Thèses : « Composition Data Analysis »

1

Morais, Joanna. « Impact of media investments on brands’ market shares : a compositional data analysis approach ». Thesis, Toulouse 1, 2017. http://www.theses.fr/2017TOU10040/document.

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L’objectif de cette thèse CIFRE, réalisée avec la société d’études de marché BVA en collaboration avec le constructeur automobile Renault, est de mesurer l’impact des investissements media pour différents canaux (télévision, affichage, etc.) sur les parts de marché de différentes marques, en prenant en compte la concurrence et les potentiels effets croisés et synergies entre ces marques, ainsi qu’en tenant compte du prix des véhicules, du contexte réglementaire (i.e. prime à la casse), et des effets retard de la publicité.Nous avons puisé dans les littératures marketing et statistique pour développer, comparer et interpréter plusieurs modèles qui respectent la contrainte de somme unitaire des parts de marché. Une application concrète au marché automobile français est présentée, pour laquelle nous montrons que les parts de marché des marques sont plus ou moins sensibles aux investissements publicitaires consentis dans chaque canal, et qu’il existe de synergies entre certaines marques
The aim of this CIFRE thesis, realized with the market research institute BVA in collaboration with the automobile manufacturer Renault, is to build a model in order to measure the impact of media investments of several channels (television, outdoor, etc.) on the brands’ market shares, taking into account the competition and the potential cross effects and synergies between brands, as well as accounting for the price, the regulatory context (scrapping incentive), and the lagged effects of advertising. We have drawn from marketing and statistical literatures to develop, compare and interpret several models which respect the unit sum constraint of market shares. A practical application to the French automobile market is presented, for which it is shown that brands’ market shares are more or less sensitive to advertising investments made in each channel, and that synergies between brands exist

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2

Boenn, Georg. « Automated analysis and transcription of rhythm data and their use for composition ». Thesis, University of Bath, 2011. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.538138.

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Filonik, Daniel. « Participatory data analytics : Designing visualisation and composition interfaces for collaborative sensemaking on large interactive screens ». Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/110597/1/Daniel_Filonik_Thesis.pdf.

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This thesis proposes the development of interfaces to support collaborative, community-led inquiry into data, which is referred to as Participatory Data Analytics. Over the course of multiple iterations spanning across three use cases, we developed a novel visualisation interface named DataChopin. Its distinctive characteristics are the use of large-scale, vertical displays as a shared desktop, along with natural, touch-based interactions for incremental construction of visualisations. The evaluation of this prototype has yielded recommendations for participatory research practitioners and designers of data exploration interfaces. In doing so, this research takes steps towards greater accessibility and democratisation of data analytics capabilities.

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MAARADJI, Abderrahmane. « End-user service composition from a social networks analysis perspective ». Phd thesis, Institut National des Télécommunications, 2011. http://tel.archives-ouvertes.fr/tel-00762647.

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Service composition has risen from the need to make information systems more flexible and open. The Service Oriented Architecture has become the reference architecture model for applications carried by the impetus of Internet (Web). In fact, information systems are able to expose interfaces through the Web which has increased the number of available Web services. On the other hand, with the emergence of the Web 2.0, service composition has evolved toward web users with limited technical skills. Those end-users, named Y generation, are participating, creating, sharing and commenting content through the Web. This evolution in service composition is translated by the reference paradigm of Mashup and Mashup editors such as Yahoo Pipes! This paradigm has established the service composition within end users community enabling them to meet their own needs, for instance by creating applications that do not exist. Additionally, Web 2.0 has brought also its social dimension, allowing users to interact, either directly through the online social networks or indirectly by sharing, modifying content, or adding metadata. In this context, this thesis aims to support the evolving concept of service composition through meaningful contributions. The main contribution of this thesis is indeed the introduction of the social dimension within the process of building a composite service through end users' dedicated environments. In fact, this concept of social dimension considers the activity of compositing services (creating a Mashup) as a social activity. This activity reveals social links between users based on their similarity in selecting and combining services. These links could be an interesting dissemination means of expertise, accumulated by users when compositing services. In other terms, based on frequent composition patterns, and similarity between users, when a user is editing a Mashup, dynamic recommendations are proposed. These recommendations aim to complete the initial part of Mashup already introduced by the user. This concept has been explored through (i) a step-by-step Mashup completion by recommending a single service at each step, and (ii) a full Mashup completion approaches by recommending the whole sequence of services that could complete the Mashup. Beyond pushing a vision for integrating the social dimension in the service composition process, this thesis has addressed a particular constraint for this recommendation system which conditions the interactive systems requirements in terms of response time. In this regard, we have developed robust algorithms adapted to the specificities of our problem. Whereas a composite service is considered as a sequence of basic service, finding similarities between users comes first to find frequent patterns (subsequences) and then represent them in an advantageous data structure for the recommendation algorithm. The proposed algorithm FESMA, provide exactly those requirements based on the FSTREE structure with interesting results compared to the prior art. Finally, to implement the proposed algorithms and methods, we have developed a Mashup creation framework, called Social Composer (SoCo). This framework, dedicated to end users, firstly implements abstraction and usability requirements through a workflow-based graphic environment. As well, it implements all the mechanisms needed to deploy composed service starting from an abstract description entered by the user. More importantly, SoCo has been augmented by including the dynamic recommendation functionality, demonstrating by the way the feasibility of this concept.

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Bryant, Donald. « ANALYSIS OF KOLMOGOROV'S SUPERPOSITION THEOREM AND ITS IMPLEMENTATION IN APPLICATIONS WITH LOW AND HIGH DIMENSIONAL DATA ». Doctoral diss., University of Central Florida, 2008. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2578.

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In this dissertation, we analyze Kolmogorov's superposition theorem for high dimensions. Our main goal is to explore and demonstrate the feasibility of an accurate implementation of Kolmogorov's theorem. First, based on Lorentz's ideas, we provide a thorough discussion on the proof and its numerical implementation of the theorem in dimension two. We present computational experiments which prove the feasibility of the theorem in applications of low dimensions (namely, dimensions two and three). Next, we present high dimensional extensions with complete and detailed proofs and provide the implementation that aims at applications with high dimensionality. The amalgamation of these ideas is evidenced by applications in image (two dimensional) and video (three dimensional) representations, the content based image retrieval, video retrieval, de-noising and in-painting, and Bayesian prior estimation of high dimensional data from the fields of computer vision and image processing.
Ph.D.
Department of Mathematics
Sciences
Mathematics PhD

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Allain, James D. « Comparison of nutrient data obtained through laboratory analysis with results generated by diet analysis software programs to determine a valid method for evaluating the nutrient content of select menu items at Pizza King, Inc ». Virtual Press, 2005. http://liblink.bsu.edu/uhtbin/catkey/1327788.

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The purpose of this research study was to test the validity of three diet analysis programs — Food Processor, Diet Analysis Plus 7.0 Online, and NutritionData.com — by comparing results of each program to the nutrient data from an independent food science lab for menu items served at Pizza King, Inc. restaurants.NutritionData.com had a slightly higher positive correlation, as well as more nutrient values within 10% of the chemical analyses, than ESHAs Food Processor. However, due to several ancillary factors (e.g., limited data base, inability to save laboratory-obtained data into a permanent database, and the minimal cost for the program for a company the size of Pizza King), this researcher has recommended ESHA Food Processor to Pizza King for the current and future analyses of their menu items. Diet Analysis Plus 7.0 Online could not be tested because the program did not generate analyzable results.
Department of Family and Consumer Sciences

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Maaradji, Abderrahmane. « End-user service composition from a social networks analysis perspective ». Thesis, Evry, Institut national des télécommunications, 2011. http://www.theses.fr/2011TELE0028/document.

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Le paradigme de service dans les nouvelles technologies de l’information et de communication est omniprésent, si bien qu’on parle de science des services. Les services Web sont définis dans le cadre des architectures orientées services (SOA) qui permet de distinguer le fournisseur de service, le répertoire de services, et enfin le consommateur du service. Cette distinction permet de créer de nouveaux services en composant des services déjà existants. Cependant, la composition de services est principalement bénéfique aux utilisateurs expérimentés comme les développeurs de logiciels car elle requiert un niveau technique élevé. Par opposition, la tendance actuelle traduite par l’émergence du Web2.0, vise à permettre aux utilisateurs du Web de créer leurs propres services à travers les environnements de Mashup, ou de collaborer et de capitaliser des connaissances à travers les réseaux et les médias sociaux. Nous croyons qu’il existe un grand potentiel pour “démocratiser” la composition de services dans de tels contextes. L’émergence du Web 2.0, basé sur des paradigmes tels que le contenu généré par l’utilisateur (UGC, Mashups) et le web social, constitue, une opportunité intéressante pour améliorer la productivité de services par l’utilisateur final et accélérer son processus créatif en capitalisant les connaissances générées par tous les utilisateurs. Dans ce contexte, cette thèse vise à soutenir l'évolution du concept de composition de services par le biais de contributions significatives. La principale contribution de cette thèse est en effet l'introduction de la dimension sociale dans le processus de construction d'un service composite à travers les environnements dédiés aux utilisateurs finaux. Ce concept considère l'activité de composition de services (création d'un Mashup) comme une activité sociale. Cette activité révèle les liens sociaux entre les utilisateurs en fonction de leur similitude dans le choix et la combinaison des services. Ces liens permettent de diffuser d'expertise de composition de services. En d'autres termes, sur la base des schémas fréquents de composition, et la similitude entre les utilisateurs, lorsqu’un utilisateur est en train d’éditer un Mashup, des recommandations dynamiques lui sont proposées. Ces recommandations visent à compléter la première partie de Mashup déjà mis en place par l'utilisateur. Ce concept a été exploré à travers (i) la complétion de Mashup étape par étape en recommandant à chaque étape un service unique, et (ii) la complétion totale de Mashup en recommandant la séquence complète de services qui pourraient le compléter. Au-delà de l’introduction de la dimension sociale dans le processus de composition de services, cette thèse a adressé une contrainte particulière du système de recommandation liée aux exigences des systèmes interactifs en termes de temps de réponse. À cet égard, nous avons développé des algorithmes robustes et adaptées aux spécificités de notre problème. Alors qu’un service composite est considéré comme une séquence de service, la recherche de similarités entre les utilisateurs revient d'abord à trouver des modèles fréquents, puis de les représenter dans une structure de données avantageuse pour l'algorithme de recommandation. L’algorithme proposé FESMA répond à ces exigences en se basant sur la structure FSTREE et offrant des résultats intéressants par rapport à l'art antérieur. Enfin, pour mettre en œuvre les algorithmes et les méthodes proposées, nous avons développé un environnement de création de Mashup, appelé ‘Social Composer’ (SoCo). Cet environnement, dédié aux utilisateurs finaux, respecte les critères d'utilisation en se basant sur le workflow graphique. En outre, il met en œuvre tous les mécanismes nécessaires pour déployer le service composé à partir d'une description abstraite introduite par l'utilisateur. De plus, SoCo a été augmentée en y incluant la fonctionnalité de recommandation dynamique, démontrant la faisabilité de ce concept
Service composition has risen from the need to make information systems more flexible and open. The Service Oriented Architecture has become the reference architecture model for applications carried by the impetus of Internet (Web). In fact, information systems are able to expose interfaces through the Web which has increased the number of available Web services. On the other hand, with the emergence of the Web 2.0, service composition has evolved toward web users with limited technical skills. Those end-users, named Y generation, are participating, creating, sharing and commenting content through the Web. This evolution in service composition is translated by the reference paradigm of Mashup and Mashup editors such as Yahoo Pipes! This paradigm has established the service composition within end users community enabling them to meet their own needs, for instance by creating applications that do not exist. Additionally, Web 2.0 has brought also its social dimension, allowing users to interact, either directly through the online social networks or indirectly by sharing, modifying content, or adding metadata. In this context, this thesis aims to support the evolving concept of service composition through meaningful contributions. The main contribution of this thesis is indeed the introduction of the social dimension within the process of building a composite service through end users’ dedicated environments. In fact, this concept of social dimension considers the activity of compositing services (creating a Mashup) as a social activity. This activity reveals social links between users based on their similarity in selecting and combining services. These links could be an interesting dissemination means of expertise, accumulated by users when compositing services. In other terms, based on frequent composition patterns, and similarity between users, when a user is editing a Mashup, dynamic recommendations are proposed. These recommendations aim to complete the initial part of Mashup already introduced by the user. This concept has been explored through (i) a step-by-step Mashup completion by recommending a single service at each step, and (ii) a full Mashup completion approaches by recommending the whole sequence of services that could complete the Mashup. Beyond pushing a vision for integrating the social dimension in the service composition process, this thesis has addressed a particular constraint for this recommendation system which conditions the interactive systems requirements in terms of response time. In this regard, we have developed robust algorithms adapted to the specificities of our problem. Whereas a composite service is considered as a sequence of basic service, finding similarities between users comes first to find frequent patterns (subsequences) and then represent them in an advantageous data structure for the recommendation algorithm. The proposed algorithm FESMA, provide exactly those requirements based on the FSTREE structure with interesting results compared to the prior art. Finally, to implement the proposed algorithms and methods, we have developed a Mashup creation framework, called Social Composer (SoCo). This framework, dedicated to end users, firstly implements abstraction and usability requirements through a workflow-based graphic environment. As well, it implements all the mechanisms needed to deploy composed service starting from an abstract description entered by the user. More importantly, SoCo has been augmented by including the dynamic recommendation functionality, demonstrating by the way the feasibility of this concept

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Kagundu, Paul. « The Quality of Governance, Composition of Public Expenditures, and Economic Growth : An Empirical Analysis ». unrestricted, 2006. http://etd.gsu.edu/theses/available/etd-07192006-184035/.

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Thesis (Ph. D.)--Georgia State University, 2006.
Title from title screen. Jorge L. Martinez-Vazquez, committee chair; James R. Alm, Roy W. Bahl, Mary Beth Walker, Neven T. Valev, Martin F. Grace, committee members. Electronic text (150 p.) : digital, PDF file. Description based on contents viewedAug. 17, 2007. Includes bibliographical references (p. 139-148).

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Ba, Mouhamadou. « Composition guidée de services : application aux workflows d’analyse de données en bio-informatique ». Thesis, Rennes, INSA, 2015. http://www.theses.fr/2015ISAR0024/document.

