Influence of a classical homogeneous gravitational field on dissipative dynamics of the Jaynes-Cummings model with phase damping

In this paper, we study the dissipative dynamics of the Jaynes-Cummings model with phase damping in the presence of a classical homogeneous gravitational field. The model consists of a moving two-level atom simultaneously exposed to the gravitational field and a single-mode traveling radiation field in the presence of the phase damping. We present a quantum treatment of the internal and external dynamics of the atom based on an alternative su(2) dynamical algebraic structure. By making use of the super-operator technique, we obtain the solution of the master equation for the density operator of the quantum system, under the Markovian approximation. Assuming that initially the radiation field is prepared in a Glauber coherent state and the two-level atom is in the excited state, we investigate the influence of gravity on the temporal evolution of collapses and revivals of the atomic population inversion, atomic dipole squeezing, atomic momentum diffusion, photon counting statistics and quadrature squeezing of the radiation field in the presence of phase damping.Comment: 25 pages, 15 figure

Van Etten (Henry) Journal d'un Quaker de notre temps (1893-1962)

Séguy Jean. Van Etten (Henry) Journal d'un Quaker de notre temps (1893-1962). In: Archives de sociologie des religions, n°20, 1965. p. 209

Dynamic MCDM with future knowledge for supplier selection

Dynamic multi-criteria decision making (DMCDM) is an emerging subject in the decision-making area and in the last decade the challenge to consider time as an important variable has become important. Some frameworks already exist in this area but when compared with other types of decision-making models, DMCDM needs more work to be applicable in real industrial problems. In this work we extend a dynamic spatial-temporal framework, designed to deal with historical data (feedback), to address the problem of considering future information/knowledge (feed-forward). The main objective is to enrich dynamic decision-making models with explicit knowledge (existing historical data) and tacit knowledge (e.g. expert predictions) in time-evolving problems, such as supplier selection. Considering supplier-predicted information for future situations (e.g. investments in capacity) and, simultaneously, learning from historical data can help a company to find less risky and consistent alternatives. The proposed model is successfully implemented in a real case study for supplier selection in one automotive industry to demonstrate the capability and applicability of the model.- (undefined