Motiviert durch strukturelle Eigenschaften molekularer Ähnlichkeitsnetzwerke werden die Evolution der größten Komponente eines Netzwerkes in zwei verschiedenen stochastischen Modellen, zufälligen Hypergraphen und zufälligen Schnittgraphen, untersucht. Zuerst wird bewiesen, dass die Anzahl der Knoten in der größten Komponente d-uniformer Hypergraphen einer Normalverteilung folgt. Der Beweis nutzt dabei ausschließlich probabilistische Argumente und keine enumerative Kombinatorik. Diesem grundlegenden Resultat folgen weitere Grenzwertsätze für die gemeinsame Verteilung von Knoten- und Kantenzahl sowie Sätze zur Zusammenhangswahrscheinlichkeit zufälliger Hypergraphen und zur asymptotischen Anzahl zusammenhängender Hypergraphen. Da das Hypergraphenmodell einige Eigenschaften der Realweltdaten nur unzureichend abbildet, wird anschließend die Evolution der größten Komponente in zufälligen Schnittgraphen, die Clustereigenschaften realer Netzwerke widerspiegeln, untersucht. Es wird gezeigt, dass zufällige Schnittgraphen sich von zufälligen (Hyper-)Graphen dadurch unterscheiden, dass (bei einer durchschnittlichen Nachbaranzahl von mehr als eins) weder die größte Komponente linear noch die zweitgrößte Komponente logarithmisch groß in Abhängigkeit von der Knotenzahl ist. Weiterhin wird ein Polynomialzeitalgorithmus zur Überdeckung der Kanten eines Graphen mit möglichst wenigen Cliquen (vollständigen Graphen) beschrieben und seine asymptotische Optimalität im Modell der zufälligen Schnittgraphen bewiesen. Anschließend wird die Entwicklung der chromatischen Zahl untersucht und gezeigt, dass zufällige Schnittgraphen mit hoher Wahrscheinlichkeit mittels verschiedener Greedystrategien optimal gefärbt werden können. Letztendlich zeigen Experimente auf realen Netzen eine Übereinstimmung mit den theoretischen Vorhersagen und legen eine gegenseitige Zertifizierung der Optimalität von Cliquen- und Färbungszahl durch Heuristiken nahe. / Motivated by structural properties of molecular similarity networks we study the behaviour of the component evolution in two different stochastic network models, that is random hypergraphs and random intersection graphs. We prove gaussian distribution for the number of vertices in the giant component of a random d-uniform hypergraph. We provide a proof using only probabilistic arguments, avoiding enumerative methods completely. This fundamental result is followed by further limit theorems concerning joint distributions of vertices and edges as well as the connectivity probability of random hypergraphs and the number of connected hypergraphs. Due to deficiencies of the hypergraph model in reflecting properties of the real--world data, we switch the model and study the evolution of the order of the largest component in the random intersection graph model which reflects some clustering properties of real--world networks. We show that for appropriate choice of the parameters random intersection graphs differ from random (hyper-)graphs in that neither the so-called giant component, appearing when the average number of neighbours of a vertex gets larger than one, has linear order nor is the second largest of logarithmic order in the number of vertices. Furthermore we describe a polynomial time algorithm for covering graphs with cliques, prove its asymptotic optimality in a random intersection graph model and study the evolution of the chromatic number in the model showing that, in a certain range of parameters, these random graphs can be coloured optimally with high probability using different greedy algorithms. Experiments on real network data confirm the positive theoretical predictions and suggest that heuristics for the clique and the chromatic number can work hand in hand proving mutual optimality.

