Undersampled Critical Branching Processes on Small-World and Random Networks Fail to Reproduce the Statistics of Spike Avalanches

The power-law size distributions obtained experimentally for neuronal avalanches are an important evidence of criticality in the brain. This evidence is supported by the fact that a critical branching process exhibits the same exponent t~3=2. Models at criticality have been employed to mimic avalanche propagation and explain the statistics observed experimentally. However, a crucial aspect of neuronal recordings has been almost completely neglected in the models: undersampling. While in a typical multielectrode array hundreds of neurons are recorded, in the same area of neuronal tissue tens of thousands of neurons can be found. Here we investigate the consequences of undersampling in models with three different topologies (two-dimensional, small-world and random network) and three different dynamical regimes (subcritical, critical and supercritical). We found that undersampling modifies avalanche size distributions, extinguishing the power laws observed in critical systems. Distributions from subcritical systems are also modified, but the shape of the undersampled distributions is more similar to that of a fully sampled system. Undersampled supercritical systems can recover the general characteristics of the fully sampled version, provided that enough neurons are measured. Undersampling in two-dimensional and small-world networks leads to similar effects, while the random network is insensitive to sampling density due to the lack of a well-defined neighborhood. We conjecture that neuronal avalanches recorded from local field potentials avoid undersampling effects due to the nature of this signal, but the same does not hold for spike avalanches. We conclude that undersampled branching-process-like models in these topologies fail to reproduce the statistics of spike avalanches.

A confiança interorganizacional nas compras

This research is part of the field of organizational studies, focusing on organizational purchase behavior and, specifically, trust interorganizational at the purchases. This topic is current and relevant by addressing the development of good relations between buyer-supplier that increases the exchange of information, increases the length of relationship, reduces the hierarchical controls and improves performance. Furthermore, although there is a vast literature on trust, the scientific work that deal specifically at the trust interorganizational still need further research to synthesize and validate the variables that generate this phenomenon. In this sense, this investigation is to explain the antecedents of trust interorganizational by the relationship between the variable operational performance, organizational characteristics, shared values and interpersonal relationships on purchases by manufacturing industries, in order to develop a robust literature, most consensual, that includes the current sociological and economic, considering the effect of interpersonal relationships in this phenomenon. This proposal is configured in a new vision of the antecedents of interorganizational trust, described as significant quantitative from models Morgan and Hunt (1994), Doney and Cannon (1997), Zhao and Cavusgil (2006) and Nyaga, Whipple, Lynch (2011), as well as qualitative analysis of Tacconi et al. (2011). With regard to methodological aspects, the study assumes the form of a descriptive, survey type, and causal trace theoretical and empirical. As for his nature, the investigation, explicative character, has developed a quantitative approach with the use of exploratory factor analysis and structural equation modeling SEM, with the use of IBM software SPSS Amos 18.0, using the method of maximum verisimilitude, and supported by technical bootstraping. The unit of analysis was the buyer-supplier relationship, in which the object under investigation was the supplier organization in view of the purchasing company. 237 valid questionnaires were collected among key informants, using a simple random sampling developed in manufacturing industries (SIC 10-33), located in the city of Natal and in the region of Natal. The first results of descriptive analysis demonstrate the phenomenon of interorganizational trust, in which purchasing firms believe, feel secure about the supplier. This demonstration showed high levels of intensity, predominantly among the vendors that supply the company with materials that are used directly in the production process. The exploratory and confirmatory factor analysis, performed on each variable alone, generated a set of observable and unobservable variables more consistent, giving rise to a model, that needed to be further specified. This again specify model consists of trajectories was positive, with a good fit, with a composite reliability and variance extracted satisfactory, and demonstrates convergent and discriminant validity, in which the factor loadings are significant and strong explanatory power. Given the findings that reinforce the model again specify data, suggesting a high probability that this model may be more suited for the study population, the results support the explanation that interorganizational trust depends on purchases directly from interpersonal relationships, sharing value and operating performance and indirectly of personal relationships, social networks, organizational characteristics, physical and relational aspect of performance. It is concluded that this trust can be explained by a set of interactions between these three determinants, where the focus is on interpersonal relationships, with the largest path coefficient for the factor under study

Simulação de fluxo de fluidos em meios porosos desordenados uma análise de efeito de escala na estimativa da permeabilidade e do coeficiente de arrasto

