie nous permet d'investiguer des questions qui, jusqu'à maintenant, demeuraient sans réponse. Même si ce mémoire se concentre sur l'application de techniques d'apprentissage automatique aux observations en rayons X des amas de galaxies, nous explorons l'usage de ces techniques à son homologue à une échelle réduite : les galaxies elles-mêmes. Malgré le fait que les trois articles présentés dans ce mémoire se concentrent sur différents aspects de la physique, sur de différentes échelles et sur de différentes techniques, ils forment une base d'études que je continuerai pendant mon doctorat : l'usage des nouvelles techniques pour investiguer la physique des régions galactiques et extragalactiques. Dans le premier article, nous introduisons les premières observations en rayons X d'un amas de galaxies lointain qui détient des attributs particuliers comme une formation stellaire hors pair (∽ 900 M⊙/an). Dans cet article, nous employons les techniques traditionnelles en astronomie rayons X pour déterminer si ce taux de formation stellaire est dû à un courant de refroidissement désinhibé. Puisque l'objet est très lointain (z=1.7), il faut faire nos calculs sans beaucoup de photons et il faut donc utiliser les indices par procuration. Nous déterminons que la galaxie centrale est séparée d'une distance de plus de 50 kpc du coeur froid de l'amas. À cause de cette séparation, le trou noir supermassif central n'est pas alimenté et il ne peut pas prévenir le courant de refroidissement. Ceci est le premier exemple d'un amas de galaxies où nous observons l'échec de la rétroaction d'un trou noir supermassif. De plus, ceci nous fournit un autre mécanisme qui peut créer la lumière intra-amas. Dans le deuxième article présenté ici, nous examinons l'émission rayons X provenant du milieu intra-amas extrêmement chaud des amas de galaxies. Nous développons une méthode compréhensive qui détermine le nombre de composantes thermiques sous-jacentes dans un spectre de plasma. Notre nouvelle technique est basée sur une combinaison d'algorithmes d'apprentissage automatique non supervisé (analyse de composantes principales) et d'apprentissage automatique supervisé (arbre aléatoire). Nous créons un ensemble de 100 000 observations synthétiques et réalistes de Chandra qui représentent le gaz chaud dans les amas de galaxies voisines. Après la réduction de notre ensemble d'entraînement à ses 25 composantes principales, nous entraînons notre classificateur afin qu'il puisse déterminer le nombre de composantes thermiques sous-jacentes. Une fois l'étape d'entraînement terminée et l'optimisation des hyperparamètres terminée, nous appliquons la méthodologie à l'amas de galaxies de Persée. En plus de créer une carte de l'amas qui indique le nombre de composantes thermiques nécessaires afin de modéliser précisément l'émission du gaz chaud, nous avons développé un ensemble d'outils numériques qui calculent les températures associées. Nos résultats sont en accord avec plus d'une décennie d'études sur l'amas de galaxies de Persée et nous indiquent qu'il faut utiliser plusieurs composantes thermiques pour modéliser le milieu intra-amas correctement. Le troisième article présenté dans ce mémoire emploie de nouveau l'apprentissage automatique pour résoudre une question précédemment sans réponse nécessaire pour la caractérisation précise de la cinématique de gaz chaud dans les galaxies. Nous avons construit un réseau de neurones convolutif qui estime la vitesse et l'élargissement des raies d'émission d'un spectre de galaxies en visible. Une fois construit, nous l'appliquons aux données synthétiques qui répliquent les observations réelles de SITELLE du programme SIGNALS. En utilisant notre réseau bien entraîné, nous caractérisons l'émission d'une cible de SIGNALS : la galaxie M33. Nos résultats indiquent que notre algorithme surpasse les méthodes standards et s'adapte bien aux procédures d'ajustement spectral. En outre, notre méthodologie augmente la vitesse des calculs par plus d'un ordre de grandeur. Bien que l'algorithme soit entraîné spécifiquement pour SITELLE, on peut faire de petites modifications pour l'utiliser avec des autres interféromètres tels que MUSE et ses futurs analogues dans la prochaine génération de télescopes. Notez que j'ai mené à titre de premier auteur deux des trois articles présentés dans ce mémoire et apporté des contributions majeures au troisième. Les trois articles ont déjà été acceptés pour publication ou ont déjà été soumis et révisés une fois. / Galaxy clusters are one of the largest structures in the universe and host several complex physical phenomena. Although a wealth of knowledge already exists on their formation and evolution, the recent advent of machine learning in the astronomical sciences has allowed us to probe questions heretofore unanswered. While this thesis does focus heavily on the application of machine