La connectivité marine est le transfert de larves et/ou d'individus entre des habitats marins éloignés. Grâce à la connectivité, les populations marines éloignées peuvent faire face à la pression de l'habitat en s'appuyant sur le transfert qui vient des populations éloignées de la même espèce. Le transfert entre les populations éloignées dans l'océan est possible par le transport dû aux courants. Cependant, il est pas encore clair si le champ des courants détermine totalement la persistance des espèces marines ou si la démographie locale joue un rôle. Les mesures in situ de la connectivité sont extrêmement difficiles. Par conséquence, notre connaissance de la connectivité est déduite des simulations numériques de dispersion. Le but de cette thèse est de préciser si la persistance de la connaissance du champ des courants et d’étudier l'effet des paramétrisations numériques dans l'estimation de la connectivité. Premièrement, je compare la théorie des graphes et le modèle de métapopulation pour déterminer si les courants ont un rôle prédominant. Cela permet d'identifier quelles mesures de la théories des graphes identifient de manière fiable les sites reproductifs importants pour la persistance en s'appuyant sur la connaissance des seuls courants. Deuxièmement, j’étudie les avantages et les lacunes de différents schémas de fermeture de turbulence. Ceci permet de préciser quel schéma reproduit mieux l'activité de turbulence dans des modèles numériques. Troisièmement, j'étudie les mécanismes générateurs de turbulence aux limites du fond. Ceci permet de connaître le coefficient de traînée effectif dû aux flux sur la topographie brute et de mieux estimer les flux turbulents. / Marine connectivity is the transfer of larvae and/or individuals between distant marine habitats. Thanks to connectivity, distant marine population can face habitat pressure by relying on the transfer from distant populations of the same species. The transfer between distant populations in the ocean is made possible by the transport due to the currents. However, it is still not clear if the current field totally determines the persistence of the marine species or if the local demography plays a role. Crucially, in situ measurements of connectivity are extremely difficult. Therefore, our knowledge about connectivity is inferred from numerical dispersal simulations. The aim of this thesis is to clarify if we can deduce the persistence from the knowledge of the current field and to investigate the effect of numerical turbulence parameterizations in estimating connectivity. Firstly, I compare graph theory and metapopulation model to determine if currents have a predominant role. This allows to identify which graph theory measures reliably identifies reproductive sites important for persistence by relying on the knowledge of currents only. Secondly, I investigate the advantages and shortcomings of different turbulence closure models. This allows to clarify which TCS better reproduces turbulence activity in numerical models. Thirdly, I investigate generating mechanisms of bottom boundary turbulence. This allows to know the effective drag coefficient due to flow over rough topography and better estimate turbulent fluxes.

Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2017-01-16T10:13:02Z No. of bitstreams: 2 Dissertação - Áurea Valéria Pereira da Silva - 2016.pdf: 2576793 bytes, checksum: 3b3472812df3319818244bcd2e0482d7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2017-01-16T10:13:30Z (GMT) No. of bitstreams: 2 Dissertação - Áurea Valéria Pereira da Silva - 2016.pdf: 2576793 bytes, checksum: 3b3472812df3319818244bcd2e0482d7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2017-01-16T10:13:30Z (GMT). No. of bitstreams: 2 Dissertação - Áurea Valéria Pereira da Silva - 2016.pdf: 2576793 bytes, checksum: 3b3472812df3319818244bcd2e0482d7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2016-12-14 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Introduction: OpenEHR is a standardization of Health Information Systems (HIS) that is mainly concerned with the exchange of Electronic Health Records (EHR). One of the major obstacles to the adoption of openEHR is the lack of database persistence specifications. Objective: The aim of this work is the mapping of storage structures from the Archetype Definition Language (ADL) specifications which describe clinical knowledge structure. Method: This research initially makes a systematic mapping about persistence structures that are derived directly from ADL specifications, i.e. without dependences from the Reference Model (RM) of openEHR. A new persistence strategy is proposed and compared with ones found in the literature. Results: Assumptions, criteria and rules were used to present the proposed approach. Its evaluation considered quantitative aspects of persistence structures. Conclusion: The evaluation revealed some advantages of proposed approach, such as: reductions of reference