Orientadores: Sergio Furtado dos Reis, Jose Alexandre Felizola Diniz-Filho / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Biologia / Made available in DSpace on 2018-08-12T20:24:03Z (GMT). No. of bitstreams: 1 Jose_Juliana_D.pdf: 6444913 bytes, checksum: 544e7d18e4de1a8e195d188567f9b007 (MD5) Previous issue date: 2009 / Resumo: A quantidade de variabilidade genética presente nas espécies pode ser influenciada por diversos fatores que atuam em diferentes níveis de organização biológica. Dentre esses fatores, os que afetam a dinâmica populacional têm sido extensivamente estudados. No entanto, a influência da história evolutiva tem sido negligenciada ao se estudar a variabilidade genética das espécies. Nós investigamos pela primeira vez a influência da história evolutiva das espécies sobre sua variabilidade genética, e como a história evolutiva compartilhada afeta as relações já estabelecidas entre a variabilidade genética e outros traços, através dos métodos filogenéticos comparativos e de métodos de análise de redes complexas. Simulações computacionais de modelos neutros de evolução indicaram influência da história evolutiva, e nos deram previsões acerca do sinal filogenético presente na variabilidade genética. Nós de fato observamos o sinal filogenético previsto nas simulações em grupos animais variados que compõe um banco de dados de 1521 espécies amostradas para a diversidade genética de aloenzimas. Detectamos também a influência da história evolutiva das espécies sobre sua variabilidade no padrão modular de redes que representam a similaridade na diversidade genética das espécies. Quando consideramos a história evolutiva na análise das relações entre a variabilidade genética e outros traços das espécies, observamos relações mais fracas do que as que foram previamente estabelecidas na literatura. / Abstract: The amount of genetic variability on species can be influenced by factors acting in different levels of biological organization, and the ones related to population dynamics have been extensively studied. However, past studies neglected the influence of evolutionary history on genetic variability. We studied for the first time the influence of evolutionary history on species genetic variability and how the influence of evolutionary history changes pre-established relationships between variability and other species traits. For our investigations we used phylogenetic comparative methods and complex network analysis. Computer simulations on neutral models of evolution showed influence of evolutionary history and also provide us expectations for a phylogenetic signal on genetic variability. We in fact observed the previously expected phylogenetic signal in a wide variety of animal groups, which compose a database of 1521 species sampled for allozymic genetic diversity. We also detected the influence of species evolutionary history on its genetic variability in the modularity patterns of networks representing genetic diversity similarities between species. When considering evolutionary history on the analysis of genetic variability relationships with other species traits, we observed weaker relationships than those previously established on literature. / Doutorado / Genetica Animal e Evolução / Doutor em Genetica e Biologia Molecular

Em sistemas biológicos, o estudo da estabilidade das redes de regulação gênica é visto como uma contribuição importante que a Matemática pode proporcionar a pesquisas sobre câncer e outras doenças genéticas. Neste trabalho, utilizamos o conceito de ``canalização\'\' como sinônimo de estabilidade em uma rede biológica. Como as características de uma rede de regulação canalizada ainda são superficialmente compreendidas, estudamos esse conceito sob o ponto de vista computacional: propomos um modelo matemático simplificado para descrever o fenômeno e realizamos algumas análises sobre o mesmo. Mais especificamente, a estabilidade da maior bacia de atração das redes Booleanas - um clássico paradigma para a modelagem de redes de regulação - é analisada. Os resultados indicam que a estabilidade da maior bacia de atração está relacionada com dados biológicos sobre o crescimento de colônias de leveduras e que considerações sobre a interação entre as funções Booleanas e a topologia da rede devem ser realizadas conjuntamente na análise de redes estáveis. / In biological systems, the study of gene regulatory networks stability is seen as an important contribution that Mathematics can make to cancer research and that of other genetic diseases. In this work, we consider the concept of ``canalization\'\' as a consequence of stability in gene regulatory networks. The characteristics of canalized regulatory networks are superficially understood. Hence, we study the canalization concept under a computational framework: a simplified model is proposed to describe the phenomenon using Boolean Networks - a classical paradigm to modeling regulatory networks. Specifically, the stability of the largest basin of attraction in gene regulatory networks is analyzed. Our results indicate that the stability of the largest basin of attraction is related to biological data on growth of yeast colonies, and that thoughts about the interaction between Boolean functions and network topologies must be given in the analysis of stable networks.

