In the last decades, although the scientific community has attempted to explain a series of complex phenomena, ranging from natural hazards to physical conditions and economic crises, aspects of their generation process still escape our full understanding. The present thesis intends to promote our understanding of the spatiotemporal behavior and the generation mechanisms that govern large and strong earthquakes, employing a broad multidisciplinary perspective for the interpretation of catastrophic events. Two main questions are debated. The first question concentrates on “whether the generation process of an extreme event has more than one facets prior to its final appearance”. In the scientific study of earthquakes, attention is drawn to the predictive capability and monitoring of different precursory observations. Among them preseismic electromagnetic emissions have been also observed indicating that the science of earthquake prediction should be from the start multidisciplinary. Drawing on recently introduced models for earthquake dynamics, that address issues such as long-range correlations, self-affinity, complexity-organization and fractal structures, the present work endeavors to further penetrate on the analysis of preseismic electromagnetic emissions and elucidate their link with the generation process of large and strong earthquakes. A second question deals with “whether there is a unified approach for the study of catastrophic events”. This question implies the possibility for common statistical behavior of diverse extreme events and the potential for transferability of methods from the study of earthquake dynamics across other fields. On these grounds, the present work extends the focus of inquiry to the analysis of electroencephalogram recordings related to epileptic seizures, in the prospect to identify common mechanisms that may explain the nature and the generation process of both phenomena, and to open up different directions for future research. Finally, with a view to consider alternative ways of studying key theoretical principles associated with the generation process of catastrophic phenomena, a relevant framework based on proposed algorithms is presented, focusing on parameters such as: the energy of earthquakes, the mean and maximum magnitude of the sample, the probability that two samples may come from the same population. Such an attempt aims to contribute to the knowledge of natural phenomena, by extending the existing theory and models and providing a few more ways for their interpretation.

The purpose of this research work is to propose an improved method for real-time sensitivity analysis (SA) applicable to large-scale complex systems. Borrowed from the EventTracker principle of the interrelation of causal events, it deploys the Rank Order Clustering (ROC) method to automatically group every relevant system input to parameters that represent the system state (i.e. output). The fundamental principle of event modelling is that the state of a given system is a function of every acquirable piece of knowledge or data (input) of events that occur within the system and its wider operational environment unless proven otherwise. It therefore strives to build the theoretical and practical foundation for the engineering of input data. The event modelling platform proposed attempts to filter unwanted data, and more importantly, include information that was thought to be irrelevant at the outset of the design process. The underpinning logic of the proposed Event Clustering technique (EventiC) is to build causal relationship between the events that trigger the inputs and outputs of the system. EventiC groups inputs with relevant corresponding outputs and measures the impact of each input variable on the output variables in short spans of time (relative real-time). It is believed that this grouping of relevant input-output event data by order of its importance in real-time is the key contribution to knowledge in this subject area. Our motivation is that components of current complex and organised systems are capable of generating and sharing information within their network of interrelated devices and systems. In addition to being an intelligent recorder of events, EventiC could also be a platform for preliminary data and knowledge construction. This improvement in the quality, and at times the quantity of input data, may lead to improved higher level mathematical formalism. It is hoped that better models will translate into superior controls and decision making. It is therefore believed that the projected outcome of this research work can be used to predict, stabilize (control), and optimize (operational research) the work of complex systems in the shortest possible time. For proof of concept, EventiC was designed using the MATLAB package and implemented using real-time data from the monitoring and control system of a typical cement manufacturing plant. The purpose for this deployment was to test and validate the concept, and to demonstrate whether the clusters of input data and their levels of importance against system performance indicators could be approved by industry experts. EventiC was used as an input variable selection tool for improving the existing fuzzy controller of the plant. Finally, EventiC was compared with its predecessor EventTracker using the same case study. The results revealed improvements in both computational efficiency and the quality of input variable selection.

