Estimating the Impacts of Acculturation on the Health of Immigrants in the United States

The growth in the immigration population in the U.S. has transformed American demographic profile and has led to magnification in health disparities in the United States. The main purpose of this dissertation is to examine the relationship between acculturation and health behavior, mental health outcomes, and access to health care service among immigrants in the U.S. Relying on the acculturation framework, the dissertation intends to increase the understanding of health disparities and health patterns among immigrants. The dissertation utilizes the secondary data from National Health Interview Survey (NHIS) data to examine the effects of acculturation on health of immigrants. Measures of Immigrants’ acculturation include English language proficiency, duration of living in the U.S., and the citizenship. Statistical modeling is applied to examine how acculturation influences health behavior of immigrants, mental health outcomes of immigrants, and access to health care service of immigrants. The main findings indicate that immigrants’ acculturation can have both detrimental and beneficial effects on health-related behaviors, mental health outcomes, and access to health care service. All measures of acculturation, in particular English language proficiency, are significant factors related to the health status of immigrants. For future research, this dissertation suggests that improving health and reducing health disparities will need to address acculturation, the educational, economic and environmental factors that affect health behavior, mental health outcomes, and access to health care service among immigrants in the U.S

RMSE-ELM: Recursive Model based Selective Ensemble of Extreme Learning Machines for Robustness Improvement

Extreme learning machine (ELM) as an emerging branch of shallow networks has shown its excellent generalization and fast learning speed. However, for blended data, the robustness of ELM is weak because its weights and biases of hidden nodes are set randomly. Moreover, the noisy data exert a negative effect. To solve this problem, a new framework called RMSE-ELM is proposed in this paper. It is a two-layer recursive model. In the first layer, the framework trains lots of ELMs in different groups concurrently, then employs selective ensemble to pick out an optimal set of ELMs in each group, which can be merged into a large group of ELMs called candidate pool. In the second layer, selective ensemble is recursively used on candidate pool to acquire the final ensemble. In the experiments, we apply UCI blended datasets to confirm the robustness of our new approach in two key aspects (mean square error and standard deviation). The space complexity of our method is increased to some degree, but the results have shown that RMSE-ELM significantly improves robustness with slightly computational time compared with representative methods (ELM, OP-ELM, GASEN-ELM, GASEN-BP and E-GASEN). It becomes a potential framework to solve robustness issue of ELM for high-dimensional blended data in the future.Comment: Accepted for publication in Mathematical Problems in Engineering, 09/22/201

New constructions of signed difference sets

Signed difference sets have interesting applications in communications and coding theory. A $(v,k,\lambda)$-difference set in a finite group $G$ of order $v$ is a subset $D$ of $G$ with $k$ distinct elements such that the expressions $xy^{-1}$ for all distinct two elements $x,y\in D$, represent each non-identity element in $G$ exactly $\lambda$ times. A $(v,k,\lambda)$-signed difference set is a generalization of a $(v,k,\lambda)$-difference set $D$, which satisfies all properties of $D$, but has a sign for each element in $D$. We will show some new existence results for signed difference sets by using partial difference sets, product methods, and cyclotomic classes