Experimentos têm revelado que redes de neurônios, tanto in vitro como in vivo, mantêm atividade descrita por avalanches e se organizam em um estado crítico no qual essas avalanches são distribuídas de acordo com leis de potência. Mostramos no presente trabalho que um modelo de rede de elementos excitáveis com sinapses dinâ- micas é capaz de exibir criticalidade auto-organizada para ampla região do espaço de parâmetros. Nossos resultados estão de acordo com outros estudos que indicam que a depressão sináptica de curto prazo constitui mecanismo suciente para produzir criticalidade em avalanches neuronais. No entanto, segundo diversos pesquisadores, embora o ajuste de parâmetros seja grosso para que haja criticalidade no modelo, é mais preciso dizer que o sistema não apresenta criticalidade auto-organizada genu ína, mas sim quasi-criticalidade auto-organizada, como os demais modelos não conservativos presentes na literatura. / Experiments have shown that neuronal networks, both in vitro and in vivo, maintain activity described by avalanches and they are organized into a critical state in which these avalanches are distributed according to power laws. We have demonstrated that a model based on a network of excitable elements with dynamical synapses is able to exhibit self-organized criticality for a wide range of the parameter\'s space. Our results are consistent with other studies that suggest short-term synaptic depression is enough to produce criticality in neuronal avalanches. However, according to several researchers, in spite of the tuning to be gross to ensure that there is criticality in the model, it is more accurate do not say that the system presents genuine self-organized criticality, but self-organized quasi-criticality as the other non-conservative models in the literature.

Experimentos têm revelado que redes de neurônios, tanto in vitro como in vivo, mantêm atividade descrita por avalanches e se organizam em um estado crítico no qual essas avalanches são distribuídas de acordo com leis de potência. Mostramos no presente trabalho que um modelo de rede de elementos excitáveis com sinapses dinâ- micas é capaz de exibir criticalidade auto-organizada para ampla região do espaço de parâmetros. Nossos resultados estão de acordo com outros estudos que indicam que a depressão sináptica de curto prazo constitui mecanismo suciente para produzir criticalidade em avalanches neuronais. No entanto, segundo diversos pesquisadores, embora o ajuste de parâmetros seja grosso para que haja criticalidade no modelo, é mais preciso dizer que o sistema não apresenta criticalidade auto-organizada genu ína, mas sim quasi-criticalidade auto-organizada, como os demais modelos não conservativos presentes na literatura. / Experiments have shown that neuronal networks, both in vitro and in vivo, maintain activity described by avalanches and they are organized into a critical state in which these avalanches are distributed according to power laws. We have demonstrated that a model based on a network of excitable elements with dynamical synapses is able to exhibit self-organized criticality for a wide range of the parameter\'s space. Our results are consistent with other studies that suggest short-term synaptic depression is enough to produce criticality in neuronal avalanches. However, according to several researchers, in spite of the tuning to be gross to ensure that there is criticality in the model, it is more accurate do not say that the system presents genuine self-organized criticality, but self-organized quasi-criticality as the other non-conservative models in the literature.

Over the last decades sleep research has focused on epidemiological studies of how different factors affect sleep, and how sleep influences other physiologic and cognitive functions. However, the complex dynamics of sleep stage transitions and arousals which occur at time scales of seconds to minutes during healthy sleep and constitute the sleep micro-architecture are not yet understood. I analyze long-term continuous EEG recordings in rats and human, and dissect emergent signatures of criticality in the dynamics of cortical rhythm bursts in relation to their correlation properties and reciprocal coupling. I show that active states durations follow a power-law distribution while the quiet states durations follow an exponential-like behavior. Such emerging bursting activity in the brain rhythm dynamics described by power-laws and exhibiting long-range spatio-temporal correlations has been proposed as an indication of self-organized criticality (SOC). To have a deeper understanding of SOC in cortical rhythm bursting dynamics, it is essential to study the dynamical evolution of an entire network of physiologic interactions in the context of different physiologic states and pathologic conditions. The human organism comprises various physiological systems, each with its own structural organization and dynamic complexity, leading to transient, fluctuating and nonlinear signals. Understanding integrated physiologic function as emergent phenomena from complex interactions among diverse organ systems is the main focus of a new field, Network Physiology. I apply Network Physiology approach and the novel concept of time delay stability (TDS), and I demonstrate their utility to study transient synchronous bursts in systems dynamics as a fundamental form of physiologic network communications. My results demonstrate that during a given physiological state, the physiological network is characterized by a specific topology and coupling strength between systems. Probing physiological network connectivity and the stability of physiological coupling across physiological states provide new insights on integrated physiological function. / 2023-03-04T00:00:00Z

