Using P-Splines to Estimate Nonlinear Covariate Effects in Latent Factor Models.

Latent factor models are useful for summarizing information among multiple outcomes. In this thesis I apply semiparametric methods based on P-splines to latent factor models in order to estimate and test non-constant factor loadings, as well as estimate and test the nonlinear relationship between an observed continuous predictor and multiple observed outcomes that measure a latent factor. In the first chapter, I develop a modeling strategy that estimates non-constant factor loadings as functions of multiple covariates. A highlight of my algorithm is the optimization of a type of generalized cross-validation criterion within each iteration of the EM algorithm for estimating the smoothing parameters of the splines. Through simulation studies I show the advantage of correctly estimating the non-constant factor loadings in reducing bias for the estimated factor score. I apply my model to studying the correlation among four highly correlated PM2.5 constituents. In the second chapter I examine the use of likelihood ratio test (LRT) in assessing whether a factor loading is constant. In order to take into account the estimation of smoothing parameters in my testing procedure, I use maximum likelihood approach to smooth the P-splines, which treats the spline coefficients as random and I test the variance of the spline coefficients. importance sampling to compute the likelihood. I use a data-driven chi-square mixture approximation as the null LRT distribution. The method is applied to estimating the underlying lead exposure represented by four types of lead measurements on mothers from the ELEMENT study. In the third chapter I use P-splines to estimate and test deviations of the latent factor mean from a linear trend. I also make the connection between my semiparametric latent factor model to a class of linear mixed models that estimate an overall exposure effect for multiple outcomes. My algorithm is based on standard linear mixed model and is implemented by adapting PROC MIXED from SAS into an iterative procedure. I apply my model to studying the lead exposure effect on children's behaviors as measured by the psychometric battery BASC-2.PhDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/116762/1/zhzh_1.pd

Cross-corpus Readability Compatibility Assessment for English Texts

Text readability assessment has gained significant attention from researchers in various domains. However, the lack of exploration into corpus compatibility poses a challenge as different research groups utilize different corpora. In this study, we propose a novel evaluation framework, Cross-corpus text Readability Compatibility Assessment (CRCA), to address this issue. The framework encompasses three key components: (1) Corpus: CEFR, CLEC, CLOTH, NES, OSP, and RACE. Linguistic features, GloVe word vector representations, and their fusion features were extracted. (2) Classification models: Machine learning methods (XGBoost, SVM) and deep learning methods (BiLSTM, Attention-BiLSTM) were employed. (3) Compatibility metrics: RJSD, RRNSS, and NDCG metrics. Our findings revealed: (1) Validated corpus compatibility, with OSP standing out as significantly different from other datasets. (2) An adaptation effect among corpora, feature representations, and classification methods. (3) Consistent outcomes across the three metrics, validating the robustness of the compatibility assessment framework. The outcomes of this study offer valuable insights into corpus selection, feature representation, and classification methods, and it can also serve as a beginning effort for cross-corpus transfer learning.Comment: 14 pages,17 figure

Evaluation of Finnish Diabetes Risk Score in screening undiagnosed diabetes and prediabetes among U.S. adults by gender and race: NHANES 1999-2010.

