Dispersion of gamma-Alumina Nano-Sized Spherical Particles in a Calamitic Liquid Crystal. Study and Optimization of the Confinement Effects

We report an experimental study on confined systems formed by butyloxybenzylidene octylaniline liquid crystal (4O.8) + gamma-alumina nanoparticles. The effects of the confinement in the thermal and dielectric properties of the liquid crystal under different densities of nanoparticles is analyzed by means of high resolution Modulated Differential Scanning Calorimetry (MDSC) and broadband dielectric spectroscopy. First, a drastic depression of the N-I and SmA-N transition temperatures is observed with confinement, the more concentration of nanoparticles the deeper this depression is, driving the nematic range closer to the room temperature. An interesting experimental law is found for both transition temperatures. Second, the change in shape of the heat capacity peaks is quantified by means of the full width half maximum (FWHM). Third, the confinement does not noticeably affect the molecular dynamics. Finally, the combination of nanoparticles and the external applied electric field tends to favor the alignment of the molecules in metallic cells. All these results indicate that the confinement of liquid crystals by means of gamma-alumina nanoparticles could be optimum for liquid crystal-based electrooptic devices.

Learning in Network Games

We report the findings of an experiment designed to study how people learn and make decisions in network games. Network games offer new opportunities to identify learning rules, since on networks (compared to e.g. random matching) more rules differ in terms of their information requirements. Our experimental design enables us to observe both which actions participants choose and which information they consult before making their choices. We use this information to estimate learning types using maximum likelihood methods. There is substantial heterogeneity in learning types. However, the vast majority of our participants' decisions are best characterized by reinforcement learning or (myopic) best-response learning. The distribution of learning types seems fairly stable across contexts. Neither network topology nor the position of a player in the network seem to substantially affect the estimated distribution of learning types.

Analysis of Spanish text-thesaurus as a complex network

[EN]Based on the theoretical tools of Complex Networks, this work provides a basic descriptive study of a synonyms dictionary, the Spanish Open Thesaurus represented as a graph. We study the main structural measures of the network compared with those of a random graph. Numerical results show that Open-Thesaurus is a graph whose topological properties approximate a scale-free network, but seems not to present the small-world property because of its sparse structure. We also found that the words of highest betweenness centrality are terms that suggest the vocabulary of psychoanalysis: placer (pleasure), ayudante (in the sense of assistant or worker), and regular (to regulate).