A Clusterização de dados em grupos oferece uma maneira de entender e extrair informações relevantes de grandes conjuntos de dados. A abordagem em relação a aspectos como a representação dos dados e medida de similaridade entre clusters, e a necessidade de ajuste de parâmetros iniciais são as principais diferenças entre os algoritmos de clusterização, influenciando na qualidade da divisão dos clusters. O uso cada vez mais comum de grandes conjuntos de dados aliado à possibilidade de melhoria das técnicas já existentes tornam a clusterização de dados uma área de pesquisa que permite inovações em diferentes campos. Nesse trabalho é feita uma revisão dos métodos de clusterização já existentes, e é descrito um novo método de clusterização de dados baseado na identificação de comunidades em redes complexas e modelos computacionais inspirados biologicamente. A técnica de clusterização proposta é composta por duas etapas: formação da rede usando os dados de entrada; e particionamento dessa rede para obtenção dos clusters. Nessa última etapa, a técnica de otimização por nuvens de partículas é utilizada a fim de identificar os clusters na rede, resultando em um algoritmo de clusterização hierárquico divisivo. Resultados experimentais revelaram como características do método proposto a capacidade de detecção de clusters de formas arbitrárias e a representação de clusters com diferentes níveis de refinamento. / DAta clustering is an important technique to understand and to extract relevant information in large datasets. Data representation and similarity measure adopted, and the need to adjust initial parameters, are the main differences among clustering algorithms, interfering on clusters quality. The crescent use of large datasets and the possibility to improve existing techniques make data clustering a research area that allows innovation in different fields. In this work is made a review of existing data clustering methods, and it is proposed a new data clustering technique based on community dectection on complex networks and bioinspired models. The proposed technique is composed by two steps: network formation to represent input data; and network partitioning to identify clusters. In the last step, particle swarm optimization technique is used to detect clusters, resulting in an hierarchical clustering algorithm. Experimental results reveal two main features of the algorithm: the ability to detect clusters in arbitrary shapes and the ability to generate clusters with different refinement degrees

