The conventional signalized diamond interchange provides numerous essential freeway-to-arterial connections. However, it tends to become operationally inefficient when high traffic demands exist on the arterial or off-ramps. To address this problem, diverging diamond interchanges (DDIs) have been implemented at many sites to replace conventional diamond interchanges (CDIs). While DDIs have been shown to outperform CDIs under high left-turn demands, their operational performance diminishes when through demands on the arterial become heavy. In this work, I proposed a new service interchange design named the “one-sided diverging diamond interchange” (one-sided DDI) as a replacement for congested CDIs and DDIs. Through a comprehensive series of microscopic simulation tests with the software PTV Vissim, I analyzed and compared the operational attributes of the one-sided DDI to those of the CDI and DDI over a range of traffic demands. Overall, the results from the simulation tests indicate that the one-sided DDI significantly outperforms both the CDI and DDI in vehicle travel time and throughput when through demands on the arterial are dominant. On the other hand, the one-sided DDI tends to modestly outperform the traditional DDI in travel time when handling moderate to high proportions of left-turning traffic.

Noisy environments pose difficulty for efficiency of communication and understanding of conversation. However, some individuals are able to hear better in these environments than others, with age and hearing ability controlled. A contributing factor may be the natural rhythm of speech. All of speech contains rhythm, we speak at different paces with words timed at certain paces. When this rhythm is altered, it is shown to reduce speech understanding. This study investigates whether people are the same in terms of how much they rely on speech rhythm to decipher speech in background noise. The purpose of this study is to use these results to create more effective and customizable hearing aids catered to each individual's hearing differences. We looked at two promising factors in this study that may predict increased ability to use the natural rhythms of speech in order to better understand it: working memory and non-speech rhythmic abilities. We created remote tasks on an online platform, Gorilla, to measure these factors in normal hearing individuals. More specifically, these tasks included a working memory task, Reading Span; a rhythmic abilities task, Beat-Based Advantage; and a rhythmic speech in noise task. We used stepwise linear regression to analyze whether these two factors impact one’s reliance on rhythm in speech. The findings of this study support that better working memory skills and better rhythmic abilities improve speech understanding in noisy environments, as these participants utilized speech rhythm to do so. We can use this knowledge on future research with participants with hearing loss. We still live in a “one-size fits all” model for hearing aids. However, by understanding the factors that improve speech comprehension in noise, we can create individually-tailored hearing aids that adapt to people’s abilities and thus improve communication in all types of environments.

Despite trends in hearing technology, there is little understanding of how listeners judge quality of speech. The lack of understanding of these perceptual mechanisms prevents development of efficient approaches to improving speech quality technologies. The proposed research addresses this knowledge gap and is among the first studies to elucidate the differences in listening strategies between speech understanding and speech quality judgments. It is well established that the frequencies in speech contributing to speech understanding varies--certain frequencies are more essential than others. The relative importance of different frequencies, known as spectral weights, represents listeners' strategies when performing speech recognition tasks (i.e., repeating back the presented speech). We hypothesize the spectral weightings of listeners judging speech quality compared to speech understanding will differ--our preliminary results have proved our hypothesis true as frequencies are proving to contribute disparately for understanding versus recognition. Previous research demonstrates clarity of pitch being a key contributor to perceived speech quality, while speech understanding may be maintained when voice pitch is heavily distorted. Therefore, the frequencies carrying information in pitch may become more important in speech quality judgment. As research continues, I will test this hypothesis by analyzing the differences in the participants' spectral weightings of a speech recognition task and a speech quality judgment task. To discern the spectral weighting, a correlational analysis is done for each of the two tasks using averages in the responses. This correlational analysis averages data from the participants' responses to find how each frequency band of interest contributed to understanding versus recognition. This project provides one of the first estimates on the contribution of frequencies in perceptual judgments of speech quality, which is valuable for future hearing technology aiming to improve perceived speech quality in that it pinpoints the frequency regions requiring attention in audiological research to come.