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Dans les domaines scientifiques, particulièrement en bioinformatique, des services élémentaires sont composés sous forme de workflows pour effectuer des expériences d’analyse de données complexes. À cause de l’hétérogénéité des ressources, la composition de services est une tâche difficile. Les utilisateurs, en composant des workflows, manquent d’assistance pour retrouver et interconnecter les services compatibles. Les solutions existantes utilisent des services spéciaux définis de manière manuelle pour gérer les conversions de formats de données entre les entrées et sorties des services dans les workflows. Cela est pénible pour un utilisateur final. Gérer les incompatibilités des services avec des convertisseurs manuels prend du temps et est lourd. Il existe des solutions automatisées pour faciliter la composition de workflows mais elles sont généralement limitées dans le guidage et l’adaptation des données entre services. La première contribution de cette thèse propose de détecter systématiquement la convertibilité des sorties vers les entrées des services. La détection de convertibilité repose sur un système de règles basé sur une abstraction des types d’entrée et sortie des services. L’abstraction de types permet de considérer la nature et la composition des données d’entrée et sortie. Les règles permettent la décomposition et la composition ainsi que la spécialisation et la généralisation de types. Elles permettent également de générer des convertisseurs de données à utiliser entre services dans les workflows. La deuxième contribution propose une approche interactive qui permet de guider des utilisateurs à composer des workflows en fournissant des suggestions de services et de liaisons compatibles basées sur la convertibilité de types d’entrée et sortie des services. L’approche est basée sur le modèle des Systèmes d’Information Logiques (LIS) qui permettent des requêtes et une navigation guidées et sûres sur des données représentées avec une logique uniforme. Avec notre approche, la composition de workflows est sûre et complète vis-à-vis de propriétés désirées. Les résultats et les expériences, effectués sur des services et des types de données en bioinformatique, montrent la pertinence de nos approches. Nos approches offrent des mécanismes adaptés pour gérer les incompatibilités de services dans les workflows, en prenant en compte la structure composite des données d’entrée et sortie. Elles permettent également de guider, étape par étape, des utilisateurs à définir des workflows bien formés à travers des suggestions pertinentes
In scientific domains, particularly in bioinformatics, elementary services are composed as workflows to perform complex data analysis experiments. Due to the heterogeneity of resources, the composition of services is a difficult task. Users, when composing workflows, lack assistance to find and interconnect compatible services. Existing solutions use special services manually defined to manage data format conversions between the inputs and outputs of services in workflows, it is difficult for an end user. Managing service incompatibilities with manual converters is time-consuming and heavy. There are automated solutions to facilitate composing workflows but they are generally limited in the guidance and the data adaptation between services they offer. The first contribution of this thesis proposes to systematically detect convertibility from outputs to inputs of services. Convertibility detection relies on a rule system based on an abstraction of input and output types of services. Type abstraction enables to consider the nature and the composition of input and output data. Rules enable decomposition and composition as well as specialization and generalization of types. They also enable to generate data converters to use between services in workflows. The second contribution proposes an interactive approach that enables to guide users to compose workflows by providing suggestions of compatible services and links based on convertibility of input and output types of services. The approach is based on the framework of Logical Information Systems (LIS) that enables safe and guided requests and navigation on data represented with a uniform logic. With our approach, composition of workflows is safe and complete w.r.t. desired properties. The results and experiences, conducted on bioinformatics services and datatypes, show the relevance of our approaches. Our approaches offer adapted mechanisms to manage service incompatibilities in workflows, by taking into account the composite structure of inputs and outputs data. They enable to guide, step by step, users to define well-formed workflows through relevant suggestions

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Zambrana, Prado Natalia. « Spectroscopic diagnostics of the elemental composition of the solar corona ». Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASP063.

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Un des objectifs principaux de la mission Solar Orbiter est d'établir un lien entre l'activité à la surface du Soleil et l'évolution de la couronne et de l'héliosphère interne. Le satellite emporte à cette fin une combinaison unique d'instruments permettant de faire tant des mesures in situ du plasma héliosphérique que des observations à distance du Soleil. Ces mesures nous permettront par exemple de déterminer la région source du vent solaire mesuré in-situ au niveau du satellite. Un outil essentiel pour établir un tel lien sont les mesures de composition. En effet, différentes structures solaires sont caractérisées par des abondances d'éléments chimiques différentes, en raison de l'effet FIP (premier potentient d'ionisation). Comparer les mesures de composition in situ et à distance, en lien avec la modélisation de l'effet FIP, nous permettra de déterminer les sources du plasma héliosphérique. Lors de la thèse, j'ai développé une nouvelle méthode de mesure d'abondances relatives de la couronne solaire grâce à la spectroscopie UV, la Linear Combination Ratio (LCR) method. Cette méthode peut être peu coûteuse en télémétrie tout en restant fiable; elle se base sur des combinaisons linéaires optimisées de raies spectrales. Cette méthode a été testée sur des spectres synthétiques et sur des données d'observations spectroscopiques. Grâce à une approche bayésienne, j'ai ensuite développé une manière de déterminer les incertitudes liées aux mesures obtenues avec la méthode LCR. Une des applications de la méthode fut de fournir des mesures de composition élémentaire fiables dans le cadre d'une collaboration dont le but est de trouver les caractéristiques du plasma et la région source d'un jet. La propagation dans la couronne et dans le milieu héliosphérique du jet a été ensuite modélisée pour déterminer sa composition in situ et s'il a atteint 1 UA. L'ensemble des méthodes et des outils nécessaires au travail de la thèse ont été développés avec la mission Solar Orbiter (lancée en février 2020) en tête. J'ai modélisé le bruit que nous obtiendrons dans les observations de SPICE et j'ai fourni trois ensembles de raies spectrales qui pourront être utilisés pour faire des mesures de composition. Ces trois ensembles seront utilisés pour concevoir des observations optimales de SPICE pour la production de cartes d'abondance coronales
Linking solar activity on the surface and in the corona to the inner heliosphere is one of the main goals of Solar Orbiter. Its unique combination of in-situ and remote sensing instruments can be used to shed light on this difficult task by, e.g., determining the source region of the solar wind measured in-situ at the spacecraft position. A key element in this are data on the elemental composition. Indeed, different structures on the Sun have different abundances as a consequence of the FIP (First Ionization Potential) effect. Comparing in-situ and remote sensing composition data, coupled with modeling, will allow us to trace back the source of heliospheric plasma. During my thesis, I developed a new method for measuring relative abundances of the solar corona using UV spectroscopy, the Linear Combination Ratio (LCR) method. This method can be telemetry efficient while remaining reliable; it is based on optimized linear combinations of spectral lines. This method has been tested on synthetic spectra and on spectroscopic observation data. Using a Bayesian approach, I then developed a way to determine the uncertainties related to the measurements obtained with the LCR method. One of the applications of the method was to provide reliable measurements of elemental composition in the framework of a collaboration whose goal is to find the characteristics of the plasma and the source region of a jet, a jet whose propagation in the corona and in the heliospheric medium will then be modeled to determine its composition in situ and whether it has reached 1 AU. All the methods and tools necessary for the thesis work have been developed with the Solar Orbiter mission (launched in February 2020) in mind. I have modeled the noise that we will obtain in the SPICE observations and I have provided three sets of spectral lines that could in principle be used to make composition measurements and that will be used to design optimal SPICE studies for abundance maps

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Siepka, Damian. « Development of multidimensional spectral data processing procedures for analysis of composition and mixing state of aerosol particles by Raman and FTIR spectroscopy ». Thesis, Lille 1, 2017. http://www.theses.fr/2017LIL10188/document.

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Les méthodologies de traitement de données multidimensionnelles peuvent considérablement améliorer la connaissance des échantillons. Les techniques spectroscopiques permettent l’analyse moléculaire avancée d’échantillons variés et complexes. La combinaison des techniques spectroscopiques aux méthodes de chimiométrie trouve des applications dans de nombreux domaines. Les particules atmosphériques affectent la qualité de l’air, la santé humaine, les écosystèmes et jouent un rôle important dans le processus de changement climatique. L’objectif de cette thèse a été de développer des outils de chimiométrie, simples d’utilisation, permettant de traiter un grand nombre de données spectrales provenant de l’analyse d’échantillons complexes par microspectrométrie Raman (RMS) et spectroscopie d’absorption IRTF. Dans un premier temps, nous avons développé une méthodologie combinant les méthodes de résolution de courbes et d’analyse multivariée afin de déterminer la composition chimique d’échantillons de particules analysées par RMS. Cette méthode appliquée à l’analyse de particules collectées dans les mines en Bolivie, a ouvert une nouvelle voie de description des échantillons. Dans un second temps, nous avons conçu un logiciel facilement accessible pour le traitement des données IRTF et Raman. Ce logiciel inclue plusieurs algorithmes de prétraitement ainsi que les méthodes d’analyse multivariées adaptées à la spectroscopie vibrationnelle. Il a été appliqué avec succès pour le traitement de données spectrales enregistrées pour divers échantillons (particules de mines de charbon, particules biogéniques, pigments organiques)
Sufficiently adjusted, multivariate data processing methods and procedures can significantly improve the process for obtaining knowledge of a sample composition. Spectroscopic techniques have capabilities for fast analysis of various samples and were developed for research and industrial purposes. It creates a great possibility for advanced molecular analysis of complex samples, such as atmospheric aerosols. Airborne particles affect air quality, human health, ecosystem condition and play an important role in the Earth’s climate system. The purpose of this thesis is twofold. On an analytical level, the functional algorithm for evaluation of quantitative composition of atmospheric particles from measurements of individual particles by Raman microspectrocopy (RMS) was established. On a constructive level, the readily accessible analytical system for Raman and FTIR data processing was developed. A potential of a single particle analysis by RMS has been exploited by an application of the designed analytical algorithm based on a combination between a multicurve resolution and a multivariate data treatment for an efficient description of chemical mixing of aerosol particles. The algorithm was applied to the particles collected in a copper mine in Bolivia and provides a new way of a sample description. The new user-friendly software, which includes pre-treatment algorithms and several easy-to access, common multivariate data treatments, is equipped with a graphical interface. The created software was applied to some challenging aspects of a pattern recognition in the scope of Raman and FTIR spectroscopy for coal mine particles, biogenic particles and organic pigments

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Öqvist, Per-Olof. « Multivariate Data Analysis on (Ti,Al)N Arc-PVD coating process : MVDA of the growth parameters thickness, stress,composition, and cutting performance ». Thesis, Uppsala universitet, Oorganisk kemi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-448547.

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This diploma work was done at Seco Tools AB (SECO) in Fagersta and aimed to evaluate the possibility to model the relationship between deposition data, deposition properties and, cutting performance of a (Ti,Al)N coating on cutting inserts by applying the Multivariate Data Analysis (MVDA) modeling technique Partial Least Squares Projection to Latent Structures Modeling (PLS). Cathodic Arc Deposition (Arc-PVD) was the PVD technique focused on this study. The deposition technique that was focused on in this study was Cathodic Arc Deposition (Arc-PVD). For this purpose, two series of Arc-PVD coatings were manufactured. The first series aimed to generate a supervised explorative model for the deposition process. The second manufactured series was aimed to generate a batch-to-batch variation model of a deposition process. In the first supervised explorative model, the deposition parameters were set by a Design of Experiment (DOE) setup using a quarter factorial design with resolution III. In the second batch-to-batch model, the non-fixed deposition parameters and the cathode wear were monitored, and all other parameters were kept the same for every run. The results demonstrate good possibilities to model Arc-PVD coating properties and its performance in metal cutting with respect to the applied deposition parameters. The supervised explorative model confirmed previously established relationships, while the batch-to-batch model shows that variations between batches could be related to the wear of the cathode. This wear was shown to have a negative influence on the properties of the deposited coating.

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Schlosser, Joseph S., Rachel A. Braun, Trevor Bradley, Hossein Dadashazar, Alexander B. MacDonald, Abdulmonam A. Aldhaif, Mojtaba Azadi Aghdam, Ali Hossein Mardi, Peng Xian et Armin Sorooshian. « Analysis of aerosol composition data for western United States wildfires between 2005 and 2015 : Dust emissions, chloride depletion, and most enhanced aerosol constituents ». AMER GEOPHYSICAL UNION, 2017. http://hdl.handle.net/10150/626273.

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This study examines major wildfires in the western United States between 2005 and 2015 to determine which species exhibit the highest percent change in mass concentration on day of peak fire influence relative to preceding nonfire days. Forty-one fires were examined using the Environmental Protection Agency (EPA) Interagency Monitoring of Protected Visual Environments (IMPROVE) data set. Organic carbon (OC) and elemental carbon (EC) constituents exhibited the highest percent change increase. The sharpest enhancements were for the volatile (OC1) and semivolatile (OC2) OC fractions, suggestive of secondary organic aerosol formation during plume transport. Of the noncarbonaceous constituents, Cl, P, K, NO3-, and Zn levels exhibited the highest percent change. Dust was significantly enhanced in wildfire plumes, based on significant enhancements in fine soil components (i.e., Si, Ca, Al, Fe, and Ti) and PMcoarse (i.e., PM10-PM2.5). A case study emphasized how transport of wildfire plumes significantly impacted downwind states, with higher levels of fine soil and PMcoarse at the downwind state (Arizona) as compared to the source of the fires (California). A global model (Navy Aerosol Analysis and Prediction System, NAAPS) did not capture the dust influence over California or Arizona during this case event because it is not designed to resolve dust dynamics in fires, which motivates improved treatment of such processes. Significant chloride depletion was observed on the peak EC day for almost a half of the fires examined. Size-resolved measurements during two specific fires at a coastal California site revealed significant chloride reductions for particle aerodynamic diameters between 1 and 10 mu m.

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Ferreira, Daniela Souza 1978. « Aplicação de espectroscopia no infravermelho e análise multivariada para previsão de parâmetros de qualidade em soja e quinoa = Application of infrared spectroscopy and multivariate analysis to predict quality parameters in soybean and quinoa ». [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/254641.

