A Note on Pretzelosity TMD Parton Distribution

We show that the transverse-momentum-dependent parton distribution, called as Pretzelosity function, is zero at any order in perturbation theory of QCD for a single massless quark state. This implies that Pretzelosity function is not factorized with the collinear transversity parton distribution at twist-2, when the struck quark has a large transverse momentum. Pretzelosity function is in fact related to collinear parton distributions defined with twist-4 operators. In reality, Pretzelosity function of a hadron as a bound state of quarks and gluons is not zero. Through an explicit calculation of Pretzelosity function of a quark combined with a gluon nonzero result is found.Comment: improved explanation, published version in Phys. Lett.

Image Properties of Embedded Lenses

We give analytic expressions for image properties of objects seen around point mass lenses embedded in a flat $\Lambda$CDM universe. An embedded lens in an otherwise homogeneous universe offers a more realistic representation of the lens's gravity field and its associated deflection properties than does the conventional linear superposition theory. Embedding reduces the range of the gravitational force acting on passing light beams thus altering all quantities such as deflection angles, amplifications, shears and Einstein ring sizes. Embedding also exhibits the explicit effect of the cosmological constant on these same lensing quantities. In this paper we present these new results and demonstrate how they can be used. The effects of embedding on image properties, although small i.e., usually less than a fraction of a percent, have a more pronounced effect on image distortions in weak lensing where the effects can be larger than 10%. Embedding also introduces a negative surface mass density for both weak and strong lensing, a quantity altogether absent in conventional Schwarzschild lensing. In strong lensing we find only one additional quantity, the potential part of the time delay, which differs from conventional lensing by as much as 4%, in agreement with our previous numerical estimates.Comment: 17 pages, 6 figure

Analysis of elastically tailored viscoelastic damping member

For more than two decades, viscoelastic materials have been commonly used as a passive damping source in a variety of structures because of their high material loss factors. In most of the applications, viscoelastic materials are used either in series with or parallel to the structural load path. The latter is also known as the constrained-layer damping treatment. The advantage of the constrained-layer damping treatment is that it can be incorporated without loss in structural integrity, namely, stiffness and strength. However, the disadvantages are that: (1) it is not the most effective use of the viscoelastic material when compared with the series-type application, and (2) weight penalty from the stiff constraining layer requirement can be excessive. To overcome the disadvantages of the constrained-layer damping treatment, a new approach for using viscoelastic material in axial-type structural components, e.g., truss members, was studied in this investigation

Adaptive Bayesian decision feedback equalizer for dispersive mobile radio channels

The paper investigates adaptive equalization of time dispersive mobile ratio fading channels and develops a robust high performance Bayesian decision feedback equalizer (DFE). The characteristics and implementation aspects of this Bayesian DFE are analyzed, and its performance is compared with those of the conventional symbol or fractional spaced DFE and the maximum likelihood sequence estimator (MLSE). In terms of computational complexity, the adaptive Bayesian DFE is slightly more complex than the conventional DFE but is much simpler than the adaptive MLSE. In terms of error rate in symbol detection, the adaptive Bayesian DFE outperforms the conventional DFE dramatically. Moreover, for severely fading multipath channels, the adaptive MLSE exhibits significant degradation from the theoretical optimal performance and becomes inferior to the adaptive Bayesian DFE