2019 MU dissertations - Access restricted to MU
Permanent URI for this collection
[Collection created 2019 October]
Browse
Recent Submissions
Item Detecting latent mean differences between non-invariant groups using ordered categorical variables(University of Missouri--Columbia, 2019) Zhang, Ti; Bonifay, Wes[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT REQUEST OF AUTHOR.] Though more and more applied researchers have begun to treat response options as ordered-categorical variables when conducting measurement invariance (MI) testing, little is known about the role of ordered-categorical variables when comparing latent means between groups. Therefore, this study simulated ordered-categorical data to specifically examine the detection of latent mean differences between non-invariant groups across a variety of conditions, including the number of items, population latent mean differences, etc. The purpose of this study was to investigate the relative parameter bias, power rates, and Type I error rates that may arise when ignoring various types of MI in both the configural invariance and metric invariance models. In summary, the most important contributors to relative bias of the true latent mean difference estimates were a) the number of items and the size of the factor loadings in the configural invariance model, b) the size of the factor loading and threshold differences in the metric invariance model that ignored group parameter differences, and c) the number of items in the metric invariance model that addressed the group parameter differences. Thus, in order to reduce the bias in estimating the true latent mean difference between groups, practitioners should identify and address the non-invariance and use a test instrument with more items. The dominant effect on the power to identify whether the latent mean difference was different from 0, in both the configural invariance model and the metric invariance model that ignored true group differences, was the population latent mean difference. In the metric invariance model that addressed the group differences, the most important effects were a) population latent mean differences, and b) loading and threshold differences. When the latent mean difference was at least moderate or the large threshold difference was ignored, the power rate was inflated to be above .90. Applied researchers should know that it will be easier to detect relatively large latent mean differences if both the loading and threshold differences are free to differ between groups. The dominant effect on Type I error rate in the configural invariance model was the number of items. In the metric invariance model that ignored the group parameter differences, the most important effects were a) the size of threshold differences, b) the loading and threshold differences, and c) the number of items. In the metric invariance model that addressed the group parameter differences, the most important effect on Type I error was the number of noninvariant items, which also significantly interacted with the number of items. Often, applied researchers assume their groups are equal, and may not concern themselves with detecting the true latent mean differences. Of course, true population differences cannot be known, so it is recommended that researchers should still conduct a MI analysis. It is especially important to note that in the metric invariance model that addressed group parameter differences, the Type I error rate was below .05. This result suggests that conducting MI testing will help applied researchers detect the true latent mean difference regardless of the magnitude of that difference (i.e., 0, .2 and .5 in this study).Item A comparative numerical study on the failure evolution at different simulation scales(University of Missouri--Columbia, 2019) Su, Yu-Chen; Chen, Zhen, 1958-[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT REQUEST OF AUTHOR.] In computational mechanics, different numerical approaches and simulation scales may lead to different results. It is because each approach has its own solution scheme feature, such as the governing equation, forcing function, and integration algorithm, for the specific scale including the nanoscale, mesoscale, and macroscale. Recently, a particle method that is the material point method (MPM) has become a popular research topic at macroscale due to its advantage combining the Eulerian and Lagrangian descriptions. However, the MPM has not been evaluated in a systematic manner for the fully coupled thermodynamic fluid-structure interaction (FSI) cases. Since the constitutive models and heat transfer in solid and fluid materials are quite different, a fully coupled computational scheme is designed in this dissertation for simulating the FSI with the MPM, in which the governing equations for both solid and fluid material points are related to each other. Additionally, the MPM has been upgraded to the generalized interpolation MPM (GIMP), also described in this work, for solving the problems with large deformation. At nanoscale, the object is usually divided by atoms or molecules. Therefore, another computational particle method, molecular dynamics (MD), has been widely utilized for simulating the movements of atoms or molecules. Although MD is an accurate tool at nanoscale, it would cost numerous computational resources. To obtain similar nanoscale information with less cost, a developed coarse-grained MD (CG-MD), which traverses the time and spatial scales, is introduced, verified, and validated in this dissertation. Although the MPM (or GIMP) and MD both belong to particle methods, their forcing functions are different, namely, they are continuous and discrete forcing functions for MPM and MD, respectively. With these similarity and difference, a series of mechanical simulations involving large deformation by using the GIMP, MD, and CG-MD were conducted and discussed in this dissertation to investigate the failure evolution at different simulation scales.Item Use of geographic information systems and remote sensing as automated support tool for variable rate irrigation prescriptions(University of Missouri--Columbia, 2019) Nguyen, Anh Thi Tuan; Thompson, Allen[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT REQUEST OF AUTHOR.] Economic as well as water shortage pressure on