We report the results of simulations of the many atom wave function when a cold gas is excited to highly excited states. We simulated the many body wave function by direct numerical solution of Schrödinger's equation. We investigated the fraction of atoms excited and the correlation of excited atoms in the gas for different types of excitation when the blockade region was small compared to the sample size. We also investigated the blockade effect when the blockade region is comparable to the sample size to determine the sensitivity of this system and constraints for quantum information.

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We have observed resonant energy transfer between cold Rydberg atoms in spatially separated cylinders. Resonant dipole-dipole coupling excites the 49s atoms in one cylinder to the 49p state while the 41d atoms in the second cylinder are transferred down to the 42p state. We have measured the production of the 49p state as a function of separation of the cylinders (0-80 microm) and the interaction time (0-25 micros). In addition, we measured the width of the electric field resonances. A full many-body quantum calculation reproduces the main features of the experiments.

The use of neural network algorithms for predicting minor and major disruptions in tokamaks is explored by analysing disruption data from the TEXT tokamak with two network architectures. Future values of the fluctuating magnetic signal are predicted based on L past values of the magnetic fluctuation signal measured by a single Mirnov coil. The time step used (=0.04 ms) corresponds to the experimental data sampling rate. Two kinds of approach are adopted for the network: the contiguous future prediction and the multi-time-scale prediction. Both networks are trained through the back-propagation algorithm with inertial terms and the strengths of the results are compared. The use of additional diamagnetic signals as a method of increasing the performance is suggested. The degree of success indicates that the magnetic fluctuations associated with the TEXT disruption data may be characterized by a low dimensional dynamical system

dissolved in dioxane (40 mL) was hydrogenated over palladiumon-barium sulfate catalyst (0.02 g) in the presence of quinoline (0.2 g). When the hydrogen absorption ceased, the catalyst was filtered off and the solvent was removed in vacuo, leaving a residue (0.081 g), which was dehydrated in xylene-tetrahydrofuran (4:1 v/v) solution with boron trifluoride etherate (0.9 mL) in the presence of hydroquinone (0.02 g) by boiling in a reflux condenser during 3.5 h. After cooling, the reaction mixture was treated with 10% aqueous sodium hydroxide (4 mL), taken in ethyl ether (150 mL), washed with water, and dried over anhydrous magnesium sulfate. Upon removal of the solvent the residue was purified by chromatography which gave 4 oil (0.052 g, 56% yield):

We use neural nets to construct nonlinear models to forecast the AL index given solar wind and interplanetary magnetic field (IMF) data. We follow two approaches: 1) the state space reconstruction approach, which is a nonlinear generalization of autoregressive‐moving average models (ARMA) and 2) the nonlinear filter approach, which reduces to a moving average model (MA) in the linear limit. The database used here is that of Bargatze et al. [1985].

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We describe how optical contact lithography based on plasmon particle array masks allows generation of a large number of different subwavelength exposure patterns using a single mask. Within an exact point dipole model, we study the local response of silver particles in small two-dimensional arrays with 50-200 nm spacing. We show how illumination with unfocused light allows optically addressing particles either individually or in controlled configurations; which pattern will be exposed by the mask is programmed by varying the wavelength, incidence angle, and polarization of the incident wave.