Supersonic Jet Noise Reduction by Coaxial Jets with Coplanar and Staggered Exits

Far-field noise radiated from coaxial cold underexpanded jet flows issuing from convergent two-nozzle configurations with coplanar and staggered-exits is investigated experimentally. The coaxial jets are operated in the "inverted" mode, i.e., the outer (annular) jet flow Mach number is higher than that of the inner (round) jet. Keeping all other geometrical and operating conditions the same, the exit-stagger of the inner (round) and the outer (annular) nozzles was varied. It is shown that the extent of the exit-stagger affects both the flows and the radiated noise from such coaxial underexpanded jet flows and that comparatively, the lowest noise levels are achieved when the coaxial nozzle-exits are coplanar. Moreover, the effectiveness of the co-flowing inner jet flow in reducing the noise radiated from either the annular or the coaxial underexpanded jet flows decreases noticeably as the exit-stagger is increased

Smoothness without Smoothing: Why Gaussian Naive Bayes is Not Naive for Multi-Subject Searchlight Studies

Spatial smoothness is helpful when averaging fMRI signals across multiple subjects, as it allows different subjects\u27 corresponding brain areas to be pooled together even if they are slightly misaligned. However, smoothing is usually not applied when performing multivoxel pattern-based analyses (MVPA), as it runs the risk of blurring away the information that fine-grained spatial patterns contain. It would therefore be desirable, if possible, to carry out pattern-based analyses which take unsmoothed data as their input but which produce smooth images as output. We show here that the Gaussian Naive Bayes (GNB) classifier does precisely this, when it is used in “searchlight” pattern-based analyses. We explain why this occurs, and illustrate the effect in real fMRI data. Moreover, we show that analyses using GNBs produce results at the multi-subject level which are statistically robust, neurally plausible, and which replicate across two independent data sets. By contrast, SVM classifiers applied to the same data do not generate a replication, even if the SVM-derived searchlight maps have smoothing applied to them. An additional advantage of GNB classifiers for searchlight analyses is that they are orders of magnitude faster to compute than more complex alternatives such as SVMs. Collectively, these results suggest that Gaussian Naive Bayes classifiers may be a highly non-naive choice for multi-subject pattern-based fMRI studies

Feshbach resonances in a quasi-2D atomic gas

Strongly confining an ultracold atomic gas in one direction to create a quasi-2D system alters the scattering properties of this gas. We investigate the effects of confinement on Feshbach scattering resonances and show that strong confinement results in a shift in the position of the Feshbach resonance as a function of the magnetic field. This shift, as well as the change of the width of the resonance, are computed. We find that the resonance is strongly damped in the thermal gas, but in the condensate the resonance remains sharp due to many-body effects. We introduce a 2D model system, suited for the study of resonant superfluidity, and having the same scattering properties as the tightly confined real system near a Feshbach resonance. Exact relations are derived between measurable quantities and the model parameters.Comment: 8 pages, 2 figure

A Fast and Accurate Diagnostic Test for Severe Sepsis Using Kernel Classifiers

Severe sepsis occurs frequently in the intensive care unit (ICU) and is a leading cause of admission, mortality, and cost. Treatment guidelines recommend early intervention, however gold standard blood culture test results may return in up to 48 hours. Insulin sensitivity (SI) is known to decrease with worsening condition and inflammatory response, and could thus be used to aid clinical treatment decisions. Some glycemic control protocols are able to accurately identify SI in real-time. A biomarker for severe sepsis was developed from retrospective SI and concurrent temperature, heart rate, respiratory rate, blood pressure, and SIRS score from 36 adult patients with sepsis. Patients were identified as having sepsis based on a clinically validated sepsis score (ss) of 2 or higher (ss = 0–4 for increasing severity). Kernel density estimates were used for the development of joint probability density profiles for ss = 2 and ss < 2 data hours (213 and 5858 respectively of 6071 total hours) and for classification. From the receiver operator characteristic (ROC) curve, the optimal probability cutoff values for classification were determined for in-sample and out-of-sample estimates. A biomarker including concurrent insulin sensitivity and clinical data for the diagnosis of severe sepsis (ss = 2) achieves 69–94% sensitivity, 75–94% specificity, 0.78–0.99 AUC, 3–17 LHR+, 0.06–0.4 LHR-, 9–38% PPV, 99–100% NPV, and a diagnostic odds ratio of 7–260 for optimal probability cutoff values of 0.32 and 0.27 for in-sample and out-of-sample data, respectively. The overall result lies between these minimum and maximum error bounds. Thus, the clinical biomarker shows good to high accuracy and may provide useful information as a real-time diagnostic test for severe sepsis

Micro Membrane Filters for Passive Plasma Extraction From Whole Human Blood Using Silicon Nitride-based Microfilters and Plama Collection Using Agarose Gels

