Fluid thrust control system

A pure fluid thrust control system is described for a pump-fed, regeneratively cooled liquid propellant rocket engine. A proportional fluid amplifier and a bistable fluid amplifier control overshoot in the starting of the engine and take it to a predetermined thrust. An ejector type pump is provided in the line between the liquid hydrogen rocket nozzle heat exchanger and the turbine driving the fuel pump to aid in bringing the fluid at this point back into the regular system when it is not bypassed. The thrust control system is intended to function in environments too severe for mechanical controls

Gibrat's law for cities: uniformly most powerful unbiased test of the Pareto against the lognormal

We address the general problem of testing a power law distribution versus a log-normal distribution in statistical data. This general problem is illustrated on the distribution of the 2000 US census of city sizes. We provide definitive results to close the debate between Eeckhout (2004, 2009) and Levy (2009) on the validity of Zipf's law, which is the special Pareto law with tail exponent 1, to describe the tail of the distribution of U.S. city sizes. Because the origin of the disagreement between Eeckhout and Levy stems from the limited power of their tests, we perform the {\em uniformly most powerful unbiased test} for the null hypothesis of the Pareto distribution against the lognormal. The $p$-value and Hill's estimator as a function of city size lower threshold confirm indubitably that the size distribution of the 1000 largest cities or so, which include more than half of the total U.S. population, is Pareto, but we rule out that the tail exponent, estimated to be $1.4 \pm 0.1$, is equal to 1. For larger ranks, the $p$-value becomes very small and Hill's estimator decays systematically with decreasing ranks, qualifying the lognormal distribution as the better model for the set of smaller cities. These two results reconcile the opposite views of Eeckhout (2004, 2009) and Levy (2009). We explain how Gibrat's law of proportional growth underpins both the Pareto and lognormal distributions and stress the key ingredient at the origin of their difference in standard stochastic growth models of cities \cite{Gabaix99,Eeckhout2004}.Comment: 7 pages + 2 figure

XMM-Newton observation of the ULIRG NGC 6240: The physical nature of the complex Fe K line emission

We report on an XMM-Newton observation of the ultraluminous infrared galaxy NGC 6240. The 0.3-10 keV spectrum can be successfully modelled with: (i) three collisionally ionized plasma components with temperatures of about 0.7, 1.4, and 5.5 keV; (ii) a highly absorbed direct power-law component; and (iii) a neutral Fe K_alpha and K_beta line. We detect a significant neutral column density gradient which is correlated with the temperature of the three plasma components. Combining the XMM-Newton spectral model with the high spatial resolution Chandra image we find that the temperatures and the column densities increase towards the center. With high significance, the Fe K line complex is resolved into three distinct narrow lines: (i) the neutral Fe K_alpha line at 6.4 keV; (ii) an ionized line at about 6.7 keV; and (iii) a higher ionized line at 7.0 keV (a blend of the Fe XXVI and the Fe K_beta line). While the neutral Fe K line is most probably due to reflection from optically thick material, the Fe XXV and Fe XXVI emission arises from the highest temperature ionized plasma component. We have compared the plasma parameters of the ultraluminous infrared galaxy NGC 6240 with those found in the local starburst galaxy NGC 253. We find a striking similarity in the plasma temperatures and column density gradients, suggesting a similar underlying physical process at work in both galaxies.Comment: 8 pages including 9 figures. Accepted for publication in A&

Heavy Ion Collisions and the Density Dependence of the Local Mean Field

We study the effect of the density dependence of the scalar and the vector part of the nucleonic self-energy in Relativistic Quantum Molecular Dynamics (RQMD) on observables like the transversal flow and the rapidity distribution. The stability of nuclei in RQMD is greatly improved if the density dependence is included in the self-energies compared to a calculation assuming always saturation density of nuclear matter. Different approaches are studied: The main results are calculated with self-energies extracted from a Dirac-Br\"uckner-Hartree-Fock G-matrix of a one boson exchange model, i.e. the Bonn potential. These results are compared with those obtained by a generalization of static Skyrme force, with calculations in the simple linear Walecka model and results of the Br\"uckner-Hartree-Fock G-matrix of the Reid soft core potential. The transversal flow is very sensitive to these different approaches. A comparison with the data is given.Comment: LaTex-file, 13 pages, 5 figures (available upon request), submitted to Nuclear Physics

Re-imagining community participation at the district level: Lessons from the DIALHS collaboration.

In South Africa, the value of community participation as one of the central components of a primary health care approach is highlighted in legislation, policy documents and strategic plans. There is widespread acceptance that community participation strengthens community empowerment, disease prevention and access to services. Since 2010, the District Innovation and Action Learning for Health System Development collaboration has co-produced knowledge about how to strengthen district health systems. Nested within this collaboration is a series of engagements seeking to understand and strengthen community participation including a multi-stakeholder health risks and assets mapping activity; ‘Local Action Group’ initiatives; reflective meetings with service colleagues about community participation experiences; and a capacity-development initiative (community participation-related short courses and mentoring). These engagements hold a number of lessons for those interested in enhancing the population orientation of primary health care and the district health system, the first of which is the clear benefit to those interested in community roles and engagement of convening spaces for dialogue. However, it is not easy to generate and sustain these spaces. Through the application of a framework of collective capacity, this chapter aims to shed light on why this is the case, and in so doing, to highlight a second lesson, which is the perhaps unrecognised capacities of certain cadres, particularly environmental health practitioners, in the implementation of community participation. Ultimately, the chapter seeks to stimulate thinking and engagement about the ways in which dialogue and participation can enrich the South African health system