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Orientadores: Juliana Azevedo Lima Pallone, Ronei Jesus Poppi
Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia de Alimentos
Made available in DSpace on 2018-08-22T02:38:43Z (GMT). No. of bitstreams: 1 Ferreira_DanielaSouza_D.pdf: 2148960 bytes, checksum: 45b2c46dd9d82dfd00f454c5a430d494 (MD5) Previous issue date: 2013
Resumo: A avaliação da qualidade nutricional de alimentos é realizada principalmente por meio da determinação dos componentes majoritários, conhecida como composição centesimal (umidade, proteína, cinza, lipídio, carboidrato e fibra). No entanto, os métodos tradicionais de análise são demorados e utilizam materiais, equipamentos e diversos reagentes químicos, que além de oferecerem risco ao analista, geram resíduos tóxicos. Diante disto, uma alternativa para a análise química de grãos, rápida, de baixo custo e sem uso de reagentes químicos é a espectroscopia na região do infravermelho. Visando atender a demanda do Brasil por pesquisas empregando espectroscopia no infravermelho para análise de alimentos, o objetivo desse trabalho foi avaliar a possibilidade de utilização das técnicas espectroscopia no infravermelho próximo (NIR), principalmente, e médio MIR, associadas à quimiometria, para previsão de parâmetros de qualidade da soja brasileira e quinoa da América do Sul. Para comparar a aplicação de NIR e MIR, amostras de soja provenientes do Paraná foram analisadas pelas duas técnicas para previsão da composição centesimal. Os erros relativos (E%) entre os valores de referência e os valores previstos pelos modelos de calibração PLS, foram pequenos tanto para o NIR como para o MIR, no entanto, os resultados sugerem o uso de NIR para previsão de lipídios (0,2 a 9,2%) e o uso de MIR para proteínas (0,2 a 5,6%), cinzas (0 a 5,0%) e umidade (0,1 a 2,0%). Posteriormente, foram construídos modelos de calibração PLS com NIR para previsão dos parâmetros de qualidade em soja moída e para a quinoa, grão inteiro e moído. Os melhores modelos de calibração para soja encontrados neste estudo foram para o conteúdo de proteína e umidade, com melhores coeficientes de determinação e raiz quadrada do erro médio quadrático de calibração (R2= 0,81, RMSEC = 0,58% e R2 = 0,80, RMSEC = 0,28%, respectivamente), contudo, a técnica mostrou capacidade adequada de predição para todos os parâmetros, incluindo lipídios, cinzas, carboidratos e fibras. Para amostras de quinoa, os espectros NIR foram inicialmente submetidos a uma análise de componentes principais (PCA) para tentar separá-las em grupos, de acordo com a origem geográfica destes grãos, os quais eram provenientes do Brasil, Bolívia e Peru. Duas componentes principais explicaram 98,2% do total da variância e três grupos foram observados na separação por PCA de acordo com o país de origem. A técnica de calibração por PLS produziu modelos adequados, que permitiu a quantificação da composição majoritária tanto para o grão inteiro como farinha de quinoa, mostrando boa correlação entre o valor previsto e o valor real, com R2 > 0,65 e RMSEC< 1,70%. Portanto, este estudo demonstra que a técnica de NIR é potencialmente útil como um método analítico não destrutivo para determinações rápidas e simples de constituintes alimentares, além de não necessitar nenhum tipo de preparo de amostra, já que os espectros dos grãos inteiros de quinoa forneceram bons resultados para previsão dos parâmetros estudados
Abstract: Evaluation of nutritional quality of food has been mainly performed by determination of major compounds, which is known as centesimal composition (moisture, protein, ash, lipid, carbohydrate and fiber). However, the traditional methods of analysis are time-consuming, use many materials and equipment, and also toxic reagents, that generate waste and are a risk for the analyst. Thus, infrared spectroscopy is an alternative to chemical analysis of grains, as it is a rapid, low cost technique and it does not use toxic reagents. In coming years, Brazilian researches using infrared for food analysis should increase, thus the objective of this work was to evaluate the possibility of application mainly of near-infrared (NIR) and mid-infrared (MIR) spectroscopy techniques coupled with chemometrics to predict quality parameters in Brazilian soybean and South America quinoa. In order to compare NIR and MIR techniques, the soybean group from Paraná (Brazil) was analyzed using both techniques to predict centesimal composition. The related errors (E%) between reference values and predicted values by partial least square (PLS) were low for both the NIR and the MIR. However, the results propose the use of NIR to predict lipid (E% of 0.2 to 9.2) content and the use of MIR to predict protein (E% of 0.2 to 5.6), ash (E% of 0 to 5.0), and moisture (E% of 0.1 to 2.0) contents. Subsequently, PLS regression models were constructed using NIR to predict quality parameters in ground soybean and quinoa, grain and ground. The best calibration models to soybean found in this study were the ones used to determine protein and moisture content (R2 = 0.81, RMSEP = 1.61% and R2 = 0.80, RMSEC = 1.55%, respectively). However, the technique shows high predictability for all parameters, including lipids, ash, carbohydrates and fibers, RMSECV of 0.40 to 2.30% and RMSEP 0.38 to 3.71%. For quinoa samples NIR spectra were obtained and principal component analysis (PCA) was applied to try to identify the geographic origin of quinoa samples, from Brazil, Peru and Bolivia. Two principal components explained 98.3% of the total variance and three groups were observed using PCA. The PLS models developed for the chemical composition showed that the proposed methodology produced adequate results, as whole grain as ground quinoa, with the graph of the real and predicted concentration having a coefficient of determination (R2) > 0.65 and RMSEC < 1.70%. The viability of the NIR technique with no waste generation, low cost, reduced time and no kind of sample preparation for replacing laborious methods of analysis was demonstrated because the results for grains were satisfactory
Doutorado
Ciência de Alimentos
Doutora em Ciência de Alimentos

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Serrà, Julià Joan. « Identification of versions of the same musical composition by processing audio descriptions ». Doctoral thesis, Universitat Pompeu Fabra, 2011. http://hdl.handle.net/10803/22674.

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This work focuses on the automatic identification of musical piece versions (alternate renditions of the same musical composition like cover songs, live recordings, remixes, etc.). In particular, we propose two core approaches for version identification: model-free and model-based ones. Furthermore, we introduce the use of post-processing strategies to improve the identification of versions. For all that we employ nonlinear signal analysis tools and concepts, complex networks, and time series models. Overall, our work brings automatic version identification to an unprecedented stage where high accuracies are achieved and, at the same time, explores promising directions for future research. Although our steps are guided by the nature of the considered signals (music recordings) and the characteristics of the task at hand (version identification), we believe our methodology can be easily transferred to other contexts and domains.
Aquest treball es centra en la identificació automàtica de versions musicals (interpretacions alternatives d'una mateixa composició: 'covers', directes, remixos, etc.). En concret, proposem dos tiupus d'estratègies: la lliure de model i la basada en models. També introduïm tècniques de post-processat per tal de millorar la identificació de versions. Per fer tot això emprem conceptes relacionats amb l'anàlisi no linial de senyals, xarxes complexes i models de sèries temporals. En general, el nostre treball porta la identificació automàtica de versions a un estadi sense precedents on s'obtenen bons resultats i, al mateix temps, explora noves direccions de futur. Malgrat que els passos que seguim estan guiats per la natura dels senyals involucrats (enregistraments musicals) i les característiques de la tasca que volem solucionar (identificació de versions), creiem que la nostra metodologia es pot transferir fàcilment a altres àmbits i contextos.

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Musaraj, Kreshnik. « Extraction automatique de protocoles de communication pour la composition de services Web ». Thesis, Lyon 1, 2010. http://www.theses.fr/2010LYO10288/document.

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La gestion des processus-métiers, des architectures orientées-services et leur rétro-ingénierie s’appuie fortement sur l’extraction des protocoles-métier des services Web et des modèles des processus-métiers à partir de fichiers de journaux. La fouille et l’extraction de ces modèles visent la (re)découverte du comportement d'un modèle mis en œuvre lors de son exécution en utilisant uniquement les traces d'activité, ne faisant usage d’aucune information a priori sur le modèle cible. Notre étude préliminaire montre que : (i) une minorité de données sur l'interaction sont enregistrées par le processus et les architectures de services, (ii) un nombre limité de méthodes d'extraction découvrent ce modèle sans connaître ni les instances positives du protocole, ni l'information pour les déduire, et (iii) les approches actuelles se basent sur des hypothèses restrictives que seule une fraction des services Web issus du monde réel satisfont. Rendre possible l'extraction de ces modèles d'interaction des journaux d'activité, en se basant sur des hypothèses réalistes nécessite: (i) des approches qui font abstraction du contexte de l'entreprise afin de permettre une utilisation élargie et générique, et (ii) des outils pour évaluer le résultat de la fouille à travers la mise en œuvre du cycle de vie des modèles découverts de services. En outre, puisque les journaux d'interaction sont souvent incomplets, comportent des erreurs et de l’information incertaine, alors les approches d'extraction proposées dans cette thèse doivent être capables de traiter ces imperfections correctement. Nous proposons un ensemble de modèles mathématiques qui englobent les différents aspects de la fouille des protocoles-métiers. Les approches d’extraction que nous présentons, issues de l'algèbre linéaire, nous permettent d'extraire le protocole-métier tout en fusionnant les étapes classiques de la fouille des processus-métiers. D'autre part, notre représentation du protocole basée sur des séries temporelles des variations de densité de flux permet de récupérer l'ordre temporel de l'exécution des événements et des messages dans un processus. En outre, nous proposons la définition des expirations propres pour identifier les transitions temporisées, et fournissons une méthode pour les extraire en dépit de leur propriété d'être invisible dans les journaux. Finalement, nous présentons un cadre multitâche visant à soutenir toutes les étapes du cycle de vie des workflow de processus et des protocoles, allant de la conception à l'optimisation. Les approches présentées dans ce manuscrit ont été implantées dans des outils de prototypage, et validées expérimentalement sur des ensembles de données et des modèles de processus et de services Web. Le protocole-métier découvert, peut ensuite être utilisé pour effectuer une multitude de tâches dans une organisation ou une entreprise
Business process management, service-oriented architectures and their reverse engineering heavily rely on the fundamental endeavor of mining business process models and Web service business protocols from log files. Model extraction and mining aim at the (re)discovery of the behavior of a running model implementation using solely its interaction and activity traces, and no a priori information on the target model. Our preliminary study shows that : (i) a minority of interaction data is recorded by process and service-aware architectures, (ii) a limited number of methods achieve model extraction without knowledge of either positive process and protocol instances or the information to infer them, and (iii) the existing approaches rely on restrictive assumptions that only a fraction of real-world Web services satisfy. Enabling the extraction of these interaction models from activity logs based on realistic hypothesis necessitates: (i) approaches that make abstraction of the business context in order to allow their extended and generic usage, and (ii) tools for assessing the mining result through implementation of the process and service life-cycle. Moreover, since interaction logs are often incomplete, uncertain and contain errors, then mining approaches proposed in this work need to be capable of handling these imperfections properly. We propose a set of mathematical models that encompass the different aspects of process and protocol mining. The extraction approaches that we present, issued from linear algebra, allow us to extract the business protocol while merging the classic process mining stages. On the other hand, our protocol representation based on time series of flow density variations makes it possible to recover the temporal order of execution of events and messages in the process. In addition, we propose the concept of proper timeouts to refer to timed transitions, and provide a method for extracting them despite their property of being invisible in logs. In the end, we present a multitask framework aimed at supporting all the steps of the process workflow and business protocol life-cycle from design to optimization.The approaches presented in this manuscript have been implemented in prototype tools, and experimentally validated on scalable datasets and real-world process and web service models.The discovered business protocols, can thus be used to perform a multitude of tasks in an organization or enterprise

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Phillips, Stephen Paul. « Discriminant Analysis of XRF Data from Sandstones of Like Facies and Appearance : A Method for Identifying a Regional Unconformity, Paleotopography,and Diagenetic Histories ». BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/3373.

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The placement of an unconformable surface within a stratal succession affects the interpreted thickness of units and sequences in contact with that surface. Unit thickness influences the interpretation of basin subsidence, paleotopography, diagenesis, and depositional style. Accurate placement of an unconformity results in true formational thicknesses for formations associated with that unconformity. True thicknesses aid in producing more precise surface to subsurface correlations, isopach maps, and paleogeographic maps. An unconformity may be difficult to identify in the stratal succession due to similar rocks above and below the unconformity and the presence of multiple candidate surfaces. Using statistical discriminant analysis of XRF data, formations bounding an unconformity can be discriminated by elemental composition which results in delineation of the associated unconformity. This discrimination is possible even for rocks that do not have significant differences in provenance if they have experienced distinct diagenetic histories. Elemental differences can be explained by quantity and type of cement. Three discriminant models were created. These models were tested with samples from three formations of similar facies, appearance, and provenance that are all associated with the same regional unconformity. All data, regardless of location, facies, or tectonic feature were used to create the first model. This model achieved moderate success by correctly classifying 80% of known samples. In a second model, data were grouped by facies trends. Separating the data by facies resulted in 94% of known samples being correctly classified. This model was most useful for delineation of an unconformity and discrimination of formations. A third model based solely on location or local tectonic feature produced the best results statistically. 96% of known samples were classified correctly. This third model does not compare locations to each other, thus making it less robust. This last model contributes by adding detail to interpretations made with the facies trend model.

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Vigo-Valentin, Alexander. « The Food Behavior Considerations, Physical Activity Behavior Patterns, and Body Composition Indices of Adolescents in Puerto Rico ». The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1219429985.

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Rousseau, Batiste. « Étude de la composition et des propriétés physiques de surface de la comète 67P/Churyumov-Gerasimenko : interprétation des données VIRTIS/Rosetta etmesure en réflectance d’analogues cométaires ». Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEO018/document.

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Lors de leur formation il y a 4,6 milliards d’années, les comètes ont intégré des matériaux transformés selon les conditions physiques et dynamiques du disque d’accrétion mais aussi une part de composés issus du milieu interstellaire. Parce qu’elles ont préservé leurs propriétés, étudier les comètes permet de mieux comprendre les conditions régnant dans le disque proto-planétaire entourant le jeune Soleil à une époque qui nous est inaccessible. Cela permet également de comprendre les différentes populations de comètes, leur processus de formation, leurs évolutions dynamiques, leur activité lorsqu’elles s’approchent du Soleil ou encore leur structure.La sonde européenne Rosetta a accompagné la comète 67P/Churyumov-Gerasimenko pendant deux ans. À son bord, une dizaine d’instruments ont permis d’étudier l’évolution de son activité, les gaz, la morphologie de surface ou les poussières parmi d’autres objectifs. VIRTIS est le spectromètre visible-infrarouge de Rosetta. Sa composante d’imagerie spectrale, VIRTIS-M, permet d’avoir accès à la dimension spatiale tout en bénéficiant d’une résolution spectrale modérée tandis que VIRTIS-H est un spectromètre ponctuel bénéficiant d’une plus grande résolution spectrale. Mon travail a reposé sur le traitement et l’analyse des données de ces instruments et se découpe en deux parties concentrées sur l’étude de la surface du noyau.La première est une analyse des paramètres spectraux et photométriques : albédo, pente spectrale, direction principale de la diffusion de la lumière par les particules, rugosité macroscopique. Dans une étude globale, j’ai mis en évidence les variations spatiales de certains de ces paramètres ; comparé les résultats issus de différents modèles ainsi que des deux instruments. J’ai ensuite déterminé localement ces paramètres, soulignant des différences selon le type de terrains ciblé. Ces études permettent de mieux comprendre les mécanismes liés à l’activité (dépôt/soulèvement de poussières, altération spatiale, variations de la teneur en glace) ou aux variations des propriétés de la surface (composition, texture).Le deuxième enjeu de cette thèse était de reproduire en laboratoire les observations réalisées par VIRTIS, et ce afin d’apporter des contraintes sur la composition et la texture de la surface. En collaboration avec l’IPAG de Grenoble j’ai donc mené des expériences consistant à produire des poudres très fines constituées de matériaux similaires à ceux que l’on suspecte d’être présents sur le noyau de 67P : matière organique (imitée par un charbon), silicates (olivine) et sulfures de fer (pyrite et pyrrhotite) sont ainsi tous observés dans les comètes ou leurs analogues. Je les ai ici broyés à des échelles micrométriques à nanométriques puis j’ai réalisé des mesures en réflectance dans la même gamme spectrale que VIRTIS. J’ai pu ainsi étudier les effets provoqués par les variations de la taille des grains, de la composition ou de la texture du mélange, mettant en avant des combinaisons reproduisant le spectre moyen de la comète. De manière générale, cette étude permet de mieux comprendre l’influence de matériaux rarement étudiés comme les sulfures de fer ainsi que le comportement spectral de poudres dont la taille des grains atteint un ordre de grandeur proche de celle de la longueur d’onde, ce qui est primordial dans l’étude des surfaces cométaires
During the Solar System formation, 4.6 billion years ago, comets accreted materials which have been transformed according to the physical and dynamical conditions of the accretion disk but also a part of components coming from the interstellar medium. By preserving a primordial composition, the study of comets allows us to better understand the conditions of the proto-planetary disk surrounding the young Sun of an epoch which is now inaccessible. Moreover, it consists also to understand the various comets populations, their formation process, dynamical and activity evolution as they inward and outward the Sun or their structure.The ESA/Rosetta mission followed the comet 67P/Churyumov-Gerasimenko during two years. A ten of instruments has been dedicated to the study of the evolution of its activity, gas release, surface morphology, dust and other objectives. VIRTIS is a visible/infrared spectrometer instrument. It is composed of VIRTIS-M, an imaging spectrometer which gives access to spatial information with moderate spectral resolution and VIRTIS-H, a point spectrometer with a higher spectral resolution. This study is based on the data analysis of VIRTIS instruments and is divided into two parts focused on the study of the nucleus surface.The first part is an analysis of the spectral and photometric parameters: albedo, spectral slope, the main direction of the light diffusion by particles, macroscopic roughness. In a global study, I highlighted the spatial variations of albedo and spectral slope; compared results derived from different models as well as from both instruments. Then, I determined these parameters locally, revealing differences between two types of terrains. This approach allows to better understand the mechanisms linked to the activity (dust drop-off/uprising, space weathering, ice content variation) and also to the surface properties (composition, texture).The second goal of the thesis is to reproduce in the laboratory the observations realized by VIRTIS to give constraints on the composition and texture of the surface. In collaboration with IPAG (Grenoble, France) I led experiments consisting of the production of very fine powders made of materials which look like those we suspect to be present on the nucleus of 67P: organic matter (mimicked by a coal), silicates (olivine) and iron sulfides (pyrite and pyrrhotite) are all observed on comets or their analogues. I ground them to micrometric to nanometric scales and I realized reflectance measurements in the same spectral range than VIRTIS. Then, I have been able to observe effects caused by the variations of the grain size, composition or texture of the mixture and to highlight combinations reproducing the mean comet VIRTIS spectrum. Finally, this work enables us understanding the influence of material poorly studied such as iron sulfides as well as the spectral behaviour of powders composed of grain sizes reaching an order of magnitude close to the wavelengths, which is essential in the study of cometary surfaces

