Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- Creators: Forzani, Erica
Description
Control engineering offers a systematic and efficient approach to optimizing the effectiveness of individually tailored treatment and prevention policies, also known as adaptive or ``just-in-time'' behavioral interventions. These types of interventions represent promising strategies for addressing many significant public health concerns. This dissertation explores the development of decision algorithms for adaptive sequential behavioral interventions using dynamical systems modeling, control engineering principles and formal optimization methods. A novel gestational weight gain (GWG) intervention involving multiple intervention components and featuring a pre-defined, clinically relevant set of sequence rules serves as an excellent example of a sequential behavioral intervention; it is examined in detail in this research.
A comprehensive dynamical systems model for the GWG behavioral interventions is developed, which demonstrates how to integrate a mechanistic energy balance model with dynamical formulations of behavioral models, such as the Theory of Planned Behavior and self-regulation. Self-regulation is further improved with different advanced controller formulations. These model-based controller approaches enable the user to have significant flexibility in describing a participant's self-regulatory behavior through the tuning of controller adjustable parameters. The dynamic simulation model demonstrates proof of concept for how self-regulation and adaptive interventions influence GWG, how intra-individual and inter-individual variability play a critical role in determining intervention outcomes, and the evaluation of decision rules.
Furthermore, a novel intervention decision paradigm using Hybrid Model Predictive Control framework is developed to generate sequential decision policies in the closed-loop. Clinical considerations are systematically taken into account through a user-specified dosage sequence table corresponding to the sequence rules, constraints enforcing the adjustment of one input at a time, and a switching time strategy accounting for the difference in frequency between intervention decision points and sampling intervals. Simulation studies illustrate the potential usefulness of the intervention framework.
The final part of the dissertation presents a model scheduling strategy relying on gain-scheduling to address nonlinearities in the model, and a cascade filter design for dual-rate control system is introduced to address scenarios with variable sampling rates. These extensions are important for addressing real-life scenarios in the GWG intervention.
A comprehensive dynamical systems model for the GWG behavioral interventions is developed, which demonstrates how to integrate a mechanistic energy balance model with dynamical formulations of behavioral models, such as the Theory of Planned Behavior and self-regulation. Self-regulation is further improved with different advanced controller formulations. These model-based controller approaches enable the user to have significant flexibility in describing a participant's self-regulatory behavior through the tuning of controller adjustable parameters. The dynamic simulation model demonstrates proof of concept for how self-regulation and adaptive interventions influence GWG, how intra-individual and inter-individual variability play a critical role in determining intervention outcomes, and the evaluation of decision rules.
Furthermore, a novel intervention decision paradigm using Hybrid Model Predictive Control framework is developed to generate sequential decision policies in the closed-loop. Clinical considerations are systematically taken into account through a user-specified dosage sequence table corresponding to the sequence rules, constraints enforcing the adjustment of one input at a time, and a switching time strategy accounting for the difference in frequency between intervention decision points and sampling intervals. Simulation studies illustrate the potential usefulness of the intervention framework.
The final part of the dissertation presents a model scheduling strategy relying on gain-scheduling to address nonlinearities in the model, and a cascade filter design for dual-rate control system is introduced to address scenarios with variable sampling rates. These extensions are important for addressing real-life scenarios in the GWG intervention.
ContributorsDong, Yuwen (Author) / Rivera, Daniel E (Thesis advisor) / Dai, Lenore (Committee member) / Forzani, Erica (Committee member) / Rege, Kaushal (Committee member) / Si, Jennie (Committee member) / Arizona State University (Publisher)
Created2014
Description
Among the alternative processes for the traditional distillation, adsorption and membrane separations are the two most promising candidates and metal-organic frameworks (MOFs) are the new material candidate as adsorbent or membrane due to their high surface area, various pore sizes, and highly tunable framework functionality. This dissertation presents an investigation of the formation process of MOF membrane, framework defects, and two-dimensional (2D) MOFs, aiming to explore the answers for three critical questions: (1) how to obtain a continuous MOF membrane, (2) how defects form in MOF framework, and (3) how to obtain isolated 2D MOFs. To solve the first problem, the accumulated protons in the MOF synthesis solution is proposed to be the key factor preventing the continuous growth among Universitetet I Oslo-(UiO)-66 crystals. The hypothesis is verified by the growth reactivation under the addition of deprotonating agent. As long as the protons were sufficiently coordinated by the deprotonating agent, the continuous growth of UiO-66 is guaranteed. Moreover, the modulation effect can impact the coordination equilibrium so that an oriented growth of UiO-66 film was achieved in membrane structures. To find the answer for the second problem, the defect formation mechanism in UiO-66 was investigated and the formation of missing-cluster (MC) defects is attributed to the partially-deprotonated ligands. Experimental results show the number of MC defects is sensitive to the addition of deprotonating agent, synthesis temperature, and reactant concentration. Pore size distribution allows an accurate and convenient characterization of the defects. Results show that these defects can cause significant deviations of its pore size distribution from the perfect crystal. The study of the third questions is based on the established bi-phase synthesis method, a facile synthesis method is adopted for the production of high quality 2D MOFs in large scale. Here, pyridine is used as capping reagent to prevent the interplanar hydrogen bond formation. Meanwhile, formic acid and triethylamine as modulator and deprotonating agent to balance the anisotropic growth, crystallinity, and yield in the 2D MOF synthesis. As a result, high quality 2D zinc-terephthalic acid (ZnBDC) and copper-terephthalic acid (CuBDC) with extraordinary aspect ratio samples were successfully synthesized.
ContributorsShan, Bohan (Author) / Mu, Bin (Thesis advisor) / Forzani, Erica (Committee member) / Dai, Lenore (Committee member) / Lin, Jerry (Committee member) / Liu, Jingyue (Committee member) / Arizona State University (Publisher)
Created2019