Mass balance processes in glaciers are important for determining the growth or retreat of ice. Calving, the mechanical breakage of ice bergs from a glacier front, is a poorly understood phenomenon. This process has great importance to the mass balance of many glaciers, for example on Antarctica and in the Arctic. A recent paper by Åström et al. (2014) compare calving fronts to Self-Organized Critical (SOC) systems, especially the Abelian sand pile model, meaning that the calving front will stay at a critical state at all times. Fluctuations in external conditions will cause the glacier front to either retreat or advance. The calving frequency and size distribution of Tunabreen, a tidewater glacier in Svalbard, was studied during August and September, 2014, with the use of a time-lapse camera set up in front of the calving front. An 11-day period is studied in detail and compared to certain external factors, i.e. tide, air temperature, humidity, atmospheric pressure, wind speed and wind direction. The results are also compared to the relationships found by Åström et al. (2014). The results vary: tide relationships are found as the amplitude reaches above 1 meter, but seize to correlate as the tide falls off. Temperature trends are found for certain periods, but are of low credibility. Humidity, atmospheric pressure, wind speed and wind direction show low to no correlation with the calving size distribution. Fragment size distribution and calving rates show good correlation with the results from Åström et al. (2014). This helps to confirm the theory of SOC applied to calving fronts. Time-lapse photography is deemed as a good way to observe calving fronts, but have certain problems which are mostly related to the weather. Longer time periods would be needed to find better long term relationships between external conditions and calving frequencies, but data is hard to acquire and time consuming to process. The theory of SOC applied to calving fronts is promising and opens up new discussions for the research community. / Massbalansprocesser för glaciärer är viktiga för att bestämma om isen drar sig tillbaka eller avancerar. Den mekaniska brytningen av isberg från glaciärer kallas kalvning. Kalvning är väldigt viktig för ett flertal glaciärers massbalans, exempelvis för landisen på Antarktis och glaciärer i Arktis. Ny forskning visar att kalvande glaciärfronter alltid försöker befinna sig i ett kritiskt läge, liknande ett så kallat Self-Organized Critical (SOC) system. Detta kan liknas vid hur en sandhög försöker befinna sig vid sin kritiska sluttningsvinkel när ett konstant flöde av sandkorn adderas. Adderandet av sandkorn kan jämföras med hur externa förhållanden, så som temperatur och tidvatten, ändras. När dessa värden ändras med tid så kommer fronten kalva, mycket likt hur sandhögen rasar när sandkorn tillförs. Externa förhållanden kommer alltså styra om glaciären kalvar eller inte, och när.En time-lapse-kamera installerades framför Tunabreen, en tidvatten glaciär på Svalbard, under Augusti-September, 2014. Bilderna över Tunabreens kalvningsfront, som varade över en 11-dagars period, användes för att ta ut varje enskild kalvingshändelse. Denna data jämfördes sedan med tidvatten, temperatur, luftfuktighet, atmosfäriskt tryck, vindhastighet och vindriktning. Resultaten jämfördes även med de förhållanden som visades i den nya studien som beskrevs tidigare.Resultaten är blandade. När tidvattnets amplitud var större än 1 meter så följer kalvningen tidvattnets mönster, men detta avtar när amplituden är mindre. Temperaturen visar viss korrelation, men endast för kortare perioder. Då temperaturens förhållande till kalvningen inte följer under de högsta och lägsta värden som fanns så bedöms temperaturen ha låg trovärdighet som kontrollerande faktor. Luftfuktighet, atmosfäriskt tryck, vindhastighet och vindriktning visar låg, till ingen, korrelation med kalvning. Storleksfördelningen av fragment och kalvningshastigheten har god korrelation med forskningen kring SOC, resultaten hjälper till att bekräfta denna teori. Time-lapse-fotografi bedöms som en bra metod för att observera kalvningsfronter, men har ett flertal problem som relaterar till det lokala vädret.Längre tidsperioder behövs för att bedöma om förhållanden stämmer på lång sikt. Data är svår att förvärva och tidskrävande att behandla. SOC stämmer bra in på kalvningsfronter vilket öppnar upp nya diskussioner inom forskningsvärlden.