Ziel dieser Arbeit ist es, die Rolle von Pfaden für die Ausbreitung von Infektionskrankheiten auf komplexen Netzwerken zu untersuchen. Wir zeigen die Relevanz von Pfaden im Kontext der Epidemiologie in statischen und zeitabhängigen Netzwerken. Ein zentrales Ergebnis ist hierbei die Erreichbarkeitsentwicklung, die eine Analyse der Pfadstruktur zeitabhängiger Netzwerke erlaubt. In dieser Dissertation wird der Einfluss zweier bestimmter Merkmale statischer Netzwerke auf die Eigenschaften ihrer Pfadstruktur untersucht. Als Fallbeispiel analysieren wir hierfür ein Viehhandelsnetzwerk in Deutschland. Dieses Netzwerk besitzt eine Riesenkomponente und eine modulare Struktur. Die wichtigsten Ergebnisse sind hierbei, dass Netzwerke, die nahe an der Perkolationsschwelle liegen, mit großer Wahrscheinlichkeit zwei disjunkte Risikoklassen für Knoten aufweisen und, dass eine modulare Struktur eine signifikante Verzögerung von Krankheitsausbrüchen zur Folge hat. Hervorzuheben sind außerdem die Methoden, die hier zur Analyse zeitabhängiger Netzwerke vorgestellt werden. Das sind Systeme, in denen das Auftreten von Kanten mit der Zeit variiert. In dieser Arbeit stellen wir eine neue Methode vor, mit der die kausale Erreichbarkeit eines zeitabhängigen Netzwerks berechnet werden kann. Darüber hinaus stellen wir Erreichbarkeitsentwicklung als eine neue Methode zur Berechnung kürzester Pfaddauern in zeitabhängigen Netzwerken vor. Diese Herangehensweise ermöglicht es, charakteristische Zeitskalen für das Durchqueren von zeitabhängigen Netzwerken aufzuzeigen. Die Kenntnis solcher Zeitskalen ist von fundamentaler Wichtigkeit für die Abschätzung von Zeiten, die für die Verbreitung von Epidemien benötigt werden. Die Erreichbarkeit eines zeitabhängigen Netzwerks kann mit ihrem aggregierten Gegenstück verglichen werden. Damit definieren wir die Kausalitätstreue, die die Güte einer statischen Approximation eines zeitabhängigen Netzwerks quantifiziert. / The objective of this thesis is to examine the role of paths for the spread of infectious diseases on complex networks. We demonstrate the importance of paths in the context of epidemiology for the case of static and temporal networks. As a central result, we introduce the unfolding accessibility method, that allows for the analysis of the path structure of temporal networks. In this thesis, we analyze the impact of two particular attributes of static networks on the properties of their path structure. As a case study, we analyze the properties of a livestock trade network in Germany. This network exhibits a giant component and a modular structure. The main findings here are that networks close to the percolation threshold are likely to show two disjoint risk classes for the nodes and, a modular structure causes a significant delay for disease outbreaks. Furthermore, special emphasis should be placed on the methods introduced in this thesis for the analysis of temporal networks, i.e. systems where the occurrence of edges varies over time. In this work we introduce a novel method to obtain the causal accessibility graph of a temporal network. Moreover, we introduce unfolding accessibility as a novel formalism for the evaluation of shortest path durations in temporal networks. This approach is able to reveal characteristic timescales for the traversal of temporal networks. Knowledge of these timescales is of fundamental importance for the estimation of times needed for the spread of infectious diseases. The accessibility graph of a temporal network can be compared to its aggregated counterpart. Hence we define the causal fidelity, which quantifies the goodness of the static approximation of a temporal network from the causal point of view.

