The purpose of this study is to examine the feasibility of virtual reality meditation (VRM) for symptom management in outpatients with rheumatoid arthritis (RA). The specific aims of this feasibility study include: 1) examining the feasibility of implementing VR meditation; 2) determining the acceptability of using VR-delivered meditation; and 3) exploring a patient’s experience of using VR-delivered meditation for symptom management. RA is a chronic disease that affects more than 1 million people in the U.S. While recent advances in medicine have shown promising results in managing physical symptoms, a large portion of outpatients with RA still suffer from fatigue. Recent studies have found that fatigue may be managed through meditation, but VR meditation has yet to be tested and deployed in this population. This feasibility study implements a mixed-methods design. Eight adults (18 years and older) with clinically-diagnosed rheumatoid arthritis were enrolled from a local rheumatology clinic. Participants used a VR headset incorporated with meditation software over the course of four consecutive weeks. Patient Reported Outcome Measurement Information System (PROMIS) measures of fatigue, pain, depression, anxiety, physical activity, and mood were collected at baseline and weekly intervals for 4 weeks. Two semi-structured interviews were conducted to capture the patient’s experience of RA, fatigue, as well as experience of the virtual environment. I was personally in charge of analyzing the interview transcriptions and adding coding measurement tags for the quantitative analysis. Results are currently pending. Expected results include that participants will find VRM both feasible and acceptable for fatigue management, and that participants will report reduced fatigue levels after using the VR device. Results of this study will inform future clinical trials using VRM, implementation of VRM into clinical use, and give a better understanding of the patient’s experience of utilizing VRM for fatigue management.

Evaluation of and diagnosis based on core needle biopsies at present requires trained pathologists on site for both sample manipulation and analysis of tissue structures. Microfluidic lab-on-chip architectures have been studied for automating cell-level pathology for decades, and the CoreView system developed in Eric Seibel’s lab applies similar technologies at the millimeter scale to tissue level analysis. Pulsatile flow has proven to be a reliable means by which to transport tissue samples without damage or adhesion to the flow channels, and high resolution microscope images can be taken in glass-covered channels for analysis by a remote pathologist or image processing system. I am developing a low cost and manufacturable on chip device to accurately cut and sort these tissue samples for further processing. To avoid unnecessary complexity and unreliability, compliant elastomeric actuators will be employed to actuate an on-chip knife which also acts as a valve controlling transport flow routing. This integrated compact device will achieve all of these goals in a system that can be mass-produced for low-cost and disposability if required for sterility. Such capabilities will enable tumor-rich regions to be sampled for genomic analyses that allow precision therapy, making cancer a treatable disease.

Water is universally critical for life on earth, and snowmelt plays an essential role in the hydrologic cycle, contributing up to 75% of water supply in much of the western United States. As a result, estimating the timing and magnitude of snowmelt is an integral water resources challenge; snow surface temperature observations are key to this issue. It is well established that snow melts at 0°C, so frequent snow temperature measurements supply crucial information for evaluating snowmelt. However, few ground observations of snow surface temperature are available, and those that are only represent a small area. By measuring infrared radiation, satellite thermal imaging can remotely determine surface temperature over large areas and time scales where ground observations are sparse or nonexistent. However, this imagery is limited by its coarse spatial resolution, which results in a blurring of temperatures across study regions. This challenge is especially prevalent for mixed pixels, or pixels with varied land surfaces such as a mix of snow and vegetation or changing topography. Thus, this project investigates how well satellite imaging represents snow surface temperature. Airborne thermal imagery were acquired by the UW Applied Physics Laboratory over Yosemite National Park, California, coincident with ASTER satellite imagery on 21 April 2017. I apply methods including data analysis and zonal statistics over the study area in order to compare finer resolution airborne imaging to coarser resolution satellite imagery. Furthermore, I calculate characteristics of land variation to evaluate where satellite imagery can be scaled for more accurate results. This project works towards the implementation of satellite thermal infrared for use in snow models, creating new datasets for hydrologists to more effectively plan our water resources.

 Perovskites are a class of hybrid organic-inorganic semiconductors being researched as potential absorbers in cheaply made solution processed solar cells. One significant challenge in commercialization of perovskite solar cells is degradation due to environmental factors. To ensure the multi-decade stability of devices studied over a data collection window of hours to days, being able to predict long-term stability from early-time data is essential. Here, we use machine learning to predict degradation times of methylammonium lead iodide (MAPI) materials aged in identical environmental conditions. Using the Lasso algorithm, we train a model to predict the time elapsed before the MAPI film reaches 75% of its initial carrier diffusion length (tLD75), achieving 31% mean linear error. Starting with a suite of early-time data as features, these models identify the first derivatives of transmittance and carrier diffusion length as the most important predictors of tLD75. We then investigate the effect increasing the prediction time window, the last time point that model features include data from, has on model accuracy. This work is the first demonstration of accurate prediction of tLD75 in identically prepared MAPI films, allowing our models to account for the wide sample-to-sample variation in perovskite material degradation. Predictive models of these stability differences between samples within batches are necessary to provide warranties of commercially produced modules using these materials.

Alternative intersection designs brought significant benefits to motorized traffic in the United States and other countries over the last several decades. Given these breakthroughs, it seems logical that the concepts of innovative geometric design and improved traffic signal phasing can be combined and utilized to enhance safety and reduce congestion for non-motorized users – particularly pedestrians – at signalized intersections in urban and suburban environments. At existing signalized intersections, pedestrians are prohibited from entering the crosswalk during much of the green interval for a parallel pedestrian phase. This study introduces and analyzes a new intersection design invented by the author – known as the Protected Overlapped Pedestrian (POP) Intersection – which prioritizes non-motorized traffic and seeks to improve safety and traffic operations for pedestrians and bicyclists, while at the same time offering a reasonable level of service for motorized vehicles. By adding pedestrian refuge islands and splitting each crosswalk into four distinct short crossings, each short crossing can be “assigned” and “overlapped” to multiple concurrent, non-conflicting vehicle phases, which allows the pedestrian clearance time of a short crossing to be contained entirely within the yellow change and red clearance intervals of its “last” concurrent vehicle phase; this allows the walk interval of a short pedestrian crossing to last as long as the green intervals of its concurrent vehicle phases. The traffic microsimulation software PTV Vissim was used to analyze the operational performance of the POP Intersection design in comparison to a traditional protected intersection design, under multiple volume intensity scenarios. Overall, the POP Intersection design produced great benefits and showed excellent promise in terms of decreasing pedestrian delays and allowing pedestrians to more effectively utilize each traffic signal cycle. Delays experienced by motor vehicles and bicycles were not substantially negatively affected.