A refined analysis of the low-mass eclipsing binary system T-Cyg1-12664

The observational mass-radius relation of main sequence stars with masses between ~0.3 and 1.0 Msun reveals deviations between the stellar radii predicted by models and the observed radii of stars in detached binaries. We generate an accurate physical model of the low-mass eclipsing binary T-Cyg1-12664 in the Kepler mission field to measure the physical parameters of its components and to compare them with the prediction of theoretical stellar evolution models. We analyze the Kepler mission light curve of T-Cyg1-12664 to accurately measure the times and phases of the primary and secondary eclipse. In addition, we measure the rotational period of the primary component by analyzing the out-of-eclipse oscillations that are due to spots. We accurately constrain the effective temperature of the system using ground-based absolute photometry in B, V, Rc, and Ic. We also obtain and analyze V, Rc, Ic differential light curves to measure the eccentricity and the orbital inclination of the system, and a precise Teff ratio. From the joint analysis of new radial velocities and those in the literature we measure the individual masses of the stars. Finally, we use the PHOEBE code to generate a physical model of the system. T-Cyg1-12664 is a low eccentricity system, located d=360+/-22 pc away from us, with an orbital period of P=4.1287955(4) days, and an orbital inclination i=86.969+/-0.056 degrees. It is composed of two very different stars with an active G6 primary with Teff1=5560+/-160 K, M1=0.680+/-0.045 Msun, R1=0.799+/-0.017 Rsun, and a M3V secondary star with Teff2=3460+/-210 K, M2=0.376+/-0.017 Msun, and R2=0.3475+/-0.0081 Rsun. The primary star is an oversized and spotted active star, hotter than the stars in its mass range. The secondary is a cool star near the mass boundary for fully convective stars (M~0.35 Msun), whose parameters appear to be in agreement with low-mass stellar model.Comment: 18 pages, 15 figures, 15 table

Residential segregation and cultural dissemination: An Axelrod-Schelling model

In the Axelrod's model of cultural dissemination, we consider mobility of cultural agents through the introduction of a density of empty sites and the possibility that agents in a dissimilar neighborhood can move to them if their mean cultural similarity with the neighborhood is below some threshold. While for low values of the density of empty sites the mobility enhances the convergence to a global culture, for high enough values of it the dynamics can lead to the coexistence of disconnected domains of different cultures. In this regime, the increase of initial cultural diversity paradoxically increases the convergence to a dominant culture. Further increase of diversity leads to fragmentation of the dominant culture into domains, forever changing in shape and number, as an effect of the never ending eroding activity of cultural minorities

Evaluation of the changes in working limits in an automobile assembly line using simulation

The aim of the work presented in this paper consists of the development of a decision-making support system, based on discrete-event simulation models, of an automobile assembly line which was implemented within an Arena simulation environment and focused at a very specific class of production lines with a four closed-loop network configuration. This layout system reflects one of the most common configurations of automobile assembly and preassembly lines formed by conveyors. The sum of the number of pallets on the intermediate buffers, remains constant, except for the fourth closed-loop, which depends on the four-door car ratio (x) implemented between the door disassembly and assembly stations of the car body. Some governing equations of the four closed-loops are not compatible with the capacities of several intermediate buffers for certain values of variable x. This incompatibility shows how the assembly line cannot operate in practice for x0,97 in a stationary regime, due to the starvation phenomenon or the failure of supply to the machines on the production line. We have evaluated the impact of the pallet numbers circulating on the first closed-loop on the performance of the production line, translated into the number of cars produced/hour, in order to improve the availability of the entire manufacturing system for any value of x. Until the present date, these facts have not been presented in specialized literature. © 2012 American Institute of Physics

The role of the organization structure in the diffusion of innovations

Diffusion and adoption of innovations is a topic of increasing interest in economics, market research, and sociology. In this paper we investigate, through an agent based model, the dynamics of adoption of innovative proposals in different kinds of structures. We show that community structure plays an important role on the innovation diffusion, so that proposals are more likely to be accepted in homogeneous organizations. In addition, we show that the learning process of innovative technologies enhances their diffusion, thus resulting in an important ingredient when heterogeneous networks are considered. We also show that social pressure blocks the adoption process whatever the structure of the organization. These results may help to understand how different factors influence the diffusion and acceptance of innovative proposals in different communities and organizations

