A stochastic model for sediment yield using the Principle of Maximum Entropy

An edited version of this paper was published by AGU. Copyright 1987 American Geophysical Union.The principle of maximum entropy was applied to derive a stochastic model for sediment yield from upland watersheds. By maximizing the conditional entropy subject to certain constraints, a probability distribution of sediment yield conditioned on the probability distribution of direct runoff volume was obtained. This distribution resulted in minimally prejudiced assignment of probabilities on the basis of given information. The parameters of this distribution were determined from such prior information about the direct runoff volume and sediment yield as their means and covariance. The stochastic model was verified by using three sets of field data and was compared with a bivariate normal distribution. The model yielded sediment yield reasonably accurately.This study was supported in part by funds provided by the Geological Survey, U.S. Department of Agriculture, through the Louisiana Water Resources Research Institute, under the project, A Multivariate Stochastic Analysis of Flood Magnitude, Duration and Volume

Why Are Outcomes Different for Registry Patients Enrolled Prospectively and Retrospectively? Insights from the Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF).

Background: Retrospective and prospective observational studies are designed to reflect real-world evidence on clinical practice, but can yield conflicting results. The GARFIELD-AF Registry includes both methods of enrolment and allows analysis of differences in patient characteristics and outcomes that may result. Methods and Results: Patients with atrial fibrillation (AF) and ≥1 risk factor for stroke at diagnosis of AF were recruited either retrospectively (n = 5069) or prospectively (n = 5501) from 19 countries and then followed prospectively. The retrospectively enrolled cohort comprised patients with established AF (for a least 6, and up to 24 months before enrolment), who were identified retrospectively (and baseline and partial follow-up data were collected from the emedical records) and then followed prospectively between 0-18 months (such that the total time of follow-up was 24 months; data collection Dec-2009 and Oct-2010). In the prospectively enrolled cohort, patients with newly diagnosed AF (≤6 weeks after diagnosis) were recruited between Mar-2010 and Oct-2011 and were followed for 24 months after enrolment. Differences between the cohorts were observed in clinical characteristics, including type of AF, stroke prevention strategies, and event rates. More patients in the retrospectively identified cohort received vitamin K antagonists (62.1% vs. 53.2%) and fewer received non-vitamin K oral anticoagulants (1.8% vs . 4.2%). All-cause mortality rates per 100 person-years during the prospective follow-up (starting the first study visit up to 1 year) were significantly lower in the retrospective than prospectively identified cohort (3.04 [95% CI 2.51 to 3.67] vs . 4.05 [95% CI 3.53 to 4.63]; p = 0.016). Conclusions: Interpretations of data from registries that aim to evaluate the characteristics and outcomes of patients with AF must take account of differences in registry design and the impact of recall bias and survivorship bias that is incurred with retrospective enrolment. Clinical Trial Registration: - URL: http://www.clinicaltrials.gov . Unique identifier for GARFIELD-AF (NCT01090362)

Special issue on construction on expansive soils

Peer reviewed: YesNRC publication: Ye

Studying Consolidation Characteristics of Ohio Clays Using GIS

Geographical information systems (GIS) can be useful in gathering information for soil properties especially at the preliminary design phases of projects. However, the usefulness and accuracy of the GIS analysis depend on the database that it relies on. The database becomes usually more populated around metropolitan areas or major cities, simply because of the increased need for infrastructure to serve larger population in and around these cities. A total of 255 consolidation test data collected from numerous transportation projects in the State of Ohio were analyzed, corrected for sample disturbance using Schmertmann\u27s correction, and entered into a GIS database for analysis. The analyses indicate that reasonably accurate compression index (Cc) values can be obtained both in metropolitan areas and statewide. The paper presents the database, the uses of GIS with spreadsheets to analyze the data, and several compression index correlations developed for Ohio soils