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Wagner, Louis. « Precise nuclear data of the 14N(p,gamma)15O reaction for solar neutrino predictions ». Helmholtz-Zentrum Dresden-Rossendorf, 2018. https://tud.qucosa.de/id/qucosa%3A31122.

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The 14N(p,gamma)15O reaction is the slowest stage of the carbon-nitrogen-oxygen cycle of hydrogen burning and thus determines its reaction rate. Precise knowledge of its rate is required to improve the model of hydrogen burning in our sun. The reaction rate is a necessary ingredient for a possible solution of the solar abundance problem that led to discrepancies between predictions of the solar standard model and helioseismology. The solar 13N and 15O neutrino fluxes are used as independent observables that probe the carbon and nitrogen abundances in the solar core. This could settle the disagreement, if the 14N(p,gamma)15O reaction rate is known with high precision. After a review of several measurements its cross section was revised downward due to a much lower contribution by one particular transition, capture to the ground state in 15O. The evaluated total relative uncertainty is still 7.5%, in part due to an unsatisfactory knowledge of the excitation function over a wide energy range. The present work reports experimentally determined cross sections as astrophysical S-factor data at twelve energies between 0.357 - 1.292 MeV for the strongest transition, capture to the 6.79 MeV excited state in 15O with lower uncertainties than before and at ten energies between 0.479 - 1.202 MeV for the second strongest transition, capture to the ground state in 15O. In addition, an R-matrix fit is performed to estimate the impact of the new data on the astrophysical relevant energy range. The recently suggested slight S-factor enhancement at the Gamow window could not be confirmed and differences to previous measurements at energies around 1 MeV were observed. The present extrapolated zero-energy S-factors are S_6.79(0) = (1.19+-0.10) keV b and S_GS(0) = (0.25+-0.05) keV b and they are within the uncertainties consistent with values recommended by the latest review.
Die 14N(p,gamma)15O Reaktion ist die langsamste Phase im Bethe-Weizsäcker-Zyklus des Wasserstoffbrennens und bestimmt deshalb die Reaktionsrate des gesamten Zyklus. Präzise Werte für die Reaktionsrate sind notwendig um das Wasserstoffbrennen in unserer Sonne besser zu verstehen. Besonders das Problem widersprüchlicher Ergebnisse aus Vorhersagen des aktuellen Sonnenmodells und helioseismologischen Experimenten könnte durch genauer bekannte 14N(p,gamma)15O Reaktionsraten aufgelöst werden. Dafür soll der solare 13N und 15O Neutrinofluss von den beta+-Zerfällen als direkter Informationsträger über die Häufigkeit von Stickstoff und Kohlenstoff im Sonneninneren genutzt werden. Der für die Berechnung der Häufigkeiten benötigte Wirkungsquerschnitt der 14N(p,gamma)15O Reaktion wurde in einer Evaluation verschiedener Messungen reduziert, da der Anteil des direkten Protoneneinfang mit Übergang in den Grundzustand deutlich weniger zum gesamten Wirkungsquerschnitt beiträgt als zuvor angenommen. Die evaluierte relative Gesamtunsicherheit ist mit 7.5% dennoch hoch, was zu einem großen Teil an ungenügendem Wissen über die Anregungsfunktion in einem weiten Energiebereich liegt. In der vorliegenden Arbeit werden experimentell ermittelte Wirkungsquerschnitte in Form von astrophysikalischen S-Faktoren für zwei Übergänge vorgestellt. Für den stärksten Übergang, den Protoneneinfang zum angeregten Zustand bei 6.79 MeV in 15O, wurden zwölf S-Faktoren bei Energien zwischen 0.357 – 1.292 MeV mit geringeren Unsicherheiten als zuvor ermittelt und für den direkten Übergang in den Grundzustand zehn Werte zwischen 0.479 – 1.202 MeV. Außerdem wurde ein R-Matrix Fit durchgeführt um den Einfluss der neuen Daten auf Extrapolationen zum astrophysikalisch relevanten Energiebereich zu prüfen. Die kürzlich vorgeschlagene Erhöhung des S-Faktors im Gamow-Fenster konnte nicht bestätigt werden und es wurden auch Unterschiede zu bisherigen Messungen im Energiebereich um 1 MeV deutlich. Die neuen extrapolierten S-Faktoren sind S679(0) = (1.19±0.10) keV b und SGS(0) = (0.25 ± 0.05) keV b und sie stimmen mit den von der Evaluation empfohlenen Werten im Rahmen ihrer Unsicherheiten überein.

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陳志昌 et Chee-cheong Chan. « Compositional data analysis of voting patterns ». Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1993. http://hub.hku.hk/bib/B31977236.

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Brunsdon, T. M. « Time series analysis of compositional data ». Thesis, University of Southampton, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.378257.

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Chan, Chee-cheong. « Compositional data analysis of voting patterns ». [Hong Kong : University of Hong Kong], 1993. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13787160.

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Fuschi, Alessandro. « Compositional data analysis applied to human microbiome network reconstruction ». Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21711/.

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The comprehension of the human gut microbiome has been made possible by technological advances for performing culture-independent analyzes. Next Generation Sequencing techniques produce discrete counts as a result, describing only the relative abundances of each identified bacterial species: such data are of compositional type. Unfortunately the classic methods of analysis on this type of data can lead to completely wrong conclusions: the development of analysis methods for compositional data is still an open issue. The purpose of this work is the description of several analyzes based on compositional data of human gut microbiome. The first result, obtained with t-SNE dimensionality reduction algorithm, is that a different sample clustering was obtained based on the metrics used to define neighborhood. Next ,I applied a biomarker identification method based on the log-ratio variance, a statistical observable used with compositional data, that allowed to identify bacterial species associated to our case/control study design. In the last part I analyzed the problem of the reconstruction of networks of bacterial species. The main objective of the network analysis was to characterize microbiota ecosystem of healthy and infected subjects in our database. Several methods have been proposed to characterize the complex relationships between bacterial populations: in this work I applied the SPIEC-EASI method to reconstruct the correlation structure of the data, and compared the different results obtained. Finally, I propose a new method inspired by Kendall’s Tau correlation, adapted to the peculiarities of compositional data, that provided promising results. This research was made possible thanks to the collaboration with Prof. George Weinstock at the Jackson Laboratory research center (USA). One of the researche aims of JAX is the study and understanding of the gut microbiota for diagnostic purposes (in our case related to infection and diabetes).

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Xia, Fan, et 夏凡. « Some topics on statistical analysis of genetic imprinting data and microbiome compositional data ». Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/206673.

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Genetic association study is a useful tool to identify the genetic component that is responsible for a disease. The phenomenon that a certain gene expresses in a parent-of-origin manner is referred to as genomic imprinting. When a gene is imprinted, the performance of the disease-association study will be affected. This thesis presents statistical testing methods developed specially for nuclear family data centering around the genetic association studies incorporating imprinting effects. For qualitative diseases with binary outcomes, a class of TDTI* type tests was proposed in a general two-stage framework, where the imprinting effects were examined prior to association testing. On quantitative trait loci, a class of Q-TDTI(c) type tests and another class of Q-MAX(c) type tests were proposed. The proposed testing methods flexibly accommodate families with missing parental genotype and with multiple siblings. The performance of all the methods was verified by simulation studies. It was found that the proposed methods improve the testing power for detecting association in the presence of imprinting. The class of TDTI* tests was applied to a rheumatoid arthritis study data. Also, the class of Q-TDTI(c) tests was applied to analyze the Framingham Heart Study data. The human microbiome is the collection of the microbiota, together with their genomes and their habitats throughout the human body. The human microbiome comprises an inalienable part of our genetic landscape and contributes to our metabolic features. Also, current studies have suggested the variety of human microbiome in human diseases. With the high-throughput DNA sequencing, the human microbiome composition can be characterized based on bacterial taxa relative abundance and the phylogenetic constraint. Such taxa data are often high-dimensional overdispersed and contain excessive number of zeros. Taking into account of these characteristics in taxa data, this thesis presents statistical methods to identify associations between covariate/outcome and the human microbiome composition. To assess environmental/biological covariate effect to microbiome composition, an additive logistic normal multinomial regression model was proposed and a group l1 penalized likelihood estimation method was further developed to facilitate selection of covariates and estimation of parameters. To identify microbiome components associated with biological/clinical outcomes, a Bayesian hierarchical regression model with spike and slab prior for variable selection was proposed and a Markov chain Monte Carlo algorithm that combines stochastic variable selection procedure and random walk metropolis-hasting steps was developed for model estimation. Both of the methods were illustrated using simulations as well as a real human gut microbiome dataset from The Penn Gut Microbiome Project.
published_or_final_version
Statistics and Actuarial Science
Doctoral
Doctor of Philosophy

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SENEDA, JOSE A. « Separação e recuperação de chumbo-208 dos resíduos de tório terras raras gerados na unidade piloto de purificação de nitrato de tório ». reponame:Repositório Institucional do IPEN, 2006. http://repositorio.ipen.br:8080/xmlui/handle/123456789/11424.

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Tese (Doutoramento)
IPEN/T
Instituto de Pesquisas Energeticas e Nucleares - IPEN/CNEN-SP

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Jha, Rajesh. « Combined Computational-Experimental Design of High-Temperature, High-Intensity Permanent Magnetic Alloys with Minimal Addition of Rare-Earth Elements ». FIU Digital Commons, 2016. http://digitalcommons.fiu.edu/etd/2621.

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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.

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Rivera, Pinto Javier. « Statistical methods for the analysis of microbiome compositional data in HIV studies ». Doctoral thesis, Universitat de Vic - Universitat Central de Catalunya, 2018. http://hdl.handle.net/10803/665037.

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The human microbiome is involved in many essential functions, such as food digestion and immune system maintenance. Alterations in its composition may have important effects on human health and they have been associated to high impact diseases such as obesity, asthma, cancer or cardiovascular disease among others. This thesis is focused on the study of the link between the gut microbiome and HIV infection. The interest arises because of the important damages that the virus causes in the gut epithelium, which houses most of our immune system. Because of this damage, HIV patients present systemic and chronic inammation responsible of an increase in their risk of having non-AIDS related diseases. Thus, understanding how gut microbiome alterations after HIV infection are related to immune dysregulation is of major importance. The analysis of microbiome data is challenging. Since microbiome abundances are obtained from high-throughput DNA sequencing techniques, the total number of reads per sample is constrained by the maximum number of sequence reads that the DNA sequencer can provide. This total count constraint induces strong dependencies among the abundances of the different taxa and confers the compositional nature of microbiome data. This means that the abundance values are not informative by themselves and that the relevant information is contained in the ratios of abundances between the diferent taxa. Ignoring the compositional nature of microbiome data may have important negative effects, such as spurious correlations, subcompositional incoherences, and the increase of type I error. In this context, we have proposed two novel statistical methods for microbiome analysis that preserve the principles of compositional data analysis: MiRKAT-CoDA (weighted and unweighted) and selbal algorithm. MiRKAT-CoDA algorithm is a distance-based method for testing the overall association between microbial composition and a response variable of interest. It extends Kernel machine regression to compositional data analysis by considering a subcompositional dominant distance, such as Aitchison distance. The weighted version of MiRKAT-CoDA provides a measure of the contribution of each taxon to the global association with the response variable. selbal algorithm is a new approach for the identification of microbial signatures associated to an outcome. The approach is innovative because, instead of defining the microbial signature as a linear combination of a set of taxa abundances, it is defined as a balance between two groups of taxa, a mathematical notion that preserves the principles of compositional data analysis. In summary, the major contributions of this thesis are two new methodological strategies: MiRKAT-CoDA (weighted and unweighted) and selbal algorithm, for microbiome association testing and for the identification of microbiome signatures, respectively. Moreover, the results of this thesis have helped to advance the study of the role of the gut microbiome in HIV infection.
El microbioma humano participa en muchas funciones esenciales como la digestión de alimentos y el mantenimiento del sistema inmunitario. Alteraciones en su composición pueden afectar a la salud del individuo, habiendo sido relacionados cambios en el microbioma con enfermedades tales como obesidad, asma, cáncer o enfermedades cardiovasculares entre otras. Esta tesis está centrada en el estudio de la relación entre el microbioma intestinal y la infección por VIH. Este interés surge debido al importante daño que el VIH produce sobre el epitelio intestinal, el cuál contiene la mayor parte del sistema inmunitario. Debido a este daño, los pacientes infectados por VIH presentan una inflamación sistémica y crónica, responsable del incremento del riesgo de padecer enfermedades no relacionadas directamente con el SIDA. Así pues, resulta importante entender las alteraciones en el microbioma intestinal asociadas a la infección y patogénesis del VIH. El análisis de los datos de microbioma resulta todo un desafio desde el punto de vista estadístico. Dado que los datos de abundancia del microbioma se obtienen por técnicas de secuenciación del ADN, el número total de reads por muestra viene limitado por el número máximo de secuencias que puede proporcionar el secuenciador. Esta limitación en el número de reads genera fuertes dependencias entre las abundancias de las diferentes taxas y define la naturaleza composicional de este tipo de datos. Este hecho supone que los valores de abundancia no son informativos en sí mismos, sino que la información la proporcionan realmente los ratios entre distintas componentes. De ignorar la composicionalidad de los datos de abundancia microbiana, los resultados obtenidos pueden ser confusos e incoherentes. Así, pueden aparecer correlaciones espurias, incoherencias subcomposicionales o incluso un incremento de los falsos positivos a la hora de definir las diferencias entre distintos grupos de individuos. En este contexto, presentamos dos nuevas propuestas para el estudio del microbioma que preservan los principios del análisis de datos composicionales: los algoritmos MiRKAT-CoDA (ponderada y sin ponderar) y selbal. El algoritmo MiRKAT-CoDA es un método basado en distancias que permite evaluar si existe una asociación global entre la composición microbiana y una variable respuesta de interés. Este método es una extensión de la Kernel machine regression dentro del ámbito del análisis de datos composicionales, considerando una distancia subcomposicionalmente dominante como es la distancia de Atichison. La versión ponderada de MiRKAT- CoDA proporciona para cada variable un valor que mide la contribución de cada una de las taxas en la asociación global con la variable respuesta. Por otra parte, el algoritmo selbal es una nueva propuesta focalizada en la identificación de firmas microbianas asociadas a una variable de interés. El método es novedoso debido a que en lugar de definir la firma microbiana como una combinación lineal de un conjunto de variables, se define como un balance entre dos grupos de taxas, una noción matemática que preserva los principios del análisis de datos composiconales. En resumen, las mayores aportaciones de esta tesis son dos estrategias metodológicas diferentes: MiRKAT-CoDA (ponderada y sin ponderar) y selbal. Estas propuestas resultan útiles para evaluar la asociación entre microbioma y variable respuesta así como identifiar firmas microbianas, respectivamente. Además, los resultados de esta tesis han contribuido al avance en el estudio del papel que desempeña el microbioma intestinal en la infección por VIH.

