Collision integrals and high temperature transport properties for N-N, O-O, and N-O

Accurate collision integrals are reported for the interactions of N(4 S 0) + N(4 S 0), O(3 P), and N(4 S 0) + O(3 P). These are computed from a semiclassical formulation of the scattering using the best available representations of all of the potential energy curves needed to describe the collisions. Experimental RKR curves and other accurate measured data are used where available; the results of accurate ab initio electronic structure calculations are used to determine the remaining potential curves. The high-lying states are found to give the largest contributions to the collision cross sections. The nine collision integrals, needed to determine transport properties to second order, are tabulated for translational temperatures in the range 250 K to 100,000 K. These results are intended to reduce the uncertainty in future predictions of the transport properties of nonequilibrium air, particularly at high temperatures. The viscosity, thermal conductivity, diffusion coefficient, and thermal diffusion factor for a gas composed of nitrogen and oxygen atoms in thermal equilibrium are calculated. It was found that the second order contribution to the transport properties is small. Graphs of these transport properties for various mixture ratios are presented for temperatures in the range 5000 to 15000 K

The effect of elevated hydrostatic pressure on the spectral absorption of deep-sea fish visual pigments

The effect of hydrostatic pressure (0.1-54 MPa, equivalent to pressures experienced by fish from the ocean's surface to depths of ca. 5400 m) on visual pigment absorption spectra was investigated for rod visual pigments extracted from the retinae of 12 species of deep-sea fish of diverse phylogeny and habitat. The wavelength of peak absorption (λmax) was shifted to longer wavelengths by an average of 1.35 nm at 40 MPa (a pressure approximately equivalent to average ocean depth) relative to measurements made at one atmosphere (ca. 0.1 MPa), but with little evidence of a change in absorbance at the λmax. We conclude that previousλ max measurements of deep-sea fish visual pigments, made at a pressure close to 0.1 MPa, provide a good indication ofλ max values at higher pressures when considering the ecology of vision in the deep-sea. Although not affecting the spectral sensitivity of the animal to any important degree, the observed shift inλ max may be of interest in the context of understanding opsin-chromophore interaction and spectral tuning of visual pigments

Bright soliton trains of trapped Bose-Einstein condensates

We variationally determine the dynamics of bright soliton trains composed of harmonically trapped Bose-Einstein condensates with attractive interatomic interactions. In particular, we obtain the interaction potential between two solitons. We also discuss the formation of soliton trains due to the quantum mechanical phase fluctuations of a one-dimensional condensate.Comment: 4 pages, 2 figures, submitted to PR

RMS Radio Source Contributions to the Microwave Sky

Cross-correlations of the WMAP full sky K, Ka, Q, V, and W band maps with the 1.4 GHz NVSS source count map and the HEAO I A2 2-10 keV full sky X-ray flux map are used to constrain rms fluctuations due to unresolved microwave sources in the WMAP frequency range. In the Q band (40.7 GHz), a lower limit, taking account of only those fluctuations correlated with the 1.4 GHz radio source counts and X-ray flux, corresponds to an rms Rayleigh-Jeans temperature of ~ 2 microKelvin for a solid angle of one square degree. The correlated fluctuations at the other bands are consistent with a beta = -2.1 +- 0.4 frequency spectrum. Using the rms fluctuations of the X-ray flux and radio source counts, and the cross-correlation of these two quantities as a guide, the above lower limit leads to a plausible estimate of ~ 5 microKelvin for Q-band rms fluctuations in one square degree. This value is similar to that implied by the excess, small angular scale fluctuations observed in the Q band by WMAP, and is consistent with estimates made by extrapolating low-frquency source counts.Comment: 17 pages, 8 figures, submitted to Ap

The Bayesian Decision Tree Technique with a Sweeping Strategy

The uncertainty of classification outcomes is of crucial importance for many safety critical applications including, for example, medical diagnostics. In such applications the uncertainty of classification can be reliably estimated within a Bayesian model averaging technique that allows the use of prior information. Decision Tree (DT) classification models used within such a technique gives experts additional information by making this classification scheme observable. The use of the Markov Chain Monte Carlo (MCMC) methodology of stochastic sampling makes the Bayesian DT technique feasible to perform. However, in practice, the MCMC technique may become stuck in a particular DT which is far away from a region with a maximal posterior. Sampling such DTs causes bias in the posterior estimates, and as a result the evaluation of classification uncertainty may be incorrect. In a particular case, the negative effect of such sampling may be reduced by giving additional prior information on the shape of DTs. In this paper we describe a new approach based on sweeping the DTs without additional priors on the favorite shape of DTs. The performances of Bayesian DT techniques with the standard and sweeping strategies are compared on a synthetic data as well as on real datasets. Quantitatively evaluating the uncertainty in terms of entropy of class posterior probabilities, we found that the sweeping strategy is superior to the standard strategy

Optically Faint Microjansky Radio Sources

We report on the identifications of radio sources from our survey of the Hubble Deep Field and the SSA13 fields, both of which comprise the deepest radio surveys to date at 1.4 GHz and 8.5 GHz respectively. About 80% of the microjansky radio sources are associated with moderate redshift starburst galaxies or AGNs within the I magnitude range of 17 to 24 with a median of I = 22 mag. Thirty-one (20%) of the radio sources are: 1) fainter than $I>$25 mag, with two objects in the HDF $I_{AB}>$28.5, 2) often identified with very red objects $I-K>$4, and 3) not significantly different in radio properties than the brighter objects. We suggest that most of these objects are associated with heavily obscured starburst galaxies with redshifts between 1 and 3. However, other mechanisms are discussed and cannot be ruled out with the present observations.Comment: to appear in Astrophysical Journal Letters, 3 figures, 1 tabl

Conversion of an Atomic Fermi Gas to a Long-Lived Molecular Bose Gas

We have converted an ultracold Fermi gas of $^6$Li atoms into an ultracold gas of $^6$Li$_2$ molecules by adiabatic passage through a Feshbach resonance. Approximately $1.5 \times 10^5$ molecules in the least-bound, $v = 38$, vibrational level of the X$^1 \Sigma ^+_g$ singlet state are produced with an efficiency of 50%. The molecules remain confined in an optical trap for times of up to 1 s before we dissociate them by a reverse adiabatic sweep.Comment: Accepted for publication in Phys. Rev. Letter

Near-optimal energy management for plug-in hybrid fuel cell and battery propulsion using deep reinforcement learning

Plug-in hybrid fuel cell and battery propulsion systems appear promising for decarbonising transportation applications such as road vehicles and coastal ships. However, it is challenging to develop optimal or near-optimal energy management for these systems without exact knowledge of future load profiles. Although efforts have been made to develop strategies in a stochastic environment with discrete state space using Q-learning and Double Q-learning, such tabular reinforcement learning agents’ effectiveness is limited due to the state space resolution. This article aims to develop an improved energy management system using deep reinforcement learning to achieve enhanced cost-saving by extending discrete state parameters to be continuous. The improved energy management system is based upon the Double Deep Q-Network. Real-world collected stochastic load profiles are applied to train the Double Deep Q-Network for a coastal ferry. The results suggest that the Double Deep Q-Network acquired energy management strategy has achieved a further 5.5% cost reduction with a 93.8% decrease in training time, compared to that produced by the Double Q-learning agent in discrete state space without function approximations. In addition, this article also proposes an adaptive deep reinforcement learning energy management scheme for practical hybrid-electric propulsion systems operating in changing environments