O presente trabalho tem como meta auxiliar na resposta a um dos principais problemas estudados no campo das ciências econômicas: o quanto e como intervenções regulatórias afetam a dinâmica dos mercados. Para isso será feita uma análise dos dados contratuais de compra e venda de energia elétrica no ambiente livre de comercialização de energia brasileiro por meio de uma metodologia que utiliza métricas de análise de redes complexas para avaliação da competitividade. Os dados abordam a atividade dos agentes comercializadores de energia nesse mercado durante o período de 2006 a 2015. É estabelecido então um ranking mensal desses agentes e criada a rede por meio da verificação das trocas de posições nesses rankings. Os resultados da análise indicam em quais anos houve maior variação na competitividade no mercado e pela análise das redes resultantes verifica-se a formação de estruturas de mercado. Posteriormente os resultados são comparados com métricas tradicionais de avaliação de competitividade e concentração de mercado e, por fim, é feita uma avaliação qualitativa dos índices sob a luz das principais alterações regulatórias ocorridas no período / The main goal of this project is to assist in the answer to one of the main issues in the study of Economics: how regulatory interventions affect the dynamics of the markets, in this case specifically, electricity markets. This will be achieved through an analysis of the contractual data of electric energy in the free Brazilian energy market environment through a methodology that uses complex network analysis for the evaluation of competitiveness. The data covers the contracts of all energy traders of this market in the period from 2006 to 2015. A monthly ranking of these agents is established and a network is created through the verification of position changes in these rankings. The results of the analysis indicates in which years there was greater variation in competitiveness and the analysis of the resulting networks indicates market structures formation. The results are then compared with traditional metrics for competitiveness and market concentration. Finally, a qualitative assessment of the results is made considering the major regulatory changes that have occurred in the study period

<div>This dissertation focuses on developing a series of mathematical models to understand the role of urban transportation system, urban mobility and information dissemination in the spreading process of infectious diseases within metropolitan areas. Urban transportation system serves as the catalyst of disease contagion since it provides the mobility for bringing people to participate in intensive urban activities and has high passenger volume and long commuting time which facilitates the spread of contagious diseases. In light of significant needs in understanding the connection between disease contagion and the urban transportation systems, both macroscopic and microscopic models are developed and the dissertation consists of three main parts. </div><div></div><div>The first part of the dissertation aims to model the macroscopic level of disease spreading within urban transportation system based on compartment models. Nonlinear dynamic systems are developed to model the spread of infectious disease with various travel modes, compare models with and without contagion during travel, understand how urban transportation system may facilitate or impede epidemics, and devise control strategies for mitigating epidemics at the network level. The hybrid automata is also introduced to account for systems with different levels of control and with uncertain initial epidemic size, and reachability analysis is used to over-approximate the disease trajectories of the nonlinear systems. The 2003 Beijing SARS data are used to validate the effectiveness of the model. In addition, comprehensive numerical experiments are conducted to understand the importance of modeling travel contagion during urban disease outbreaks and develop control strategies for regulating the entry of urban transportation system to reduce the epidemic size. </div><div></div><div>The second part of the dissertation develops a data-driven framework to investigate the disease spreading dynamics at individual level. In particular, the contact network generation algorithm is developed to reproduce individuals' contact pattern based on smart card transaction data of metro systems from three major cities in China. Disease dynamics are connected with contact network structures based on individual based mean field and origin-destination pair based mean field approaches. The results suggest that the vulnerability of contact networks solely depends on the risk exposure of the most dangerous individual, however, the overall degree distribution of the contact network determines the difficulties in controlling the disease from spreading. Moreover, the generation model is proposed to depict how individuals get into contact and their contact duration, based on their travel characteristics. The metro data are used to validate the correctness of the generation model, provide insights on monitoring the risk level of transportation systems, and evaluate possible control strategies to mitigate the impacts due to infectious diseases. </div><div></div><div>Finally, the third part of the dissertation focuses on the role played by information in urban travel, and develops a multiplex network model to