Current monitoring tools in the intensive care units focus on displaying physiologically monitored parameters (e.g. vital signs such as heart rate, respiratory rate and blood pressure) at the present moment. Added clinical utility can be found by analyzing how the conditions of a patient evolve with time, and automatically relating that dynamics to population trends. Variability analysis consists of monitoring patterns of variation over intervals in time of physiological signals such as heart rate and respiratory rate. Given that illness has been associated in multiple studies with altered variability, most commonly lack of variation, variability monitoring represents a tool whose contribution at the bedside still needs to be explored. With the long term objective of improving care, this thesis promotes the use of variability analysis through three distinct types of analysis: facing the technical challenges involved with the dimensionality of variability analysis, enhancing the physiological understanding of variability, and showing its utility in real world clinical applications. In particular, the contributions of this thesis include: the review and classification into domains of a large array of measures of variability; the design of system and methods to integrate multiple measures of variability into a unique score, called composite measure, bringing relevant information to specific clinical problems; the comparison of patterns of heart rate variability during exercise and sepsis development, showing the inability of single measures of variability to discriminate between the two kinds of stressors; the analysis of variability produced from a physiologically-based model of the cardiovascular system, showing that each single measure of variability is an unspecific sensor of the body, thereby promoting multivariate analysis to the only means of understanding the physiology underlying variability; the study of heart rate variability in a population at high risk of sepsis development, showing the ability of variability to predict the occurrence of sepsis more than 48 hours in advance respect to the time of diagnosis of the clinical team; the study of heart and respiratory rate variability in intubated intensive care unit patients, showing how variability can provide a better way of assessing extubation readiness respect to commonly used clinical parameters. Overall, it is hoped that these novel contributions will help promoting bedside applications of variability monitoring to improve patient care.

This thesis is a complexity-informed case study of a language school in Greece, which provides a rich description of how language pedagogy develops in the periphery of the English-using world. In addition, this study demonstrates the feasibility and potential of using Complex Systems Theory (CST) in the study of educational settings. The thesis begins by describing English Language Teaching (ELT) in Greece, thus setting the scene for the empirical investigation. This is followed by a review of ELT literature, with particular reference to theories of language, pedagogy and society, and by an overview of CST, which pragmatically synthesises complex realism and post-modern ways of knowing, and defines a set of principles to guide complexity-informed empirical inquiry. Having conceptualised the language school as a complex system, it is suggested that activity in the school was sustained by multiple intentionalities, i.e., collective, emergent, nested and generative drivers of activity. These included: (a) an imperative to provide certification to learners, (b) some learners’ desire to integrate in transnational discourse communities, (c) the expectation that language learning should lead to increased awareness of ‘English’ culture, (d) competition against the state school system, and (e) the unstated aim of protecting the professional interests of the school’s staff and stakeholders. Intentionalities were associated with specific pedagogical outcomes and cultural outlooks, and their synthesis is defined as a dynamic of intentions. Next, the thesis looks into the learning materials used at the language school, and it is suggested that these generate affordances which impacted pedagogy. The distribution of learning activities in the books was associated with synchronic and diachronic changes in the dynamics of intentions underpinning activity in the school. Complexity-inspired conceptual instruments, such as an ‘affordance landscape’ and ‘attractors’, are developed to describe the influence of the learning materials, and it is suggested that the learning resources used at the language school made transmissive and communicative pedagogy more likely. The empirical component of the study concludes by describing prototypical instruction sequences that typified ELT in the language school, which evidenced traces of transmissive and communicative pedagogy. Some sequences (e.g., Reading and Vocabulary, and Transmissive Grammar) evidenced transmissive influences, which were associated with local pedagogical traditions, whereas others, such as Process-Based Writing, were more closely aligned with the communicative ideology that is mainstream in ELT. The thesis concludes by synthesising the findings with insights from the CST literature. In doing so, it demonstrates the theoretically generative potential of a complexity-informed inquiry, which can help to formulate understandings of ELT that are sensitive to the interface between systems and their environments, while providing ontologically coherent accounts of structure and agency, and of behaviours that are neither completely random nor entirely predictable.

This thesis concerns with both practical and theoretical aspects of phenomenon called Emergence. First part has been devoted to the research of available specialized resources on emergent topic and also on main features of complex systems. Acquired knowledge of two topics has been implemented on a real practical example of complex information system by creation of method which purpose is to help finding and determining emergent behavior. Last part of the thesis brings outcome of analysis of real system and discusses the recommendations for researchers on how to determine emergent behavior in their own systems.

