Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

Tallgrass prairie restoration is an important conservation activity in rural areas. However, little is known about prairie restoration in urban environments. The overall objective of this study was to characterize and better understand urban prairie restoration. This was carried out through an examination of 29 restoration sites within Winnipeg, Manitoba. The results indicated that actively restored urban prairies were successful and high in diversity. Multiple attributes of the restorations were examined as indicators of success including vegetation, the propagule bank and insects. However, not all attributes delivered equivocal results. This suggests that multiple measures should be used to assess a restoration site. Anthropogenic and biophysical variables were found to influence vegetation of the restorations equally, highlighting the importance of incorporating a human component in urban ecological research. These urban restorations were seen to surpass larger rural restorations in quality; thus, efforts should be made to increase their prevalence.

Understanding pesticide transport is crucial to evaluate their ecological impact on ecosystems. Current knowledge on the spatial variability of pesticide deposition, the impact of erosion on pesticides export and the in situ pesticide degradation is very limited at the catchment scale. In this thesis, characterisation and modeling at two scales, the plot and catchment, were combined in two agricultural contexts. A formalism was developed to predict pesticide transport in runoff and was integrated in LISEM (LImbourg Soil Erosion Model). The results show that impermeable roads contributed to more than 40% to the overall load of fungicides exported via runoff from the vineyard. Pesticide partitioning between suspended solids and runoff water differed largely according to the molecules and the hydrological dynamics. The occurrence of degradation products and the enrichment of one enantiomer were observed suggesting the potential of chiral analyses for assessing biodegradation of chiral pesticides at such scale. Overall, the investigation demonstrated that combining different approaches enable a better understanding of pesticide transport.

Tallgrass prairie restoration is an important conservation activity in rural areas. However, little is known about prairie restoration in urban environments. The overall objective of this study was to characterize and better understand urban prairie restoration. This was carried out through an examination of 29 restoration sites within Winnipeg, Manitoba. The results indicated that actively restored urban prairies were successful and high in diversity. Multiple attributes of the restorations were examined as indicators of success including vegetation, the propagule bank and insects. However, not all attributes delivered equivocal results. This suggests that multiple measures should be used to assess a restoration site. Anthropogenic and biophysical variables were found to influence vegetation of the restorations equally, highlighting the importance of incorporating a human component in urban ecological research. These urban restorations were seen to surpass larger rural restorations in quality; thus, efforts should be made to increase their prevalence.

Organizations own data sources that contain millions, billions or even trillions of rows and these data are usually highly dimensional in nature. Typically, these raw repositories are comprised of numerous independent data sources that are too big to be copied or joined, with the consequence that aggregations become highly problematic. Data cubes play an essential role in facilitating fast Online Analytical Processing (OLAP) in many multi-dimensional data warehouses. Current data cube computation techniques have had some success in addressing the above-mentioned aggregation problem. However, the combined problem of reducing data cube size for very large and highly dimensional databases, while guaranteeing fast query response times, has received less attention. Another issue is that most OLAP tools often causes users to be lost in the ocean of data while performing data analysis. Often, most users are interested in only a subset of the data. For example, consider in such a scenario, a business manager who wants to answer the crucial location-related business question. "Why are my sales declining at location X"? This manager wants fast, unambiguous location-aware answers to his queries. He requires access to only the relevant ltered information, as found from the attributes that are directly correlated with his current needs. Therefore, it is important to determine and to extract, only that small data subset that is highly relevant from a particular user's location and perspective. In this thesis, we present the Personalized Smart Cube approach to address the abovementioned scenario. Our approach consists of two main parts. Firstly, we combine vertical partitioning, partial materialization and dynamic computation to drastically reduce the size of the computed data cube while guaranteeing fast query response times. Secondly, our personalization algorithm dynamically monitors user query pattern and creates a personalized data cube for each user. This ensures that users utilize only that small subset of data that is most relevant to them. Our experimental evaluation of our Personalized Smart Cube approach showed that our work compared favorably with other state-of-the-art methods. We evaluated our work focusing on three main criteria, namely the storage space used, query response time and the cost savings ratio of using a personalized cube. The results showed that our algorithm materializes a relatively smaller number of views than other techniques and it also compared favourable in terms of query response time. Further, our personalization algorithm is superior to the state-of-the art Virtual Cube algorithm, when evaluated in terms of the number of user queries that were successfully answered when using a personalized cube, instead of the base cube.

