Mechanical Analysis of Polycarbonate/Polysiloxane Block Copolymers and Blends

Polydimethylsiloxane (PDMS) can be used to react with polycarbonate (PC) to generate PC-PDMS multiblock copolymers and PC/PC-PDMS-PC triblock blends to overcome the notch sensitivity of PC while maintaining its transparency. It was found in this study that PDMS can act as a rubber particle to absorb energy and promote multicrazing. As a result, the incorporation of PDMS can increase PC's toughness. Meanwhile, high optical clarity can be observed even at 62 wt% PDMS in the multiblock copolymers with uniform morphology. However, PC/PC-PDMS-PC triblock blends damage PC's transparency and become opaque due to the phase separation. Furthermore, compared to compression molding, injection molding introduces shear due to the decrease of the area at the nozzle, which leads to the orientation of polymer chains and, subsequently, better properties of specimens

Puritan Jeremiad and American Myth: Sacvan Bercovitch’s Study in the Puritan Rhetoric and Imagination

Sacvan Bercovitch is the most influential and prominent Americanist and literary and cultural critic after Perry Miller. In a close textual reading of classic Puritan texts, Bercovitch concludes that the major legacy of Puritan New England is not religious, or moral, or institutional, but in the realm of rhetoric. Rhetoric for Bercovitch is more than verbal ornamentation. It is a set of aesthetic devices that constitute a particular structure of perception, a particular pattern of thought and mode of expression. Bercovitch tries to grasp the imaginative structure and symbolic pattern of American thought underlying in rhetorical devices and believes that the Puritan rhetoric is the primary force that drives and shapes the American imagination. Bercovitch analyzes the Puritan jeremiad, a particular Puritan literary mode, to be a case of his study in the Puritan rhetoric. By the rhetorical device of typology, the Puritans identified America as the new promised land foretold in Scripture. Their migration to New England was a flight from another Babylon or Egypt; their conflicts with the Indians were foreshadowed by Joshua’s conquest of Cannan; and New England would in due time be the site of new Jerusalem. Considered as “a kind of imperialism by interpretation” by Bercovtich, the Puritan typology enables the immigrants to usurp the very meaning of the story of the ancient Jews. The Puritan jeremiad survives the decline of Puritanism and persists throughout the 18th and 19th century in all forms of the literature. It bespeaks an “ideological consensus” and helps sustain the myth of America through three hundred years of turbulence and change in American history

Illness Experiences and Racial Identity in Sickness: a Memoir

In the Iranian American writer Porochista Khakpour’s powerful memoir Sick: a Memoir, Khakpour’s racial identity plays an important part in her illness experiences. Different from traditional American autopathographies, Sick mixes Khakpour’s illness experiences and autobiographical experiences. The memoir is not only about the mysterious Lyme disease and its symptoms but about the feeling of alienation and homelessness and the trauma of being an outsider in America. Khakpour’s experiences of living with Lyme becomes a trope of her experiences of living in America as an racial other. In combining illness and racial experiences, Sick enriches the genre of autopathography which is mainly white and middle-class and provides a new perspective to observe illness in a multi-cultural context

Robust Methods for Causal Inference Using Penalized Splines

Observational studies are important for evaluating treatment effects, especially when randomization of treatments is unethical or expensive. Without randomization, valid inferences about treatment effects can only be drawn by controlling for confounders. Propensity scores (PS) -- the probability of treatment assignment as a function of covariates -- are often used to control for confounders. PS-based methods are vulnerable to bias and inefficiency when outcome or propensity score models are misspecified or there is limited overlap in the propensity score distributions between treatment groups. In this dissertation, we develop new robust methods for estimating causal effects from observational studies and address two closely related topics on causal inference -- the problem of limited overlap and variable selection for propensity score model. In Chapter 2, we propose a robust multiple imputation based approach to causal inference called Penalized Spline of Propensity Methods for Treatment Comparison (PENCOMP). PENCOMP estimates causal effects by imputing missing potential outcomes with flexible spline models, and draws inference based on imputed and observed outcomes. Under the standard causal inference assumptions, PENCOMP is doubly robust, that is, yields consistent estimates of causal effects if either the propensity or the outcome model is correctly specified. Simulations suggest that it tends to outperform doubly-robust marginal structural modeling, especially when the weights are highly variable. We apply our method to the Multicenter AIDS Cohort study (MACS) to estimate the short term effect of antiretroviral treatment on CD4 counts in HIV+ patients. In Chapter 3, we address the issue of limited overlap in the propensity score distributions across treatment groups. We investigate appropriate restrictions of the causal estimand, and compare alternative estimation methods, including various simple and augmented inverse propensity weighting approaches, matching and PENCOMP. We demonstrate the flexibility of PENCOMP for estimating different estimands. We apply these methods to the MACS dataset to estimate the effects of antiretroviral treatment on CD4 counts in HIV+ patients. In Chapter 4, we consider variable selection techniques that seek to restrict predictors in the propensity model to true confounders, thus improving overlap in the propensity distributions and increasing efficiency. We also propose a new version of PENCOMP via bagging that incorporates the variability of model selection, which can be advantageous when the data are noisy. We examine by simulation studies the impact of various variable selection techniques, including an extension of the adaptive lasso, on inferences from PENCOMP and weighting approaches. We demonstrate our methods and variable selection techniques using the MACS dataset.PHDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147507/1/tkzhou_1.pd