investigate the co-evolution of disease dynamics and information dissemination. The model considers that individuals may obtain information on the state of diseases by observing the disease symptoms from the people they met during travel and from centralized information sources such as news agencies and social medias. As a consequence, the multiplex networks model is developed with one layer capturing information percolation and the other layer modeling the disease dynamics, and the dynamics on one layer depends on the dynamics of the other layer. The multiplex network model is found to have three stable states and their corresponding threshold values are analytically derived. In the end, numerical experiments are conducted to investigate the effectiveness of local and global information in reducing the size of disease outbreaks and the synchronization between disease and information dynamics is discussed. </div><div></div>

O presente trabalho tem como meta auxiliar na resposta a um dos principais problemas estudados no campo das ciências econômicas: o quanto e como intervenções regulatórias afetam a dinâmica dos mercados. Para isso será feita uma análise dos dados contratuais de compra e venda de energia elétrica no ambiente livre de comercialização de energia brasileiro por meio de uma metodologia que utiliza métricas de análise de redes complexas para avaliação da competitividade. Os dados abordam a atividade dos agentes comercializadores de energia nesse mercado durante o período de 2006 a 2015. É estabelecido então um ranking mensal desses agentes e criada a rede por meio da verificação das trocas de posições nesses rankings. Os resultados da análise indicam em quais anos houve maior variação na competitividade no mercado e pela análise das redes resultantes verifica-se a formação de estruturas de mercado. Posteriormente os resultados são comparados com métricas tradicionais de avaliação de competitividade e concentração de mercado e, por fim, é feita uma avaliação qualitativa dos índices sob a luz das principais alterações regulatórias ocorridas no período / The main goal of this project is to assist in the answer to one of the main issues in the study of Economics: how regulatory interventions affect the dynamics of the markets, in this case specifically, electricity markets. This will be achieved through an analysis of the contractual data of electric energy in the free Brazilian energy market environment through a methodology that uses complex network analysis for the evaluation of competitiveness. The data covers the contracts of all energy traders of this market in the period from 2006 to 2015. A monthly ranking of these agents is established and a network is created through the verification of position changes in these rankings. The results of the analysis indicates in which years there was greater variation in competitiveness and the analysis of the resulting networks indicates market structures formation. The results are then compared with traditional metrics for competitiveness and market concentration. Finally, a qualitative assessment of the results is made considering the major regulatory changes that have occurred in the study period

In welcher Weise hängt die Dynamik eines neuronalen Systems von den Eigenschaften seiner Netzwerkstruktur ab? Diese wichtige Fragestellung der Neurowissenschaft untersuchen wir in dieser Dissertation anhand einer analytischen und numerischen Modellierung der Aktivität großer neuronaler Netzwerke mit komplexer Struktur. Im Fokus steht die Relevanz zweier bestimmter Merkmale für die Dynamik: strukturelle Heterogenität und Gradkorrelationen. Ein zentraler Bestandteil der Dissertation ist die Entwicklung einer Molekularfeldnäherung, mit der die mittlere Aktivität heterogener, gradkorrelierter neuronaler Netzwerke berechnet werden kann, ohne dass einzelne Neuronen explizit simuliert werden müssen. Die Netzwerkstruktur wird von einer reduzierten Matrix erfasst, welche die Verbindungsstärke zwischen den Neuronengruppen beschreibt. Für einige generische Zufallsnetzwerke kann diese Matrix analytisch berechnet werden, was eine effiziente Analyse der Dynamik dieser Systeme erlaubt. Mit der Molekularfeldnäherung und numerischen Simulationen zeigen wir, dass assortative Gradkorrelationen einem neuronalen System ermöglichen, seine Aktivität bei geringer externer Anregung aufrecht zu erhalten und somit besonders sensitiv auf schwache Stimuli zu reagieren. / An important question in neuroscience is how the structure and dynamics of a neuronal network relate to each other. We approach this problem by modeling the spiking activity of large-scale neuronal networks that exhibit several complex network properties. Our main focus lies on the relevance of two particular attributes for the dynamics, namely structural heterogeneity and degree correlations. As a central result, we introduce a novel mean-field method that makes it possible to calculate the average activity of heterogeneous, degree-correlated neuronal networks without having to simulate each neuron explicitly. We find that the connectivity structure is sufficiently captured by a reduced matrix that contains only the coupling between the populations. With the mean-field method and numerical simulations we demonstrate that assortative degree correlations enhance the network’s ability to sustain activity for low external excitation, thus making it more sensitive to small input signals.