Vários problemas do mundo real podem ser analisados como problemas de otimização. Na bioinformática, em especial, como exemplos podem ser citados o alinhamento múltiplo de sequências, a filogenia, a predição de estruturas de proteínas e RNA, entre outros. As Meta-heurísticas Populacionais (MhP) são técnicas baseadas em interações de conjuntos de soluções candidatas, como elementos de uma população, utilizadas na otimização de funções. Seu uso é especialmente interessante na otimização de problemas onde há conhecimento parcial ou nenhum do espaço de busca. O objetivo deste trabalho é investigar o uso de auto-organização da população de um sistema imunológico artificial (AIS) a fim de aplicá-lo no problema de docking, que pode ser visto como um problema de otimização multimodal complexo. O AIS é um tipo de MhP inspirado na microevolução do sistema imunológico adaptativo de organismos complexos. Neste, as soluções candidatas representam células do sistema imunológico que busca se adaptar para a eliminação de um patógeno. O desenvolvimento do algoritmo foi baseado no opt-aiNet, que utiliza dos princípios das teorias de seleção clonal e maturação de afinidade para realizar a otimização de funções. Adicionalmente, o opt-aiNet, inspirado na teoria de redes imunológicas, realiza uma etapa de supressão, que busca eliminar soluções semelhantes, aumentando assim a diversidade populacional. Esta etapa é computacionalmente custosa, dado que é feito o cálculo da distância entre todos os possíveis pares de células (soluções) afim de eliminar aquelas próximas de acordo com um dado critério. A proposta deste trabalho é o desenvolvimento de um algoritmo de supressão auto-organizável, inspirado no fenômeno da criticalidade auto-organizada, buscando diminuir a influência da seleção de parâmetros e a complexidade da etapa de supressão. O algoritmo proposto foi testado em um conjunto de funções contínuas conhecidas e comumente utilizadas pela comunidade de computação evolutiva. Os resultados obtidos foram comparados com aqueles de uma implementação do opt-aiNet. Em adição, foi proposta a utilização de operadores de mutação com distribuição q-gaussiana nos AISs desenvolvidos. O algoritmo foi também aplicado no problema de docking rígido baseado em complementaridade de superfícies e minimização de colisões, especificamente no docking de proteínas. Os resultados foram comparados com aqueles de um algoritmo genético, resultando em um melhor desempenho obtido pelo algoritmo proposto. / Many real world problems can be described as optimization problems. In bioinformatics in special, there is multiple sequence alignment, filogeny and RNA and Protein structure prediction, among others. Population based metaheuristics are techniques based in the interaction of a set of candidate solutions as elements of a population. Its use is specially interesting in optimization problems where there is little or no knowledge of the search space. The objective of this work is to study the use of self-organization of population in an artificial imune system for use in the docking problem, considered a complex multimodal optimization problem. The artificial imunme system is a type of population based methaheuristics inspired in the microevolution of the adaptive immune system of complex organisms. Candidate solutions represent cells of the immune system adapting its antibodies to eliminate a pathogen. The development of the algorithm was based in the opt-aiNet, based in the principles of clonal selection and affinity maturation for function optimization. Additionally, the opt-aiNet, inspired in theories of immune network, makes a suppression stage to eliminate similiar solutions and control diversity. This stage is computationally expensive as it calculates the distance between every possible pair of cells (solutions) eliminating those closer than a threshold. This work proposes a self-organized suppression algorithm inspired in the self-organized criticality, looking to minimize the influence of parameter selection and complexity of the suppression stage in opt-aiNet. The proposed algorithm was tested in a set of well-known functions in the evolutionary computation community. The results were compared to those of an implementation of the opt-aiNet. In addition, we proposed a mutation operator with q-Gaussian distribution for the artificial immune systems. The algorithm was then applied in the rigid protein docking problem based in surface complementarity and colision avoidance. The results were compared with a genetic algorithm and achieved a better performance.