The present study provides a methodology that gives a predictive character the computer simulations based on detailed models of the geometry of a porous medium. We using the software FLUENT to investigate the flow of a viscous Newtonian fluid through a random fractal medium which simplifies a two-dimensional disordered porous medium representing a petroleum reservoir. This fractal model is formed by obstacles of various sizes, whose size distribution function follows a power law where exponent is defined as the fractal dimension of fractionation Dff of the model characterizing the process of fragmentation these obstacles. They are randomly disposed in a rectangular channel. The modeling process incorporates modern concepts, scaling laws, to analyze the influence of heterogeneity found in the fields of the porosity and of the permeability in such a way as to characterize the medium in terms of their fractal properties. This procedure allows numerically analyze the measurements of permeability k and the drag coefficient Cd proposed relationships, like power law, for these properties on various modeling schemes. The purpose of this research is to study the variability provided by these heterogeneities where the velocity field and other details of viscous fluid dynamics are obtained by solving numerically the continuity and Navier-Stokes equations at pore level and observe how the fractal dimension of fractionation of the model can affect their hydrodynamic properties. This study were considered two classes of models, models with constant porosity, MPC, and models with varying porosity, MPV. The results have allowed us to find numerical relationship between the permeability, drag coefficient and the fractal dimension of fractionation of the medium. Based on these numerical results we have proposed scaling relations and algebraic expressions involving the relevant parameters of the phenomenon. In this study analytical equations were determined for Dff depending on the geometrical parameters of the models. We also found a relation between the permeability and the drag coefficient which is inversely proportional to one another. As for the difference in behavior it is most striking in the classes of models MPV. That is, the fact that the porosity vary in these models is an additional factor that plays a significant role in flow analysis. Finally, the results proved satisfactory and consistent, which demonstrates the effectiveness of the referred methodology for all applications analyzed in this study.

Algoritmos genéticos: uso de lógica nebulosa e análise de convergência por cadeia de Markov

In this work, the Markov chain will be the tool used in the modeling and analysis of convergence of the genetic algorithm, both the standard version as for the other versions that allows the genetic algorithm. In addition, we intend to compare the performance of the standard version with the fuzzy version, believing that this version gives the genetic algorithm a great ability to find a global optimum, own the global optimization algorithms. The choice of this algorithm is due to the fact that it has become, over the past thirty yares, one of the more importan tool used to find a solution of de optimization problem. This choice is due to its effectiveness in finding a good quality solution to the problem, considering that the knowledge of a good quality solution becomes acceptable given that there may not be another algorithm able to get the optimal solution for many of these problems. However, this algorithm can be set, taking into account, that it is not only dependent on how the problem is represented as but also some of the operators are defined, to the standard version of this, when the parameters are kept fixed, to their versions with variables parameters. Therefore to achieve good performance with the aforementioned algorithm is necessary that it has an adequate criterion in the choice of its parameters, especially the rate of mutation and crossover rate or even the size of the population. It is important to remember that those implementations in which parameters are kept fixed throughout the execution, the modeling algorithm by Markov chain results in a homogeneous chain and when it allows the variation of parameters during the execution, the Markov chain that models becomes be non - homogeneous. Therefore, in an attempt to improve the algorithm performance, few studies have tried to make the setting of the parameters through strategies that capture the intrinsic characteristics of the problem. These characteristics are extracted from the present state of execution, in order to identify and preserve a pattern related to a solution of good quality and at the same time that standard discarding of low quality. Strategies for feature extraction can either use precise techniques as fuzzy techniques, in the latter case being made through a fuzzy controller. A Markov chain is used for modeling and convergence analysis of the algorithm, both in its standard version as for the other. In order to evaluate the performance of a non-homogeneous algorithm tests will be applied to compare the standard fuzzy algorithm with the genetic algorithm, and the rate of change adjusted by a fuzzy controller. To do so, pick up optimization problems whose number of solutions varies exponentially with the number of variables

Modelagem de usinas eólicas através de um processo de Markov e técnicas de confiabilidade para a estimativa anual da energia produzida

This study aims to use a computational model that considers the statistical characteristics of the wind and the reliability characteristics of a wind turbine, such as failure rates and repair, representing the wind farm by a Markov process to determine the estimated annual energy generated, and compare it with a real case. This model can also be used in reliability studies, and provides some performance indicators that will help in analyzing the feasibility of setting up a wind farm, once the power curve is known and the availability of wind speed measurements. To validate this model, simulations were done using the database of the wind farm of Macau PETROBRAS. The results were very close to the real, thereby confirming that the model successfully reproduced the behavior of all components involved. Finally, a comparison was made of the results presented by this model, with the result of estimated annual energy considering the modeling of the distribution wind by a statistical distribution of Weibull

Undersampled Critical Branching Processes on Small-World and Random Networks Fail to Reproduce the Statistics of Spike Avalanches

The power-law size distributions obtained experimentally for neuronal avalanches are an important evidence of criticality in the brain. This evidence is supported by the fact that a critical branching process exhibits the same exponent t~3=2. Models at criticality have been employed to mimic avalanche propagation and explain the statistics observed experimentally. However, a crucial aspect of neuronal recordings has been almost completely neglected in the models: undersampling. While in a typical multielectrode array hundreds of neurons are recorded, in the same area of neuronal tissue tens of thousands of neurons can be found. Here we investigate the consequences of undersampling in models with three different topologies (two-dimensional, small-world and random network) and three different dynamical regimes (subcritical, critical and supercritical). We found that undersampling modifies avalanche size distributions, extinguishing the power laws observed in critical systems. Distributions from subcritical systems are also modified, but the shape of the undersampled distributions is more similar to that of a fully sampled system. Undersampled supercritical systems can recover the general characteristics of the fully sampled version, provided that enough neurons are measured. Undersampling in two-dimensional and small-world networks leads to similar effects, while the random network is insensitive to sampling density due to the lack of a well-defined neighborhood. We conjecture that neuronal avalanches recorded from local field potentials avoid undersampling effects due to the nature of this signal, but the same does not hold for spike avalanches. We conclude that undersampled branching-process-like models in these topologies fail to reproduce the statistics of spike avalanches.