A quantitative analysis of measures of quality in science

Condensing the work of any academic scientist into a one-dimensional measure of scientific quality is a difficult problem. Here, we employ Bayesian statistics to analyze several different measures of quality. Specifically, we determine each measure's ability to discriminate between scientific authors. Using scaling arguments, we demonstrate that the best of these measures require approximately 50 papers to draw conclusions regarding long term scientific performance with usefully small statistical uncertainties. Further, the approach described here permits the value-free (i.e., statistical) comparison of scientists working in distinct areas of science.Comment: 11 pages, 8 figures, 4 table

Effect of periodic parametric excitation on an ensemble of force-coupled self-oscillators

We report the synchronization behavior in a one-dimensional chain of identical limit cycle oscillators coupled to a mass-spring load via a force relation. We consider the effect of periodic parametric modulation on the final synchronization states of the system. Two types of external parametric excitations are investigated numerically: periodic modulation of the stiffness of the inertial oscillator and periodic excitation of the frequency of the self-oscillatory element. We show that the synchronization scenarios are ruled not only by the choice of parameters of the excitation force but depend on the initial collective state in the ensemble. We give detailed analysis of entrainment behavior for initially homogeneous and inhomogeneous states. Among other results, we describe a regime of partial synchronization. This regime is characterized by the frequency of collective oscillation being entrained to the stimulation frequency but different from the average individual oscillators frequency.Comment: Comments and suggestions are welcom

Distribution of reflection eigenvalues in many-channel chaotic cavities with absorption

The reflection matrix R=S^{\dagger}S, with S being the scattering matrix, differs from the unit one, when absorption is finite. Using the random matrix approach, we calculate analytically the distribution function of its eigenvalues in the limit of a large number of propagating modes in the leads attached to a chaotic cavity. The obtained result is independent on the presence of time-reversal symmetry in the system, being valid at finite absorption and arbitrary openness of the system. The particular cases of perfectly and weakly open cavities are considered in detail. An application of our results to the problem of thermal emission from random media is briefly discussed.Comment: 4 pages, 2 figures; (Ref.[5b] added, appropriate modification in text

Longitudinal Reproducibility of Neurite Orientation Dispersion and Density Imaging (NODDI) Derived Metrics in the White Matter

Diffusion-weighted magnetic resonance imaging (DWI) is undergoing constant evolution with the ambitious goal of developing in-vivo histology of the brain. A recent methodological advancement is Neurite Orientation Dispersion and Density Imaging (NODDI), a histologically validated multi-compartment model to yield microstructural features of brain tissue such as geometric complexity and neurite packing density, which are especially useful in imaging the white matter. Since NODDI is increasingly popular in clinical research and fields such as developmental neuroscience and neuroplasticity, it is of vast importance to characterize its reproducibility (or reliability). We acquired multi-shell DWI data in 29 healthy young subjects twice over a rescan interval of 4 weeks to assess the within-subject coefficient of variation (CVWS), between-subject coefficient of variation (CVBS) and the intraclass correlation coefficient (ICC), respectively. Using these metrics, we compared regional and voxel-by-voxel reproducibility of the most common image analysis approaches (tract-based spatial statistics [TBSS], voxel-based analysis with different extents of smoothing [“VBM-style”], ROI-based analysis). We observed high test–retest reproducibility for the orientation dispersion index (ODI) and slightly worse results for the neurite density index (NDI). Our findings also suggest that the choice of analysis approach might have significant consequences for the results of a study. Collectively, the voxel-based approach with Gaussian smoothing kernels of ≥4 mm FWHM and ROI-averaging yielded the highest reproducibility across NDI and ODI maps (CVWS mostly ≤3%, ICC mostly ≥0.8), respectively, whilst smaller kernels and TBSS performed consistently worse. Furthermore, we demonstrate that image quality (signal-to-noise ratio [SNR]) is an important determinant of NODDI metric reproducibility. We discuss the implications of these results for longitudinal and cross-sectional research designs commonly employed in the neuroimaging field

Finding Exogenous Variables in Data with Many More Variables than Observations

Many statistical methods have been proposed to estimate causal models in classical situations with fewer variables than observations (p<n, p: the number of variables and n: the number of observations). However, modern datasets including gene expression data need high-dimensional causal modeling in challenging situations with orders of magnitude more variables than observations (p>>n). In this paper, we propose a method to find exogenous variables in a linear non-Gaussian causal model, which requires much smaller sample sizes than conventional methods and works even when p>>n. The key idea is to identify which variables are exogenous based on non-Gaussianity instead of estimating the entire structure of the model. Exogenous variables work as triggers that activate a causal chain in the model, and their identification leads to more efficient experimental designs and better understanding of the causal mechanism. We present experiments with artificial data and real-world gene expression data to evaluate the method.Comment: A revised version of this was published in Proc. ICANN201