As redes complexas surgiram como uma nova e importante maneira de representação e abstração de dados capaz de capturar as relações espaciais, topológicas, funcionais, entre outras características presentes em muitas bases de dados. Dentre as várias abordagens para a análise de dados, destacam-se a classificação e a detecção de outliers. A classificação de dados permite atribuir uma classe aos dados, baseada nas características de seus atributos e a detecção de outliers busca por dados cujas características se diferem dos demais. Métodos de classificação de dados e de detecção de outliers baseados em redes complexas ainda são pouco estudados. Tendo em vista os benefícios proporcionados pelo uso de redes complexas na representação de dados, o presente trabalho apresenta o desenvolvimento de um método baseado em redes complexas para detecção de outliers que utiliza a caminhada aleatória e um índice de dissimilaridade. Este método possibilita a identificação de diferentes tipos de outliers usando a mesma medida. Dependendo da estrutura da rede, os vértices outliers podem ser tanto aqueles distantes do centro como os centrais, podem ser hubs ou vértices com poucas ligações. De um modo geral, a medida proposta é uma boa estimadora de vértices outliers em uma rede, identificando, de maneira adequada, vértices com uma estrutura diferenciada ou com uma função especial na rede. Foi proposta também uma técnica de construção de redes capaz de representar relações de similaridade entre classes de dados, baseada em uma função de energia que considera medidas de pureza e extensão da rede. Esta rede construída foi utilizada para caracterizar mistura entre classes de dados. A caracterização de classes é uma questão importante na classificação de dados, porém ainda é pouco explorada. Considera-se que o trabalho desenvolvido é uma das primeiras tentativas nesta direção / Complex networks have emerged as a new and important way of representation and data abstraction capable of capturing the spatial relationships, topological, functional, and other features present in many databases. Among the various approaches to data analysis, we highlight classification and outlier detection. Data classification allows to assign a class to the data based on characteristics of their attributes and outlier detection search for data whose characteristics differ from the others. Methods of data classification and outlier detection based on complex networks are still little studied. Given the benefits provided by the use of complex networks in data representation, this study developed a method based on complex networks to detect outliers based on random walk and on a dissimilarity index. The method allows the identification of different types of outliers using the same measure. Depending on the structure of the network, the vertices outliers can be either those distant from the center as the central, can be hubs or vertices with few connections. In general, the proposed measure is a good estimator of outlier vertices in a network, properly identifying vertices with a different structure or a special function in the network. We also propose a technique for building networks capable of representing similarity relationships between classes of data based on an energy function that considers measures of purity and extension of the network. This network was used to characterize mixing among data classes. Characterization of classes is an important issue in data classification, but it is little explored. We consider that this work is one of the first attempts in this direction

abstract: Hyperbolic geometry, which is a geometry which concerns itself with hyperbolic space, has caught the eye of certain circles in the machine learning community as of late. Lauded for its ability to encapsulate strong clustering as well as latent hierarchies in complex and social networks, hyperbolic geometry has proven itself to be an enduring presence in the network science community throughout the 2010s, with no signs of fading into obscurity anytime soon. Hyperbolic embeddings, which map a given graph to hyperbolic space, have particularly proven to be a powerful and dynamic tool for studying complex networks. Hyperbolic embeddings are exploited in this thesis to illustrate centrality in a graph. In network science, centrality quantifies the influence of individual nodes in a graph. Eigenvector centrality is one type of such measure, and assigns an influence weight to each node in a graph by solving for an eigenvector equation. A procedure is defined to embed a given network in a model of hyperbolic space, known as the Poincare disk, according to the influence weights computed by three eigenvector centrality measures: the PageRank algorithm, the Hyperlink-Induced Topic Search (HITS) algorithm, and the Pinski-Narin algorithm. The resulting embeddings are shown to accurately and meaningfully reflect each node's influence and proximity to influential nodes. / Dissertation/Thesis / Masters Thesis Computer Science 2020

The need to understand the structure of complex networks increases as both their complexity and the dependency of human society on them grows. Network centralities help to recognize the key elements of these networks. Betweenness centrality is a network centrality measure based on shortest paths. More precisely, the contribution of a pair of vertices u, v to a vertex w ̸= u, v is the fraction of the shortest uv-paths which lead through w. Betweenness centrality is then given by the sum of contributions of all pairs of vertices u, v ̸= w to w. In this work, we have summarized known results regarding both exact values and bounds on betweenness. Additionally, we have improved an existing bound and obtained more exact formulation for r-regular graphs. We have made two major contributions about betweenness uniform graphs, whose vertices have uniform betweenness value. The first is that all betweenness uniform graphs of order n with maximal degree n − k have diameter at most k, by which we have solved a conjecture posed in the literature. The second major result is that betweenness uniform graphs nonisomorphic to a cycle that are either vertex- or edge-transitive are 3-connected, by which we have partially solved another conjecture. 1