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Biagi, Lyvia. « Condition assessment of patients with Type 1 diabetes using compositional data analysis ». Doctoral thesis, Universitat de Girona, 2019. http://hdl.handle.net/10803/667966.

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Type 1 Diabetes Mellitus (T1DM) is a chronic disease that leads to absolute insulin deficiency. People with T1DM need exogenous insulin to maintain glucose at proper levels. Achieving optimal glycemic control is a major hurdle due to the large intra-patient variability, and continuous glucose monitoring (CGM) plays an essential role for individuals with T1DM. This thesis is devoted to describe the condition assessment of patients with T1DM through the analysis of glucose data obtained from CGM. This work focuses on understanding and dissecting the CGM measures. A model of the error of a CGM sensor has been obtained and the accuracy of the CGM has been assessed during challenging conditions. Additionally, a novel approach for the categorization of daily glucose profiles based on the analysis of compositional data (CoDa) is proposed. Finally, a probabilistic model of transition between different categories of periods of glucose data obtained with CoDa analysis is presented.
La diabetes mellitus tipo 1 (T1DM) es una enfermedad crónica que conduce a una deficiencia absoluta de insulina. Las personas con T1DM requieren insulina exógena para mantener los niveles de glucosa apropiados. Alcanzar un control glicémico óptimo tiene una gran dificultad debido a la variabilidad intrapaciente, y el monitoreo continuo de glucosa (CGM) desempeña un papel esencial para los individuos con T1DM. Este trabajo se enfoca en entender y diseccionar las medidas obtenidas de CGM. Se ha obtenido un modelo de error de un sensor CGM y se ha evaluado la precisión del CGM en condiciones difíciles. Además, se presenta un nuevo enfoque para la caracterización de perfiles de glucosa diarios con base en el análisis de datos composicionales (CoDa). Finalmente, se presenta un modelo de transición probabilístico entre diferentes categorías de periodos de datos de glucosa que fue obtenido usando técnicas CoDa.

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Yamane, Danilo Ricardo [UNESP]. « Nutrient diagnosis of orange crops applying compositional data analysis and machine learning techniques ». Universidade Estadual Paulista (UNESP), 2018. http://hdl.handle.net/11449/180576.

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O manejo eficiente de nutrientes é crucial para atingir alta produtividade de frutos. Resultados da análise do tecido são comumente interpretados usando faixas críticas de concentração de nutrientes (CNCR) e Sistema Integrado de Diagnose e Recomendação (DRIS) em culturas de laranja. No entanto, ambos os métodos ignoram as propriedades inerentes à classe dos dados composicionais, não considerando adequadamente as interações de nutrientes e a influência varietal na composição nutricional da planta. Portanto, ferramentas eficazes de modelagem são necessárias para corrigir vieses e incorporar efeitos genéticos na avaliação do estado nutricional. O objetivo deste estudo foi desenvolver uma abordagem diagnóstica precisa para avaliar o estado nutricional de variedades de copa de laranjeira (Citrus sinensis), usando a análise composicional dos dados e algoritmos de inteligência artificial. Foram coletadas 716 amostras foliares de ramos frutíferos em pomares comerciais de laranjeiras não irrigadas (“Valência”, “Hamlin”, “Pera”, “Natal”, “Valencia Americana” e “Westin”) distribuídos pelo estado de São Paulo (Brasil), analisadas as concentrações de N, S, P, K, Ca, Mg, B, Cu, Zn, Mn e Fe, e avaliadas as produções de frutos. Balanços de nutrientes foram computados como relações-log isométricas (ilr). Análises discriminantes dos valores de ilr diferenciaram os perfis de nutrientes das variedades de copa, indicando composições nutricionais específicas. A acurácia diagnóstica dos balanços de nutrientes atingiu 88% com a produtividade de corte correspondente a 60 t ha-1, utilizando-se ilrs e o algoritmo de classificação knn, o que possibilitou o desenvolvimento de padrões nutricionais confiáveis para a obtenção de elevado nível de produtividade de frutos. Os citricultores do estado de São Paulo devem adotar o conceito de balanços de nutrientes, onde grupos de nutrientes estão equilibrados de maneira ideal. Fornecer mais Ca através de calcário ou gesso, reduzir as aplicações de fertilizantes P e K, e aumentar a fertilização de B via solo pode reequilibrar os balanços [Mg | Ca], [Ca, Mg | K], [P | N, S], [K, Ca, Mg | N, S, P] e [B | N, S, P, K, Ca, Mg] em pomares de laranjas com produtividade inferior a 60 t ha-1. O software “CND-Citros” pode auxiliar os citricultores, engenheiros agrônomos e técnicos a diagnosticar o estado nutricional das lavouras de laranja com base no método proposto, utilizando os resultados da análise química das folhas.
Efficient nutrient management is crucial to attain high fruit productivity. Results of tissue analysis are commonly interpreted using critical nutrient concentration ranges (CNCR) and Diagnosis and Recommendation Integrated System (DRIS) on orange crops. Nevertheless, both methods ignore the inherent properties of compositional data class, not accounting adequately for nutrient interactions and varietal influence on plant ionome. Therefore, effective modeling tools are needed to rectify biases and incorporate genetic effects on nutrient composition. The objective of this study was to develop an accurate diagnostic approach to evaluate the nutritional status across orange (Citrus sinensis) canopy varieties using compositional data analysis and machine learning algorithms. We collected 716 foliar samples from fruit-bearing shoots in plots of non-irrigated commercial orange orchards (“Valencia”, “Hamlin”, “Pera”, “Natal”, “Valencia Americana” and “Westin”) distributed across São Paulo state (Brazil), analyzed N, S, P, K, Ca, Mg, B, Cu, Zn, Mn and Fe, and measured fruit yields. Sound nutrient balances were computed as isometric log-ratios (ilr). Discriminant analysis of ilr values differentiated the nutrient profiles of canopy varieties, indicating plant-specific ionomes. Diagnostic accuracy of nutrient balances reached 88% about cutoff yield of 60 Mg ha-1 using ilrs and a k-nearest neighbors classification, allowing the development of reliable nutritional standards at high fruit yield level. Citrus growers from São Paulo state should adopt the concept of yield-limiting nutrient balances, where groups of nutrients are optimally balanced. Supplying more Ca as lime or gypsum materials, reducing the P and K fertilizer applications and enhancing soil B fertilization could re-establish the [Mg | Ca], [Ca, Mg | K], [P | N, S], [K, Ca, Mg | N, S, P] and [B | N, S, P, K, Ca, Mg] balances in orange orchards yielding less than 60 Mg ha-1. The software “CND-Citros” can assist citrus growers, agronomy engineers and technicians to diagnose the nutrient status of orange crops based on the proposed method, using the results of leaf chemical analysis.

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Yamane, Danilo Ricardo. « Nutrient diagnosis of orange crops applying compositional data analysis and machine learning techniques / ». Jaboticabal, 2018. http://hdl.handle.net/11449/180576.

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Orientador: Arthur Bernardes Cecílio Filho
Resumo: O manejo eficiente de nutrientes é crucial para atingir alta produtividade de frutos. Resultados da análise do tecido são comumente interpretados usando faixas críticas de concentração de nutrientes (CNCR) e Sistema Integrado de Diagnose e Recomendação (DRIS) em culturas de laranja. No entanto, ambos os métodos ignoram as propriedades inerentes à classe dos dados composicionais, não considerando adequadamente as interações de nutrientes e a influência varietal na composição nutricional da planta. Portanto, ferramentas eficazes de modelagem são necessárias para corrigir vieses e incorporar efeitos genéticos na avaliação do estado nutricional. O objetivo deste estudo foi desenvolver uma abordagem diagnóstica precisa para avaliar o estado nutricional de variedades de copa de laranjeira (Citrus sinensis), usando a análise composicional dos dados e algoritmos de inteligência artificial. Foram coletadas 716 amostras foliares de ramos frutíferos em pomares comerciais de laranjeiras não irrigadas (“Valência”, “Hamlin”, “Pera”, “Natal”, “Valencia Americana” e “Westin”) distribuídos pelo estado de São Paulo (Brasil), analisadas as concentrações de N, S, P, K, Ca, Mg, B, Cu, Zn, Mn e Fe, e avaliadas as produções de frutos. Balanços de nutrientes foram computados como relações-log isométricas (ilr). Análises discriminantes dos valores de ilr diferenciaram os perfis de nutrientes das variedades de copa, indicando composições nutricionais específicas. A acurácia diagnóstica dos balanços de... (Resumo completo, clicar acesso eletrônico abaixo)
Abstract: Efficient nutrient management is crucial to attain high fruit productivity. Results of tissue analysis are commonly interpreted using critical nutrient concentration ranges (CNCR) and Diagnosis and Recommendation Integrated System (DRIS) on orange crops. Nevertheless, both methods ignore the inherent properties of compositional data class, not accounting adequately for nutrient interactions and varietal influence on plant ionome. Therefore, effective modeling tools are needed to rectify biases and incorporate genetic effects on nutrient composition. The objective of this study was to develop an accurate diagnostic approach to evaluate the nutritional status across orange (Citrus sinensis) canopy varieties using compositional data analysis and machine learning algorithms. We collected 716 foliar samples from fruit-bearing shoots in plots of non-irrigated commercial orange orchards (“Valencia”, “Hamlin”, “Pera”, “Natal”, “Valencia Americana” and “Westin”) distributed across São Paulo state (Brazil), analyzed N, S, P, K, Ca, Mg, B, Cu, Zn, Mn and Fe, and measured fruit yields. Sound nutrient balances were computed as isometric log-ratios (ilr). Discriminant analysis of ilr values differentiated the nutrient profiles of canopy varieties, indicating plant-specific ionomes. Diagnostic accuracy of nutrient balances reached 88% about cutoff yield of 60 Mg ha-1 using ilrs and a k-nearest neighbors classification, allowing the development of reliable nutritional standards at high fruit... (Complete abstract click electronic access below)
Doutor

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Förstner, Konrad Ulrich. « Computational analysis of metagenomic data : delineation of compositional features and screens for desirable enzymes ». kostenfrei, 2008. http://www.opus-bayern.de/uni-wuerzburg/volltexte/2009/3357/.

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Lienhard, Jasper Z. (Jasper Zebulon). « What is measured is managed : statistical analysis of compositional data towards improved materials recovery ». Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/98661.

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Thesis: S.B., Massachusetts Institute of Technology, Department of Materials Science and Engineering, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 35-36).
As materials consumption increases globally, minimizing the end-of-life impact of solid waste has become a critical challenge. Cost-effective methods of quantifying and tracking municipal solid waste contents and disposal processes are necessary to drive and track increases in material recovery and recycling. This work presents an algorithm for estimating the average quantity and composition of municipal waste produced by individual locations. Mass fraction confidence intervals for different types of waste were calculated from data collected by sorting and weighing waste samples from municipal sites. This algorithm recognizes the compositional nature of mass fraction waste data. The algorithm developed in this work also evaluated the value of additional waste samples in refining mass fraction confidence intervals. Additionally, a greenhouse gas emissions model compared carbon dioxide emissions for different disposal methods of waste, in particular landfilling and recycling, based on the waste stream. This allowed for identification of recycling opportunities based on carbon dioxide emission savings from offsetting the need for primary materials extraction. Casework was conduced with this methodology using site-specific waste audit data from industry. The waste streams and carbon dioxide emissions of three categories of municipal waste producers, retail, commercial, and industrial, were compared. Paper and plastic products, whose mass fraction averages ranged from 40% to 52% and 26% to 29%, respectively, dominated the waste streams of these three industries. Average carbon dioxide emissions in each of these three industries ranged from 2.18 kg of CO₂ to 2.5 kg of CO₂ per kilogram of waste thrown away. On average, Americans throw away about 2 kilograms per person per day of solid waste.
by Jasper Z. Lienhard.
S.B.

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Nguyen, Thi Huong An. « Contribution to the statistical analysis of compositional data with an application to political economy ». Thesis, Toulouse 1, 2019. http://www.theses.fr/2019TOU10032/document.