Orientador: Marcus Aloizio Martinez de Aguiar / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Física Gleb Wataghin / Made available in DSpace on 2018-08-26T15:46:41Z (GMT). No. of bitstreams: 1 Moreira_CarolinaArruda_M.pdf: 4382090 bytes, checksum: 433897428b81157ccaeb3d7af604a731 (MD5) Previous issue date: 2015 / Resumo: Nesta dissertação, estudamos um processo dinâmico binário similar ao modelo do eleitor em redes complexas. Este modelo descreve uma eleição com dois candidatos e um grupo de N eleitores indecisos, que podem mudar de ideia adotando a opinião de um de seus contatos em uma rede de amigos ou de formadores de opinião. Os nós da rede social possuem estado interno rotulados por 0 ou 1, de acordo com a atual intenção de voto de cada indivíduo. Os formadores de opinião têm ideia fixa e podem influenciar a rede inteira dos eleitores indecisos. Estes são modelados por nós "fixos" na rede, conectados a todos os nós livres da rede de eleitores, aos quais quantificamos por N0 fixos no estado 0 e N1 fixos no estado 1. Calculamos a distribuição de probabilidade P(m) de que o candidato 1 conquiste m votos. Estudamos a dinâmica em redes estrela e comparamos os resultados com os obtidos para redes totalmente conectadas. Em ambos os casos a transição de fase entre os estados de equilíbrio ordenado e desordenado é observada à medida que N0 e N1 se aproximam de zero; no entanto, esse comportamento difere consideravelmente para as duas topologias: enquanto o ponto crítico ocorre exatamente para N0 = N1 = 1para qualquer tamanho N na rede totalmente conectada, tornando a distribuição de equilíbrio uniforme, nas redes estrela o ponto crítico depende de N e se escala com N0 = N1 ? ?N, levando a distribuição de equilíbrio se separar em dois picos, o que reflete os dois estados possíveis do nó central. Obtemos também soluções analíticas aproximadas que se mantêm perto da transição de fase e esclarecem o papel do nó central no processo. Além disso, estendemos a dinâmica abordada para o caso em que cada nó é representado por dois "bits", de modo a existirem quatro estados internos possíveis, (0,0), (0,1), (1,0) e (1,1). Esta abordagem objetiva buscar quais as características dinâmicas de um sistema que apresente nós com restrições na interação. Nesta situação, não encontramos novidades entre as dinâmicas de um e dois bits / Abstract: In this work, we study a dynamical process similar to the voter model on complex networks. This model describes an election with two candidates and a group of N undecided voters that can change their minds by adopting either the opinion of a contact in a social network or the opinion of opinion makers. The nodes of the social network have an internal state labeled by 0 or 1 according to the current voting intention of each individual. The opinion makers have a fixed opinion and can influence the entire set of undecided voters. They are modeled as ''fixed'' nodes connected to all free nodes on the social network and we quantify by N0 the number of nodes fixed in state 0 and by N1 those fixed in state 1. We calculate the probability distribution P(m) that candidate 1 receives m votes. We study this dynamics on star networks and we compare the results with those obtained from fully connected networks. In both cases the transition between the ordered and disordered equilibrium states is observed as N0 and N1 approach zero; however, this behavior differs significantly between the two topologies: while the critical point occurs exactly in N0 = N1 = 1 for fully connected networks and it is independent of the network size N, which leads to a uniform probability distribution, for star networks the critical point depends on N and scales as N0 = N1 ? ?N, and the distribution probability splits into two peaks, reflecting the two possible states of the central node. We also obtain an approximate analytical solution that holds near the phase transition, which clarifies the role of the central node in the process. Besides, we extend the dynamics approach to the case where each node is represented by two ''bits'' such that there are four possible internal states (0,0), (0,1), (1,0) and (1,1). This approach aims to search what are the systems' dynamical characteristics under restrictions in the interaction. In this case, we didn't find any new results between the one and two bits dynamics / Mestrado / Física / Mestra em Física