Avui en dia, la música està esbiaixada cap al consum d'alguns artistes molt populars. Per exemple, el 2007 només l'1% de totes les cançons en format digital va representar el 80% de les vendes. De la mateixa manera, només 1.000 àlbums varen representar el 50% de totes les vendes, i el 80% de tots els àlbums venuts es varen comprar menys de 100 vegades. Es clar que hi ha una necessitat per tal d'ajudar a les persones a filtrar, descobrir, personalitzar i recomanar música, a partir de l'enorme quantitat de contingut musical disponible. Els algorismes de recomanació de música actuals intenten predir amb precisió el que els usuaris demanen escoltar. Tanmateix, molt sovint aquests algoritmes tendeixen a recomanar artistes famosos, o coneguts d'avantmà per l'usuari. Això fa que disminueixi l'eficàcia i utilitat de les recomanacions, ja que aquests algorismes es centren bàsicament en millorar la precisió de les recomanacions. És a dir, tracten de fer prediccions exactes sobre el que un usuari pugui escoltar o comprar, independentment de quant útils siguin les recomanacions generades. En aquesta tesi destaquem la importància que l'usuari valori les recomanacions rebudes. Per aquesta raó modelem la corba de popularitat dels artistes, per tal de poder recomanar música interessant i desconeguda per l'usuari. Les principals contribucions d'aquesta tesi són: (i) un nou enfocament basat en l'anàlisi de xarxes complexes i la popularitat dels productes, aplicada als sistemes de recomanació, (ii) una avaluació centrada en l'usuari, que mesura la importància i la desconeixença de les recomanacions, i (iii) dos prototips que implementen la idees derivades de la tasca teòrica. Els resultats obtinguts tenen una clara implicació per aquells sistemes de recomanació que ajuden a l'usuari a explorar i descobrir continguts que els pugui agradar. / Actualmente, el consumo de música está sesgada hacia algunos artistas muy populares. Por ejemplo, en el año 2007 sólo el 1% de todas las canciones en formato digital representaron el 80% de las ventas. De igual modo, únicamente 1.000 álbumes representaron el 50% de todas las ventas, y el 80% de todos los álbumes vendidos se compraron menos de 100 veces. Existe, pues, una necesidad de ayudar a los usuarios a filtrar, descubrir, personalizar y recomendar música a partir de la enorme cantidad de contenido musical existente. Los algoritmos de recomendación musical existentes intentan predecir con precisión lo que la gente quiere escuchar. Sin embargo, muy a menudo estos algoritmos tienden a recomendar o bien artistas famosos, o bien artistas ya conocidos de antemano por el usuario.Esto disminuye la eficacia y la utilidad de las recomendaciones, ya que estos algoritmos se centran en mejorar la precisión de las recomendaciones. Con lo cuál, tratan de predecir lo que un usuario pudiera escuchar o comprar, independientemente de lo útiles que sean las recomendaciones generadas. En este sentido, la tesis destaca la importancia de que el usuario valore las recomendaciones propuestas. Para ello, modelamos la curva de popularidad de los artistas con el fin de recomendar música interesante y, a la vez, desconocida para el usuario.Las principales contribuciones de esta tesis son: (i) un nuevo enfoque basado en el análisis de redes complejas y la popularidad de los productos, aplicada a los sistemas de recomendación,(ii) una evaluación centrada en el usuario que mide la calidad y la novedad de las recomendaciones, y (iii) dos prototipos que implementan las ideas derivadas de la labor teórica. Los resultados obtenidos tienen importantes implicaciones para los sistemas de recomendación que ayudan al usuario a explorar y descubrir contenidos que le puedan gustar. / Music consumption is biased towards a few popular artists. For instance, in 2007 only 1% of all digital tracks accounted for 80% of all sales. Similarly, 1,000 albums accounted for 50% of all album sales, and 80% of all albums sold were purchased less than 100 times. There is a need to assist people to filter, discover, personalise and recommend from the huge amount of music content available along the Long Tail.Current music recommendation algorithms try to accurately predict what people demand to listen to. However, quite often these algorithms tend to recommend popular -or well-known to the user- music, decreasing the effectiveness of the recommendations. These approaches focus on improving the accuracy of the recommendations. That is, try to make accurate predictions about what a user could listen to, or buy next, independently of how useful to the user could be the provided recommendations. In this Thesis we stress the importance of the user's perceived quality of the recommendations. We model the Long Tail curve of artist popularity to predict -potentially- interesting and unknown music, hidden in the tail of the popularity curve. Effective recommendation systems should promote novel and relevant material (non-obvious recommendations), taken primarily from the tail of a popularity distribution. The main contributions of this Thesis are: (i) a novel network-based approach for recommender systems, based on the analysis of the item (or user) similarity graph, and the popularity of the items, (ii) a user-centric evaluation that measures the user's relevance and novelty of the recommendations, and (iii) two prototype systems that implement the ideas derived from the theoretical work. Our findings have significant implications for recommender systems that assist users to explore the Long Tail, digging for content they might like.