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L’objectif de cette thèse est d’étudier le résultat d’élections et l’impact des facteurs socio-économiques sur les parts de vote dans le système multipartite d’un point de vue mathématique. Les votes de l’élection départementale en France en 2015 forment un vecteur appelé composition. Ainsi, le modèle de régression classique ne peut pas être utilisé directement pour modéliser ces parts de vote en raison de contraintes de données de composition. Au chapitre 2, nous présentons un modèle de régression dans lequel la variable dépendante est une variable de composition et les variables explicatives contiennent à la fois des variables classiques et des variables de composition. Nous analysons les impacts des facteurs socio-économiques sur l’issue de l’élection en prédisant les parts de vote en fonction d’une variable explicative classique ou d’une variable explicative de composition. Quelques techniques graphiques sont également présentées. Néanmoins, il serait plus judicieux d’interpréter les coefficients du modèle de régression sur le simplexe. Par ailleurs, certains auteurs montrent que les données électorales présentent souvent un comportement extrême. Nous proposons donc de remplacer la distribution Normale par la distribution de Student. Cependant, il existe deux versions de la distribution Student : la distribution Student non corrélée (UT) et la distribution Independent Student(IT). Dans la troisième partie, nous présentons un résumé complet de la distribution Student, comprenant les distributions Student univariée et multivariée, IT et UT à degrés de liberté fixes. Nous prouvons que l’estimateur de maximum de vraisemblance de la matrice de covariance dans le modèle UT est asymptotiquement biaisé. Nous fournissons également un algorithme itératif repondéré pour calculer l’estimateur du maximum de vraisemblance du paramètre du modèle IT. Une simulation est fournie et certains tests de Kolmogorov – Smirnov basés sur la distance de Mahalanobis sont effectués pour sélectionner le bon modèle. Cependant, cela ne fonctionne pas pour le modèle UT en raison d’une seule réalisation de n observations de la distribution multivariée. Enfin, nous étudions l’hypothèse d’indépendance statistique entre unités territoriales, qui peut être mise en doute du fait de l’autocorrélation spatiale potentielle des données de composition. Nous développons un modèle autorégressif spatial simultané pour les données de composition qui permet à la fois la corrélation spatiale et les corrélations entre équations en utilisant des méthodes de moindres carrés à deux étages et à trois étages. Nous présentons une étude de simulation pour illustrer ces méthodes. Une application à un ensemble de données de l’élection départementale française de 2015 est également présentée. Il reste encore du travail à faire pour surmonter le problème des zéros dans les parts de vote. Ce problème est déjà présent pour les élections départementales françaises au niveau cantonal lorsque l’on regroupe les partis électoraux en trois catégories. Cela aurait été encore plus grave si l’on considérait les partis politiques d’origine sans agrégation. En outre, une autre direction consiste à examiner la distribution multivariée de Student pour le modèle spatial
The objective of this thesis is to investigate the outcome of an election and the impacts of the socio-economics factors on the vote shares in the multiparty system from mathematical point of view. The vote shares of the departmental election in France in 2015 form a vector called composition. Thus, the classical regression model cannot be used directly to model these vote shares because of contraints of compositional data. In Chapter 2, we present a regression model in which the dependent variable is a compositional variable and the set of explanatory variables contains both classical variables and compositional variables. We analyze the impacts of socio-economic factors on the outcome of the election through predicting the vote shares according to either a classical explanatory variable or a compositional explanatory variable. Some graphical techniques are also presented. However, it would be more appreciated to interpret the coefficients of regression model on the simplex. Furthermore, some authors show that electoral data often exhibit heavy tail behavior. Thus, we propose to replace the Normal distribution by the Student distribution. However, there are two versions of the Student distribution: the uncorrelated Student(UT) distribution and the independent Student (IT) distribution. In Chapter 3, we present a complete summary for the Student distributions which includes the univariate and multivariate Student, the IT and the UT distribution with fixed degrees of freedom. We prove that the maximum likelihood estimator of the covariance matrix in the UTmodel is asymptotically biased. We also provide an iterative reweighted algorithm to compute the maximum likelihood estimator of parameter of the IT model. A simulation is provided and some Kolmogorov–Smirnov tests based on the Mahalanobis distance are carried out to select the right model. However, this does not work for the UT model because of a single realization of n observation of the multivariate distribution. In Chapter 4, we apply the multivariate Student (IT) regression model to our political economy data. We then compare this model to the multivariate Normal regression model. We also apply the Kolmogorov–Smirnov tests based on the Mahalanobis distance which is proposed in chapter 3 to select a better model. Finally, we investigate the assumption of statistical independence across territorial units which may be questionable due to potential spatial autocorrelation for compositional data. We develop a simultaneous spatial autoregressive model for compositional data which allows for both spatial correlation and correlations across equations by using two-stage and three-stage least squares methods. We present a simulation study to illustrate these methods. An application to a data set from the 2015 French departmental election are also showed. There is still work to continue in the direction of overcoming the problem of zeros in vote shares. This problem is already present for the departmental French elections at the canton level when aggregating the electoral parties in three categories. It would have been even more serious when considering the original political parties with no aggregation. Besides, another direction consists in considering the multivariate Student distribution for a spatial model

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Prado, Raul Ribeiro. « Estudo da composição de raios cósmicos de altas energias através da análise de dados medidos pelo Observatório Pierre Auger ». Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/76/76131/tde-23042014-155508/.

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O conhecimento sobre a composição de raios cósmicos de altas energias é fundamental na abordagem da maior parte das grandes questões referentes à astrofísica de altas energias. Entretanto, do ponto de vista experimental, determinar o tipo de partícula medida nesse regime de energia ainda é um enorme desafio e essa tarefa tem recebido especial atenção por parte das colaborações responsáveis pelos experimentos em atividade. A principal dificuldade está no fato das medidas serem realizadas indiretamente através das cascatas de partículas formadas a partir da interação do raio cósmico inicial com átomos da atmosfera, os chamados chuveiros atmosféricos. Entre os principais experimentos em funcionamento, o Observatório Pierre Auger se destaca por ter a maior área de detecção (3000 km2) e por utilizar pioneiramente um sistema híbrido de detecção, com detectores de superfície e de fluorescência funcionando simultaneamente. Os telescópios de fluorescência medem o número de partículas do chuveiro em função da profundidade, o que chamamos de perfil longitudinal. Alguns parâmetros extraídos desse perfil são sensíveis à composição das partículas primárias. No presente trabalho, aplicamos métodos estatísticos novos aos dados extraídos dos perfis longitudinais de chuveiros medidos pelo Auger com o objetivo de inferir informações sobre a massa média, ou seja, a composição dos raios cósmicos. A primeira análise apresentada é baseada no parâmetro XMax. A evolução do valor médio de XMax com a energia contém informações sobre a composição inicial. Com o objetivo de eliminar vieses experimentais, corrigindo os efeitos dos detectores, aplicamos métodos de deconvolução às distribuições de XMax. A segunda análise é do tipo multiparamétrica e aplica redes neurais do tipo Multilayer Perceptrons a outros parâmetros extraídos dos perfis longitudinais. A partir desse procedimento é possível obter informações sobre a composição média das partículas e também reconstruir a energia dos eventos.
The knowledge about high energy cosmic rays composition is fundamental to approach most of the big questions regarding high energy astrophysics. However, from the experimental point of view, to determine the kind of the measured particle in this energy range is still a huge challenge and this task has received special attention from the collaborations responsible for running the experiments in activity. The main difficulty is on the fact that the measurements are made indirectly by the secondary particles cascades formed by the interaction of primary particles with atmosphere atoms, which are called air showers. Among the main experiments in operation, Pierre Auger Observatory has the larger collecting area (3000 km2) and uses a pioneer hybrid detection system, with surface detectors and fluorescence telescopes working simultaneously. The fluorescence telescopes measure the number of particles in the shower as a function of atmospheric depth, which we call longitudinal profiles. Some parameters extracted from these profiles are sensitive to primary composition. In this study, we applied new statistical methods to the data from longitudinal profiles measured by the Pierre Auger Observatory aiming to infer information about the mean mass, in other words, the composition of cosmic rays. The first analysis shown (chapter 4) is based on the known parameter called XMax. The evolution of XMax mean value with energy contains information about primary composition. Unfolding methods have been applied to the XMax distribution in order to minimize experimental bias and to correct detector effects. The second analysis shown is of the multi-parametric type and applies neural networks of the Multilayer Perceptrons class to longitudinal profiles parameters. From this procedure, it is possible to obtain information about average composition and to reconstruct the energy of events.

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Gao, Lei. « Determination of quantitative nutritional labeling compositional data of lipids by Nuclear Magnetic Resonance (NMR) spectroscopy ». Thesis, McGill University, 2008. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=111577.

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The application of Nuclear Magnetic Resonance (NMR) spectroscopy in the determination of nutrition labeling component data (NLCD) was investigated, with the intent of using this methodology as a primary method to calibrate FTIR instrumentation for NLCD confirmation or screening on a routine basis. Unlike previous NMR studies, this work used three strategies to attain accuracy and reproducibility of NLCD through: (i) appropriate setting of operational parameters for spectral acquisition; (ii) resonance selection by optimizing the signal in proportion to the nuclei population and (iii) integration of resonances by pre-defined fixed chemical shift ranges. Both of 13C NMR spectra and 1H NMR spectra were shown to provide robust and acceptable results on the condition of appropriate acquisition of spectra for quantization purposes and the adoption of standard procedures for spectral processing, integration and calculation purposes. A quantitative approach of NLCD including trans content was determined by the interpretation resonance signals of 13C's and 1H's from methylene groups presented in triglyceride complex of fats and oils. An alternative method based on partial-least-squares (PLS) calibrations was provided as well, the latter proved to be especially useful in dealing with overlapping bands frequently found in 1H spectra. With the diagnostic provided by PLS, the trans and cis signals were shown to be separated in 1H spectra. It is the premise for the trans fat determination based on 1H spectra. Unit conversion from mole to weight % was addressed and a solution was developed based on NMR data per se, without significant assumptions. Validation involving the analysis of three different lipid types (model triacylglycerols, refined and hydrogenated oils) demonstrated that NMR predictions of NLCD were in good agreement with those results either from samples' actual values as well as those obtained using GC and FTIR predictions. Thus with appropriate integration of instrumentation, software and spectral processing accessories, both 13C and 1H NMR can determine NLCD, but with the capability to determine trans, 1H NMR is more practical than 13C NMR due to its much shorter spectral acquisition time. Thus NMR can serve as a primary method for the calibration of FTIR instrumentation, a practical instrumental method for routine NLCD determination and screening.

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Zheng, Zhilin. « Learning Group Composition and Re-composition in Large-scale Online Learning Contexts ». Doctoral thesis, Humboldt-Universität zu Berlin, 2017. http://dx.doi.org/10.18452/18412.

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Die Erforschung der Zusammenstellung kleiner Lerngruppen beschäftigt sich mit dem Problem, eine passende Gruppenzusammensetzung in einer Population von Lernern zu finden, die jeder Gruppe optimalen Nutzen bringen könnte. In letzter Zeit sind viele Studien zu diesem Problem der Kleingruppenzusammenstellung durchgeführt worden. Allerdings waren diese Forschungen nur selten auf den Kontext großer Lerner-Populationen ausgerichtet. Angesichts des zunehmenden Aufkommens von MOOCs muss jedoch das Problem der Gruppenzusammenstellung entsprechend erweitert betrachtet werden, und zwar mit neuen Forschungen, die den Kontext derartig großer Lerner-Populationen berücksichtigen. Anders als in Klassenzimmer-Settings könnte die beobachtete hohe Abbruchquote in MOOCs in einer Unterbesetzung der Gruppengröße resultieren und könnte somit viele Lerner dazu bringen, neue Gruppen zu bilden. Zusätzlich zur Gruppenzusammenstellung muss daher die Gruppenneuzusammenstellung als neues Thema in aktuellen Kontexten großer Lerner-Populationen ebenfalls erforscht werden. Die Untersuchungen der vorliegenden Arbeit gliedern sich in zwei Teile. Der erste Teil beschäftigt sich mit Gruppenzusammenstellung. In diesem Teil stelle ich einen diskreten-PSO Algorithmus zur Zusammenstellung kleiner Lerngruppen vor und vergleiche bislang bestehende Gruppenzusammenstellungs-Algorithmen unter den Gesichtspunkten Zeitaufwand und Gruppierungsqualität. Um Gruppenzusammenstellung in MOOCs anzuwenden wurde ein Gruppenzusammenstellungsexperiment in einem MOOC durchgeführt. Die Hauptergebnisse deuten darauf hin, dass die Gruppenzusammenstellung die Abbruchsquote reduzieren kann, jedoch lediglich einen sehr schwachen Bezug zur Lernperformanz der Lerner aufweist. Der zweite Teil beschäftigt sich mit Gruppenneuzusammenstellung. Die vorliegende Arbeit stellt eine datengesteuerte Herangehensweise vor, die umfassenden Gebrauch von Gruppeninteraktionsdaten macht sowie Gruppendynamik mit einbezieht. Mittels einer in einem Simulationsexperiment durchgeführten Evaluation zeigen sich die Vorteile dieses Verfahrens: Der Lerngruppenzusammenhalt wird verbessert und die Abbruchsquote im Vergleich zu einer Zufallsverteilung reduziert. Darüberhinaus wurde hier ein Gruppen-Lern-Werkzeug entwickelt und für die Praxis vorbereitet, das die Anforderungen des geforderten Ansatzes der Gruppenneuzusammenstellung erfüllt.
Small learning group composition addresses the problem of seeking such matching among a population of students that it could bring each group optimal benefits. Recently, many studies have been conducted to address this small group composition problem. Nevertheless, the focus of such a body of research has rarely been cast to large-scale contexts. Due to the recent come of MOOCs, the topic of group composition needs to be accordingly extended with new investigations in such large learning contexts. Different from classroom settings, the reported high drop-out rate of MOOCs could result in group’s incompletion in size and thus might compel many students to compose new groups. Thus, in addition to group composition, group re-composition as a new topic needs to be studied in current large-scale learning contexts as well. In this thesis, the research is structured in two stages. The first stage is group composition. In this part, I proposed a discrete-PSO algorithm to compose small learning groups and compared the existing group composition algorithms from the perspectives of time cost and grouping quality. To implement group composition in MOOCs, a group composition experiment was conducted in a MOOC. The main results indicate that group composition can reduce drop-out rate, yet has a very weak association with students’ learning performance. The second stage is to cope with group re-composition. This thesis suggests a data-driven approach that makes full use of group interaction data and accounts for group dynamics. Through evaluation in a simulation experiment, it shows its advantages of bringing us more cohesive learning groups and reducing the drop-out rate compared to a random condition. Apart from these, a group learning tool that fulfills the goals of the proposed group re-composition approach has been developed and is made ready for practice.

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Yu, Shiyong. « A Hierarchical Bayesian Model for the Unmixing Analysis of Compositional Data subject to Unit-sum Constraints ». ScholarWorks@UNO, 2015. http://scholarworks.uno.edu/td/2016.

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Modeling of compositional data is emerging as an active area in statistics. It is assumed that compositional data represent the convex linear mixing of definite numbers of independent sources usually referred to as end members. A generic problem in practice is to appropriately separate the end members and quantify their fractions from compositional data subject to nonnegative and unit-sum constraints. A number of methods essentially related to polytope expansion have been proposed. However, these deterministic methods have some potential problems. In this study, a hierarchical Bayesian model was formulated, and the algorithms were coded in MATLABÒ. A test run using both a synthetic and real-word dataset yields scientifically sound and mathematically optimal outputs broadly consistent with other non-Bayesian methods. Also, the sensitivity of this model to the choice of different priors and structure of the covariance matrix of error were discussed.

