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Title: The Association Between Executive Functioning, Verbal Memory, and Internalized Stigma in Predicting Psychotropic Medication Adherence Behaviors Among People with Serious Mental Illness
Creator: Powell, Karina
Date: 2020
Description: Long-term symptom management is an important consideration for many individuals with serious mental illness to maintain recovery over time,...
Show moreLong-term symptom management is an important consideration for many individuals with serious mental illness to maintain recovery over time, prevent relapse and rehospitalization, and decrease symptom severity. Psychotropic medications can be an important aspect of treatment planning; however, many barriers exist that can impact medication adherence and treatment efficacy. Research suggests that internalized stigma and neurocognition may impact medication adherence, but research has yet to investigate the relationship between these three constructs in tandem. In this study, medication adherence was measured via medication possession ratio calculated from pharmacy records (objective) and self-reported medication use (subjective). Selection of neurocognitive domains and assessment measures was informed by MATRICS and ISBD-BANC consensus batteries. The primary expectations were that verbal memory and executive functioning would be related to medication adherence behaviors and that these domains would moderate the relationship between internalized stigma and medication adherence. A consistent association was seen between measures of self-reported medication use over time, suggesting participants were generally consistent in their reported use. While analyses testing the primary hypotheses were nonsignificant, in the context of limitations of statistical power, trends emerged that may be worthy of further investigation. Future research should continue to explore the potentially multifaceted relationships between factors contributing to adherence. A more comprehensive understanding these relationships has implications for clinicians and consumers in addressing treatment barriers and disengagement by developing more nuanced and targeted interventions.
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Title: A Novel Remote Sensing System Using Reflected GNSS Signals
Creator: Parvizi, Roohollah
Date: 2020
Description: This dissertation presents a method to remotely sense freshwater surface ice and water using reflected signals from Global Navigation...
Show moreThis dissertation presents a method to remotely sense freshwater surface ice and water using reflected signals from Global Navigation Satellite Systems (GNSS). A portable ground-based sensor system is designed and built for collecting both scattered Global Positioning System (GPS) signals and independent validation data (lidar and camera) from the surface. GPS front-end signals are collected from both a direct receiving antenna facing upward and from a reflection-receiving antenna facing downward. Multiple data campaigns are conducted on the Lake Michigan waterfront in Chicago. A customized software receiver tests a new signal processing method to detect and acquire Global Navigation Satellite System (GNSS) signals reflected from the lake surface ice and collected by a downward-facing antenna. The method, modified differential coherent integration, multiplies time-shifted auto-correlation samples. The new method is evaluated against three conventional integration methods (coherent, incoherent, and differential integration) with signals from the direct antenna. With front-end samples from the reflection antenna, the new method is the only one of the four methods compared that acquires satellites in the reflected GPS signals, with three acquired using 10 ms of integration.The lidar surface scans are mapped with camera images and estimated reflection points to indicate the surface reflection type and to provide surface height relative to the sensors. For one satellite whose specular point is estimated to be on the ice surface, a Delay Doppler Map (DDM), signal-to-noise (SNR) ratio, and surface reflectivity (SR) are computed with the modified differential coherent integration method using the GPS. The DDM shows that, with modified differential integration, the satellite can be acquired in the reflected signal. For two satellites whose reflection points scan across ice and water over time the SNR and SR are computed over time. The SR is shown to be lower for liquid water than lake ice. This system concept may be used in the future for more complete mapping of phase changes in the cryosphere.
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Title: The Impact of Depression and Cognitive Dietary Restraint on the Association between Obstructive Sleep Apnea and Disordered Eating Behaviors
Creator: Schwartz, Natalie
Date: 2020
Description: Obstructive sleep apnea (OSA) is characterized by loud snoring and repeated episodes of obstruction in the upper airway during sleep that...
