Near- and subbarrier elastic and quasielastic scattering of the weakly bound (6)Li projectile on (144)Sm

High-precision data of backward-angle elastic and quasielastic scattering for the weakly bound (6)Li projectile on (144)Sm target at deep-sub-barrier, near-, and above-barrier energies were measured. From the deep-sub-barrier data, the surface diffuseness of the nuclear interacting potential was studied. Barrier distributions were extracted from the first derivatives of the elastic and quasielastic excitation functions. It is shown that sequential breakup through the first resonant state of the (6)Li is an important channel to be included in coupled-channels calculations, even at deep-sub-barrier energies.

Knowledge acquisition by networks of interacting agents in the presence of observation errors

In this work we investigate knowledge acquisition as performed by multiple agents interacting as they infer, under the presence of observation errors, respective models of a complex system. We focus the specific case in which, at each time step, each agent takes into account its current observation as well as the average of the models of its neighbors. The agents are connected by a network of interaction of Erdos-Renyi or Barabasi-Albert type. First, we investigate situations in which one of the agents has a different probability of observation error (higher or lower). It is shown that the influence of this special agent over the quality of the models inferred by the rest of the network can be substantial, varying linearly with the respective degree of the agent with different estimation error. In case the degree of this agent is taken as a respective fitness parameter, the effect of the different estimation error is even more pronounced, becoming superlinear. To complement our analysis, we provide the analytical solution of the overall performance of the system. We also investigate the knowledge acquisition dynamic when the agents are grouped into communities. We verify that the inclusion of edges between agents (within a community) having higher probability of observation error promotes the loss of quality in the estimation of the agents in the other communities.