Physical Sciences and Mathematics Commons^™

Understanding The Role Of Interactivity And Explanation In Adaptive Experiences, Lijie Guo

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Adaptive experiences have been an active area of research in the past few decades, accompanied by advances in technology such as machine learning and artificial intelligence. Whether the currently ongoing research on adaptive experiences has focused on personalization algorithms, explainability, user engagement, or privacy and security, there is growing interest and resources in developing and improving these research focuses. Even though the research on adaptive experiences has been dynamic and rapidly evolving, achieving a high level of user engagement in adaptive experiences remains a challenge. %????? This dissertation aims to uncover ways to engage users in adaptive experiences by incorporating …

The Influence Of Allostery Governing The Changes In Protein Dynamics Upon Substitution, Joseph Hess

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The focus of this research is to investigate the effects of allostery on the function/activity of an enzyme, human immunodeficiency virus type 1 (HIV-1) protease, using well-defined statistical analyses of the dynamic changes of the protein and variants with unique single point substitutions ¹. The experimental data¹ evaluated here only characterized HIV-1 protease with one of its potential target substrates. Probing the dynamic interactions of the residues of an enzyme and its variants can offer insight of the developmental importance for allosteric signaling and their connection to a protein’s function. The realignment of the secondary structure elements can …

Explaining Spatio-Temporal Evolution Of Extreme Hydro-Climatic Events Using A Complex Network Framework, Somnath Mondal

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Severe hydroclimatic extreme events, such as droughts, heatwaves, and heavy rainfall, are occurring with increasing frequency and causing significant impacts on both people and the environment. These events also compound in space and time, leading to even more significant consequences. Therefore, it is essential to comprehend these phenomena' concurrent and time-delayed progression across different temporal and spatial scales to address adaptation and mitigation effectively. To accurately understand and map the co-evolution of extreme events, it's necessary to have a thorough grasp of their spatiotemporal patterns, how they propagate and interact with one another, and the underlying mechanisms driving their occurrence. …

Modeling Antihypertensive Therapeutic Inertia And Intensification To Support Clinical Action Toward Hypertension Control, Benjamin Martin

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Background

Hypertension is the leading modifiable risk factor for cardiovascular disease and consequent mortality worldwide. In the U.S., more than half of hypertension cases remain uncontrolled, despite availability of effective pharmaceutical treatment options. Evidence suggests that therapeutic inertia, defined as clinician failure to initiate or increase therapy when treatment goals are unmet, is the most influential barrier to improving hypertension control. Substantial rates of therapeutic inertia have been reported in ambulatory primary care settings where hypertension is typically treated and managed. Understanding and overcoming the forces driving therapeutic inertia in hypertension management is a critical strategy to reach population health …

Machine Learning-Based Data And Model Driven Bayesian Uncertanity Quantification Of Inverse Problems For Suspended Non-Structural System, Zhiyuan Qin

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Inverse problems involve extracting the internal structure of a physical system from noisy measurement data. In many fields, the Bayesian inference is used to address the ill-conditioned nature of the inverse problem by incorporating prior information through an initial distribution. In the nonparametric Bayesian framework, surrogate models such as Gaussian Processes or Deep Neural Networks are used as flexible and effective probabilistic modeling tools to overcome the high-dimensional curse and reduce computational costs. In practical systems and computer models, uncertainties can be addressed through parameter calibration, sensitivity analysis, and uncertainty quantification, leading to improved reliability and robustness of decision and …