agricultural use of water has placed added emphasis on efficient irrigation management. Center pivot technology has made great improvement with variable rate irrigation (VRI) technology to vary water application spatially and temporally to maximize the economic and environmental return. Proper management of VRI systems depends on correctly matching the pivot application to specific field temporal and areal conditions. There is need for a tool to accurately and inexpensively define dynamic management zones, to sense within-field variability in real time, and control variable rate water application so that producers are more willing to adopt and utilize the advantages of VRI systems. This study included tests of the center pivot system uniformity performance in 2014 at Delta Research Center in Portageville, MO. The goal of this research was to develop MOPivot software with an algorithm to determine unique management areas under center pivot systems based on system design and limitations. The MOPivot tool automates prescriptions for VRI center pivot based on non-uniform water needs while avoiding potential runoff and deep percolation. The software was validated for use in real-time irrigation management in 2018 for VRI control system of a Valley 8000 center pivot planted to corn. The water balance model was used to manage irrigation scheduling. Field data, together with soil moisture sensor measurement of soil water content, were used to develop the regression model of remote sensing-based crop coefficient (Kc). Remote sensing vegetation index in conjunction with GDD and crop growth stages in regression models showed high correlation with Kc. Validation of those regression models was done using Centralia, MO, field data in 2016. The MOPivot successfully created prescriptions to match system capacity of the management zone based on system limitations for center pivot management. Along with GIS data sources, MOPivot effectively provides readily available graphical prescription maps, which can be edited and directly uploaded to a center pivot control panel. The modeled Kc compared well with FAO Kc. By combining GDD and crop growth in the models, these models would account for local weather conditions and stage of crop during growing season as time index in estimating Kc. These models with Fraction of growth (FrG) and cumulative growing degree days (cGDD) had a higher coefficient of efficiency, higher Nash-Sutcliffe coefficient of efficiency and higher Willmott index of agreement. Future work should include improvement in the MOPivot software with different crops and aerial remote sensing imagery to generate dynamic prescriptions during the season to support irrigation scheduling for real-time monitoring of field conditions.Item Imaging-based customization of lattice structures for biomedical applications(University of Missouri--Columbia, 2019) Ma, Xuewei; Feng, Frank[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT REQUEST OF AUTHOR.] Synovial joints can provide movement and articulation, however, with overuse, aging, and trauma, joint replacement surgeries may be needed. Commercially available joint reconstruction implants have undergone great improvement during the past decades. Nevertheless, existing solutions using available implant designs and materials have limitations that lead to potential failure, particularly with young active patients. Bone cement and stress shielding have been identified as the major reasons for premature artificial joint failures. A breakdown of the cement may happen and revision surgery may be needed because of the aseptic loosening. The stress shielding problem is caused by the significant mismatch of stiffness properties between the patient trabecular bones and metallic implant materials for joint replacement surgeries. This research introduces a novel method to develop customized lattice structures with graded properties according to the mechanical properties derived from clinical Computed Tomography (CT) scan of the bone. Various lattice design variables are being analyzed for their effects on mechanical performance and geometrical features needed for biological fixation and manufacturability. The introduced mathematical models and techniques in the proposed work facilitate generic direct digital design and manufacturing of effective customized lattice structures with graded properties for joint reconstruction applications.Item Role of S100A9 in diabetic retinopathy(University of Missouri--Columbia, 2019) Lim, Rayne Ruiyi; Chaurasia, Shyam Sunder[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT REQUEST OF AUTHOR.] Diabetic retinopathy (DR) is a microvasculature disease causing irreversible blindness in 50 [percent] of working adults with type 2 diabetes mellitus (T2DM). Chronic sterile inflammation is one of the key players involved in DR progression, however no treatment strategy is available for the early inflammatory milieu. S100A9 is a damage-associated molecular patterns (DAMPs) protein found at the injury sites of inflammatory diseases. However, the function of S100A9 in the retina and DR is yet to be studied. This study was designed to characterize S100A9 in the retina and its role in DR pathogenesis. Firstly, S100A9 protein was identified to associate with severity of DR in human patient plasma. In the retina, S100A9 was found upregulated in the retina of Ossabaw pig model of early DR. Obese pigs fed a western diet showed signs of retinal damage including pericyte loss, basement membrane thickening, and neuronal degeneration. S100A9 was increased in the inner and outer plexiform layers of the retina and localized to the activated retinal microglial cells. We then established the culture of primary pig retinal microglial (pMicroglia) cells for further examination of S100A9 mechanism of action in DR. Activated pMicroglia were shown to produce S100A9 protein, which induced pro-inflammatory response via production of IL-1[beta], IL-6 and IL-8. Paquinimod, a Q-compound, inhibited S100A9 binding to TLR4 and thus dampened the pro-inflammatory response via downstream regulation of TLR4 and NLRP3 inflammasome. In conclusion, this study reports microglial-derived S100A9 to be a possible instigator of early damage seen in DR pathogenesis, which can be targeted for therapeutic intervention in human DR patients.