A confiança interorganizacional nas compras

This research is part of the field of organizational studies, focusing on organizational purchase behavior and, specifically, trust interorganizational at the purchases. This topic is current and relevant by addressing the development of good relations between buyer-supplier that increases the exchange of information, increases the length of relationship, reduces the hierarchical controls and improves performance. Furthermore, although there is a vast literature on trust, the scientific work that deal specifically at the trust interorganizational still need further research to synthesize and validate the variables that generate this phenomenon. In this sense, this investigation is to explain the antecedents of trust interorganizational by the relationship between the variable operational performance, organizational characteristics, shared values and interpersonal relationships on purchases by manufacturing industries, in order to develop a robust literature, most consensual, that includes the current sociological and economic, considering the effect of interpersonal relationships in this phenomenon. This proposal is configured in a new vision of the antecedents of interorganizational trust, described as significant quantitative from models Morgan and Hunt (1994), Doney and Cannon (1997), Zhao and Cavusgil (2006) and Nyaga, Whipple, Lynch (2011), as well as qualitative analysis of Tacconi et al. (2011). With regard to methodological aspects, the study assumes the form of a descriptive, survey type, and causal trace theoretical and empirical. As for his nature, the investigation, explicative character, has developed a quantitative approach with the use of exploratory factor analysis and structural equation modeling SEM, with the use of IBM software SPSS Amos 18.0, using the method of maximum verisimilitude, and supported by technical bootstraping. The unit of analysis was the buyer-supplier relationship, in which the object under investigation was the supplier organization in view of the purchasing company. 237 valid questionnaires were collected among key informants, using a simple random sampling developed in manufacturing industries (SIC 10-33), located in the city of Natal and in the region of Natal. The first results of descriptive analysis demonstrate the phenomenon of interorganizational trust, in which purchasing firms believe, feel secure about the supplier. This demonstration showed high levels of intensity, predominantly among the vendors that supply the company with materials that are used directly in the production process. The exploratory and confirmatory factor analysis, performed on each variable alone, generated a set of observable and unobservable variables more consistent, giving rise to a model, that needed to be further specified. This again specify model consists of trajectories was positive, with a good fit, with a composite reliability and variance extracted satisfactory, and demonstrates convergent and discriminant validity, in which the factor loadings are significant and strong explanatory power. Given the findings that reinforce the model again specify data, suggesting a high probability that this model may be more suited for the study population, the results support the explanation that interorganizational trust depends on purchases directly from interpersonal relationships, sharing value and operating performance and indirectly of personal relationships, social networks, organizational characteristics, physical and relational aspect of performance. It is concluded that this trust can be explained by a set of interactions between these three determinants, where the focus is on interpersonal relationships, with the largest path coefficient for the factor under study

Simulação de fluxo de fluidos em meios porosos desordenados uma análise de efeito de escala na estimativa da permeabilidade e do coeficiente de arrasto

The present study provides a methodology that gives a predictive character the computer simulations based on detailed models of the geometry of a porous medium. We using the software FLUENT to investigate the flow of a viscous Newtonian fluid through a random fractal medium which simplifies a two-dimensional disordered porous medium representing a petroleum reservoir. This fractal model is formed by obstacles of various sizes, whose size distribution function follows a power law where exponent is defined as the fractal dimension of fractionation Dff of the model characterizing the process of fragmentation these obstacles. They are randomly disposed in a rectangular channel. The modeling process incorporates modern concepts, scaling laws, to analyze the influence of heterogeneity found in the fields of the porosity and of the permeability in such a way as to characterize the medium in terms of their fractal properties. This procedure allows numerically analyze the measurements of permeability k and the drag coefficient Cd proposed relationships, like power law, for these properties on various modeling schemes. The purpose of this research is to study the variability provided by these heterogeneities where the velocity field and other details of viscous fluid dynamics are obtained by solving numerically the continuity and Navier-Stokes equations at pore level and observe how the fractal dimension of fractionation of the model can affect their hydrodynamic properties. This study were considered two classes of models, models with constant porosity, MPC, and models with varying porosity, MPV. The results have allowed us to find numerical relationship between the permeability, drag coefficient and the fractal dimension of fractionation of the medium. Based on these numerical results we have proposed scaling relations and algebraic expressions involving the relevant parameters of the phenomenon. In this study analytical equations were determined for Dff depending on the geometrical parameters of the models. We also found a relation between the permeability and the drag coefficient which is inversely proportional to one another. As for the difference in behavior it is most striking in the classes of models MPV. That is, the fact that the porosity vary in these models is an additional factor that plays a significant role in flow analysis. Finally, the results proved satisfactory and consistent, which demonstrates the effectiveness of the referred methodology for all applications analyzed in this study.