attributes (referential integrity) and redundancy in clinical database; production of unidentified clinical records in relation to the patient; creation of a single base table for each archetype, regardless of its use in the form of slots; scalability of database schema (stable number of database tables), even in the occurrence of recursion through slots. / Introdução: OpenEHR é uma padronização dos Sistemas de Informação em Saúde (SIS) que se preocupa principalmente com a troca de Registros de Saúde Eletrônicos (RES). Um dos maiores obstáculos à adoção do openEHR é a carência de especificações de persistência de banco de dados. Objetivo: O objetivo deste trabalho é o mapeamento de estruturas de armazenamento a partir das especificações Archetype Definition Language (ADL) que descrevem a estrutura do conhecimento clínico. Métodos: Esta pesquisa inicialmente faz um mapeamento sistemático sobre estruturas de persistência que são derivadas diretamente de especificações ADL, isto é, sem dependências do Modelo de Referência (RM) de openEHR. Uma nova estratégia de persistência é proposta e comparada com as encontradas na literatura. Resultados: Foram utilizados pressupostos, critérios e regras para apresentar a abordagem proposta. Uma avaliação considerou aspectos quantitativos das estruturas de persistência, em comparação com o que foi encontrado na literatura. Conclusões: A avaliação revelou algumas vantagens da abordagem proposta, tais como: reduções de atributos de referência (integridade referencial) e redundância em banco de dados clínicos; produção de registros clínicos não identificados em relação ao paciente; criação de uma tabela de base única para cada arquétipo, independentemente da sua utilização sob a forma de slots; escalabilidade do esquema de banco de dados (número estável de tabelas de banco de dados), mesmo na ocorrência de recursão através de slots.

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College completion agendas necessarily presume year-to-year student persistence. Institutional efforts to retain admitted students has emerged for a variety of reasons, some intrinsic and others extrinsic. Some of these reasons include (1) financial exigency as institutions strive to retain tuition-paying students or meet prescribed enrollment and retention criteria currently used in performance funding strategies; (2) reputation enhancement as institutions attempt to ascend annual publications such as the U.S. News & World Report which rely on retention rates as one of several indicators used to measure institutional quality; (3) gaining a perceived advantage in admissions, marketing, and fundraising as persistence rates have, for better or worse, become a de facto measure of quality undergraduate programs; and (4) mission fulfillment as institutions, especially public institutions, are tasked with contributing towards broadly cast social goals such as access to education, economic competitiveness, and community development. Knowledge about forces that impact student attrition is critical to the development of preventative strategies that seek to improve student persistence rates. One such environmental force that has an impact on student persistence is financial aid and a student’s ability to pay for their college education. While research examining the impact of financial aid on student persistence has accumulated over the years, little is known about how the loss of certain types of aid, specifically, state-based merit aid, affects students once they enroll in an institution. The majority of studies about financial aid’s impact on student persistence were conducted prior to the establishment of many state-wide merit scholarship programs. Tinto’s (1975, 1986, 1993) interactional theory of student departure serves as the theoretical framework employed in this study. Tinto (1975) states that entering college students bring with them specific background characteristics and initial commitments that influence the student’s social and academic integration at the institution that, in turn, impact subsequent institutional and goal commitments and, ultimately, persistence. This study intends to examine pre- and post-matriculation data gathered through the admissions and financial aid processes to develop predictive models useful in calculating the probabilities associated with Bright Futures scholarship retention, institutional persistence after losing a Bright Futures scholarship award at the conclusion of a student’s first year of enrollment, and a student’s eligibility to recapture a Bright Futures scholarship award in their third year of enrollment. Data was collected passively from institutional databases on 2,418 students meeting the study criteria for inclusion in the model building process. Findings indicate that the models developed throughout the course of this study hold potential for informing institutional retention initiatives among Bright Futures scholarship award recipients.

