O objetivo deste trabalho é estudar algumas classes de anéis. Para isso, introduzimos o conceito de módulo pobre e provamos algumas propriedades básicas destes módulos. Além disso, estudamos quais hipóteses sobre um anel R fazem com que alguma família da classe dos R-módulos seja uma família destituída (famílias tais que todo R-módulo é pobre), uma família sem classe média (famílias tais que todo R-módulo ou é pobre ou é injetivo) ou uma família que é uma utopia (famílias tais que todo R-módulo não é pobre). / The goal of this dissertation is to study certain classes of rings. To this end, we introduce the definition of a poor module and prove some basic properties of these modules. Furthermore, we study which hypotheses on a ring R turn some classes of R-modules into a destitute family (families such that every R-module is poor), a family with no middle class (families such that every R-module is either poor or injective) or a family that is an utopia (families such that every R-module is not poor)

Two modules have been developed which qualify mesoscale atmospheric models for simulating the chemical transport at resolutions much higher than the model grid. Compared with total fine-grid application this method proves to be nearly so efficient but more economic. The modules solve the chemical transport equations (a) and submit the horizontal subgrid (b) for the meteorological and chemical calculations: (a) The chemical transport module considers the triad NO-N02-03 together with a simplified hydrocarbon chemistry. Involved are chemical reactions, anthropogenic and biogenic emission, dry deposition, passive transport, and turbulent diffusion. For these calculations a special vertical subgrid was introduced within the lowest atmospheric model layer. lt eliminates the frequently used approach of constant vertical particle fluxes near the surface. (b) The horizontal-subgrid module splits the horizontal model grid equidistantly into subgrid cells. The vertical surface fluxes of momentum, sensible and latent heat, radiation, soil heat and wetness, and chemical components are explicitly treated on this subgrid. The subgrid-averaged surface fluxes are employed for the (coarser) normal-grid calculations of the atmospheric meteorological variables. In contrast to the meteorological quantities, the chemical components and processes are perf ormed at all model layers on the horizontal subgrid. Several results are compared to conventional simulations of variable model resolution. / Zwei Programm-Module für mesoskalige Atmosphärenmodelle sind entwickelt worden, die Chemie-Transport-Vorgänge in höherer als der normalen Modellgitter-Auflösung simulieren. Im Vergleich zu hochaufgelösten Standardmodell-Anwendungen erweist sich diese Methode als effizienter. Die Module lösen die Chemie-Transport-Gleichungen (a) und schaffen das horizontale Untergitter für die meteorologischen und chemischen Berechnungen (b): (a) Im Chemie-Transport-Modul wird die Triade NO-N02-03 gemeinsam mit einer vereinfachten Kohlenwasserstoff-Chemie betrachtet. Berücksichtigt werden chemische Reaktionen, anthropogene und biogene Emissionen, trockene Deposition, passiver Transport und turbulente Diffusion. Für diese Berechnungen wurde innerhalb der untersten Modellschicht ein spezielles vertikales Untergitter eingeführt, um die in Oberflächennähe häufig angewendete Näherung konstanter Stoffflüsse zu eliminieren. (b) Das Untergitter-Modul unterteilt das horizontale Modellgitter in Unterzellen, auf welche die Berechnung der Boden- und Oberflächenflüsse bezogen wird. Die vertikalen Oberflächenflüsse von Impuls, sensibler und latenter Wärme, Strahlung, Bodenwärme und -feuchte sowie der chemischen Komponenten werden explizit im Untergitter bestimmt. Die über die Unterzellen gemittelten Flüsse werden für die im (gröberen) Modellgitter ablaufenden Berechnungen der meteorologischen Größen genutzt. Im Gegensatz dazu werden die chemischen Komponenten und Prozesse in allen Modellschichten vollständig auf dem Untergitter behandelt. Einige Ergebnisse dieser Methode werden im Vergleich mit Standard-Simulationen unterschiedlichen Auflösungsgrades gezeigt.