Show moreObstructive sleep apnea (OSA) is characterized by loud snoring and repeated episodes of obstruction in the upper airway during sleep that cause partial or total stop in the airflow, often resulting in short, disruptive sleep. Sleep disruption as a result of OSA has been shown to result in reduced cognitive functioning and disordered eating behaviors, however, few studies to date have sought to determine whether OSA is associated with disordered eating behaviors. Bariatric patients are asked to restrict their food intake prior to surgery; however, restraint theory suggests that the eating behavior of those intending to restrict their intake can be disinhibited by a variety of conditions, which cause the restrained eater to engage in disinhibited and overeating behaviors. Depression has also demonstrated an association with disordered eating behaviors and cognitive dysfunction. Affect regulation theory is often cited as a possible explanation for this association, with the desire to avoid unpleasant affective states resulting in disordered eating behaviors. A sample of 93 bariatric surgery candidates were recruited from an academic medical center in a large metropolitan city to participate in the current study. Participants were recruited during their routine pre-surgical assessment for bariatric surgery and completed measures of disordered eating and depression. Both body mass index and diagnosis of OSA were obtained from patient medical chart. It was hypothesized that the presence of OSA would be related to higher levels of disordered eating behaviors, including disinhibited eating, frequency of loss of control and frequency of binge eating. Additionally, it was hypothesized that cognitive dietary restraint would moderate the association between OSA and disordered eating in bariatric patients, such that the greater the intent to strictly control food intake (i.e., greater cognitive dietary restraint), the more strongly sleep disturbance will be associated with disordered eating. Finally, it was hypothesized that depression would mediate the association between OSA and disordered eating in bariatric patients, such that depressive symptomatology accounts for the relation between OSA and disordered eating behaviors. Results indicated that the presence of OSA was not related to higher levels of disordered eating behaviors, nor did depression mediate the association between OSA and the disordered eating outcomes. Two of the moderation models were significant, including the disinhibited eating model and the frequency of loss of control over eating model. Although bariatric patients are encouraged to restrict their food intake, findings suggested that restraint does not appear to function as intended in reducing disinhibited eating behaviors in individuals with OSA. The findings for the loss of control model supported restraint theory and suggested that those with OSA who also endorse high restraint are at increased risk for experiencing loss of control over eating, whereas those who do not have cognitive dysfunction associated with OSA may or may not exhibit the same association. The current study highlights the impact of restraint on bariatric patients’ disordered eating behaviors, suggesting that restraint theory may not be entirely applicable to bariatric populations and may depend on the disordered eating behavior being examined. The current study also points to numerous gaps in the current literature and provides future directions for research on OSA and disordered eating in bariatric populations.
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Title: GAME THEORY BASED LOCATION-AWARE CHARGING SOLUTIONS FOR NETWORKED ELECTRIC VEHICLES
Creator: Laha, Aurobinda
Date: 2020
Description: The recent explosive adoption of electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) has sparked considerable interest of...
Show moreThe recent explosive adoption of electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) has sparked considerable interest of academia in developing efficient charging schemes. Supported by the advanced vehicle-to-grid (V2G) network, vehicles and charging stations can respectively make better charging and pricing decisions via real-time information sharing. In this research, we study the charging problem in an intelligent transportation system (ITS), which consists of smart-grid enabled charging stations and networked EVs. Each vehicle aims to select a station with the lowest charging cost by considering the charging prices and its location while the objective of a charging station is to maximize its revenue given the charging strategy of the vehicles. We employ a multileader multi-follower Stackelberg game to model the interplay between the vehicles and charging stations, in which the location factor plays an important role. We show that there exists a unique equilibrium for the followers’ subgame played by the vehicles, while the stations are able to reach an equilibrium of their subgame with respect to the charging prices. Therefore, the Nash equilibrium of the Stackelberg game is achievable through the proposed charging scheme. We further evaluate the price of anarchy (PoA) of the proposed charging scheme by using a centralized optimization model, in which a modified matching algorithm is applied. In state-of-the-art research works, PHEVs tend to charge or discharge to a smart grid individually. In our extended work, we also consider the discharging scenarios for PHEVs, which is generally during the peak hours of a micro-grid system. We propose that by leveraging the cooperation between charging and discharging PHEVs, the grid will be able to properly disperse the charging load in the load valley and discharging during the load peak hours. As a consequence, the electricity load will be well balanced. In this process, the PHEVs also receive greater benefit, thus serving the PHEV charging and discharging cooperation as a win-win strategy for both the grid and the PHEV users. We formulate and resolve the PHEV charging and discharging cooperation in the framework of a coalition game. Finally, simulation results confirm the uniqueness of the equilibrium in both the game strategies. A performance comparison between the proposed distributed and centralized strategy with existing solutions are presented. We also provide the results of the coalition game when both charging and discharging PHEVs are present in the network. The proper management of charging and discharging of EVs poses one of the most challenging and interesting issues in our research. We aim to provide a complete demand response management solution to PHEVs and micro-grids in a real-time scenario.
