The largest uncertainty for cosmological studies using clusters of galaxies is introduced by our limited knowledge of the statistics of galaxy cluster structure, and of the scaling relations between ...observables and cluster mass. To improve on this situation we have started an XMM-Newton Large Programme for the in-depth study of a representative sample of 33 galaxy clusters, selected in the redshift range z=0.055 to 0.183 from the REFLEX Cluster Survey, having X-ray luminosities above 0.4 X 10^44 h_70^-2 erg s^-1 in the 0.1 - 2.4 keV band. This paper introduces the sample, compiles properties of the clusters, and provides detailed information on the sample selection function. We describe the selection of a nearby galaxy cluster sample that makes optimal use of the XMM-Newton field-of-view, and provides nearly homogeneous X-ray luminosity coverage for the full range from poor clusters to the most massive objects in the Universe. For the clusters in the sample, X-ray fluxes are derived and compared to the previously obtained fluxes from the ROSAT All-Sky Survey. We find that the fluxes and the flux errors have been reliably determined in the ROSAT All-Sky Survey analysis used for the REFLEX Survey. We use the sample selection function documented in detail in this paper to determine the X-ray luminosity function, and compare it with the luminosity function of the entire REFLEX sample. We also discuss morphological peculiarities of some of the sample members. The sample and some of the background data given in this introductory paper will be important for the application of these data in the detailed studies of cluster structure, to appear in forthcoming publications.
One of the challenges all hospitals, especially designated trauma centers, face is how to make sure they have adequate staffing on various days of the week and at various times of the year. A number ...of studies have explored whether factors such as weather, temporal variation, holidays, and events that draw mass gatherings may be useful for predicting patient volume. This article looks at the effects of weather, mass gatherings, and calendar variables on daily trauma admissions at the three Level I trauma hospitals in the Minneapolis-St. Paul metropolitan area. Using ARIMA statistical modeling, we found that weekends, summer, lack of rain, and snowfall were all predictive of daily trauma admissions; holidays and mass gatherings such as sporting events were not. The forecasting model was successful in reflecting the pattern of trauma admissions; however, it's usefulness was limited in that the predicted range of daily trauma admissions was much narrower than the observed number of admissions. Nonetheless, the observed pattern of increased admission in the summer months and year-round on Saturdays should be helpful in resource planning.