Battery electric buses (BEBs) bring several advantages to public transportation systems. With fixed routes and scheduled trips, the implementation of BEBs in the transit context is considered a seamless transition towards a zero greenhouse gases transit system. However, energy consumption uncertainty is a significant deterrent for mainstream implementation of BEBs. Demonstration and trial projects are often conducted to better understand the uncertainty in energy consumption (EC). However, the BEB's energy consumption varies due to uncertainty in operational, topological, and environmental attributes. This thesis aims at developing simulation, data-driven, and low-resolution models using big data to quantify the EC of BEBs, with the overarching goal of developing a comprehensive planning framework for BEB implementation in bus transit networks. This aim is achieved through four interwind objectives. 1) Quantify the operational and topological characteristics of bus transit networks using complex network theory. This objective provides a fundamental base to understanding the behaviour of bus transit networks under disruptive events. 2) Investigate the impacts of the vehicular, operational, topological, and external parameters on the EC of BEBs. 3) Develop and evaluate the feasibility of big-data analytics and data-driven models to numerically estimate BEB's EC. 4) Create an open-source low-resolution data-based framework to estimate the EC of BEBs. This framework integrates the modelling efforts in objectives 1-3 and offers practical knowledge for transit providers. Overall, the thesis provides genuine contributions to BEB research and offers a practical framework for addressing the EC uncertainty associated with BEB operation in the transit context. Further, the results offer transit planners the means to set up the optimum transit operations profile that improves BEB energy utilization, and in turn, reduces transit-related greenhouse gases. / Thesis / Doctor of Engineering (DEng)

An application area of increasing importance is creating agent-based simulations to model human societies. One component of developing these simulations is the ability to generate realistic human social networks. Online social networking websites, such as Facebook, Google+, and Twitter, have increased in popularity in the last decade. Despite the increase in online social networking tools and the importance of studying human behavior in these networks, collecting data directly from these networks is not always feasible due to privacy concerns. Previous work in this area has primarily been limited to 1) network generators that aim to duplicate a small subset of the original network's properties and 2) problem-specific generators for applications such as the evaluation of community detection algorithms. In this thesis, we extended two synthetic network generators to enable them to duplicate the properties of a specific dataset. In the first generator, we consider feature similarity and label homophily among individuals when forming links. The second generator is designed to handle multiplex networks that contain different link types. We evaluate the performance of both generators on existing real-world social network datasets, as well as comparing our methods with a related synthetic network generator. In this thesis, we demonstrate that the proposed synthetic network generators are both time efficient and require only limited parameter optimization.

Modern societies are fully dependent on critical infrastructures networks to support the economy, security, and prosperity. Energy infrastructure network is of paramount importance to our societies. As a pillar of the economy, it is necessary that energy infrastructure networks continue to operate safely and be resilient to provide reliable power to other critical infrastructure networks. Nonetheless, frequent large-scale blackouts in recent years have highlighted the vulnerability in the power grids, where disruptions can trigger cascading failures causing a catastrophic regional-level blackout. Such catastrophic blackouts call for a systemic risk assessment approach whereby the entire network/system is assessed against such failures considering the dynamic power flow within. However, the lack of detailed data combining both topological and functional information, and the computational resources typically required for large-scale modelling, considering also operational corrective actions, have impeded large-scale resilience studies. In this respect, the research in the present dissertation focuses on investigating, analyzing, and evaluating the vulnerability of power grid infrastructure networks in an effort to enhance their resilience. Through a Complex Network Theory (CNT) lens, the power grid robustness has been evaluated against random and targeted attacks through evaluating a family of centrality measures. The results shows that CNT models provide a quick and potential indication to identify key network components, which support regulators and operators in making informed decisions to maintain and upgrade the network, constrained by the tolerable risk and allocated financial resources. Furthermore, a dynamic Cascade Failure Model (CFM) has been employed to develop a Physical Flow-Based Model (PFBM). The CFM considers the operational corrective actions in case of failure to rebalance the supply and demand (i.e., dispatch and load shedding). The CFM was subsequently utilized to construct a grid vulnerability map function of the Link Vulnerability Index (LVI), which can be used to rank the line maintenance priority. In addition, a Node Importance Index (NII) has been developed for power substations ranking according to the resulting cascade failure size. The results from CNT and CFM approaches were compared to address the impact of considering the physical behavior of the power grid. The comparison results indicate that relying solely on CNT topology-based model could result in erroneous conclusions pertaining to the grid behavior. Moving forward, a systemic risk mitigation strategy based on the Intentional Controlled Islanding (ICI) approach has been introduced to suppress the failure propagation. The proposed mitigation strategy integrated the operation- with structure-guided strategies has shown excellent capabilities in terms of enhancing the network robustness and minimizing the possibility of catastrophic large-scale blackouts. This research demonstrates the model application on a real large-scale network with data ranging from low to high voltage. In the future, the CFM model can be integrated with other critical infrastructure network systems to establish a network-of-networks interaction model for assessing the systemic risk throughout and between multiple network layers. Understanding the interdependence between different networks will provide stakeholders with insight on enhancing resilience and support policymakers in making informed decisions pertaining to the tolerable systemic risk level to take reliable actions under abnormal conditions. / Thesis / Doctor of Philosophy (PhD)

