Goertler instability in compressible boundary layers along curved surfaces with suction and cooling

The Goertler instability of the laminar compressible boundary layer flows along concave surfaces is investigated. The linearized disturbance equations for the three-dimensional, counter-rotating streamwise vortices in two-dimensional boundary layers are presented in an orthogonal curvilinear coordinate. The basic approximation of the disturbance equations, that includes the effect of the growth of the boundary layer, is considered and solved numerically. The effect of compressibility on critical stability limits, growth rates, and amplitude ratios of the vortices is evaluated for a range of Mach numbers for 0 to 5. The effect of wall cooling and suction of the boundary layer on the development of Goertler vortices is investigated for different Mach numbers

Surgical technique for arthroscopic onlay suprapectoral biceps tenodesis with an all-suture anchor.

The long head of the biceps is a frequent pain generator in the shoulder. Tendinopathy of the long head of the biceps may be treated with biceps tenodesis. There has been great debate about the optimal technique for biceps tenodesis, without a clear distinction between different techniques. Biceps tenodesis fixation may include interference fixation, suspensory fixation, all-suture anchors, and soft tissue fixation. In this technical note, we describe an all-arthroscopic onlay suprapectoral biceps tenodesis with an all-suture anchor

Computation of Kolmogorov's Constant in Magnetohydrodynamic Turbulence

In this paper we calculate Kolmogorov's constant for magnetohydrodynamic turbulence to one loop order in perturbation theory using the direct interaction approximation technique of Kraichnan. We have computed the constants for various $E^u(k)/E^b(k)$, i.e., fluid to magnetic energy ratios when the normalized cross helicity is zero. We find that $K$ increases from 1.47 to 4.12 as we go from fully fluid case $(E^b=0)$ to a situation when $E^u/E^b=0.5$, then it decreases to 3.55 in a fully magnetic limit $(E^u=0)$. When $E^u/E^b=1$, we find that $K=3.43$.Comment: Latex, 10 pages, no figures, To appear in Euro. Phys. Lett., 199

Effect of sintering temperature and heat treatment on electrical properties of indium oxide based ceramics

Indium oxide based ceramics with bismuth oxide addition were sintered in air in the temperature range 800-1300 ºC. Current-voltage characteristics of In2O3-Bi2O3 ceramics sintered at different temperatures are weakly nonlinear. After an additional heat treatment in air at about 200 ºC samples sintered at a temperature within the narrow range of about 1050-1100 ºC exhibit a current-limiting effect accompanied by low-frequency current oscillations. It is shown that the observed electrical properties are controlled by the grain-boundary barriers and the heat treatment in air at 200 ºC leads to the decrease in the barrier height. Electrical measurements, scanning electron microscopy and X-ray photoelectron spectroscopy results suggest that the current-limiting effect observed in In2O3-Bi2O3 can be explained in terms of the modified barrier model proposed earlier for the explanation of similar effect in In2O3-SrO ceramics

Optimal Data-Dependent Hashing for Approximate Near Neighbors

We show an optimal data-dependent hashing scheme for the approximate near neighbor problem. For an $n$-point data set in a $d$-dimensional space our data structure achieves query time $O(d n^{\rho+o(1)})$ and space $O(n^{1+\rho+o(1)} + dn)$, where $\rho=\tfrac{1}{2c^2-1}$ for the Euclidean space and approximation $c>1$. For the Hamming space, we obtain an exponent of $\rho=\tfrac{1}{2c-1}$. Our result completes the direction set forth in [AINR14] who gave a proof-of-concept that data-dependent hashing can outperform classical Locality Sensitive Hashing (LSH). In contrast to [AINR14], the new bound is not only optimal, but in fact improves over the best (optimal) LSH data structures [IM98,AI06] for all approximation factors $c>1$. From the technical perspective, we proceed by decomposing an arbitrary dataset into several subsets that are, in a certain sense, pseudo-random.Comment: 36 pages, 5 figures, an extended abstract appeared in the proceedings of the 47th ACM Symposium on Theory of Computing (STOC 2015