Search

Kimberly D Brunisholz,1,2,* Pascal Briot,1,2,* Sharon Hamilton,1 Elizabeth A Joy,3 Michael Lomax,2 Nathan Barton,2 Ruthann Cunningham,3 Lucy A Savitz,2 Wayne Cannon1 1Primary Care Clinical Program, Intermountain Healthcare, Salt Lake City, UT, USA; 2Institute for Healthcare Delivery, Intermountain Healthcare, Salt Lake City, UT, USA; 3Office of Research, Intermountain Healthcare, Salt Lake City, UT, USA*Joint first authors Purpose: The purpose of this study was to determine the impact of diabetes self-management education (DSME) in improving processes and outcomes of diabetes care as measured by a five component diabetes bundle and HbA1c, in individuals with type 2 diabetes mellitus (T2DM). Methods: A retrospective analysis was performed for adult T2DM patients who received DSME training in 2011–2012 from an accredited American Diabetes Association center at Intermountain Healthcare (IH) and had an HbA1c measurement within the prior 3 months and 2–6 months after completing their first DSME visit. Control patients were selected from the same clinics as case-patients using random number generator to achieve a 1 to 4 ratio. Case and control patients were included if 1) pre-education HbA1c was between 6.0%–14.0%; 2) their main provider was a primary care physician; 3) they met the national Healthcare Effectiveness Data and Information Set criteria for inclusion in the IH diabetes registry. The IH diabetes bundle includes retinal eye exam, nephropathy screening or prescription of angiotensin converting enzyme or angiotensin receptor blocker; blood pressure

Dynamic compression experiments in condensed matter are of interest in part because they provide opportunities to examine material response under extreme conditions; however, the inference of material behavior from dynamic experiments is challenging in the presence of phase transitions exhibiting kinetic processes. Demonstrating an approach to quantitative interpretation of such dynamic experiments, we present a Bayesian model calibration of strength and phase transformation parameters to data drawn from pulsed power and gas gun shot experiments. The posterior predictions of the Bayesian model capture the experimental measurements and account for the various uncertainties in the experimental configurations. This holistic approach to model calibration utilizing multiple types of experimental data identifies important cross correlations among kinetics, strength, and the phase boundary. Improved insight into potential sources of current model form error is provided by comparing the differences between calibrations against different subsets of the experimental data. [ABSTRACT FROM AUTHOR]

Spall failure is of interest due to its prevalence in high strain rate problems in which the spallation is driven by the interaction of release waves. In this article, a porosity-based damage model that includes microinertial effects is used to examine spall failure. The model is successfully calibrated to plate impact-driven experiments and then used to evaluate experimental conditions producing more extreme strain rate conditions, such as those in laser-driven experiments. The incorporation of microinertia allows us to better understand the increase in apparent macroscopic spall strength seen at high strain rates. Correspondingly, we conclude that the incorporation of microinertial effects improves the model's predictive capabilities. Microinertial effects result in more severe local tensile stresses that affect the damage evolution, and microinertia can play a significant role in the approach to the material's ideal strength at extreme loading rates. A preliminary parametric study is also carried out to investigate the role of microstructural aspects such as nucleation volume fraction and initial pore radius. One counter-intuitive result from the microinertial effects is that, for a given nucleation site volume fraction, having larger initial pore nucleation sites can lead to an improved macroscopic spall strength. [ABSTRACT FROM AUTHOR]

Samples of quartz-bearing rocks were heated above the α (trigonal)–β (hexagonal) phase transformation of quartz (625–950°C) to explore changes in preferred orientation patterns. Textures were measured both in situ and ex situ with neutron, synchrotron X-ray and electron backscatter diffraction. The trigonal–hexagonal phase transformation does not change the orientation of c- and a-axes, but positive and negative rhombs become equal in the hexagonal β-phase. In naturally deformed quartzites measured by neutron diffraction a perfect texture memory was observed, i.e. crystals returned to the same trigonal orientation they started from, with no evidence of twin boundaries. Samples measured by electron back-scattered diffraction on surfaces show considerable twinning and memory loss after the phase transformation. In experimentally deformed quartz rocks, where twinning was induced mechanically before heating, the orientation memory is lost. A mechanical model can explain the memory loss but so far it does not account for the persistence of the memory in quartzites. Stresses imposed by neighboring grains remain a likely cause of texture memory in this mineral with a very high elastic anisotropy. If stresses are imposed experimentally the internal stresses are released during the phase transformation and the material returns to its original state prior to deformation. Similarly, on surfaces there are no tractions and thus texture memory is partially lost.

We study the deformation of tantalum under extreme loading conditions. Experimental velocity data are drawn from both ramp loading experiments on Sandia's Z-machine and gas gun compression experiments. The drive conditions enable the study of materials under pressures greater than 100 GPa. We provide a detailed forward model of the experiments including a model of the magnetic drive for the Z-machine. Utilizing these experiments, we simultaneously infer several different types of physically motivated parameters describing equation of state, plasticity, and anelasticity via the computational device of Bayesian model calibration. Characteristics of the resulting calculated posterior distributions illustrate relationships among the parameters of interest via the degree of cross correlation. The calibrated velocity traces display good agreement with the experiments up to experimental uncertainty as well as improvement over previous calibrations. Examining the Z-shots and gun-shots together and separately reveals a trade-off between accuracy and transferability across different experimental conditions. Implications for model calibration, limitations from model form, and suggestions for improvements are discussed. [ABSTRACT FROM AUTHOR]

Recent progress in the development of dynamic strength experimental platforms is allowing for unprecedented insight into the assumptions used to construct constitutive models operating in extreme conditions. In this work, we make a quantitative assessment of how tantalum strength scales with its shear modulus to pressures of hundreds of gigapascals through a cross-platform examination of three dynamic strength experiments. Specifically, we make use of Split–Hopkinson pressure bar and Richtmyer–Meshkov instability experiments to assess the low-pressure strain and strain rate dependence. Concurrent examination of magnetically driven ramp-release experiments up to pressures of 350 GPa allows us to examine the pressure dependence. Using a modern description of the shear modulus, validated against both ab initio theory and experimental measurements, we then assess how the experimentally measured pressure dependence scales with shear modulus. We find that the common assumption of scaling strength linearly with the shear modulus is too soft at high pressures and offer discussion as to how descriptions of slip mediated plasticity could result in an alternative scaling that is consistent with the data. [ABSTRACT FROM AUTHOR]