ObjectiveTo evaluate the performance of Finnish Diabetes Risk Score (FINDRISC) in detecting undiagnosed diabetes and prediabetes among U.S. adults by gender and race.MethodsThis cross-sectional analysis included participants (aged ≥20 years) from the National Health and Nutrition Examination Survey (NHANES) 1999-2010. Sensitivity, specificity, area under the receiver operating characteristic (ROC) curve and the optimal cutoff points for identifying undiagnosed diabetes and prediabetes were calculated for FINDRISC by gender and race/ethnicity.ResultsAmong the 20,633 adults (≥20 years), 49.8% were women and 53.0% were non-Hispanic White. The prevalence of undiagnosed diabetes and prediabetes was 4.1% and 35.6%, respectively. FINDRISC was positively associated with the prevalence of diabetes (OR = 1.48 for 1 unit increase, p<0.001) and prediabetes (OR = 1.15 for 1 unit increase, p<0.001). The area under ROC for detecting undiagnosed diabetes was 0.75 for total population, 0.74 for men and 0.78 for women (p = 0.04); 0.76 for White, 0.76 for Black and 0.72 for Hispanics (p = 0.03 for White vs. Hispanics). The area under ROC for detecting prediabetes was 0.67 for total population, 0.66 for men and 0.70 for women (p<0.001); 0.68 for White, 0.67 for Black and 0.65 for Hispanics (p<0.001 for White vs. Hispanics). The optimal cutoff point was 10 (sensitivity = 0.75) for men and 12 (sensitivity = 0.72) for women for detecting undiagnosed diabetes; 9 (sensitivity = 0.61) for men and 10 (sensitivity = 0.69) for women for detecting prediabetes.ConclusionsFINDRISC is a simple and non-invasive screening tool to identify individuals at high risk for diabetes in the U.S. adults

Enhancing Landscape Connectivity in Detroit through Multifunctional Green Corridor Modeling and Design

Maintaining habitats is important for plants and animals. However, habitats in urban environments are fragmented due to urbanization. The fragmentation is a barrier for wildlife movement because landscape connectivity is decreased in urban environments. Previous studies in conservation ecology paid more attention to natural landscapes than urban environments. Other studies aimed at improving urban landscape connectivity were not practical because of restrictions in fully developed urban environments. This study provides practical conservation methods by taking advantage of existing vacant land to develop green infrastructure and increase landscape connectivity. The city of Detroit is chosen as a case study because of its large potential to redevelop existing vacant land. The paper includes examining structural and functional connectivity by FRAGSTATS and Conefor, selecting core patches in ArcGIS, identifying potential corridors by the least-cost-path and evaluating corridors by gravity model. By comparing data before and after corridor built-up, results show that census tract-level connectivity metrics would be improved by developing proposed corridors. To further link research results with the city of Detroit, multi-functional green infrastructure typologies for vacant land re-development are provided. This paper provides a systematic and scientific method for developing vacant lands and other available lands by green infrastructure network, which benefits both humans and wildlife. By developing green infrastructure network, both social connection and ecology connection will be achieved. Furthermore, this paper connects research with real world situations, providing a founded and practical strategy for other cities having similar vacant lands situation to Detroit to redevelop.Master of Science Master of Landscape ArchitectureNatural Resources and EnvironmentUniversity of Michiganhttps://deepblue.lib.umich.edu/bitstream/2027.42/136561/1/Zhang_Zhenzhen_Thesis.pd

Diverse anisotropy of phonon transport in two-dimensional IV-VI compounds: A comparative study

New classes two-dimensional (2D) materials beyond graphene, including layered and non-layered, and their heterostructures, are currently attracting increasing interest due to their promising applications in nanoelectronics, optoelectronics and clean energy, where thermal transport property is one of the fundamental physical parameters. In this paper, we systematically investigated the phonon transport properties of 2D orthorhombic group IV-VI compounds of $GeS$, $GeSe$, $SnS$ and $SnSe$ by solving the Boltzmann transport equation (BTE) based on first-principles calculations. Despite the similar puckered (hinge-like) structure along the armchair direction as phosphorene, the four monolayer compounds possess diverse anisotropic properties in many aspects, such as phonon group velocity, Young's modulus and lattice thermal conductivity ($\kappa$), etc. Especially, the $\kappa$ along the zigzag and armchair directions of monolayer $GeS$ shows the strongest anisotropy while monolayer $SnS$ and $SnSe$ shows an almost isotropy in phonon transport. The origin of the diverse anisotropy is fully studied and the underlying mechanism is discussed in detail. With limited size, the $\kappa$ could be effectively lowered, and the anisotropy could be effectively modulated by nanostructuring, which would extend the applications in nanoscale thermoelectrics and thermal management. Our study offers fundamental understanding of the anisotropic phonon transport properties of 2D materials, and would be of significance for further study, modulation and aplications in emerging technologies.Comment: 14 pages, 8 figures, 2 table