Submitted by Geandra Rodrigues (geandrar@gmail.com) on 2018-10-30T13:13:53Z No. of bitstreams: 1 marialuizafurtuozofalci.pdf: 3709688 bytes, checksum: dfce5ab7a51878d5d6b47d99d30e5d36 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2018-11-23T12:23:48Z (GMT) No. of bitstreams: 1 marialuizafurtuozofalci.pdf: 3709688 bytes, checksum: dfce5ab7a51878d5d6b47d99d30e5d36 (MD5) / Made available in DSpace on 2018-11-23T12:23:48Z (GMT). No. of bitstreams: 1 marialuizafurtuozofalci.pdf: 3709688 bytes, checksum: dfce5ab7a51878d5d6b47d99d30e5d36 (MD5) Previous issue date: 2018-09-20 / O processo de desenvolvimento de software é uma atividade complexa, que é influenciada por diferentes fatores, e pode ser surpreendida por um comportamento inesperado do software. Devido a sua importância cada vez maior nos dias de hoje, a necessidade de melhoria na qualidade do software e seus processos é de extrema importância. Uma forma de melhorar processos de software é através da análise de dados de execuções anteriores, dados estes que para serem coletados necessitam do controle e monitoramento dos processos. O presente trabalho propõe uma arquitetura que engloba modelos de proveniência de dados, ontologia e rede complexa, para modelar a proveniência na área de processos de desenvolvimento software, além de permitir a extração de conhecimento implícito nos dados. A arquitetura conta também com uma camada de visualização para dar suporte à compreensão do comportamento dos dados a gerentes de projetos, e dessa forma os mesmos possam tomar decisões orientadas a dados e melhorar futuras execuções. A arquitetura proposta foi avaliada através da utilização de dados reais e estudo com participação de um gerente de projetos. / Software development process is a complex activity, which is influenced by many factors and can be surprised by an unexpected software behavior. Software‟s importance has grown exponentially in the past few years, which makes software improvement extremely necessary, as it is present in many different aspects of daily life. Analyze data from previous executions may be a good tactic to deal with software unpredictability, and to record processes‟ data is necessary to implement software monitoring and control. The present work proposes an architecture that encompasses provenance data, ontology and complex network models to structure data provenance in software process‟ domain and allow implicit knowledge extraction. The architecture proposed has a visualization layer to support project managers‟ data comprehension, allowing them to have data-oriented decision making and improve future process executions. The proposed architecture was evaluated with real companies‟ data and through a study with a specialist participation.

