911 resultados para Team Evaluation Models


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"Counterinsurgency (COIN) requires an integrated military, political, and economic program best developed by teams that field both civilians and soldiers. These units should operate with some independence but under a coherent command. In Vietnam, after several false starts, the United States developed an effective unified organization, Civil Operations and Revolutionary Development Support (CORDS), to guide the counterinsurgency. CORDS had three components absent from our efforts in Afghanistan today: sufficient personnel (particularly civilian), numerous teams, and a single chain of command that united the separate COIN programs of the disparate American departments at the district, provincial, regional, and national levels. This paper focuses on the third issue and describes the benefits that unity of command at every level would bring to the American war in Afghanistan. The work begins with a brief introduction to counterinsurgency theory, using a population-centric model, and examines how this warfare challenges the United States. It traces the evolution of the Provincial Reconstruction Teams (PRTs) and the country team, describing problems at both levels. Similar efforts in Vietnam are compared, where persistent executive attention finally integrated the government's counterinsurgency campaign under the unified command of the CORDS program. The next section attributes the American tendency towards a segregated response to cultural differences between the primary departments, executive neglect, and societal concepts of war. The paper argues that, in its approach to COIN, the United States has forsaken the military concept of unity of command in favor of 'unity of effort' expressed in multiagency literature. The final sections describe how unified authority would improve our efforts in Afghanistan and propose a model for the future."--P. iii.

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ASPER/PUR-76/3410/A

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Thesis (Master's)--University of Washington, 2016-06

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Thesis (Ph.D.)--University of Washington, 2016-06

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Thesis (Master's)--University of Washington, 2016-06

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Remotely sensed data have been used extensively for environmental monitoring and modeling at a number of spatial scales; however, a limited range of satellite imaging systems often. constrained the scales of these analyses. A wider variety of data sets is now available, allowing image data to be selected to match the scale of environmental structure(s) or process(es) being examined. A framework is presented for use by environmental scientists and managers, enabling their spatial data collection needs to be linked to a suitable form of remotely sensed data. A six-step approach is used, combining image spatial analysis and scaling tools, within the context of hierarchy theory. The main steps involved are: (1) identification of information requirements for the monitoring or management problem; (2) development of ideal image dimensions (scene model), (3) exploratory analysis of existing remotely sensed data using scaling techniques, (4) selection and evaluation of suitable remotely sensed data based on the scene model, (5) selection of suitable spatial analytic techniques to meet information requirements, and (6) cost-benefit analysis. Results from a case study show that the framework provided an objective mechanism to identify relevant aspects of the monitoring problem and environmental characteristics for selecting remotely sensed data and analysis techniques.

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Recently, methods for computing D-optimal designs for population pharmacokinetic studies have become available. However there are few publications that have prospectively evaluated the benefits of D-optimality in population or single-subject settings. This study compared a population optimal design with an empirical design for estimating the base pharmacokinetic model for enoxaparin in a stratified randomized setting. The population pharmacokinetic D-optimal design for enoxaparin was estimated using the PFIM function (MATLAB version 6.0.0.88). The optimal design was based on a one-compartment model with lognormal between subject variability and proportional residual variability and consisted of a single design with three sampling windows (0-30 min, 1.5-5 hr and 11 - 12 hr post-dose) for all patients. The empirical design consisted of three sample time windows per patient from a total of nine windows that collectively represented the entire dose interval. Each patient was assigned to have one blood sample taken from three different windows. Windows for blood sampling times were also provided for the optimal design. Ninety six patients were recruited into the study who were currently receiving enoxaparin therapy. Patients were randomly assigned to either the optimal or empirical sampling design, stratified for body mass index. The exact times of blood samples and doses were recorded. Analysis was undertaken using NONMEM (version 5). The empirical design supported a one compartment linear model with additive residual error, while the optimal design supported a two compartment linear model with additive residual error as did the model derived from the full data set. A posterior predictive check was performed where the models arising from the empirical and optimal designs were used to predict into the full data set. This revealed the optimal'' design derived model was superior to the empirical design model in terms of precision and was similar to the model developed from the full dataset. This study suggests optimal design techniques may be useful, even when the optimized design was based on a model that was misspecified in terms of the structural and statistical models and when the implementation of the optimal designed study deviated from the nominal design.

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Healthcare workforce shortfalls require a rethinking of models for delivering care to people with chronic disease. Chronic disease needs to be managed by a multiskilled team of healthcare professionals with specialist input. Education at undergraduate, graduate and postgraduate levels needs to prepare healthcare professionals for this new paradigm. Some tasks currently seen only as part of a doctor's purview could be performed by other trained professionals to allow doctors to concentrate on more appropriate activities. We need to explore new collaborations to deliver multidisciplinary healthcare for chronic disease and evaluate these for patient outcomes and cost effectiveness.

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The precise evaluation of electromagnetic field (EMF) distributions inside biological samples is becoming an increasingly important design requirement for high field MRI systems. In evaluating the induced fields caused by magnetic field gradients and RF transmitter coils, a multilayered dielectric spherical head model is proposed to provide a better understanding of electromagnetic interactions when compared to a traditional homogeneous head phantom. This paper presents Debye potential (DP) and Dyadic Green's function (DGF)-based solutions of the EMFs inside a head-sized, stratified sphere with similar radial conductivity and permittivity profiles as a human head. The DP approach is formulated for the symmetric case in which the source is a circular loop carrying a harmonic-formed current over a wide frequency range. The DGF method is developed for generic cases in which the source may be any kind of RF coil whose current distribution can be evaluated using the method of moments. The calculated EMFs can then be used to deduce MRI imaging parameters. The proposed methods, while not representing the full complexity of a head model, offer advantages in rapid prototyping as the computation times are much lower than a full finite difference time domain calculation using a complex head model. Test examples demonstrate the capability of the proposed models/methods. It is anticipated that this model will be of particular value for high field MRI applications, especially the rapid evaluation of RF resonator (surface and volume coils) and high performance gradient set designs.

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Our first study develops a measure of appetitive motivation and our second study compares several measures of Gray's (1987) behaviour activation system (BAS) in the prediction of the surface scales of personality. In particular, we were interested in determining the utility of the new appetitive motivation scale and Dickman's functional impulsivity scale. In comparison to other well-known measures, both scales were generally good predictors. We conclude that the appetitive motivation scale is a promising measure of BAS based upon construct validation. Contrary to previous studies which have suggested that BAS is a generally poor predictor of the surface scales of personality, we discovered appetitive motivation to be an important predictor of personality in general. Interestingly, the scale was also predictive of scores on the Baddeley reasoning test. (C) 2003 Elsevier Ltd. All rights reserved.

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The influence of three dimensional effects on isochromatic birefringence is evaluated for planar flows by means of numerical simulation. Two fluid models are investigated in channel and abrupt contraction geometries. In practice, the flows are confined by viewing windows, which alter the stresses along the optical path. The observed optical properties differ therefore from their counterpart in an ideal two-dimensional flow. To investigate the influence of these effects, the stress optical rule and the differential propagation Mueller matrix are used. The material parameters are selected so that a retardation of multiple orders is achieved, as is typical for highly birefringent melts. Errors due to three dimensional effects are mainly found on the symmetry plane, and increase significantly with the flow rate. Increasing the geometric aspect ratio improve the accuracy provided that the error on the retardation is less than one order. (C) 2004 Elsevier B.V. All rights reserved.