Line lists for the A2PI-X2Sigma+ (red) and {B2Sigma+-X2Sigma} (violet) Systems of CN, 13C14N, and 12C15N, and Application to Astronomical Spectra

New red and violet system line lists for the CN isotopologues 13C14N and 12C15N have been generated. These new transition data are combined with those previously derived for 12C14N, and applied to the determination of CNO abundances in the solar photosphere and in four red giant stars: Arcturus, the bright very low-metallicity star HD 122563, and carbon-enhanced metal-poor stars HD 196944 and HD 201626. When lines of both red and violet system lines are detectable in a star, their derived N abundances are in good agreement. The mean N abundances determined in this work generally are also in accord with published values.Comment: ApJS, in press, 37 pages, 7 figures, 3 table

Blade planform for a quiet helicopter

The effects of blade planform and tip speed on noise and performance for a Hughes 500 C rotor system were studied. A cursory examination of the effects of such planform shapes as regular, inverse, and no taper on the noise and performance of the rotor was conducted. It was found that a constant width wide chord planform at tower tip speed provided the best performance and lowest noise. The tapered planforms had lower performance figures due to the reduced solidity. However, some noise reductions were achieved

Incremental Stochastic Subgradient Algorithms for Convex Optimization

In this paper we study the effect of stochastic errors on two constrained incremental sub-gradient algorithms. We view the incremental sub-gradient algorithms as decentralized network optimization algorithms as applied to minimize a sum of functions, when each component function is known only to a particular agent of a distributed network. We first study the standard cyclic incremental sub-gradient algorithm in which the agents form a ring structure and pass the iterate in a cycle. We consider the method with stochastic errors in the sub-gradient evaluations and provide sufficient conditions on the moments of the stochastic errors that guarantee almost sure convergence when a diminishing step-size is used. We also obtain almost sure bounds on the algorithm's performance when a constant step-size is used. We then consider \ram{the} Markov randomized incremental subgradient method, which is a non-cyclic version of the incremental algorithm where the sequence of computing agents is modeled as a time non-homogeneous Markov chain. Such a model is appropriate for mobile networks, as the network topology changes across time in these networks. We establish the convergence results and error bounds for the Markov randomized method in the presence of stochastic errors for diminishing and constant step-sizes, respectively