Decoding the genomes from organisms that encompass all taxonomies provides the foundation for extensive, large scale studies of biological molecules such as RNA, protein and carbohydrates. The high-throughput studies facilitated by the existence of these genome sequences necessitate the development of new analytic methods for the interpretation of large sets of results. The work herein focuses on the development of a novel clustering method for the analysis of protein array results and examines its utilization in the analysis of integrated interaction data sets. Sets of proteins that interact with a molecule of interest were clustered according to their functional similarity. The simUI distance metric in the statistical analysis package BioConductor was applied to measure the similarity of two proteins utilizing the assembly of their Gene Ontology annotation. Clusters were identified by partitioning around medoids and interpreted using the summary label provided by the Gene Ontology annotation of the medoid. The utility of the method was tested on two published yeast protein array data sets and shown to allow interpretation of the data to yield novel biological hypotheses. We performed a protein array screen using the E3 ubiquitin ligase and PDZ domain-containing protein LNX1. We combined these results with other published LNX1 interactors to produce a set of 220 proteins that was clustered according to Gene Ontology annotation. From the clustering results, 14 proteins were selected for subsequent examination by co-immunoprecipitation, of which 8 proteins were confirmed as LNX1 interactors. Recognition of 6 proteins by specific LNX1 PDZ domains was confirmed by fusion protein pull-downs. This work supports the role of LNX1 as a signalling scaffold. The interpretation of protein array results using our novel clustering method facilitated the identification of candidate molecules for subsequent experimental analysis. Thus our analytical method facilitates identification of biologically relevant molecules within a large data set, making this method an essential component of complex, high-throughput experimentation.

Decoding the genomes from organisms that encompass all taxonomies provides the foundation for extensive, large scale studies of biological molecules such as RNA, protein and carbohydrates. The high-throughput studies facilitated by the existence of these genome sequences necessitate the development of new analytic methods for the interpretation of large sets of results. The work herein focuses on the development of a novel clustering method for the analysis of protein array results and examines its utilization in the analysis of integrated interaction data sets. Sets of proteins that interact with a molecule of interest were clustered according to their functional similarity. The simUI distance metric in the statistical analysis package BioConductor was applied to measure the similarity of two proteins utilizing the assembly of their Gene Ontology annotation. Clusters were identified by partitioning around medoids and interpreted using the summary label provided by the Gene Ontology annotation of the medoid. The utility of the method was tested on two published yeast protein array data sets and shown to allow interpretation of the data to yield novel biological hypotheses. We performed a protein array screen using the E3 ubiquitin ligase and PDZ domain-containing protein LNX1. We combined these results with other published LNX1 interactors to produce a set of 220 proteins that was clustered according to Gene Ontology annotation. From the clustering results, 14 proteins were selected for subsequent examination by co-immunoprecipitation, of which 8 proteins were confirmed as LNX1 interactors. Recognition of 6 proteins by specific LNX1 PDZ domains was confirmed by fusion protein pull-downs. This work supports the role of LNX1 as a signalling scaffold. The interpretation of protein array results using our novel clustering method facilitated the identification of candidate molecules for subsequent experimental analysis. Thus our analytical method facilitates identification of biologically relevant molecules within a large data set, making this method an essential component of complex, high-throughput experimentation.

Thesis (Ph. D.)--Miami University, Dept. of Botany, 2008. / Title from second page of PDF document. Includes bibliographical references (p. 86-88).

Αντικείμενο της παρούσας διατριβής υπήρξε αφενός η μελέτη και η αξιολόγηση των υπαρχόντων τεχνικών και αφετέρου ο σχεδιασμός, η υλοποίηση και η αποτίμηση νέων τεχνικών και μηχανικών για τη βελτιστοποίηση της απόδοσης του συστήματος με τη βέλτιστη αξιοποίηση των διαθέσιμων πόρων. / The objective of this dissertation was on the one hand the study and analysis of existing techniques and algorithms for large-scale DVEs and on the other hand the design, implementation and assessment of new techniques, methods and algorithms that can ensure the quality of the provided services and the efficient operation of the system. The algorithms and techniques developed were analysed and evaluated through extensive experiments both for identifying possible weaknesses and for highlighting the applications that can achieve optimum results. More specifically, two approaches have been designed and developed. The first approach is related to the design and development of a partitioning algorithm, that, in contrary to existing approaches, exploits the special characteristic of the virtual world and the impact those characteristics have on users’ behavior in order to predict the needs of the environment, in terms of resources, before they are required. The second approach is related to the design and development of a dynamic management approach that exploits the dynamic nature of DVEs in order to perform load balancing and to ensure performance optimization. During the elaboration of this dissertation, we found out that one of the basic problems when designing and implementing algorithms, methods and techniques for large-scale DVEs is the way that their efficiency could be examined. In most of the cases, the evaluation is based on theoretical models, which often fail to meet the circumstances and situations met in real DVEs. In particular, for supporting largescale DVEs, extended infrastructure is needed in terms of both hardware and software. Due to the fact that both researchers and application designers do not always have access to such extended infrastructure, the assessment and evaluation of developed techniques are extremely difficult. In most of the cases, both application designers and researchers adopt specialized methods for evaluating different techniques, while in other cases simulation tools have been developed from scratch. However, given the fact that the design and implementation is application or technique-specific, the reusability of these tools for different architectures and algorithms is not always successful. To this direction and for overcoming this important limitation, a simulation modeling framework for assessing DVEs performance was designed and implemented. The framework takes into account a number of both generic and special-cause parameters, which can be set on demand by the DVE designers and stake-holders and it is based on transforming system requirements to the concepts of operational management.

