Three-body forces and the limit of oxygen isotopes

The limit of neutron-rich nuclei, the neutron drip-line, evolves regularly from light to medium-mass nuclei except for a striking anomaly in the oxygen isotopes. This anomaly is not reproduced in shell-model calculations derived from microscopic two-nucleon forces. Here, we present the first microscopic explanation of the oxygen anomaly based on three-nucleon forces that have been established in few-body systems. This leads to repulsive contributions to the interactions among excess neutrons that change the location of the neutron drip-line from $^{28}$O to the experimentally observed $^{24}$O. Since the mechanism is robust and general, our findings impact the prediction of the most neutron-rich nuclei and the synthesis of heavy elements in neutron-rich environments.Comment: 4 pages, 4 figures, to be published in PR

The basic K nuclear cluster K- pp and its enhanced formation in the p + p -> K+ + X reaction

We have studied the structure of K- pp nuclear cluster comprehensively by solving this three-body system exactly in a variational method starting from the Ansatz that the Lambda(1405) resonance (Lambda*) is a K-p bound state. We have found that our original prediction for the presence of K-pp as a compact bound system with M = 2322$MeV/c2, B = 48 MeV and Gamma = 60 MeV remains unchanged by varying the Kba-rN and NN interactions widely as far as they reproduce Lambda(1405). The structure of K- pp reveals a molecular feature, namely, the K- in Lambda* as an "atomic center" plays a key role in producing strong covalent bonding with the other proton. We have shown that the elementary process, p + p -> K+ + Lambda* + p, which occurs in a short impact parameter and with a large momentum transfer (Q ~ 1.6$ GeV/c), leads to unusually large self-trapping of Lambda* by the participating proton, since the Lambda*-p system exists as a compact doorway state propagating to K- pp (R{Lambda*-p} ~ 1.67 fm).Comment: 18 pages, 14 figures. Phys, Rev. C, in pres

Predicting Airline Choices: A Decision Support Perspective and Alternative Approaches

The ability to predict the choices of prospective passengers allows airlines to alleviate the need for overbooking flights and subsequently bumping passengers, potentially leading to improved customer satisfaction. Past studies have typically focused on identifying the important factors that influence choice behaviors and applied discrete choice framework models to model passengers’ airline choices. Typical discrete choice models rely on two major assumptions: the existence of a utility function that represents the preferences over a choice set and the linearity of the utility function with respect to attributes of alternatives and decision makers. These assumptions allow the discrete choice models to be easily interpreted, as each unit change of an input attribute can be directly translated into change in utility that eventually affects the optimal choice. However, these restrictive assumptions might impede the ability of typical discrete choice models to deliver operational accurate prediction and forecasts. In this paper, we focus on developing operational models that are intended for supporting the actual prediction decisions of airlines. We propose two alternative approaches, pairwise preference learning using classification techniques and ranking function learning using evolutionary computation. We have empirically compared these approaches against the standard discrete choice framework models and report some promising results in this paper