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Hayes, Audrey A. « Analyses of coyote (canis latrans) consumption of anthropogenic material and dietary composition in urban and non-urban habitats ». Wright State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=wright1630436863238348.

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Cambianica, Pamela. « Morphological and compositional analysis of boulder distributions on comet 67P/Churyumov-Gerasimenko ». Doctoral thesis, Università degli studi di Padova, 2019. http://hdl.handle.net/11577/3423169.

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The European Space Agency's Rosetta mission consisted of the orbiter spacecraft Rosetta and the lander Philae. Launched in 2004, the space probe reached the comet 67P/Churyumov-Gerasimenko after a journey lasted more than ten years. The objectives of the mission were to map the comet, to study its composition, to investigate the chemical and thermal properties, and to monitor the activity during its journey in the inner Solar System. Rosetta was the first mission to rendezvous with a comet, and to deploy a lander on a comet's surface. From a wider prospective, the Rosetta mission allowed to investigate the origin of comets, and to define the implication for the origin of the Solar System. We used OSIRIS Narrow Angle Camera with a spatial scale smaller than 2 m=pixel to analyze the surface of comet 67P. The surface reveals a variety of terrains and geological features, suggesting to be a very active and complex environment. In the first part of the thesis, a detailed quantitative analysis of isolated boulder fields is provided. We used different techniques to supply a method for analyzing the morphology of the boulders, which represent one of the ubiquitous and most important geological features on the comet. In the second part, a method to measure the seasonal evolution of Hapi's deposit is described, providing an upper limit for Hapi's water ice fraction. Measuring the evolution of the heights of some boulders, we fixed the pristine 67P ice content, and we compared the results with the Inter-Stellar Medium and CI-Chondrites. Finally, we investigated the macroscopic thermomechanical behavior of a 40 meter boulder located on the Imhotep region by modeling its response to diurnal thermal forcing. Preliminary results reveal that stresses occur in the boulder's exteriors due to the sudden variation in temperature during sunset and sunrise. We explored whether the simulated stress is enough to propagate preexisting cracks, discussing the implications for rock breakdown.

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Le, Guern François. « Ecoulements réactifs à hautes températures, mesures et modélisations ». Paris 7, 1988. http://www.theses.fr/1988PA077222.

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Mise au point et réalisation d'une méthode de prélèvement des gaz volcaniques qui permet de reconstituer la composition élémentaire d'une source gazeuse à haute température. Cette résolution est basée sur la minimisation de l'enthalpie globale du système par calcul sur une banque de données. Cette méthode a été testée sur plusieurs volcans : Mt St Helens, l'Etna, le mont Usu. La modélisation de l'évolution physico-chimique des gaz volcaniques lors de leur refroidissement permet de relier la composition des gaz magmatiques à la genèse des aérosols atmosphériques, à la formation des incrustations fumerolliennes ou à la genèse de certains gîtes métallifères

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Patton, William. « Modelling of unequally sampled rock properties using geostatistical simulation and machine learning methods ». Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2022. https://ro.ecu.edu.au/theses/2530.

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Important orebody characteristics that determine viability of the mineral resource and ore reserve potential such as physical properties, mineralogical and geochemical compositions often vary substantially across an ore deposit. Geometallurgical models aim to capture the spatial relationships between mineral compositions, physical properties of rock and their interactions with mechanical and chemical processes during mining extraction and processing. This characterisation of physical and chemical properties of ores can in turn be used to inform mining and processing decisions that enable the extraction of the maximum value from the ore deposit most efficiently. During the construction of such spatial geometallurgical models, practitioners are presented with many challenges. These include modelling high-dimensional data of various types including categorical, continuous and compositional attributes and their uncertainties. Decisions on how to segregate samples data into spatially and statistically homogeneous groups to satisfy modelling assumptions such as stationarity are often a requirement. Secondary properties such as metallurgical test results are often few in number, acquired on larger scales than that of primary rock property data and non-additive in nature. In this thesis a data driven workflow that aims to address these challenges when constructing geometallurgical models of ore deposits is devised. Spatial machine learning techniques are used to derive geometallurgical categories, or classes, from multiscale, multiresolution, high dimensional rock properties. In supervised mode these methods are also used to predict geometallurgical classes at samples where rock property information is incomplete. Realisations of the layout of geometallurgical classes and the variabilities of associated rock properties are then mapped using geostatistical simulations and machine learning. The workflow is demonstrated using a case study at Orebody H; a complex stratabound Bedded Iron Ore deposit in Western Australia’s Pilbara. A detailed stochastic model of five compositions representing primary rock properties and geometallurgical responses in the form of lump and fine product iron ore quality specifications was constructed. The predicted product grade recoveries are realistic values that honour constraints of the predicted head grade compositions informed by more abundant and regularly spaced sampling than metallurgical tests. Finally, uncertainties are quantified to assess risk following a confidence interval based framework. This could be used to identify zones of high uncertainty where collection of additional data might help mitigate or minimise risks and in turn improve forecast production performances.

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Pospiech, Solveig [Verfasser], Hans [Akademischer Betreuer] Ruppert, Hans [Gutachter] Ruppert et Raimon [Gutachter] Tolosana-Delgado. « Geochemical Characterization of Tea Leaves (Camellia sinensis) and Soils for Provenance Studies based on Compositional Data Analysis / Solveig Pospiech ; Gutachter : Hans Ruppert, Raimon Tolosana-Delgado ; Betreuer : Hans Ruppert ». Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2019. http://d-nb.info/1188886843/34.

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Font, Moragón Carme. « Mathematical models for energy and landscape integrated analysis in agroecosystems ». Doctoral thesis, Universitat Autònoma de Barcelona, 2016. http://hdl.handle.net/10803/399906.

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Els models matemàtics s'utilitzen per explicar fenòmens naturals. Com que els fenòmens naturals són molt complexes, per tal d'aprofundir en el seu comportament i ser capaç de fer prediccions sobre ells, es necessita passar per un procés de simplificació. En el procés de creació del model, el sistema es tradueix a llenguatge matemàtic que permet l'estudi del sistema des d'un nou punt de vista. En aquesta tesi, es consideren models estadístics per estudiar el comportament dels agroecosistemes a diferents escales espacials. L'objectiu d'aquest treball és estudiar la relació entre fluxos d'energia, canvis de cobertes del sòl, la funcionalitat del paisatge i la biodiversitat que subjau en els agroecosistemes. Per a això, es proposen models basats en tals matèries. Les principals unitats d'anàlisi seran les cobertes del sòl, quan treballem a escala regional, i els usos del sòl, a escala local. En el segon capítol, es presenta un model de pertorbació-complexitat intermèdia (IDC) dels paisatges culturals. Aquest enfocament té com a objectiu avaluar com els diferents nivells de pertorbacions antropogèniques sobre els ecosistemes afecten la capacitat d'acollir la biodiversitat en funció de l'heterogeneïtat d'usos sòl. S'aplica a l'illa de Mallorca, enmig de la zona activa de la biodiversitat mediterrània, a escala regional i de paisatge. El model utilitza la pertorbació exercida pels agricultors que alteren la producció primària neta a través del canvi d'usos del sòl, així com l'eliminació d'una part d'ella, juntament amb l'índex de Shannon-Wiener de la diversitat d'usos del sòl. El model es prova en un disseny experimental a dues escales al llarg de tres punts de temps. La riquesa d'espècies d'aus nidificants i hivernants, preses com a indicador de la biodiversitat, s'utilitza en una anàlisi factorial exploratori. Seguint la idea presentada en el segon capítol, en el tercer capítol es presenta un mètode per descriure la relació entre els indicadors d'heterogeneïtat d'usos del sòl, i l'apropiació humana de la producció primària neta en una regió determinada. Aquestes quantitats són vistes com a funcions del vector de proporcions de les cobertes de sòl, que al seu torn es tracta com un vector aleatori els valors del qual depenen de la unitat de terreny que s'observa. Presentem el mètode suposant, en primer lloc, que el vector de proporcions segueix una distribució uniforme en el símplex. Després, considerem com a punt de partida un conjunt de dades mostals, de manera que primer hem d'obtenir una estimació de la seva distribució de probabilitat teòrica, i en segon lloc, generem una mostra de grans dimensions seguint la distribució estimada. Apliquem aquest procediment a dades de l'illa de Mallorca en tres moments de temps diferents. L'objectiu principal aquí és calcular el valor esperat de la diversitat del paisatge com a funció del nivell d'apropiació humana. Aquesta funció està relacionada amb l'anomenada hipòtesi d'energia i espècies, i amb l'hipòtesi de la Pertorbació Intermèdia. Finalment, el quart capítol està dedicat a tractar els processos interns dels agroecosistemes. Per a aquest propòsit, es proposa un graf que representa el patró de fluxos d'energia en un agroecosistema. Utilitzem aquest graf per calcular el nivell d'emmagatzematge d'energia dins de l'agroecosistema, així com la informació inclosa en aquesta xarxa de fluxos, a escales tant local com de paisatge. Per tant, es proposa un model d'anàlisi integrat d'energia i paisatge (ELIA) que avalua tant la complexitat dels bucles d'energia interna, com la informació continguda en tota la xarxa de fluxos d'energia soci-metabòliques, per tal de correlacionar aquesta interacció d'informació energètica amb l'estructura funcional del paisatge. A l'annex, es suggereix una millora de l'indicador d'informació. ELIA es prova en el Vallès, a la Regió Metropolitana de Barcelona.
Mathematical models are used to better explain natural phenomena. Since natural phenomena are very complex, in order to delve into their behaviour and be able to do predictions over them, a simplification process of such systems is needed. In the process of creating the model, the system is translated into mathematical language that allows the study of the system from a new point of view. In this thesis, statistical models are considered to study the behaviour of agroecosystems at different spatial scales. The aim of this work is to study the relation between energy flows, land cover changes, landscape functionality and the biodiversity that underlies in agroecosystems. For this, models based on such matters are proposed. The main units of analysis will be the land covers, when we work at regional scale, and the land uses, at local scale. In the second chapter, an intermediate disturbance-complexity model (IDC) of cultural landscapes is presented. This approach is aimed at assessing how different levels of anthropogenic disturbance on ecosystems affect the capacity to host biodiversity depending on the land matrix heterogeneity. It is applied to the Mallorca Island, amidst the Mediterranean biodiversity hotspot, at regional and landscape scales. The model uses the disturbance exerted by farmers altering the Net Primary Production (NPP) through land use change, as well as removing a share of it, together with Shannon-Wiener index of land use diversity. The model is tested with a twofold-scalar experimental design of a set of landscape units along three time points. Species richness of breeding and wintering birds, taken as a biodiversity proxy, is used in an exploratory factor analysis. Following the idea presented in the second chapter, in the third chapter we present a method to describe the relation between indicators of the land matrix heterogeneity, and the human appropriation of the net primary production in a given region. These quantities are viewed as functions of the vector of proportions of the different land covers, which is in turn treated as a random vector whose values depend on the particular small terrain cell that is observed. We illustrate the method assuming first that the vector of proportions follows a uniform distribution on the simplex. We then consider as starting point a raw dataset of proportions for each cell, for which we must first obtain an estimate of its theoretical probability distribution, and secondly generate a sample of large size from it. We apply this procedure to real historical data of the Mallorca Island in three different time points. The main goal here is to compute the mean value of the land covers diversity as a function of the level of human appropriation of net primary production. This function is related to the so-called Energy-Species hypothesis and to the Intermediate Disturbance Hypothesis. Finally, fourth chapter is devoted to deal with agroecosystems internal processes. For this purpose, a graph to represent the pattern of energy flows in an agroecosystem is presented. We use this graph model to calculate the level of energy storage within the agroecosystem provided by its ‘internal feedback’, as well as the information embedded in this network of flows, at local and landscape scales. Thus, we propose an Energy-Landscape Integrated Analysis (ELIA) model that assesses both the complexity of internal energy loops, and the information held in the whole network of socio-metabolic energy fluxes, so as to correlate this energy-information interplay with the functional landscape structure. In the annex, an improvement of the information indicator is suggested. ELIA is tested in the Vallès County of the Barcelona Metropolitan Region.

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Galván, Femenía Iván. « Compositional methodology and statistical inference of family relationships using genetic markers ». Doctoral thesis, Universitat de Girona, 2020. http://hdl.handle.net/10803/672178.

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The present thesis is a compendium of three research articles produced between 2015 and 2019. The three articles are different contributions based on compositional statistical methodology and statistical inference of genetic 2/2 relatedness. In the first work of this thesis, we review the classical graphical methods used to detect relatedness and introduce the analysis of Compositional Data for relatedness research. In the second article, we propose the analysis of identity by state genotype sharing data instead of the classical identity by state allele sharing data. The third article finishes the thesis with the development of the likelihood ratio approach to infer three-quarter siblings in genetic databases. To illustrate all the results of this doctoral thesis we use genetic markers from worldwide human population projects such as the Human Genome Diversity Project and the 1000 Genomes Project, as well as from a local prospective human cohort of the Genomes of Catalonia (GCAT)
Aquesta tesi doctoral és un compendi de tres articles de recerca produïts entre el 2015-2019. Els tres articles són aportacions diferents basades en la metodologia de les dades composicionals i en la inferència estadística de relacions familiars. En el primer treball d'aquesta tesi, revisem els mètodes gràfics clàssics utilitzats per detectar relacions familiars i introduïm l'anàlisi de les dades composicionals per a la investigació de relacions familiars. En el segon, es proposa l'anàlisi de dades de genotips compartits idèntics per estat en lloc de les clàssiques dades d'al·lels compartits. El tercer article finalitza la tesi amb l'elaboració de la raó de versemblances per inferir tres quarts germans en bases de dades genètiques. Per il·lustrar els resultats, s'utilitzen marcadors genètics de projectes de població humana com el Projecte de la Diversitat del Genoma Humà, el Projecte 1000 Genomes i una cohort humana prospectiva local dels genomes de Catalunya (GCAT)
Programa de Doctorat en Tecnologia

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Illous, Hugo. « Abstractions relationnelles de la mémoire pour une analyse compositionnelle de structures de données ». Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEE015.

