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- All Subjects: chemical engineering
- All Subjects: Health Sciences
- Creators: Forzani, Erica
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.
We have also made the attempts of using zinc nitride and copper nitride as metal sources to synthesize the boronic acid-containing MOFs. However, the attempts were not successful. The possible reason is the existence of copper and zinc ions catalyzed the decomposition of 3,5-Dicarboxyphenylboronic acid, forming isophthalic acid. The ended product has been proved to be isophthalic acid crystals by the single crystal X-ray diffraction. The effects of solvents, reaction temperature, and added bases were investigated. The addition of triethylamine has been shown to tremendously improve the sample crystallinity by facilitating ligand deprotonation
This study investigated the effect of environmental heat stress on physiological and performance measures during a ~4 mi time trial (TT) mountain hike in the Phoenix metropolitan area. Participants (n = 12; 7M/5F; age 21.6 ± 2.47 [SD]) climbed ‘A’ mountain (~1 mi) four times on a hot day (HOT; wet bulb globe temperature [WBGT] = 31.6°C) and again on a moderate day (MOD; WBGT = 19.0°C). Physiological and performance measures were made before and throughout the course of each hike. Mean pre-hike hydration status (urine specific gravity [USG]) indicated that participants began both HOT and MOD trials in a euhydrated state (1.016 ± 0.010 and 1.010 ± 0.008, respectively) and means did not differ significantly between trials (p = .085). Time trial performance was impaired by -11% (11.1 minutes) in the HOT trial (105 ± 21.7 min), compared to MOD (93.9 ± 13.1 min) (p = .013). Peak core temperatures were significantly higher in HOT (38.5 ± 0.36°C) versus MOD (38.0 ± 0.30°C) with progressively increasing differences between trials over time (p < .001). Peak ratings of perceived exertion were significantly higher in HOT (14.2 ± 2.38) compared to MOD (11.9 ± 2.02) (p = .007). Relative intensity (percent of age-predicted maximal heart rate [HR]), estimated absolute intensity (metabolic equivalents [METs]), and estimated energy expenditure (MET-h) were all increased in HOT, but not significantly so. The HOT condition reduced predicted maximal aerobic capacity (CRFp) by 6% (p = .026). Sweat rates differed significantly between HOT (1.38 ± 0.53 L/h) and MOD (0.84 ± 0.27 L/h) (p = .01). Percent body mass loss (PBML) did not differ significantly between HOT (1.06 ± 0.95%) and MOD (0.98 ± 0.84%) (p = .869). All repeated measures variables showed significant between-subjects effects (p < .05), indicating individual differences in response to test conditions. Heat stress was shown to negatively affect physiological and performance measures in recreational mountain hikers. However, considerable variation exists between individuals, and the degree of physiological and performance impairment is probably due, in part, to differences in aerobic fitness and acclimatization status rather than pre- or during-performance hydration status.
Underreporting of energy intake (EI) has been found to be an important consideration that interferes with accurate weight control assessment and the effective use of energy balance (EB) models in an intervention setting. To better understand underreporting, a variety of estimation approaches are developed; these include back-calculating energy intake from a closed-form of the EB model, a Kalman-filter based algorithm for recursive estimation from randomly intermittent measurements in real time, and two semi-physical identification approaches that can parameterize the extent of systematic underreporting with global/local modeling techniques. Each approach is analyzed with intervention participant data and demonstrates potential of promoting the success of weight control.
In addition, substantial efforts have been devoted to develop participant-validated models and incorporate into the Hybrid Model Predictive Control (HMPC) framework for closed-loop interventions. System identification analyses from Phase I led to modifications of the measurement protocols for Phase II, from which longer and more informative data sets were collected. Participant-validated models obtained from Phase II data significantly increase predictive ability for individual behaviors and provide reliable open-loop dynamic information for HMPC implementation. The HMPC algorithm that assigns optimized dosages in response to participant real time intervention outcomes relies on a Mixed Logical Dynamical framework which can address the categorical nature of dosage components, and translates sequential decision rules and other clinical considerations into mixed-integer linear constraints. The performance of the HMPC decision algorithm was tested with participant-validated models, with the results indicating that HMPC is superior to "IF-THEN" decision rules.