AbstractThe novelty of this study resides in the fabrication of a passive, operating on capillary force, penetration-flow microfluidic device for plasma separation, based on both silicon nitride combination (SiN-SiO-SiN)-based microfilters and agarose gels, and its characterization for plasma separation from whole human blood. The fabrication processes are compatible with IC process protocols, with merits of mass productions and precise size control. The fabrication process for silicon nitride membrane was reported at Lab Chip [1], and quantification its applications to affinity-based protein separation on the silicon nitride was reported at MicroTAS’07 [2]. Our method differs from that of group Yobas [3] in the specific separation method and materials, and of group Pizziconi [4] in the geometry of the filter, and fluidic components with the structure

Practical Multiple Scattering for Rough Surfaces

Microfacet theory concisely models light transport over rough surfaces. Specular reflection is the result of single mirror reflections on each facet, while exact computation of multiple scattering is either neglected, or modeled using costly importance sampling techniques. Practical but accurate simulation of multiple scattering in microfacet theory thus remains an open challenge. In this work, we revisit the traditional V-groove cavity model and derive an analytical, cost-effective solution for multiple scattering in rough surfaces. Our kaleidoscopic model is made up of both real and virtual V-grooves, and allows us to calculate higher-order scattering in the microfacets in an analytical fashion. We then extend our model to include nonsymmetric grooves, allowing for additional degrees of freedom on the surface geometry, improving multiple reflections at grazing angles with backward compatibility to traditional normal distribution functions. We validate the accuracy of our model against ground-truth Monte Carlo simulations, and demonstrate its flexibility on anisotropic and textured materials. Our model is analytical, does not introduce significant cost and variance, can be seamless integrated in any rendering engine, preserves reciprocity and energy conservation, and is suitable for bidirectional methods

Active transonic aerofoil design optimization using robust multiobjective evolutionary algorithms

The use of adaptive wing/aerofoil designs is being considered, as they are promising techniques in aeronautic/ aerospace since they can reduce aircraft emissions and improve aerodynamic performance of manned or unmanned aircraft. This paper investigates the robust design and optimization for one type of adaptive techniques: active flow control bump at transonic flow conditions on a natural laminar flow aerofoil. The concept of using shock control bump is to control supersonic flow on the suction/pressure side of natural laminar flow aerofoil that leads to delaying shock occurrence (weakening its strength) or boundary-layer separation. Such an active flow control technique reduces total drag at transonic speeds due to reduction of wave drag. The location of boundary-layer transition can influence the position and structure of the supersonic shock on the suction/pressure side of aerofoil. The boundarylayer transition position is considered as an uncertainty design parameter in aerodynamic design due to the many factors, such as surface contamination or surface erosion. This paper studies the shock-control-bump shape design optimization using robust evolutionary algorithms with uncertainty in boundary-layer transition locations. The optimization method is based on a canonical evolution strategy and incorporates the concepts of hierarchical topology, parallel computing, and asynchronous evaluation. Two test cases are conducted: the first test assumes the boundary-layer transition position is at 45% of chord from the leading edge, and the second test considers robust design optimization for the shock control bump at the variability of boundary-layer transition positions. The numerical result shows that the optimization method coupled to uncertainty design techniques produces Pareto optimal shock-control-bump shapes, which have low sensitivity and high aerodynamic performance while having significant total drag reduction

The Anderson-Mott Transition as a Random-Field Problem

The Anderson-Mott transition of disordered interacting electrons is shown to share many physical and technical features with classical random-field systems. A renormalization group study of an order parameter field theory for the Anderson-Mott transition shows that random-field terms appear at one-loop order. They lead to an upper critical dimension $d_{c}^{+}=6$ for this model. For $d>6$ the critical behavior is mean-field like. For $d<6$ an $\epsilon$-expansion yields exponents that coincide with those for the random-field Ising model. Implications of these results are discussed.Comment: 8pp, REVTeX, db/94/

Comment on "Role of heavy meson exchange in near threshold N N --> d pi"

In a recent paper by C. J. Horowitz (Phys. Rev. C {\bf 48}, 2920 (1993)) a heavy meson exchange is incorporated into threshold NN --> d pi to enhance the grossly underestimated cross section. However, that calculation uses an unjustified assumption on the initial and final momenta, which causes an overestimate of this effect by a factor of 3--4. I point out that the inclusion of the Delta(1232) isobar increases the cross section significantly even at threshold.Comment: 7 pages, figures by fax or mail from [email protected]

A general T-matrix approach applied to two-body and three-body problems in cold atomic gases

We propose a systematic T-matrix approach to solve few-body problems with s-wave contact interactions in ultracold atomic gases. The problem is generally reduced to a matrix equation expanded by a set of orthogonal molecular states, describing external center-of-mass motions of pairs of interacting particles; while each matrix element is guaranteed to be finite by a proper renormalization for internal relative motions. This approach is able to incorporate various scattering problems and the calculations of related physical quantities in a single framework, and also provides a physically transparent way to understand the mechanism of resonance scattering. For applications, we study two-body effective scattering in 2D-3D mixed dimensions, where the resonance position and width are determined with high precision from only a few number of matrix elements. We also study three fermions in a (rotating) harmonic trap, where exotic scattering properties in terms of mass ratios and angular momenta are uniquely identified in the framework of T-matrix.Comment: 14 pages, 4 figure