The purpose of this study was to investigate and predict Latino male student retention using ACT’s Engage College survey at a research university in the southwestern region of the U.S. ACT’s Engage survey was designed to predict first-year college retention using 10 psychosocial measures. However, no empirical study exists to support ACT’s claim especially for Latino male students. Data from a four-year research university between 2009 and 2011 were analyzed with logistic regression. Logistic regression analysis was performed for the whole sample (N = 8,061) and for the Latino male subsample (n = 860). In the entire sample’s first regression model, high school grade quartile and SAT score as well as demographic variables were used as predictor variables. In this model, the independent variables of high school grade point average quartile, SAT score, gender, and race made statistically significant contributions to the model (Nagelkerke R2 = .031, p < .01). In the entire sample’s second regression model, ACT’s 10 psychosocial variables were added to the first regression model as predictor variables. Results indicated the instrument was valid for the freshmen as a whole because five out of 10 psychosocial measures displayed statistically significant odds ratios (ORs) for predicting retention: (a) Commitment to College (OR = 1.006, p < .01), (b) Academic Discipline (OR = 1.005, p < .01), (c) Social Activity (OR = -.997, p < .01), (d) Social Connection (OR = 1.004, p < .01), and (e) Academic Self-Confidence (OR = -.997, p < .01). Regarding the subsample of 860 Latino males, none of the 10 psychosocial measures produced statistically significant results. The findings indicate the need to determine a new way of identifying at-risk Latino male students because current methods have failed to build a robust predictive model for this student population.

Avec l’avènement du cloud computing et des big data, de nouveaux systèmes de gestion de bases de données sont apparus, connus en général sous le vocable systèmes NoSQL. Par rapport aux systèmes relationnels, ces systèmes se distinguent par leur absence de schéma, une spécialisation pour des types de données particuliers (documents, graphes, clé/valeur et colonne) et l’absence de langages de requêtes déclaratifs. L’offre est assez pléthorique et il n’y a pas de standard aujourd’hui comme peut l’être SQL pour les systèmes relationnels. De nombreuses applications peuvent avoir besoin de manipuler en même temps des données stockées dans des systèmes relationnels et dans des systèmes NoSQL. Le programmeur doit alors gérer deux (au moins) modèles de données différents et deux (au moins) langages de requêtes différents pour pouvoir écrire son application. De plus, il doit gérer explicitement tout son cycle de vie. En effet, il a à (1) coder son application, (2) découvrir les services de base de données déployés dans chaque environnement Cloud et choisir son environnement de déploiement, (3) déployer son application, (4) exécuter des requêtes multi-sources en les programmant explicitement dans son application, et enfin le cas échéant (5) migrer son application d’un environnement Cloud à un autre. Toutes ces tâches sont lourdes et fastidieuses et le programmeur risque d’être perdu dans ce haut niveau d’hétérogénéité. Afin de pallier ces problèmes et aider le programmeur tout au long du cycle de vie des applications utilisant des bases de données multiples, nous proposons un ensemble cohérent de modèles, d’algorithmes et d’outils. En effet, notre travail dans ce manuscrit de thèse se présente sous forme de quatre contributions. Tout d’abord, nous proposons un modèle de données unifié pour couvrir l’hétérogénéité entre les modèles de données relationnelles et NoSQL. Ce modèle de données est enrichi avec un ensemble de règles de raffinement. En se basant sur ce modèle, nous avons défini notre algèbre de requêtes. Ensuite, nous proposons une interface de programmation appelée ODBAPI basée sur notre modèle de données unifié, qui nous permet de manipuler de manière uniforme n’importe quelle source de données qu’elle soit relationnelle ou NoSQL. ODBAPI permet de programmer des applications indépendamment des bases de données utilisées et d’exprimer des requêtes simples et complexes multi-sources. Puis, nous définissons la notion de bases de données virtuelles qui interviennent comme des médiateurs et interagissent avec les bases de données intégrées via ODBAPI. Ce dernier joue alors le rôle d’adaptateur. Les bases de données virtuelles assurent l’exécution des requêtes d’une façon optimale grâce à un modèle de coût et un algorithme de génération de plan d’exécution optimal que nous définis. Enfin, nous proposons une approche automatique de découverte de bases de données dans des environnements Cloud. En effet, les programmeurs peuvent décrire leurs exigences en termes de bases de données dans des manifestes, et grâce à notre algorithme d’appariement, nous sélectionnons l’environnement le plus adéquat à notre application pour la déployer. Ainsi, nous déployons l’application en utilisant une API générique de déploiement appelée COAPS. Nous avons étendue cette dernière pour pouvoir déployer les applications utilisant plusieurs sources de données. Un prototype de la solution