learning techniques to X-ray observations of galaxy clusters, it takes the techniques applied there to galaxy cluster's smaller counterparts: the individual galaxies themselves. Although the three papers presented here focus on distinct physics, scales, and techniques, they all form a basis of studies that I will continue during my doctorate: using new techniques to probe the underlying physics of galactic and extragalactic regions. The first paper introduced is a study of a galaxy cluster near the beginning of the epoch of cluster formation exhibiting peculiar attributes such as an elevated stellar formation rate (∽ 900M⊙/yr). In this paper, we employ traditional techniques in X-ray astronomy to determine whether or not the prodigious formation rate is due to an uninhibited cooling core. Since the object is so distant (z=1.7), we must execute our calculations with relatively few photons and thus rely on proxy measures. We determine that there exists a strong cooling flow offset by over 50 kpc from the central galaxy. Because of this offset, the AGN is not fueled and thus fails to heat up the cooling flow. This is the first example of a galaxy cluster in which we observe the failure of AGN feedback. Additionally, this provides another mechanism for the creation of intracluster light. The second article presented here focuses on X-ray emission from the hot intra-cluster medium within the galaxy cluster. We develop a comprehensive method for determining the number of underlying thermal components in the plasma's spectra. Our novel technique relies on a combination of an unsupervised learning algorithm (principal component analysis) and a supervised learning algorithm (random forest classification). We create a set of 100,000 realistic mock Chandra observations of the hot X-ray emitting gas in nearby galaxy clusters. After reducing our synthetic training set to its 25 most important principal components, we trained a random forest classifier to distinguish between the number of underlying thermal components. After successful testing and hyperparameter optimization, we applied the methodology third paper featured in this thesis once again employs machine learning to solve a previously undetermined question necessary for the accurate characterization of the kinematics of the warm gas in galaxies. We constructed a convolutional neural network to estimate the velocity and broadening parameters from the optical spectra of emission-line nebula and applied it to synthetic data replicating real SITELLE observations from the SIGNALS program. With a trained and optimized network in hand, we apply our methodology to a target of the SIGNALS program: the galaxy M33. Our results indicate our algorithm out-performs previous methods and nicely complements spectral fitting procedures. Moreover, the methodology increases calculation speeds by more than an order of magnitude -- thus greatly reducing the time needed to determine the kinematic parameters. Although the algorithm was trained for SITELLE data, this method can be readily ported to other IFUs such as MUSE. I have led two of the papers presented in this memoire and made major contributions to the third. All three papers have been either accepted for publication or have already been submitted and revised once. to the Perseus Cluster. In addition to creating a map of the cluster indicating the number of thermal components required to accurately model the thermal emission, we developed a suite of analysis routines to determine the temperatures of the underlying components. Our results are in agreement with over a decade of studies on the Perseus cluster and indicate that multiple temperature components are required for the accurate study of its intracluster medium.

Researchers perform multivariate techniques MANOVA, discriminant analysis and factor analysis. The most common applications in social science are to identify and test the effects from the analysis. The use of this multivariate technique is uncommon in investigating the effects of power usage and Province in South Africa on the amounts of the five power modes. This dissertation discusses this issue, the methodology and practical problems of the three multivariate techniques. The author examines the applications of each technique in social public research and comparisons are made between the three multivariate techniques. This dissertation concludes with a discussion of both the concepts of the present multivariate techniques and the results found on the use of the three multivariate techniques in the energy household consumption. The author recommends focusing on the hypotheses of the study or typical questions surrounding of each technique to guide the researcher in choosing the appropriate analysis in the social research, as each technique has some strengths and limitations. / Statistics / M. Sc. (Statistics)