This thesis undertakes an empirical study of software evolution by analyzing open source software (OSS) systems. The main purpose is to aid in understanding OSS evolution. The work centers on collecting large quantities of structural data cost-effectively and analyzing such data to understand software evolution <em>dynamics</em> (the mechanisms and causes of change or growth). <br /><br /> We propose a multipurpose systematic approach to extracting program facts (<em>e. g. </em>, function calls). This approach is supported by a suite of C and C++ program extractors, which cover different steps in the program build process and handle both source and binary code. We present several heuristics to link facts extracted from individual files into a combined system model of reasonable accuracy. We extract historical sequences of system models to aid software evolution analysis. <br /><br /> We propose that software evolution can be viewed as <em>Punctuated Equilibrium</em> (<em>i. e. </em>, long periods of small changes interrupted occasionally by large avalanche changes). We develop two approaches to study such dynamical behavior. One approach uses the evolution spectrograph to visualize file level changes to the implemented system structure. The other approach relies on automated software clustering techniques to recover system design changes. We discuss lessons learned from using these approaches. <br /><br /> We present a new perspective on software evolution dynamics. From this perspective, an evolving software system responds to external events (<em>e. g. </em>, new functional requirements) according to <em>Self-Organized Criticality</em> (SOC). The SOC dynamics is characterized by the following: (1) the probability distribution of change sizes is a power law; and (2) the time series of change exhibits long range correlations with power law behavior. We present empirical evidence that SOC occurs in open source software systems.

This thesis undertakes an empirical study of software evolution by analyzing open source software (OSS) systems. The main purpose is to aid in understanding OSS evolution. The work centers on collecting large quantities of structural data cost-effectively and analyzing such data to understand software evolution <em>dynamics</em> (the mechanisms and causes of change or growth). <br /><br /> We propose a multipurpose systematic approach to extracting program facts (<em>e. g. </em>, function calls). This approach is supported by a suite of C and C++ program extractors, which cover different steps in the program build process and handle both source and binary code. We present several heuristics to link facts extracted from individual files into a combined system model of reasonable accuracy. We extract historical sequences of system models to aid software evolution analysis. <br /><br /> We propose that software evolution can be viewed as <em>Punctuated Equilibrium</em> (<em>i. e. </em>, long periods of small changes interrupted occasionally by large avalanche changes). We develop two approaches to study such dynamical behavior. One approach uses the evolution spectrograph to visualize file level changes to the implemented system structure. The other approach relies on automated software clustering techniques to recover system design changes. We discuss lessons learned from using these approaches. <br /><br /> We present a new perspective on software evolution dynamics. From this perspective, an evolving software system responds to external events (<em>e. g. </em>, new functional requirements) according to <em>Self-Organized Criticality</em> (SOC). The SOC dynamics is characterized by the following: (1) the probability distribution of change sizes is a power law; and (2) the time series of change exhibits long range correlations with power law behavior. We present empirical evidence that SOC occurs in open source software systems.