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Les analyses statiques ont pour but d’inférer des propriétés sémantiques de programmes. Nous distinguons deux importantes classes d’analyses statiques : les analyses d’états et les analyses relationnelles. Alors que les analyses d’états calculent une sur-approximation de l’ensemble des états atteignables d’un programme, les analyses relationnelles calculent des propriétés fonctionnelles entre les états d’entrée et les états de sortie d’un programme. Les analyses relationnelles offrent plusieurs avantages, comme leur capacité à inférer des propriétés sémantiques plus expressives par rapport aux analyses d’états. De plus, elles offrent également la possibilité de rendre l’analyse compositionnelle, en utilisant les relations entrée-sortie comme des résumés de procédures, ce qui est un avantage pour le passage à l’échelle. Dans le cas des programmes numériques, plusieurs analyses ont été proposées qui utilisent des domaines abstraits numériques relationnels, pour décrire des relations. D’un autre côté, modéliser des abstractions de relations entre les états mémoires entrée-sortie tout en prenant en compte les structures de données est difficile. Dans cette Thèse, nous proposons un ensemble de nouveaux connecteurs logiques, reposant sur la logique de séparation, pour décrire de telles relations. Ces connecteurs peuvent exprimer qu’une certaine partie de la mémoire est inchangée, fraîchement allouée, ou désallouée, ou que seulement une seule partie de la mémoire est modifiée (et de quelle manière). En utilisant ces connecteurs, nous construisons un domaine abstrait relationnel et nous concevons une analyse statique compositionnelle par interprétation abstraite qui sur-approxime des relations entre des états mémoires contenant des structures de données inductives. Nous avons implémenté ces contributions sous la forme d’un plug-in de l’analyseur FRAMA-C. Nous en avons évalué l’impact sur l’analyse de petits programmes écrits en C manipulant des listes chaînées et des arbres binaires, mais également sur l’analyse d’un programme plus conséquent qui consiste en une partie du code source d’Emacs. Nos résultats expérimentaux montrent que notre approche permet d’inférer des propriétés sémantiques plus expressives d’un point de vue logique que des analyses d’états. Elle se révèle aussi beaucoup plus rapide sur des programmes avec un nombre conséquent d’appels de fonctions sans pour autant perdre en précision
Static analyses aim at inferring semantic properties of programs. We distinguish two important classes of static analyses: state analyses and relational analyses. While state analyses aim at computing an over-approximation of reachable states of programs, relational analyses aim at computing functional properties over the input-output states of programs. Relational analyses offer several advantages, such as their ability to infer semantics properties more expressive compared to state analyses. Moreover, they offer the ability to make the analysis compositional, using input-output relations as summaries for procedures, which is an advantage for scalability. In the case of numeric programs, several analyses have been proposed that utilize relational numerical abstract domains to describe relations. On the other hand, designing abstractions for relations over input-output memory states and taking shapes into account is challenging. In this Thesis, we propose a set of novel logical connectives to describe such relations, which rely on separation logic. This logic can express that certain memory areas are unchanged, freshly allocated, or freed, or that only part of the memory is modified (and how). Using these connectives, we build an abstract domain and design a compositional static analysis by abstract interpretation that over-approximates relations over memory states containing inductive structures. We implement this approach as a plug-in of the FRAMA-C analyzer. We evaluate it on small programs written in C that manipulate singly linked lists and binary trees, but also on a bigger program that consists of a part of Emacs. The experimental results show that our approach allows us to infer more expressive semantic properties than states analyses, from a logical point of view. It is also much faster on programs with an important number of function calls without losing precision

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Owen, Daniel D. « Hydrochemical and isotopic indicators of hydrological processes within coal seam gas formations and adjacent aquifers, Condamine River catchment, QLD ». Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/98525/1/Daniel_Owen_Thesis.pdf.

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This project addressed potential connection between aquifers in an area where groundwater extraction for both agriculture and coal seam gas occur. The analysis of water chemistry and isotopes, and new mathematical techniques were employed. Overall, there was no evidence of significant solute or gas transfer between aquifers. Methane gas was found to be generated within all aquifers via microbial activity. As a result, stable and radioactive isotopes results are complex. In contrast, hydrochemistry, and the analysis of very low concentrations of the lithium ion and its stable isotopes were found to be effective indicators of coal-seam gas bearing groundwater.

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48

Breillat, Noémie. « Traçage des minéralisations à molybdène à l'échelle mondiale : variation du δ₉₈Mo en complément des outils isotopiques Pb, S, Re-Os ». Thesis, Orléans, 2015. http://www.theses.fr/2015ORLE2079/document.

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Cette étude s’intéresse aux variations de la composition isotopique du Mo des molybdénites (MoS2) afin de déterminer si un lien existe entre le type d’occurrence, les processus minéralisateurs, l’âge des occurrences et les variations observées pour le δMo. Une base de données (n=391) construite à partir d’analyses effectuées au cours de cette étude (n=198) et de donnée issue de la littérature (n=193) permet l’obtention de statistiques robustes sur la composition isotopique du Mo des MoS₂. Différents types d’occurrences ont été étudiés (granites, pegmatites, greisens, filons périgranitiques, porphyres, skarns, IOGC, veines polymétalliques et fentes alpines). Tous les δ98Mo ont été normalisés au NIST3134 (δ₉₈MoNIST(NIST) = 0‰). La répartition du δ₉₈MoNIST des MoS₂ tend à suivre une loi normale avec une moyenne de 0,04±1,04‰ (2σ). Les moyennes des δ₉₈MoNIST sont plus élevées pour les fentes alpines, les greisens et les veines périgranitiques que pour les skarns, granites et porphyres. Ces derniers types d’occurrences cristallisent à plus haute température que les précédents. Dans le cas des occurrences liées aux granites, le δ₉₈MoNIST des granites est plus faible que celui des pegmatites et que celui des veines périgranitiques. Ceci pourrait montrer une influence de la température sur le fractionnement isotopique du Mo. Des variations intra-occurrence ont aussi été mises en évidence. L’amplitude de ces variations ne dépend pas du type d’occurrence. Les δ₉₈MoNIST du skarn d’Azegour varient de 1,02‰. Le processus de fractionnement proposé est le fractionnement de Rayleigh lors de la cristallisation fractionnée. Des analyses isotopiques du Pb et du S montrent une forte contribution de la série volcano-sédimentaire encaissante. Les δ₉₈MoNIST de la pegmatite de Ploumanac’h varient très peu : 0,22‰. Les analyses isotopiques du S et du Pb montrent une forte contribution crustale dans la formation des magmas
This study focuses on isotopic composition of molybdenite (MoS₂) in order to decipher possible links between occurrence type, mineralizing processes, ages and observed δMo variations. A data base (n=391) have been built thanks to data from this study and data from literature allowing to run solid statistics on Mo isotopic composition of MoS₂. Different occurrence types have been investigated (granite, pegmatite, greisens, perigranitic vein, porphyry deposit, skarn, IOCG, polymetallic epithermal vein and alpine-type fissure vein). All δ₉₈Mo have been normalized to NIST3134 (δ₉₈MoNIST(NIST) = 0‰). The distribution of all data is Gaussian with a mean value of 0.04±1.04‰ (2σ). δ₉₈MoNIST mean values are higher for alpine-type fissure vein, greisens and perigranitic vein than for skarn, granite and porphyry deposit. These last occurrence types crystallize at higher temperature. For granite-related occurrences, δ₉₈MoNIST of granite is lower than δ₉₈MoNIST of pegmatite and perigranitic vein. This suggests an influence of temperature on Mo isotopic fractionation. Intra-occurrence variations have been evidenced. The intra-occurrence variations are not depending of the occurrence type. δ₉₈MoNIST of the Azegour skarn vary on large range of 1.02‰. Rayleigh fractionation is proposed as principal fractionation process. S and Pb isotopic analyses suggest a contribution of hosting volcano-sedimentary series. δ₉₈MoNIST of Ploumanac’h pegmatite vary on a narrow range of 0.22‰. S and Pb isotopic analyses suggest a strong crustal contribution in magmas genesis

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Messias, Ricardo Matioli. « Transformações em dados composicionais para a aplicação da análise de componentes principais ». Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-12072016-211056/.

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A análise de dados composicionais está sendo amplamente utilizada nas diversas áreas do conhecimento como por exemplo na análise de sedimentos rochosos, na comparação de diferentes células e até na análise criminalística na comparação de evidências de crimes. Durante a história da análise deste tipo de dados existiram muitos tipos de ajustes utilizados para contornar o problema da soma constante das variáveis e ainda hoje não temos um consenso de qual a melhor solução a ser utilizada. Neste trabalho, temos como objetivo a enunciação das 7 transformações que mais foram utilizadas ao longo do tempo e suas vantagens e desvantagens. A análise de componentes principais foi escolhida para o comparativo destas transformações. Fizemos a aplicação destas transformações em três bancos de dados reais com características diferentes entre si, comparamos os resultados e analisamos qual das transformações apresentou o melhor desempenho em cada base de dados. Os critérios de comparação foram o percentual da variância explicada, as variáveis que foram mais importantes para a primeira componente principal, cargas das variáveis nas componentes principais mais importantes assim como suas correlações com as variáveis. Também, simulamos quatro estruturas de bases de dados composicionais para avaliar o desempenho das transformações. Para essas comparações e simulações, foram desenvolvidas algumas funções, utilizando o \\textit estatístico R, que visam facilitar a comparação entre as sete transformações, assim auxiliando na escolha de qual das transformações melhor se adapta aos dados. Pelos resultados obtidos notamos que: nas bases de dados reais, os resultados das explicações da variância das transformações são similares e as transformações Ref e Alr mostram melhores desempenhos que as demais; nas quatro estruturas simuladas as transformações Ref e Alr também possuem os melhores resultados na explicação da variância e a interpretação de suas componentes principais são parecidas, assim como as transformações Trad, Log e Clr. Com isso notamos que independentemente da aplicação do logaritmo nas transformações Alr e Log elas apresentaram resultados muitos similares às transformações Ref e Trad, respectivamente, tanto na explicação da variância como na interpretação das componentes principais.
The compositional data analysis is being widely used in several areas of knowledge such as the analysis of rocky sediments, to compare different biological cells and even in forensic analysis to compare crimes evidences. During the history of the analysis of such data, to circumvent the problem of variable\'s constant sum were used many types of adjustments. Until now, we do not have a consensus in which is the best solution to be used in this cases. In this paper, we aim to enunciate seven transformations that most were used over time and their advantages and disadvantages. The principal component analysis was chosen for the comparison of these transformations. We applied this transformations in three real databases with different characteristics, we hope to compare the results and analyze which transformation have the best performance in each database. The comparison criteria were the percentage of explained variance, the variables that were most important to the first principal component,variable\'s loads in the most important principal components as well their correlation with the variables. We also simulated four compositional data bases structures to evaluate the performance of the transformations. For these comparisons and simulations were developed some functions, using the statistical software R, to facilitate comparison between the seven transformations, thus assisting in choosing which of the best transformation fits to the data. From the results we note that: for the real databases, the results of the variance explanation of all transformations are similar, thus Ref and Alr transformations show better performances than the others; in the four simulated structures the Ref and Alr transformations also have the best results in the variance explanation and interpretation of its main components are similar, as well as the transformations Trad, Log and Clr. Thus we note that independently of applying logarithm in and Log and Alr transformations they present very similar results as Ref and Trad transformations, respectively, both in variance explanation and in the interpretation of the principal components.

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Martín, Fernández Josep Antoni. « Medidas de diferencia y clasificación automática no paramétrica de datos composicionales ». Doctoral thesis, Universitat Politècnica de Catalunya, 2001. http://hdl.handle.net/10803/6704.

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Es muy frecuente encontrar datos de tipo composicional en disciplinas tan dispares como son, entre otras, las ciencias de la tierra, la medicina, y la economía. También es frecuente en estos ámbitos el uso de técnicas de clasificación no paramétrica para la detección de agrupaciones naturales en los datos. Sin embargo, una búsqueda bibliográfica bastante exhaustiva y la presentación de resultados preliminares sobre el tema en congresos de ámbito internacional han permitido constatar la inexistencia de un cuerpo teórico y metodológico apropiado que permita desarrollar pautas y recomendaciones a seguir en el momento de realizar una clasificación no paramétrica de datos composicionales. Por estos motivos se ha elegido como tema de tesis la adaptación y desarrollo de métodos de agrupación adecuados a datos de naturaleza composicional, es decir, datos tales que el valor de cada una de sus componentes expresa una proporción respecto de un total. El título de la misma, "Medidas de diferencia y clasificación automática no paramétrica de datos composicionales", recoge no sólo este propósito, sino que añade la expresión "medidas de diferencia" con el propósito de reflejar el peso específico importante que tiene el estudio de este tipo de medida en el desarrollo del trabajo. La expresión "no paramétrica'' se refiere a que en la misma no se considerarán técnicas de clasificación que presuponen la existencia de un modelo de distribución de probabilidad para las observaciones objeto de la agrupación.

La memoria de la tesis se inicia con un capítulo introductorio donde se presentan los elementos básicos de las técnicas de clasificación automática no paramétrica. Se pone especial énfasis en aquellos elementos susceptibles de ser adaptados para su aplicación en clasificaciones de datos composicionales. En el segundo capítulo se aborda el análisis de los conceptos más importantes en torno a los datos composicionales. En este capítulo, los esfuerzos se han concentrado principalmente en estudiar las medidas de diferencia entre datos composicionales junto con las medidas de tendencia central y de dispersión. Con ello se dispone de las herramientas necesarias para proceder al desarrollo de una metodología apropiada para la clasificación no paramétrica de datos composicionales, consistente en incorporar los elementos anteriores a las técnicas habituales y adaptarlas en la medida de lo necesario. El tercer capítulo se dedica exclusivamente a proponer nuevas medidas de diferencia entre datos composicionales basadas en las medidas de divergencia entre distribuciones de probabilidad. En el cuarto capítulo se incorporan las peculiaridades de los datos composicionales a las técnicas de clasificación y se exponen las pautas a seguir en el uso práctico de estas técnicas. El capítulo se completa con la aplicación de la metodología expuesta a un caso práctico. En el quinto capítulo de esta tesis se aborda el denominado problema de los ceros. Se analizan los inconvenientes de los métodos usuales de substitución y se propone una nueva fórmula de substitución de los ceros por redondeo. El capítulo finaliza con el estudio de un caso práctico. En el epílogo de esta memoria se presentan las conclusiones del trabajo de investigación y se indican la líneas futuras de trabajo. En los apéndices finales de esta memoria se recogen los conjuntos de datos utilizados en los casos prácticos que se han desarrollado en la presente tesis. Esta memoria se completa con la lista de las referencias bibliográficas más relevantes que se han consultado para llevar a cabo este trabajo de investigación.
On March 23, 2001 Josep Antoni Martín-Fernández from the Dept. of Computer Sciences and Applied Mathematics of the University of Girona (Catalonia-Spain), presented his PhD thesis, entitled "Measures of difference and non-parametric cluster analysis for compositional data" at the Technical University of Barcelona. A short resumee follows:

Compositional data are by definition proportions of some whole. Thus, their natural sample space is the open simplex and interest lies in the relative behaviour of the components. Basic operations defined on the simplex induce a vector space structure, which justifies the developement of its algebraic-geometric structure: scalar product, norm, and distance. At the same time, hierarchic methods of classification require to establish in advance some or all of the following measures: difference, central tendency and dispersion, in accordance with the nature of the data. J. A. Martín-Fernández studies the requirements for these measures when the data are compositional in type and presents specific measures to be used with the most usual non-parametric methods of cluster analysis. As a part of his thesis he also introduced the centering operation, which has been shown to be a powerful tool to visualize compositional data sets. Furthermore, he defines a new dissimilarity based on measures of divergence between multinomial probability distributions, which is compatible with the nature of compositional data. Finally, J. A. Martín-Fernández presents in his thesis a new method to attack the "Achilles heel" of any statistical analysis of compositional data: the presence of zero values, based on a multiplicative approach which respects the essential properties of this type of data.

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