Mining of uranium ore and its extraction using the milling process generates solid and liquid waste, commonly termed uranium mine tailings. Uranium mine tailings is radioactive, as it consists of residual uranium, thorium, and radium, which amounts to 85% of the original ore’s radioactivity. Due to the extensively long half-lives of uranium (4.5x109 years), thorium (75,400 years), and radium (1,620 years) and their harmful radioactive, it is imperative to isolate uranium mine tailings from the environment for a longer period. Containment of uranium mine tailings in dam-like structures, called uranium mine tailings dam (UMTD), is the most followed disposal and storage method. Like a conventional water retention dam, UMTDs are also susceptible to failure, mainly due to adverse weather conditions. Once the UMTD fails, a fraction of the radioactive tailings infiltrates and migrate through the vadose zone contaminating the groundwater sources underlying it. Radionuclide behavior and migration in the subsurface are affected by several environmental factors. To minimize the uncertainty and improve current radionuclide fate and transport models, it is vital to study these factors and any interdependence existing between them. This study aims to understand these environmental factors by i) enlisting the factors affecting subsurface radionuclide migration through scoping review of articles and reports, and ii) analyzing the interdependence existing between the factors using the complex network theory (CNT) approach and identifying the dominant factors among them. Factors such as chemical and biological characteristics of soil stratigraphy, groundwater, and radioactive tailings plume, meteorological, and hydrogeological are found to influence radionuclide behavior and transport mechanisms in the vadose zone. CNT approach described soil microorganisms, fraction of organic carbon, infiltration rate of the soil, transmissivity, clay fraction in the soil, particulates in groundwater, and infiltrating rainwater as dominant factors in the NoF based on their centrality measures and sensitivity analysis of the network of factors (NoF). Any uncertainty associated with these factors will affect and propagate through the model. Hence, sufficient resources should be directed in the future to characterize these factors and minimize their uncertainty, which will lead to developing reliable fate and transport models for radionuclides. / Thesis / Master of Applied Science (MASc) / Waste products from uranium mining and milling operations are called uranium mine tailings, which are radioactive. Generally, uranium mine tailings are disposed of and isolated in dam-like structures referred to as uranium mine tailings dams (UMTD). One of the most common causes of UMTD failure is extreme weather conditions. When a UMTD fails, a part of tailings, consisting of radionuclides uranium, thorium, and radium, infiltrate into the subsurface through the vadose zone. Radionuclide behavior and transport in the subsurface is influenced by several environmental factors. The objective of the present study is to understand the factors affecting radionuclide migration by i) conducting a scoping review on radionuclide migration in the subsurface to describe the factors studied in the literature, and ii) understanding and analyzing any relation among the factors and deriving the most dominant factors based on their relation. This study can be used further to develop accurate and reliable radionuclide fate and transport models with minimal uncertainty.