Phenotypic effect of mutations in evolving populations of RNA molecules

Abstract Background The secondary structure of folded RNA sequences is a good model to map phenotype onto genotype, as represented by the RNA sequence. Computational studies of the evolution of ensembles of RNA molecules towards target secondary structures yield valuable clues to the mechanisms behind adaptation of complex populations. The relationship between the space of sequences and structures, the organization of RNA ensembles at mutation-selection equilibrium, the time of adaptation as a function of the population parameters, the presence of collective effects in quasispecies, or the optimal mutation rates to promote adaptation all are issues that can be explored within this framework. Results We investigate the effect of microscopic mutations on the phenotype of RNA molecules during their in silico evolution and adaptation. We calculate the distribution of the effects of mutations on fitness, the relative fractions of beneficial and deleterious mutations and the corresponding selection coefficients for populations evolving under different mutation rates. Three different situations are explored: the mutation-selection equilibrium (optimized population) in three different fitness landscapes, the dynamics during adaptation towards a goal structure (adapting population), and the behavior under periodic population bottlenecks (perturbed population). Conclusions The ratio between the number of beneficial and deleterious mutations experienced by a population of RNA sequences increases with the value of the mutation rate μ at which evolution proceeds. In contrast, the selective value of mutations remains almost constant, independent of μ, indicating that adaptation occurs through an increase in the amount of beneficial mutations, with little variations in the average effect they have on fitness. Statistical analyses of the distribution of fitness effects reveal that small effects, either beneficial or deleterious, are well described by a Pareto distribution. These results are robust under changes in the fitness landscape, remarkably when, in addition to selecting a target secondary structure, specific subsequences or low-energy folds are required. A population perturbed by bottlenecks behaves similarly to an adapting population, struggling to return to the optimized state. Whether it can survive in the long run or whether it goes extinct depends critically on the length of the time interval between bottlenecks.Support from the Spanish MICINN through research project FIS2008-05273 is gratefully acknowledged.Peer Reviewe

Cognitive hierarchy theory and two-person games

The outcome of many social and economic interactions, such as stock-market transactions, is strongly determined by the predictions that agents make about the behavior of other individuals. Cognitive hierarchy theory provides a framework to model the consequences of forecasting accuracy that has proven to fit data from certain types of game theory experiments, such as Keynesian beauty contests and entry games. Here, we focus on symmetric two-player-two-action games and establish an algorithm to find the players’ strategies according to the cognitive hierarchy approach. We show that the snowdrift game exhibits a pattern of behavior whose complexity grows as the cognitive levels of players increases. In addition to finding the solutions up to the third cognitive level, we demonstrate, in this theoretical frame, two new properties of snowdrift games: (i) any snowdrift game can be characterized by only a parameter, its class; (ii) they are anti-symmetric with respect to the diagonal of the pay-off’s space. Finally, we propose a model based on an evolutionary dynamics that captures the main features of the cognitive hierarchy theory

Effect of memory, intolerance, and second-order reputation on cooperation

The understanding of cooperative behavior in social systems has been the subject of intense research over the past few decades. In this regard, the theoretical models used to explain cooperation in human societies have been complemented with a growing interest in experimental studies to validate the proposed mechanisms. In this work, we rely on previous experimental findings to build a theoretical model based on two cooperation driving mechanisms: second-order reputation and memory. Specifically, taking the donation game as a starting point, the agents are distributed among three strategies, namely, unconditional cooperators, unconditional defectors, and discriminators, where the latter follow a second-order assessment rule: shunning, stern judging, image scoring, or simple standing. A discriminator will cooperate if the evaluation of the recipient''s last actions contained in his memory is above a threshold of (in)tolerance. In addition to the dynamics inherent to the game, another imitation dynamics, involving much longer times (generations), is introduced. The model is approached through a mean-field approximation that predicts the macroscopic behavior observed in Monte Carlo simulations. We found that, while in most second-order assessment rules, intolerance hinders cooperation, it has the opposite (positive) effect under the simple standing rule. Furthermore, we show that, when considering memory, the stern judging rule shows the lowest values of cooperation, while stricter rules show higher cooperation levels