A computerized system for care planning and documentation of patient care was initiated at a western teaching hospital, using the framework of Nursing Interventions Classification and Nursing Outcomes Classification standardized languages. The software integrates care planning and documentation, and includes both order entry as well as a charting application. Prior to initiating the project, a study was conducted to evaluate staff attitude toward computerization, time needed for documentation, and comprehensiveness of charting entries. Data from staff surveys, observations, and chart audits conducted pre- and post-computer project implementation demonstrated that the staff attitudes toward computers were less positive, the time required for charting was unchanged, and there were improvements in how completely the nurses documented charting elements.
Using data to evaluate charge nurse leadership guides continued program improvements.
Solving river engineering problems typically requires river flow characterization, including the prediction of flow depth, flow velocity, and flood extent. Hydraulic models use governing equations of the flow in motion (conservation of mass and momentum principles) to predict the flow characteristics. However, solving such equations can be substantially expensive, depending upon their spatial extension. Moreover, modeling two- or three-dimensional river flows with high-resolution topographic data for large-scale regions (national or continental scale) is next to impossible. Such simulations are required for comprehensive river modeling, where a system of connected rivers is to be simulated simultaneously. Machine Learning (ML) approaches have shown promise for different water resources problems, and they have demonstrated an ability to learn from current data to predict new scenarios, which can enhance the understanding of the systems. The aim of this paper is to present an efficient flood simulation framework that can be applied to large-scale simulations. The framework outlines a novel, quick, efficient and versatile model to identify flooded areas and the flood depth, using a hybrid of hydraulic model and ML measures. To accomplish that, a two-dimensional hydraulic model (iRIC), calibrated by measured water surface elevation data, was used to train two ML models to predict river depth over the domain for an arbitrary discharge. The first ML model included a random forest (RF) classification model, which was used to identify wet or dry nodes over the domain. The second was a multilayer perceptron (MLP) model that was developed and trained by the iRIC simulation results, in order to estimate river depth in wet nodes. For the test data the overall accuracy of 98.5 percent was achieved for the RF classification. The regression coefficient for the MLP model for depth was 0.88. The framework outlined in this paper can be used to couple hydraulics and ML models to reduce the computation time, resources and expenses of large-scale, real-time simulations, specifically for two- or three-dimensional hydraulic modeling, where traditional hydraulic models are infeasible or prohibitively expensive.
As with most dune fields, the White Sands Dune Field in New Mexico forms in a wind regime that is not unimodal. In this study, crescentic dune shape change (deformation) with migration at White Sands was explored in a time series of five LiDAR‐derived digital elevation models (DEMs) and compared to a record of wind direction and speed during the same period. For the study period of June 2007 to June 2010, 244 sand‐transporting wind events occurred and define a dominant wind mode from the SW and lesser modes from the NNW and SSE. Based upon difference maps and tracing of dune brinklines, overall dune behavior consists of crest‐normal migration to the NE, but also along‐crest migration of dune sinuosity and stoss superimposed dunes to the SE. The SW winds are transverse to dune orientations and cause most forward migration. The NNW winds cause along‐crest migration of dune sinuosity and stoss bedforms, as well as SE migration of NE‐trending dune terminations. The SSE winds cause ephemeral dune deformation, especially crestal slipface reversals. The dunes deform with migration because of differences in dune‐segment size, and differences in the lee‐face deposition rate as a function of the incidence angle between the wind direction and the local brinkline orientation. Each wind event deforms dune shape, this new shape then serves as the boundary condition for the next wind event. Shared incidence‐angle control on dune deformation and lee‐face stratification types allows for an idealized model for White Sands dunes. Copyright © 2015 John Wiley & Sons, Ltd.
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