Fundamental study of transpiration cooling

Isothermal and non-isothermal pressure drop data and heat transfer data generated on porous 304L stainless steel wire forms, sintered spherical stainless steel powder, and sintered spherical OFHC copper powder are reported and correlated. Pressure drop data was collected over a temperature range from 500 R to 2000 R and heat transfer data collected over a heat flux range from 5 to 15 BTU/in2/sec. It was found that flow data could be correlated independently of transpirant temperature and type (i.e., H2, N2). It was also found that no simple relation between heat transfer coefficient and specimen porosity was obtainable

Shock waves in ultracold Fermi (Tonks) gases

It is shown that a broad density perturbation in a Fermi (Tonks) cloud takes a shock wave form in the course of time evolution. A very accurate analytical description of shock formation is provided. A simple experimental setup for the observation of shocks is discussed.Comment: approx. 4 pages&figures, minor corrections^2, to be published as a Letter in Journal of Physics

Dominant ferromagnetism in the spin-1/2 half-twist ladder 334 compounds, Ba3Cu3In4O12 and Ba3Cu3Sc4O12

The magnetic properties of polycrystalline samples of Ba3Cu3In4O12 (In-334) and Ba3Cu3Sc4O12 (Sc-334) are reported. Both 334 phases have a structure derived from perovskite, with CuO4 squares interconnected to form half-twist ladders along the c-axis. The Cu-O-Cu angles, ~ 90o, and the positive Weiss temperatures indicate the presence of significant ferromagnetic (FM) interactions along the Cu ladders. At low temperatures, T < 20 K, sharp transitions in the magnetic susceptibility and heat capacity measurements indicate three-dimensional (3D) antiferromagnetic (AFM) ordering at TN. TN is suppressed on application of a field and a complex magnetic phase diagram with three distinct magnetic regimes below the upper critical field can be inferred from our measurements. The magnetic interactions are discussed in relation to a modified spin-1/2 FM-AFM model and the 334 half-twist ladder is compared to other 2-rung ladder spin-1/2 systems.Comment: 20 pages, 7 figure

Improving measurements of SF6 for the study of atmospheric transport and emissions

Sulfur hexafluoride (SF6) is a potent greenhouse gas and useful atmospheric tracer. Measurements of SF6 on global and regional scales are necessary to estimate emissions and to verify or examine the performance of atmospheric transport models. Typical precision for common gas chromatographic methods with electron capture detection (GC-ECD) is 1–2%. We have modified a common GC-ECD method to achieve measurement precision of 0.5% or better. Global mean SF6 measurements were used to examine changes in the growth rate of SF6 and corresponding SF6 emissions. Global emissions and mixing ratios from 2000–2008 are consistent with recently published work. More recent observations show a 10% decline in SF6 emissions in 2008–2009, which seems to coincide with a decrease in world economic output. This decline was short-lived, as the global SF6 growth rate has recently increased to near its 2007–2008 maximum value of 0.30&plusmn;0.03 pmol mol−1 (ppt) yr−1 (95% C.L.)

Formation of shock waves in a Bose-Einstein condensate

We consider propagation of density wave packets in a Bose-Einstein condensate. We show that the shape of initially broad, laser-induced, density perturbation changes in the course of free time evolution so that a shock wave front finally forms. Our results are well beyond predictions of commonly used zero-amplitude approach, so they can be useful in extraction of a speed of sound from experimental data. We discuss a simple experimental setup for shock propagation and point out possible limitations of the mean-field approach for description of shock phenomena in a BEC.Comment: 8 pages & 6 figures, minor changes, more references, to appear in Phys. Rev.

Assessing positive matrix factorization model fit: a new method to estimate uncertainty and bias in factor contributions at the daily time scale

International audienceA Positive Matrix Factorization receptor model for aerosol pollution source apportionment was fit to a synthetic dataset simulating one year of daily measurements of ambient PM2.5 concentrations, comprised of 39 chemical species from nine pollutant sources. A novel method was developed to estimate model fit uncertainty and bias at the daily time scale, as related to factor contributions. A balanced bootstrap is used to create replicate datasets, with the same model then fit to the data. Neural networks are trained to classify factors based upon chemical profiles, as opposed to correlating contribution time series, and this classification is used to align factor orderings across results associated with the replicate datasets. Factor contribution uncertainty is assessed from the distribution of results associated with each factor. Comparing modeled factors with input factors used to create the synthetic data assesses bias. The results indicate that variability in factor contribution estimates does not necessarily encompass model error: contribution estimates can have small associated variability yet also be very biased. These results are likely dependent on characteristics of the data

Assessing positive matrix factorization model fit: a new method to estimate uncertainty and bias in factor contributions at the measurement time scale

A Positive Matrix Factorization receptor model for aerosol pollution source apportionment was fit to a synthetic dataset simulating one year of daily measurements of ambient PM&lt;sub&gt;2.5&lt;/sub&gt; concentrations, comprised of 39 chemical species from nine pollutant sources. A novel method was developed to estimate model fit uncertainty and bias at the daily time scale, as related to factor contributions. A circular block bootstrap is used to create replicate datasets, with the same receptor model then fit to the data. Neural networks are trained to classify factors based upon chemical profiles, as opposed to correlating contribution time series, and this classification is used to align factor orderings across the model results associated with the replicate datasets. Factor contribution uncertainty is assessed from the distribution of results associated with each factor. Comparing modeled factors with input factors used to create the synthetic data assesses bias. The results indicate that variability in factor contribution estimates does not necessarily encompass model error: contribution estimates can have small associated variability across results yet also be very biased. These findings are likely dependent on characteristics of the data

Towards a first principles description of phonons in Ni50_{50}Pt50_{50} disordered alloys: the role of relaxation

Using a combination of density-functional perturbation theory and the itinerant coherent potential approximation, we study the effects of atomic relaxation on the inelastic incoherent neutron scattering cross sections of disordered Ni$_{50}$Pt$_{50}$ alloys. We build on previous work, where empirical force constants were adjusted {\it ad hoc} to agree with experiment. After first relaxing all structural parameters within the local-density approximation for ordered NiPt compounds, density-functional perturbation theory is then used to compute phonon spectra, densities of states, and the force constants. The resulting nearest-neighbor force constants are first compared to those of other ordered structures of different stoichiometry, and then used to generate the inelastic scattering cross sections within the itinerant coherent potential approximation. We find that structural relaxation substantially affects the computed force constants and resulting inelastic cross sections, and that the effect is much more pronounced in random alloys than in ordered alloys.Comment: 8 pages, 3 eps figures, uses revtex

Analysis of phase II methodologies for single-arm clinical trials with multiple endpoints in rare cancers:An example in Ewing's sarcoma

Trials run in either rare diseases, such as rare cancers, or rare subpopulations of common diseases are challenging in terms of identifying, recruiting and treating sufficient patients in a sensible period. Treatments for rare diseases are often designed for other disease areas and then later proposed as possible treatments for the rare disease after initial phase I testing is complete. To ensure the trial is in the best interests of the patient participants, frequent interim analyses are needed to force the trial to stop promptly if the treatment is futile or toxic. These non-definitive phase II trials should also be stopped for efficacy to accelerate research progress if the treatment proves to be particularly promising. In this paper, we review frequentist and Bayesian methods that have been adapted to incorporate two binary endpoints and frequent interim analyses. The LINES trial is used as a motivating example and provides a suitable platform to compare these approaches. The Bayesian approach provides greater design flexibility, but does not provide additional value over the frequentist approaches in a single trial setting when the prior is non-informative. However, Bayesian designs are able to borrow from any previous experience, using prior information to improve efficiency