24 resultados para hydrodynamic coefficient
Resumo:
We estimate the momentum diffusion coefficient of a heavy quark within a pure SU(3) plasma at a temperature of about 1.5Tc. Large-scale Monte Carlo simulations on a series of lattices extending up to 1923×48 permit us to carry out a continuum extrapolation of the so-called color-electric imaginary-time correlator. The extrapolated correlator is analyzed with the help of theoretically motivated models for the corresponding spectral function. Evidence for a nonzero transport coefficient is found and, incorporating systematic uncertainties reflecting model assumptions, we obtain κ=(1.8–3.4)T3. This implies that the “drag coefficient,” characterizing the time scale at which heavy quarks adjust to hydrodynamic flow, is η−1D=(1.8–3.4)(Tc/T)2(M/1.5 GeV) fm/c, where M is the heavy quark kinetic mass. The results apply to bottom and, with somewhat larger systematic uncertainties, to charm quarks.
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Novel magnetic resonance imaging sequences have and still continue to play an increasing role in neuroimaging and neuroscience. Among these techniques, diffusion-weighted imaging (DWI) has revolutionized the diagnosis and management of diseases such as stroke, neoplastic disease and inflammation. However, the effects of aging on diffusion are yet to be determined. To establish reference values for future experimental mouse studies we tested the hypothesis that absolute apparent diffusion coefficients (ADC) of the normal brain change with age. A total of 41 healthy mice were examined by T2-weighted imaging and DWI. For each animal ADC frequency histograms (i) of the whole brain were calculated on a voxel-by-voxel basis and region-of-interest (ROI) measurements (ii) performed and related to the animals' age. The mean entire brain ADC of mice <3 months was 0.715(+/-0.016) x 10(-3) mm2/s, no significant difference to mice aged 4 to 5 months (0.736(+/-0.040) x 10(-3) mm2/s) or animals older than 9 months 0.736(+/-0.020) x 10(-3) mm2/s. Mean whole brain ADCs showed a trend towards lower values with aging but both methods (i + ii) did not reveal a significant correlation with age. ROI measurements in predefined areas: 0.723(+/-0.057) x 10(-3) mm2/s in the parietal lobe, 0.659(+/-0.037) x 10(-3) mm2/s in the striatum and 0.679(+/-0.056) x 10(-3) mm2/s in the temporal lobe. With advancing age, we observed minimal diffusion changes in the whole mouse brain as well as in three ROIs by determination of ADCs. According to our data ADCs remain nearly constant during the aging process of the brain with a small but statistically non-significant trend towards a decreased diffusion in older animals.
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OBJECTIVES: Diffusion-weighted MRI is sensitive to molecular motion and has been applied to the diagnosis of stroke. Our intention was to investigate its usefulness in patients with brain tumor and, in particular, in the perilesional edema. METHODS: We performed MRI of the brain, including diffusion-weighted imaging and mapping of the apparent diffusion coefficient (ADC), in 16 patients with brain tumors (glioblastomas, low-grade gliomas and metastases). ADC values were determined by the use of regions of interest positioned in areas of high signal intensities as seen on T2-weighted images and ADC maps. Measurements were taken in the tumor itself, in the area of perilesional edema and in the healthy contralateral brain. RESULTS: ADC mapping showed higher values of peritumoral edema in patients with glioblastoma (1.75 x 10(-3)mm(2)/s) and metastatic lesions (1.61 x 10(-3)mm(2)/s) compared with those who had low-grade glioma (1.40 x10(-3)mm(2)/s). The higher ADC values in the peritumoral zone were associated with lower ADC values in the tumor itself. CONCLUSIONS: The higher ADC values in the more malignant tumors probably reflect vasogenic edema, thereby allowing their differentiation from other lesions.
Resumo:
PURPOSE: To determine how the ADC value of parotid glands is influenced by the choice of b-values. MATERIALS AND METHODS: In eight healthy volunteers, diffusion-weighted echo-planar imaging (DW-EPI) was performed on a 1.5 T system, with b-values (in seconds/mm2) of 0, 50, 100, 150, 200, 250, 300, 500, 750, and 1000. ADC values were calculated by two alternative methods (exponential vs. logarithmic fit) from five different sets of b-values: (A) all b-values; (B) b=0, 50, and 100; (C) b=0 and 750; (D) b=0, 500, and 1000; and (E) b=500, 750, and 1000. RESULTS: The mean ADC values for the different settings were (in 10(-3) mm2/second, exponential fit): (A) 0.732+/-0.019, (B) 2.074+/-0.084, (C) 0.947+/-0.020, (D) 0.890+/-0.023, and (E) 0.581+/-0.021. ADC values were significantly (P <0.001) different for all pairwise comparisons of settings (A-E) of b-values, except for A vs. D (P=0.172) and C vs. D (P=0.380). The ADC(B) was significantly higher than ADC(C) or ADC(D), which was significantly higher than ADC(E). ADC values from exponential vs. logarithmic fit (P=0.542), as well as left vs. right parotid gland (P=0.962), were indistinguishable. CONCLUSION: The ADC values calculated from low b-value settings were significantly higher than those calculated from high b-value settings. These results suggest that not only true diffusion but also perfusion and saliva flow may contribute to the ADC.
Resumo:
Software metrics offer us the promise of distilling useful information from vast amounts of software in order to track development progress, to gain insights into the nature of the software, and to identify potential problems. Unfortunately, however, many software metrics exhibit highly skewed, non-Gaussian distributions. As a consequence, usual ways of interpreting these metrics --- for example, in terms of "average" values --- can be highly misleading. Many metrics, it turns out, are distributed like wealth --- with high concentrations of values in selected locations. We propose to analyze software metrics using the Gini coefficient, a higher-order statistic widely used in economics to study the distribution of wealth. Our approach allows us not only to observe changes in software systems efficiently, but also to assess project risks and monitor the development process itself. We apply the Gini coefficient to numerous metrics over a range of software projects, and we show that many metrics not only display remarkably high Gini values, but that these values are remarkably consistent as a project evolves over time.
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INTRODUCTION To ensure root canal treatment success, endodontic microbiota should be efficiently reduced. The in vitro bactericidal effects of a hydrodynamic system and a passive ultrasonic irrigation system were compared. METHODS Single-rooted extracted teeth (n = 250) were contaminated with suspensions of Enterococcus faecalis ATCC 29212, mixed aerobic cultures, or mixed anaerobic cultures. First, the antibacterial effects of the hydrodynamic system (RinsEndo), a passive ultrasonic irrigation system (Piezo smart), and manual rinsing with 0.9% NaCl (the control) were compared. Colony-forming units were counted. Second, the 2 systems were used with 1.5% sodium hypochlorite (NaOCl) alone or NaOCl + 0.2% chlorhexidine (CHX). The colony-forming units in the treated and untreated roots were determined during a period of 5 days. RESULTS Both irrigation systems reduced bacterial numbers more effectively than manual rinsing (P < .001). With NaCl, ultrasonic activated irrigation reduced bacterial counts significantly better than hydrodynamic irrigation (P = .042). The NaOCl + CHX combination was more effective than NaOCl alone for both systems (P < .001), but hydrodynamic irrigation was more effective with NaOCl + CHX than the passive ultrasonic irrigation system. CONCLUSIONS Both irrigation systems, when combined with NaOCl + CHX, removed bacteria from root canals.