137 research outputs found
Tracking Vector Magnetograms with the Magnetic Induction Equation
The differential affine velocity estimator (DAVE) developed in Schuck (2006)
for estimating velocities from line-of-sight magnetograms is modified to
directly incorporate horizontal magnetic fields to produce a differential
affine velocity estimator for vector magnetograms (DAVE4VM). The DAVE4VM's
performance is demonstrated on the synthetic data from the anelastic
pseudospectral ANMHD simulations that were used in the recent comparison of
velocity inversion techniques by Welsch (2007). The DAVE4VM predicts roughly
95% of the helicity rate and 75% of the power transmitted through the
simulation slice. Inter-comparison between DAVE4VM and DAVE and further
analysis of the DAVE method demonstrates that line-of-sight tracking methods
capture the shearing motion of magnetic footpoints but are insensitive to flux
emergence -- the velocities determined from line-of-sight methods are more
consistent with horizontal plasma velocities than with flux transport
velocities. These results suggest that previous studies that rely on velocities
determined from line-of-sight methods such as the DAVE or local correlation
tracking may substantially misrepresent the total helicity rates and power
through the photosphere.Comment: 30 pages, 13 figure
Local Difference Measures between Complex Networks for Dynamical System Model Evaluation
Acknowledgments We thank Reik V. Donner for inspiring suggestions that initialized the work presented herein. Jan H. Feldhoff is credited for providing us with the STARS simulation data and for his contributions to fruitful discussions. Comments by the anonymous reviewers are gratefully acknowledged as they led to substantial improvements of the manuscript.Peer reviewedPublisher PD
Effects of Two Commercial Electronic Prescribing Systems on Prescribing Error Rates in Hospital In-Patients: A Before and After Study
In a before-and-after study, Johanna Westbrook and colleagues evaluate the change in prescribing error rates after the introduction of two commercial electronic prescribing systems in two Australian hospitals
Combined impact of healthy lifestyle factors on colorectal cancer: a large European cohort study
Background: Excess body weight, physical activity, smoking, alcohol consumption and certain dietary factors are individually related to colorectal cancer (CRC) risk; however, little is known about their joint effects. The aim of this study was to develop a healthy lifestyle index (HLI) composed of five potentially modifiable lifestyle factors – healthy weight, physical activity, non-smoking, limited alcohol consumption and a healthy diet, and to explore the association of this index with CRC incidence using data collected within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Methods: In the EPIC cohort, a total of 347,237 men and women, 25- to 70-years old, provided dietary and lifestyle information at study baseline (1992 to 2000). Over a median follow-up time of 12 years, 3,759 incident CRC cases were identified. The association between a HLI and CRC risk was evaluated using Cox proportional hazards regression models and population attributable risks (PARs) have been calculated. Results: After accounting for study centre, age, sex and education, compared with 0 or 1 healthy lifestyle factors, the hazard ratio (HR) for CRC was 0.87 (95% confidence interval (CI): 0.44 to 0.77) for two factors, 0.79 (95% CI: 0.70 to 0.89) for three factors, 0.66 (95% CI: 0.58 to 0.75) for four factors and 0.63 (95% CI: 0.54 to 0.74) for five factors; P-trend <0.0001. The associations were present for both colon and rectal cancers, HRs, 0.61 (95% CI: 0.50 to 0.74; P for trend <0.0001) for colon cancer and 0.68 (95% CI: 0.53 to 0.88; P-trend <0.0001) for rectal cancer, respectively (P-difference by cancer sub-site = 0.10). Overall, 16% of the new CRC cases (22% in men and 11% in women) were attributable to not adhering to a combination of all five healthy lifestyle behaviours included in the index. Conclusions: Combined lifestyle factors are associated with a lower incidence of CRC in European populations characterized by western lifestyles. Prevention strategies considering complex targeting of multiple lifestyle factors may provide practical means for improved CRC prevention. Electronic supplementary material The online version of this article (doi:10.1186/s12916-014-0168-4) contains supplementary material, which is available to authorized users
The centroid paradigm: Quantifying feature-based attention in terms of attention filters
This paper elaborates a recent conceptualization of feature-based attention in terms of attention filters (Drew et al., Journal of Vision, 10(10:20), 1-16, 2010) into a general purpose centroid-estimation paradigm for studying feature-based attention. An attention filter is a brain process, initiated by a participant in the context of a task requiring feature-based attention, which operates broadly across space to modulate the relative effectiveness with which different features in the retinal input influence performance. This paper describes an empirical method for quantitatively measuring attention filters. The method uses a "statistical summary representation" (SSR) task in which the participant strives to mouse-click the centroid of a briefly flashed cloud composed of items of different types (e.g., dots of different luminances or sizes), weighting some types of items more strongly than others. In different attention conditions, the target weights for different item types in the centroid task are varied. The actual weights exerted on the participant's responses by different item types in any given attention condition are derived by simple linear regression. Because, on each trial, the centroid paradigm obtains information about the relative effectiveness of all the features in the display, both target and distractor features, and because the participant's response is a continuous variable in each of two dimensions (versus a simple binary choice as in most previous paradigms), it is remarkably powerful. The number of trials required to estimate an attention filter is an order of magnitude fewer than the number required to investigate much simpler concepts in typical psychophysical attention paradigms
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