proposée a été développé et mis en œuvre dans des cas d'utilisation du projet OpenPaaS. Nous avons également effectué diverses expériences pour tester l'efficacité et la précision de nos contributions / The production of huge amount of data and the emergence of Cloud computing have introduced new requirements for data management. Many applications need to interact with several heterogeneous data stores depending on the type of data they have to manage: traditional data types, documents, graph data from social networks, simple key-value data, etc. Interacting with heterogeneous data models via different APIs, and multiple data stores based applications imposes challenging tasks to their developers. Indeed, programmers have to be familiar with different APIs. In addition, the execution of complex queries over heterogeneous data models cannot, currently, be achieved in a declarative way as it is used to be with mono-data store application, and therefore requires extra implementation efforts. Moreover, developers need to master and deal with the complex processes of Cloud discovery, and application deployment and execution. In this manuscript, we propose an integrated set of models, algorithms and tools aiming at alleviating developers task for developing, deploying and migrating multiple data stores applications in cloud environments. Our approach focuses mainly on three points. First, we provide a unified data model used by applications developers to interact with heterogeneous relational and NoSQL data stores. This model is enriched by a set of refinement rules. Based on that, we define our query algebra. Developers express queries using OPEN-PaaS-DataBase API (ODBAPI), a unique REST API allowing programmers to write their applications code independently of the target data stores. Second, we propose virtual data stores, which act as a mediator and interact with integrated data stores wrapped by ODBAPI. This run-time component supports the execution of single and complex queries over heterogeneous data stores. It implements a cost model to optimally execute queries and a dynamic programming based algorithm to generate an optimal query execution plan. Finally, we present a declarative approach that enables to lighten the burden of the tedious and non-standard tasks of (1) discovering relevant Cloud environments and (2) deploying applications on them while letting developers to simply focus on specifying their storage and computing requirements. A prototype of the proposed solution has been developed and implemented use cases from the OpenPaaS project. We also performed different experiments to test the efficiency and accuracy of our proposals

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The WSCC ADN program had 66.57% persistence rate between the years 2000-2004. This retrospective study analyzed 28 demographic, pre-clinical, and clinical variables to identify correlates for persistence within the WSCC ADN program. The population size was 730 first-time candidates or the entire population of five consecutive clinical classes graduating between the years of 2000-2004. The candidates were identified and the variables tabulated using the WSCC student information system. SPSS 13.0 software was employed to conduct descriptive, frequency, multiple regression, multivariate analysis of variance, and univariate analysis of variance tests. The criterion variables included persistence within the entire population, gender-specific persistence factors, and age-specific factors within the traditional and non-traditional populations that persisted. Descriptive and frequency analysis found that most candidates were female (90.82%), Caucasian (96.44%), and classified as non-traditional (63.97%). Females and particularly non-traditional females maintained the highest persistence rates. The mean pre-clinical and clinical admittance ages were 25.04 and 28.39 years. Seventy percent of the candidates lived within the WSCC service area. The mean distance commuted was 37.71 miles. Statistical tests revealed that nine predictor variables influenced persistence within the entire population. The largest contributors of variance were 2nd semester clinical GPA (η2 = .33), cumulative pre-clinical GPA (η2 = .15), and grades in microbiology (η2 = .14). These variables along with the number of course withdrawals and/or grades of “F” were found to be major indicators for persistence within the female and male sub-populations. The number of full-time semesters was a more significant contributor in the male population (η2 = .12) than the female population (η2 = .02). Data analysis revealed that non-traditional students who persisted had higher human anatomy and physiology II grades while the traditional students had a higher rate of transferring coursework into the nursing program. These findings will aid in the direction of the recruitment, evaluation, and selection of potential candidates for this very demanding program of study while validating the importance of prerequisite core knowledge. The findings should serve as predictive evidence to better identify and inform potential “at-risk” candidates of the factors that affect persistence in this nursing program.

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