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Title: IMPACT OF DATA SHAPE, FIDELITY, AND INTER-OBSERVER REPRODUCIBILITY ON CARDIAC MAGNETIC RESONANCE IMAGE PIPELINES
Creator: Obioma, Blessing Ngozi
Date: 2020
Description: Artificial Intelligence (AI) holds a great promise in the healthcare. It provides a variety of advantages with its application in clinical...
Show moreArtificial Intelligence (AI) holds a great promise in the healthcare. It provides a variety of advantages with its application in clinical diagnosis, disease prediction, and treatment, with such interests intensifying in the medical image field. AI can automate various cumbersome data processing techniques in medical imaging such as segmentation of left ventricular chambers and image-based classification of diseases. However, full clinical implementation and adaptation of emerging AI-based tools face challenges due to the inherently opaque nature of such AI algorithms based on Deep Neural Networks (DNN), for which computer-trained bias is not only difficult to detect by physician users but is also difficult to safely design in software development. In this work, we examine AI application in Cardiac Magnetic Resonance (CMR) using an automated image classification task, and thereby propose an AI quality control framework design that differentially evaluates the black-box DNN via carefully prepared input data with shape and fidelity variations to probe system responses to these variations. Two variants of the Visual Geometric Graphics with 19 neural layers (VGG19) was used for classification, with a total of 60,000 CMR images. Findings from this work provides insights on the importance of quality training data preparation and demonstrates the importance of data shape variability. It also provides gateway for computation performance optimization in training and validation time.
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Title: A Network Analysis to Examine the Construct of Acculturative Stress in Mexican Americans
Creator: Mosqueda, Andrea I
Date: 2020
Description: Acculturative stress (AS) is a negative consequence of acculturation and occurs when acculturation is problematic for the individual. There is...
Show moreAcculturative stress (AS) is a negative consequence of acculturation and occurs when acculturation is problematic for the individual. There is a substantial variation in AS phenomena across different cultural/ethnic groups, across individual variables (e.g., sex, age, education, attitude, cognitive style), and across social variables (e.g., contact, social support, status). There does not seem to be one specific way in which AS affects all groups or individuals. Elevated levels of AS are associated with multiple manifest difficulties, including family dysfunction, geographical separation from family, low-income levels, and non-positive expectations for the future. AS has been associated with outcomes such as anxiety, depression, suicidal ideation, and feelings of isolation and alienation. Using a network analysis approach, this exploratory study examined the structure of the construct of AS as represented in the Social, Attitudinal, Familial, and Environmental (SAFE) AS Scale, a measure used to assess the AS construct across four contexts. Results of the network analysis of AS aspects suggest that the AS construct is best understood as a latent variable rather than as a network of interacting AS aspects, and that distress is related to particular aspects of AS. The present study findings suggest a particular model of relations between latent AS, its manifest aspects, and clinical outcomes. Further research can help to better understand the relationship between individual AS aspects (i.e., treatment targets) and treatment goals of impacting AS-related outcomes (e.g., distress), as well as the mechanisms of change.
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