Orientador: Andriana Susana Lopes de Oliveira Campanharo / Resumo: A epilepsia é uma patologia cerebral caracterizada pela presença de crises epilépticas que afeta em torno de 1% de toda a população mundial. As crises epilépticas podem ocasionar perda de consciência gerando grande impacto na vida das pessoas que sofrem da doença. Atualmente, a técnica de EletroEncefaloGrafia (EEG) é um dos testes diagnósticos mais importantes para a identificação das crises epilépticas e de eventos entre crises, chamados de Descargas Epileptiformes Interictais (DEIs). A zona epileptogênica é a região neuronal responsável pela geração das crises em pacientes com epilepsia focal, e em alguns casos, seu tratamento consiste em uma cirurgia para a remoção desta região. Estudos que relacionam séries temporais com a teoria de redes complexas ganharam grande destaque e importância, mostrando que é possível mapear uma série temporal em uma rede complexa sem grande perdas de informações. Neste sentido, o objetivo deste trabalho consiste na utilização do mapeamento proposto por Campanharo et al., utilizando o conceito de bins, em uma aplicação inovadora: na detecção automática das crises epilépticas, das DEIs por meio da análise de sinais de EEG de pacientes com epilepsia e na localização da zona epileptogênica a partir de uma técnica não-invasiva. A metodologia proposta apresentou performance satisfatória na identificação das crises epilépticas e das DEIs, caracterizadas por uma grande variação de amplitude e frequência. Valores médios de sensibilidade de 83,3% e de 9... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Epilepsy is a cerebral pathology characterized by the presence of epileptic seizures that affects around 1% of the entire world population. Epileptic seizures can cause loss of consciousness generating great impact on the lives of people who suffer from the disease. Currently, the ElectroEncefaloGraphy(EEG) technique is one of the most important diagnostic tests for the identification of epileptic seizures and events between seizures, called Intertictal Epileptiform Discharges (DEIs). The epileptogenic zone is the neuronal region responsible for generating seizures in patients with focal epilepsy, and in some cases, treatment consists of surgery to remove this region. Studies that relate time series to the theory of complex networks have gained great prominence and importance, showing that it is possible to map a time series in a complex network without great loss of information. In this sense, the objective of this work is to use the mapping proposed by Campanharo et al., using the concept of bins, in a novel application: in the automatic detection of epileptic seizures, of DEIs through the analysis of EEG signals of patients with epilepsy and the location of the epileptogenic zone using a non-invasive technique. The proposed methodology presented satisfactory performance in the identification of epileptic seizures and EIDs, characterized by a high variation in amplitude and frequency. Average sensitivity values 83,3% and 93,2% were reached in the detection of epileptic seiz... (Complete abstract click electronic access below) / Mestre

Critical infrastructure networks (CIN) are essential systems that provide key socio-eco-nomical services. They can be classified into different sectors such as energy supply, in-formation and communication, water, food, health, transport, among others. Their pro-tection from hazards and constant improvements are crucial for ensuring the appropriate operation of a society and economy. In this context, the current study focused on analyzing the factors affecting the function-ing of one particular CIN in the water sector: the urban drainage networks (UDNs). More specifically, the present research focused on evaluating how does the structure of UDNs influence their function. Concepts and methods from complex network theory were used to evaluate structural properties of sewers systems and function was evaluated in terms wastewater flow quantitates and occurrence of node flooding and combined sewer over-flow (CSOs) events, considered as indicators if network performance. Initial results suggested that network metrics can be used as surrogate variables of UDNs main functions, i.e. transport and collection of wastewater. However, efficiency of this de-pended on the type of layout, i.e. physical arrangement of the network. Following studies focused then on developing a graph-theory based method to quantify the structure of an UDN and use it to evaluate the influence of layout on its function. Results suggested that sewer networks with a more meshed layout had a better performance, i.e. adverse events such as urban pluvial flooding and CSO discharges were less likely to occur, than UDNs with a branched layout. Furthermore, transitioning from a tree-like structure to a more meshed system was identified to be a cost-efficient measure for urban flooding manage-ment. It is concluded that the structure of an UDN, in terms of its layout, has a strong influence on its performance and therefore on its resilience. It is expected that the obtained results could serve as support for better management and operational actions of UDNs and could also serve as basis for the development of a new structural resiliency analysis based mainly on the UDN configuration.:1. General Introduction 2. Centrality and Shortest Path Length measures for the functional analysis of Urban Drainage Networks 3. Meshness of sewer networks and its implications for flooding occurrence 4. The Role of Sewer Network Structure on the Occurrence and Magnitude of Combined Sewer Overflows (CSOs) 5. Determination of Optimal Meshness of Sewer Network Based On a Cost-Benefit Analysis 6. Influence of Meshness on Urban Drainage Networks Resilience and its Implications 7. Conclusions and Outlook 8. Supplementary Information