[pt] Esta dissertação investiga a utilização de sistemas Neuro- Fuzzy Hierárquicos BSP (Binary Space Partitioning) para aplicações em classificação de padrões, previsão, sistemas de controle e extração de regras fuzzy. O objetivo é criar um modelo Neuro-Fuzzy Hierárquico BSP do tipo Mamdani a partir do modelo Neuro-Fuzzy Hierárquico BSP Class (NFHB-Class) que é capaz de criar a sua própria estrutura automaticamente e extrair conhecimento de uma base de dados através de regras fuzzy, lingüisticamente interpretáveis, que explicam a estrutura dos dados. Esta dissertação consiste de quatros etapas principais: estudo dos principais sistemas hierárquicos; análise do sistema Neuro-Fuzzy Hierárquico BSP Class, definição e implementação do modelo NFHB-Mamdani e estudo de casos. No estudo dos principais sistemas hierárquicos é efetuado um levantamento bibliográfico na área. São investigados, também, os principais modelos neuro-fuzzy utilizados em sistemas de controle - Falcon e o Nefcon. Na análise do sistema NFHB- Class, é verificado o aprendizado da estrutura, o particionamento recursivo, a possibilidade de se ter um maior número de entrada - em comparação com outros sistemas neuro-fuzzy - e regras fuzzy recursivas. O sistema NFHB- Class é um modelo desenvolvido especificamente para classificação de padrões, como possui várias saídas, não é possível utilizá-lo em aplicações em controle e em previsão. Para suprir esta deficiência, é criado um novo modelo que contém uma única saída. Na terceira etapa é definido um novo modelo Neuro-Fuzzy Hierárquico BSP com conseqüentes fuzzy (NFHB-Mamdani), cuja implementação utiliza a arquitetura do NFHBClass para a fase do aprendizado, teste e validação, porém, com os conseqüentes diferentes, modificando a estratégia de definição dos conseqüentes das regras. Além de sua utilização em classificação de padrões, previsão e controle, o sistema NFHB-Mamdani é capaz de extrair conhecimento de uma base de dados em forma de regras do tipo SE ENTÃO. No estudo de casos são utilizadas duas bases de dados típicas para aplicações em classificação: Wine e o Iris. Para previsão são utilizadas séries de cargas elétricas de seis companhias brasileiras diferentes: Copel, Cemig, Light, Cerj, Eletropaulo e Furnas. Finalmente, para testar o desempenho do sistema em controle faz-se uso de uma planta de terceira ordem como processo a controlar. Os resultados obtidos para classificação, na maioria dos casos, são superiores aos melhores resultados encontrados pelos outros modelos e algoritmos aos quais foram comparados. Para previsão de cargas elétricas, os resultados obtidos estão sempre entre os melhores resultados fornecidos por outros modelos aos quais formam comparados. Quanto à aplicação em controle, o modelo NFHB-Mamdani consegue controlar, de forma satisfatória, o processo utilizado para teste. / [en] This paper investigates the use of Binary Space Partitioning (BSP) Hierarchical Neuro-Fuzzy Systems for applications in pattern classification, forecast, control systems and obtaining of fuzzy rules. The goal is to create a BSP Hierarchical Neuro-Fuzzy Model of the Mamdani type from the BSP Hierarchical Neuro-Fuzzy Class (NFHB-Class) which is able to create its own structure automatically and obtain knowledge from a data base through fuzzy rule, interpreted linguistically, that explain the data structure. This paper is made up of four main parts: study of the main Hierarchical Systems; analysis of the BSP Hierarchical Neuro-Fuzzy Class System, definition and implementation of the NFHB-Mamdani model, and case studies. A bibliographical survey is made in the study of the main Hierarchical Systems. The main Neuro-Fuzzy Models used in control systems - Falcon and Nefcon -are also investigated. In the NFHB-Class System, the learning of the structure is verified, as well as, the recursive partitioning, the possibility of having a greater number of inputs in comparison to other Neuro-Fuzzy systems and recursive fuzzy rules. The NFHB-Class System is a model developed specifically for pattern classification, since it has various outputs, it is not possible to use it in control application and forecast. To make up for this deficiency, a new unique output model is developed. In the third part, a new BSP Hierarchical Neuro-Fuzzy model is defined with fuzzy consequents (NFHB-Mamdani), whose implementation uses the NFHB-Class architecture for the learning, test, and validation phase, yet with the different consequents, modifying the definition strategy of the consequents of the rules. Aside from its use in pattern classification, forecast, and control, the NFHB-Mamdani system is capable of obtaining knowledge from a data base in the form of rules of the type IF THEN. Two typical data base for application in classification are used in the case studies: Wine and Iris. Electric charge series of six different Brazilian companies are used for forecasting: Copel, Cemig, Light, Cerj, Eletropaulo and Furnas. Finally, to test the performance of the system in control, a third order plant is used as a process to be controlled. The obtained results for classification, in most cases, are better than the best results found by other models and algorithms to which they were compared. For forecast of electric charges, the obtained results are always among the best supplied by other models to which they were compared. Concerning its application in control, the NFHB-Mamdani model is able to control, reasonably, the process used for test.