Le variateur de vitesse est l’un des convertisseurs de puissance le plus utilisé en industrie ; il alimente les pompes, les ventilateurs, les convoyeurs, les ascenseurs …etc. Dans ces systèmes de conversion, les modules de puissance intégrants des composants IGBT représentent la partie cœur de conversion de l’énergie électrique AC/AC. En fonctionnement, ils sont sujets à des conditions fonctionnelles et environnementales sévères (cycles de température, humidité, vibrations …etc.). En raison de la puissance dissipée au niveau de leurs composants (IGBTs et diodes), le module subit des déformations d’origine thermomécanique, qui s’accumulent au fil du temps en limitant sa durée de vie et en impactant sur sa fiabilité. Dès lors, les industriels se trouvent non seulement face au développement des composants robustes et fiables, mais surtout être capable à élaborer des outils permettant la programmation des phases de maintenance. Dans cette thématique s’inscrivent les travaux de cette thèse. Durant laquelle nous essayons de répondre à ce besoin, en proposant une méthodologie de prédiction de durée de vie restante d’un module de puissance intégré au sein d’un variateur de vitesse de 15kW. En effet, nous développons un modèle de durée de vie restante nécessitant une modélisation électrothermique du système, un algorithme de comptage de cycles de température appelé le Rainflow et une loi de durée de vie obtenue à partir des tests de vieillissement. Le paramètre de durée de vie est ensuite calculé en appliquant la règle de Miner. Enfin nous proposons des stratégies de prise en compte de l’effet de dégradation pour ce modèle. / The motor drive is one of the most used power converters in industry; it drives pumps, fans, conveyers, elevators …etc. In this conversion systems, power modules integrating IGBT devices represent the core part in the AC/AC electrical energy conversion. During operation, they are subjected to severe functional and environmental conditions (temperature cycles, humidity, vibrations …etc.). Due to the dissipated power at the level of their devices (IGBTs and diodes), the module undergoes deformations of a thermomechanical origin, which accumulate over time limiting its lifetime and impacting on its reliability. Therefore, manufacturers are not faced only with the development of robust and reliable components, but above all they need to be able to develop tools for maintenance phases scheduling. In this scope, the thesis work is focused. During it, we try to bring a solution to this need, so we propose a remaining lifetime prediction methodology for an integrated power module inside a 15kW motor drive.Therefore, we develop a remaining lifetime model that requires an electrothermal modeling of the system, a temperature cycles counting algorithm called by the Rainflow and a lifetime law obtained from the aging tests. The lifetime parameter is then calculated by applying the Miner’s rule. Finally, we propose strategies to take into account the degradation effect on this model.

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京都大学 / 新制・課程博士 / 博士(理学) / 甲第23678号 / 理博第4768号 / 新制||理||1683(附属図書館) / 京都大学大学院理学研究科数学・数理解析専攻 / (主査)教授 加藤 周, 教授 雪江 明彦, 教授 池田 保 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM

Systems biology comprises the global, integrated analysis of large-scale data encoding different levels of biological information with the aim to obtain global insight into the cellular networks. Several studies have unveiled the modular and hierarchical organization inherent in these networks. In this dissertation, we propose and develop innovative systems approaches to integrate multi-source biological data in a modular manner for network inference and biomarker discovery in complex diseases such as breast cancer. The first part of the dissertation is focused on gene module identification in gene expression data. As the most popular way to identify gene modules, many cluster algorithms have been applied to the gene expression data analysis. For the purpose of evaluating clustering algorithms from a biological point of view, we propose a figure of merit based on Kullback-Leibler divergence between cluster membership and known gene ontology attributes. Several benchmark expression-based gene clustering algorithms are compared using the proposed method with different parameter settings. Applications to diverse public time course gene expression data demonstrated that fuzzy c-means clustering is superior to other clustering methods with regard to the enrichment of clusters for biological functions. These results contribute to the evaluation of clustering outcomes and the estimations of optimal clustering partitions. The second part of the dissertation presents a hybrid computational intelligence method to infer gene regulatory modules. We explore the combined advantages of the nonlinear and dynamic properties of neural networks, and the global search capabilities of the hybrid genetic algorithm and particle swarm optimization method to infer network interactions at modular level. The proposed computational framework is tested in two biological processes: yeast cell cycle, and human Hela cancer cell cycle. The identified gene regulatory modules were evaluated using several validation strategies: 1) gene set enrichment analysis to evaluate the gene modules derived from clustering results; (2) binding site enrichment analysis to determine enrichment of the gene modules for the cognate binding sites of their predicted transcription factors; (3) comparison with previously reported results in the literatures to confirm the inferred regulations. The proposed framework could be beneficial to biologists for predicting the components of gene regulatory modules in which any candidate gene is involved. Such predictions can then be used to design a more streamlined experimental approach for biological validation. Understanding the dynamics of these gene regulatory modules will shed light on the related regulatory processes. Driven by the fact that complex diseases such as cancer are “diseases of pathways”, we extended the module concept to biomarker discovery in cancer research. In the third part of the dissertation, we explore the combined advantages of molecular interaction network and gene expression profiles to identify biomarkers in cancer research. The reliability of conventional gene biomarkers has been challenged because of the biological heterogeneity and noise within and across patients. In this dissertation, we present a module-based biomarker discovery approach that integrates interaction network topology and high-throughput gene expression data to identify markers not as individual genes but as modules. To select reliable biomarker sets across different studies, a hybrid method combining group feature selection with ensemble feature selection is proposed. First, a group feature selection method is used to extract the modules (subnetworks) with discriminative power between disease groups. Then, an ensemble feature selection method is used to select the optimal biomarker sets, in which a double-validation strategy is applied. The ensemble method allows combining features selected from multiple classifications with various data subsampling to increase the reliability and classification accuracy of the final selected biomarker set. The results from four breast cancer studies demonstrated the superiority of the module biomarkers identified by the proposed approach: they can achieve higher accuracies, and are more reliable in datasets with same clinical design. Based on the experimental results above, we believe that the proposed systems approaches provide meaningful solutions to discover the cellular regulatory processes and improve the understanding about disease mechanisms. These computational approaches are primarily developed for analysis of high-throughput genomic data. Nevertheless, the proposed methods can also be extended to analyze high-throughput data in proteomics and metablomics areas. / Ph. D.

International Telemetering Conference Proceedings / October 27-30, 1997 / Riviera Hotel and Convention Center, Las Vegas, Nevada / This paper describes a Smart Sensor Network System for applications requiring sensors connected in a multidrop configuration in order to minimize interconnecting cables. The communication protocol was optimized for high speed data collection. The Smart Sensor Network System was developed with the following goals in mind: cost reduction, reliability and performance increase.

Inflammatory bowel disease encompasses a variety of heterogeneous chronic inflammatory diseases that affect the gastrointestinal tract, where Crohn’s disease and ulcerative colitis are the principal examples. The etiology of these, and many other complex human diseases, remain largely unknown and therefore pose relevant targets for novel research strategies. One such strategy is the in silico application of network theory derived methods to data sourced from publicly available repositories of e.g. gene expression data. Specifically, methods generating graphs of interconnected elements enriched by differentially expressed genes—disease modules—were inferred with data available through the Gene Expression Omnibus. Based on a previous method, the current project aimed to evaluate disease modules, combined from stand-alone inferential methods, in disease consensus modules: representing pathophenotypical motifs for the diseases of interest. The modules found to be significantly enriched by genome-wide association study inferred single-nucleotide polymorphisms, as validated using the Pathway Scoring Algorithm, were subsequently subjects for further analysis using Kyoto Encyclopedia of Genes and Genomes-pathway enrichment, and literature searches. The results of this study adheres to previous findings relating to the employed method, but lack any novelty pertaining the diseases of interest. However, the results substantiate the preceding methods’ conclusion by including parameters that increase statistical validity. In addition, the study contributed to peripheral results concerning both the methodology of consensus module methods, and the elucidation of inflammatory bowel disease etiology and disease subtype differentiation, that pose interesting subjects for future investigation.

The aim of the diploma thesis is to make functional application for design of DAQ system for productive machine by using LabVIEW and platforms cRIO. The first part is focused on productive machine. After the thesis continues with specific examples. These examples have impact on machining accuracy and their measurements. The second part of diploma thesis is focused on making measurement program. LabVIEW FPGA Module, LabVIEW Real-Time Module are used in the program.