Vários problemas do mundo real podem ser analisados como problemas de otimização. Na bioinformática, em especial, como exemplos podem ser citados o alinhamento múltiplo de sequências, a filogenia, a predição de estruturas de proteínas e RNA, entre outros. As Meta-heurísticas Populacionais (MhP) são técnicas baseadas em interações de conjuntos de soluções candidatas, como elementos de uma população, utilizadas na otimização de funções. Seu uso é especialmente interessante na otimização de problemas onde há conhecimento parcial ou nenhum do espaço de busca. O objetivo deste trabalho é investigar o uso de auto-organização da população de um sistema imunológico artificial (AIS) a fim de aplicá-lo no problema de docking, que pode ser visto como um problema de otimização multimodal complexo. O AIS é um tipo de MhP inspirado na microevolução do sistema imunológico adaptativo de organismos complexos. Neste, as soluções candidatas representam células do sistema imunológico que busca se adaptar para a eliminação de um patógeno. O desenvolvimento do algoritmo foi baseado no opt-aiNet, que utiliza dos princípios das teorias de seleção clonal e maturação de afinidade para realizar a otimização de funções. Adicionalmente, o opt-aiNet, inspirado na teoria de redes imunológicas, realiza uma etapa de supressão, que busca eliminar soluções semelhantes, aumentando assim a diversidade populacional. Esta etapa é computacionalmente custosa, dado que é feito o cálculo da distância entre todos os possíveis pares de células (soluções) afim de eliminar aquelas próximas de acordo com um dado critério. A proposta deste trabalho é o desenvolvimento de um algoritmo de supressão auto-organizável, inspirado no fenômeno da criticalidade auto-organizada, buscando diminuir a influência da seleção de parâmetros e a complexidade da etapa de supressão. O algoritmo proposto foi testado em um conjunto de funções contínuas conhecidas e comumente utilizadas pela comunidade de computação evolutiva. Os resultados obtidos foram comparados com aqueles de uma implementação do opt-aiNet. Em adição, foi proposta a utilização de operadores de mutação com distribuição q-gaussiana nos AISs desenvolvidos. O algoritmo foi também aplicado no problema de docking rígido baseado em complementaridade de superfícies e minimização de colisões, especificamente no docking de proteínas. Os resultados foram comparados com aqueles de um algoritmo genético, resultando em um melhor desempenho obtido pelo algoritmo proposto. / Many real world problems can be described as optimization problems. In bioinformatics in special, there is multiple sequence alignment, filogeny and RNA and Protein structure prediction, among others. Population based metaheuristics are techniques based in the interaction of a set of candidate solutions as elements of a population. Its use is specially interesting in optimization problems where there is little or no knowledge of the search space. The objective of this work is to study the use of self-organization of population in an artificial imune system for use in the docking problem, considered a complex multimodal optimization problem. The artificial imunme system is a type of population based methaheuristics inspired in the microevolution of the adaptive immune system of complex organisms. Candidate solutions represent cells of the immune system adapting its antibodies to eliminate a pathogen. The development of the algorithm was based in the opt-aiNet, based in the principles of clonal selection and affinity maturation for function optimization. Additionally, the opt-aiNet, inspired in theories of immune network, makes a suppression stage to eliminate similiar solutions and control diversity. This stage is computationally expensive as it calculates the distance between every possible pair of cells (solutions) eliminating those closer than a threshold. This work proposes a self-organized suppression algorithm inspired in the self-organized criticality, looking to minimize the influence of parameter selection and complexity of the suppression stage in opt-aiNet. The proposed algorithm was tested in a set of well-known functions in the evolutionary computation community. The results were compared to those of an implementation of the opt-aiNet. In addition, we proposed a mutation operator with q-Gaussian distribution for the artificial immune systems. The algorithm was then applied in the rigid protein docking problem based in surface complementarity and colision avoidance. The results were compared with a genetic algorithm and achieved a better performance.

The influence of the strain rate and heat treatment on the occurrence of plastic instabilities in extruded AlSi1MgMn (6082) and cold rolled AlMg4.5Mn0.4 (5182) alloys was studied. The samples were uniaxially loaded at various strain rates and at room temperature (RT). The results are discussed using concurrent acoustic emission (AE) monitoring during mechanical testing and the AE parameters are correlated to the microstructure and to the stress-time curves. All samples exhibited the Portevin-Le Châtelier (PLC) effect of different types, dependently on the heat treatment and the applied strain rate. The occurrence of the PLC effect is manifested by burst AE signals with high amplitudes. Statistical analysis of the AE signals has shown the power-law probability distribution.

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