Hydrodynamic capabilities of an SPH code incorporating an artificial conductivity term with a gravity-based signal velocity

This paper investigates the hydrodynamic performances of an SPH code incorporating an artificial heat conductivity term in which the adopted signal velocity is applicable when gravity is present. In accordance with previous findings it is shown that the performances of SPH to describe the development of Kelvin-Helmholtz instabilities depend strongly on the consistency of the initial condition set-up and on the leading error in the momentum equation due to incomplete kernel sampling. An error and stability analysis shows that the quartic B-spline kernel (M_5) possesses very good stability properties and we propose its use with a large neighbor number, between ~50 (2D) to ~ 100 (3D), to improve convergence in simulation results without being affected by the so-called clumping instability. SPH simulations of the blob test show that in the regime of strong supersonic flows an appropriate limiting condition, which depends on the Prandtl number, must be imposed on the artificial conductivity SPH coefficients in order to avoid an unphysical amount of heat diffusion. Results from hydrodynamic simulations that include self-gravity show profiles of hydrodynamic variables that are in much better agreement with those produced using mesh-based codes. In particular, the final levels of core entropies in cosmological simulations of galaxy clusters are consistent with those found using AMR codes. Finally, results of the Rayleigh-Taylor instability test demonstrate that in the regime of very subsonic flows the code has still several difficulties in the treatment of hydrodynamic instabilities. These problems being intrinsically due to the way in which in standard SPH gradients are calculated and not to the implementation of the artificial conductivity term.Comment: 26 pages, 15 figures, accepted for publication in A&

Semiparametric Bayesian models for human brain mapping

Functional magnetic resonance imaging (fMRI) has led to enormous progress in human brain mapping. Adequate analysis of the massive spatiotemporal data sets generated by this imaging technique, combining parametric and non-parametric components, imposes challenging problems in statistical modelling. Complex hierarchical Bayesian models in combination with computer-intensive Markov chain Monte Carlo inference are promising tools.The purpose of this paper is twofold. First, it provides a review of general semiparametric Bayesian models for the analysis of fMRI data. Most approaches focus on important but separate temporal or spatial aspects of the overall problem, or they proceed by stepwise procedures. Therefore, as a second aim, we suggest a complete spatiotemporal model for analysing fMRI data within a unified semiparametric Bayesian framework. An application to data from a visual stimulation experiment illustrates our approach and demonstrates its computational feasibility

Hydraulics are a first order control on CO2 efflux from fluvial systems

Evasion of carbon dioxide (CO2) from fluvial systems is now recognized as a significant component of the global carbon cycle. However the magnitude of, and controls on, this flux remain uncertain and improved understanding of both are required to refine global estimates of fluvial CO2 efflux. CO2 efflux data show no pattern with latitude suggesting that catchment biological productivity is not a primary control and that an alternative explanation for inter-site variability is required. It has been suggested that increased flow velocity and turbulence enhance CO2 efflux, but this is not confirmed. Here, using contemporaneous measurements of efflux (range: 0.07 – 107 µmol CO2 m-2 s-1), flow hydraulics (mean velocity range: 0.03 – 1.39 m s-1) and pCO2 (range: 174 – 10712 µatm) at six sites, we find that flow intensity is a primary control on efflux across two climatically different locations (where pH is not a limiting factor) and that the relationship is refined by incorporating the partial pressure of CO2 (pCO2) of the water. A remaining challenge is how to upscale from point to reach or river basin level. Remote imaging or river surface may be worth exploring if subjectivity in interpreting surface state can be overcome

Monte Carlo-based Noise Compensation in Coil Intensity Corrected Endorectal MRI

Background: Prostate cancer is one of the most common forms of cancer found in males making early diagnosis important. Magnetic resonance imaging (MRI) has been useful in visualizing and localizing tumor candidates and with the use of endorectal coils (ERC), the signal-to-noise ratio (SNR) can be improved. The coils introduce intensity inhomogeneities and the surface coil intensity correction built into MRI scanners is used to reduce these inhomogeneities. However, the correction typically performed at the MRI scanner level leads to noise amplification and noise level variations. Methods: In this study, we introduce a new Monte Carlo-based noise compensation approach for coil intensity corrected endorectal MRI which allows for effective noise compensation and preservation of details within the prostate. The approach accounts for the ERC SNR profile via a spatially-adaptive noise model for correcting non-stationary noise variations. Such a method is useful particularly for improving the image quality of coil intensity corrected endorectal MRI data performed at the MRI scanner level and when the original raw data is not available. Results: SNR and contrast-to-noise ratio (CNR) analysis in patient experiments demonstrate an average improvement of 11.7 dB and 11.2 dB respectively over uncorrected endorectal MRI, and provides strong performance when compared to existing approaches. Conclusions: A new noise compensation method was developed for the purpose of improving the quality of coil intensity corrected endorectal MRI data performed at the MRI scanner level. We illustrate that promising noise compensation performance can be achieved for the proposed approach, which is particularly important for processing coil intensity corrected endorectal MRI data performed at the MRI scanner level and when the original raw data is not available.Comment: 23 page