Temperature of a trapped unitary Fermi gas at finite entropy

We present theoretical predictions for the equation of state of a harmonically trapped Fermi gas in the unitary limit. Our calculations compare Monte Carlo results with the equation of state of a uniform gas using three distinct perturbation schemes. We show that in experiments the temperature can be usefully calibrated by making use of the entropy, which is invariant during an adiabatic conversion into the weakly interacting limit of molecular BEC. We predict the entropy dependence of the equation of state.

Conditional deletion of hypothalamic Y2 receptors reverts gonadectomy-induced bone loss in adult mice

Reduction in levels of sex hormones at menopause in women is associated with two common, major outcomes, the accumulation of white adipose tissue, and the progressive loss of bone because of excess osteoclastic bone resorption exceeding osteoblastic bone formation. Current antiresorptive therapies can reduce osteoclastic activity but have only limited capacity to stimulate osteoblastic bone formation and restore lost skeletal mass. Likewise, the availability of effective pharmacological weight loss treatments is currently limited. Here we demonstrate that conditional deletion of hypothalamic neuropeptide Y2 receptors can prevent ongoing bone loss in sex hormone-deficient adult male and female mice. This benefit is attributable solely to activation of an anabolic osteoblastic bone formation response that counterbalances persistent elevation of bone resorption, suggesting the Y2-mediated anabolic pathway to be independent of sex hormones. Furthermore, the increase in fat mass that typically occurs after ovariectomy is prevented by germ line deletion of Y2 receptors, whereas in male mice body weight and fat mass were consistently lower than wild-type regardless of sex hormone status. Therefore, this study indicates a role for Y2 receptors in the accumulation of adipose tissue in the hypogonadal state and demonstrates that hypothalamic Y2 receptors constitutively restrain osteoblastic activity even in the absence of sex hormones. The increase in bone formation after release of this tonic inhibition suggests a promising new avenue for osteoporosis treatment.

Entropy generation for microscale forced convection: effects of different thermal boundary conditions, velocity slip, temperature jump, viscous dissipation, and duct geometry

This work presents closed form solutions for fully developed temperature distribution and entropy generation due to forced convection in microelectromechanical systems (MEMS) in the Slip-flow regime, for which the Knudsen number lies within the range 0.001

Estimating buffer overflows in three stages using cross-entropy

In this paper we propose a fast adaptive Importance Sampling method for the efficient simulation of buffer overflow probabilities in queueing networks. The method comprises three stages. First we estimate the minimum Cross-Entropy tilting parameter for a small buffer level; next, we use this as a starting value for the estimation of the optimal tilting parameter for the actual (large) buffer level; finally, the tilting parameter just found is used to estimate the overflow probability of interest. We recognize three distinct properties of the method which together explain why the method works well; we conjecture that they hold for quite general queueing networks. Numerical results support this conjecture and demonstrate the high efficiency of the proposed algorithm.

A Novel Nonparametric Density Estimator

We present a novel nonparametric density estimator and a new data-driven bandwidth selection method with excellent properties. The approach is in- spired by the principles of the generalized cross entropy method. The pro- posed density estimation procedure has numerous advantages over the tra- ditional kernel density estimator methods. Firstly, for the first time in the nonparametric literature, the proposed estimator allows for a genuine incor- poration of prior information in the density estimation procedure. Secondly, the approach provides the first data-driven bandwidth selection method that is guaranteed to provide a unique bandwidth for any data. Lastly, simulation examples suggest the proposed approach outperforms the current state of the art in nonparametric density estimation in terms of accuracy and reliability.