The pairing of high-resolution volumetric imaging methods with cellular markers of neural activity holds network-level explanatory power over behavior. However, common statistical analysis approaches fall short in capturing the functional relationships between brain regions across multiple spatial dimensions. Here, we propose combining unsupervised machine learning clustering methods with network graph theory visualization to reveal intricacies from these data beyond conventional standards. We demonstrate the feasibility of this approach on a recent single-cell dataset describing the longitudinal changes of brain-wide activation during relapse to palatable food in mice. We applied a new analytical framework combining the functionality of two open-source programs: (i) Histo-Cytometric Multidimensional Analysis Pipeline (CytoMAP), packaged with unsupervised k-means clustering and t-distributed stochastic neighbor embedding (t-SNE), and (ii) Cytoscape, a network analysis program. Hierarchical radial network diagrams were applied to the dataset in which we visualized the anatomical organization of regions that underwent statistically significant changes in activation. Across abstinence duration, we found an initial suppression in activation followed by widespread increases in activation. This increase correlated with observed behavioral changes and appeared to be triggered by activation hotspots. We next interrogated the co-activational relationships amongst the >900 brain regions by running unsupervised t-SNE dimensionality reductions on the data from each experimental group. These results were validated using k-means clustering and Davies-Bouldin indexing. We observed an intuitive segregation of regions dependent on activation status. Cluster number decreased in a time-dependent manner, suggesting increases in modular processing are associated with increased activation due to abstinence length. This trend indicated that cluster membership of regions likely also changed in a time-dependent fashion, indicating a dynamic recruitment effect at a regional level underlies abstinence-related relapse vulnerability. By analyzing cellular whole-brain data in this novel manner, we gained new insight into a previously unexplored dimension of brain activation dynamics underlying complex behavior.

Fewer than 20% of adolescents and young adults (AYA) with diabetes achieve optimal glycemic control. The use of diabetes technology, including insulin pumps and continuous glucose monitors (CGM), are associated with improved outcomes; however, there are inequities in technology use. The University of Washington (UW) AYA Diabetes Program aims to address disparities in AYA with diabetes by providing tailored education and access to multidisciplinary specialists, including a social worker and psychologist. We examined the impact of the UW AYA Diabetes Program on change in diabetes technology use among AYA and assess whether this varied by sex, race/ethnicity, and health insurance. The study included participants seen in the UW AYA Diabetes Program between 2017-2021. For each binary outcome of insulin pump use and CGM use, we calculated proportions and 95% CIs (confidence intervals) using generalized linear models. We assessed for possible effect modification by sex, race/ethnicity, and health insurance. In this cohort of 453 patients (51% male, 87% non-Hispanic White, 27% public insurance, 55% CGM and pump use) there was evidence of effect modification by health insurance type for both pump and CGM use (p=0.01 and 0.03 respectively) but not by sex or race/ethnicity. Baseline CGM use was significantly lower among public vs private insurance participants (38%, 95% CI: 29-46%; vs 61%, 95% CI: 56-66%) but not after 6-months of program participation (58%, 95% CI: 45-72% vs. 74%, 95% CI: 68-80%). Pump use was also lower at baseline among publicly-insured AYA (29%, 95% CI: 20-37%; vs. 67%, 95% CI: 62-72% for privately-insured). However, by 12-months, pump use for publicly-insured AYA had increased and was no longer statistically significant (44%, 95% CI: 30-59% vs. 64%, 95% CI: 56-72% for privately-insured). We found that the UW AYA Diabetes Program proved to be successful in mitigating baseline disparities and increasing diabetes technology use in AYA with diabetes.

Adolescents with type 1 diabetes (T1D) are at risk for poor physical and psychosocial outcomes. Diabetes-related family conflict has previously been associated with youths' glycemic control (HbA1c). However, less is known about how family conflict associates with other health outcomes. This project aimed to explore correlations between adolescent and parent reported family conflict with diabetes-distress, depressive symptoms, resilience, and health-related quality of life (HRQOL) for both adolescent and parent. Eligible patients were enrolled in a two-site randomized controlled psychosocial intervention study. Participants were ages 13-18 with T1D for over a year and elevated levels of diabetes distress. At baseline, patients and their parent completed measures of: diabetes-specific family conflict (DFCS), HRQOL (T1DAL), diabetes distress (PAID-T), depressive symptoms (PHQ-8), and resilience (CD-RISC). HbA1c was pulled from participants medical charts. Descriptive statistics were used to summarize demographic variables, and bivariate correlation analyses were used to investigate the association between DFCS and the other psychosocial variables. Adolescents (N= 131; 53.4% female, 6.1% identified as another gender, 78.6% White, 9.9% Black, 2.3% Asian, 3.1% American Indian/Alaskan Native, and 80.9% Non-Hispanic, average age 15.38  1.5) DFCS scores correlated with more diabetes-distress (r=0.386, p<0.001), depressive symptoms (r=0.334, p<0.001), and less HQOL (r= -0.303, p<0.001). Parents’ (N=131; 79.4% White, 9.2% Latino/Mexican 6.1% Black, 2.3% Asian, 0.8% other, 79.4% private insurance) DFCS scores correlated with higher youth A1C (r=0.280, p<0.001), higher parent diabetes distress (r= 0.479, p <0.001), and lower parent resilience (r= - 0.200, p = 0.022) and HQOL (r = -0.369, p < 0.001). Both parent and patient reports appear to be an important area of further investigation for determining correlates of poor physical and psychosocial wellbeing in this high-risk group. While further investigation is needed, screening for family conflict may be important in clinical procedure, as it may become a future target for intervention.