Orientador: Fernando José Von Zuben / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-27T03:14:35Z (GMT). No. of bitstreams: 1 Kurka_DavidBurth_M.pdf: 1660677 bytes, checksum: 7258daf8129b4dac9d1f647195775d3c (MD5) Previous issue date: 2015 / Resumo: Com o surgimento e a popularização de Redes Sociais Online e de Serviços de Redes Sociais, pesquisadores da área de computação têm encontrado um campo fértil para o desenvolvimento de trabalhos com grande volume de dados, modelos envolvendo múltiplos agentes e dinâmicas espaço-temporais. Entretanto, mesmo com significativo elenco de pesquisas já publicadas no assunto, ainda existem aspectos das redes sociais cuja explicação é incipiente. Visando o aprofundamento do conhecimento da área, este trabalho investiga fenômenos de compartilhamento coletivo na rede, que caracterizam eventos de difusão de informação. A partir da observação de dados reais oriundos do serviço online Twitter, tais eventos são modelados, caracterizados e analisados. Com o uso de técnicas de aprendizado de máquina, são encontrados padrões nos processos espaço-temporais da rede, tornando possível a construção de classificadores de mensagens baseados em comportamento e a caracterização de comportamentos individuais, a partir de conexões sociais / Abstract: With the advent and popularization of Online Social Networks and Social Networking Services, computer science researchers have found fertile field for the development of studies using large volumes of data, multiple agents models and spatio-temporal dynamics. However, even with a significant amount of published research on the subject, there are still aspects of social networks whose explanation is incipient. In order to deepen the knowledge of the area, this work investigates phenomena of collective sharing on the network, characterizing information diffusion events. From the observation of real data obtained from the online service Twitter, we collect, model and characterize such events. Finally, using machine learning and computational data analysis, patterns are found on the network's spatio-temporal processes, making it possible to classify a message's topic from users behaviour and the characterization of individual behaviour, from social connections / Mestrado / Engenharia de Computação / Mestre em Engenharia Elétrica

abstract: Over the past century, the world has become increasingly more complex. Modern systems (i.e blockchain, internet of things (IoT), and global supply chains) are inherently difficult to comprehend due to their high degree of connectivity. Understanding the nature of complex systems becomes an acutely more critical skill set for managing socio-technical infrastructure systems. As existing education programs and technical analysis approaches fail to teach and describe modern complexities, resulting consequences have direct impacts on real-world systems. Complex systems are characterized by exhibiting nonlinearity, interdependencies, feedback loops, and stochasticity. Since these four traits are counterintuitive, those responsible for managing complex systems may struggle in identifying these underlying relationships and decision-makers may fail to account for their implications or consequences when deliberating systematic policies or interventions. This dissertation details the findings of a three-part study on applying complex systems modeling techniques to exemplar socio-technical infrastructure systems. In the research articles discussed hereafter, various modeling techniques are contrasted in their capacity for simulating and analyzing complex, adaptive systems. This research demonstrates the empirical value of a complex system approach as twofold: (i) the technique explains systems interactions which are often neglected or ignored and (ii) its application has the capacity for teaching systems thinking principles. These outcomes serve decision-makers by providing them with further empirical analysis and granting them a more complete understanding on which to base their decisions. The first article examines modeling techniques, and their unique aptitudes are compared against the characteristics of complex systems to establish which methods are most qualified for complex systems analysis. Outlined in the second article is a proof of concept piece on using an interactive simulation of the Los Angeles water distribution system to teach complex systems thinking skills for the improved management of socio-technical infrastructure systems. Lastly, the third article demonstrates the empirical value of this complex systems approach for analyzing infrastructure systems through the construction of a systems dynamics model of the Arizona educational-workforce system, across years 1990 to 2040. The model explores a series of dynamic hypotheses and allows stakeholders to compare policy interventions for improving educational and economic outcome measures. / Dissertation/Thesis / Doctoral Dissertation Systems Engineering 2020

Meiotic nuclear oscillations in the fission yeast Schizosaccharomyces pombe are crucial for proper chromosome pairing and recombination. We report a mechanism of these oscillations on the basis of collective behavior of dynein motors linking the cell cortex and dynamic microtubules that extend from the spindle pole body in opposite directions. By combining quantitative live cell imaging and laser ablation with a theoretical description, we show that dynein dynamically redistributes in the cell in response to load forces, resulting in more dynein attached to the leading than to the trailing microtubules. The redistribution of motors introduces an asymmetry of motor forces pulling in opposite directions, leading to the generation of oscillations. Our work provides the first direct in vivo observation of self-organized dynamic dynein distributions, which, owing to the intrinsic motor properties, generate regular large-scale movements in the cell.

付記する学位プログラム名: デザイン学大学院連携プログラム / 京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第22097号 / 情博第707号 / 新制||情||121(附属図書館) / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 黒田 知宏, 教授 吉川 正俊, 教授 矢守 克也 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM