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Where\u27s the Dim Sum? Art, Oral History, and the Chinese American Diaspora in Portland, OR
Get PDFThis thesis examines the history, transformation, and contemporary significance of Portland\u27s Chinatown through an oral history framework, emphasizing the voices of Chinese American artists. Beginning with a comprehensive historical overview from the mid-nineteenth-century Gold Rush migrations and railroad labor to twentieth-century exclusion, dispossession under urban renewal, and the late-twentieth-century dispersal into suburbs, the thesis traces how structural racism and policy shaped both the physical landscape and communal life of Oregon\u27s Chinese diaspora. Through in-depth interviews with local artists, this research aims to illuminate how individual experiences of identity, belonging, and creative expression navigate the tensions between heritage and adaptation.
Drawing on three semi-structured interviews, the study explores generational shifts in language, cultural practice, and spatial attachment. It reveals that second-generation Chinese Americans often feel peripheral to the historic enclave yet sustain cultural memory through family traditions, community associations, and artistic practice. The thesis thus highlights how art serves as both a conduit for personal narratives and a medium for resisting erasure. Artists articulate this liminal experience of diaspora: grounded in ancestral legacy yet compelled to excel in new social contexts.
Comparative analysis also situates Portland\u27s experience alongside larger Chinatowns, showing its early prominence and subsequent decline into a predominantly symbolic district. The study further examines how contemporary institutions like the Portland Chinatown Museum, Lan Su Chinese Garden, and emergent ethnoburbs seek to preserve heritage and foster cross-cultural dialogue amid ongoing challenges of gentrification and suburbanization.
Ultimately, this thesis argues that the vitality of Chinese American life in Oregon resides less in static geographic boundaries than in dynamic, transgenerational practices of storytelling, artistry, and community building. By centering oral testimonies, it not only recovers overlooked histories but also charts possibilities for sustainable cultural resilience in the region\u27s evolving urban landscape
Working Paper No. 104, \u3ci\u3eAn Gorta Mór\u3c/i\u3e: The ‘Great Hunger’ and Irish Immigrant Labor in 19th Century America
No full textThis inquiry seeks to establish that portions of the population of Ireland -- responding to what has been referred to in their Celtic language as An Gorta Mór, or in the English language as the ‘Great Hunger -- took to emigrating en masse and therewith contributed bigly to the expansion of America’s labor supply. Bolsterd by Irish immigrants, this expanding mid-nineteenth century labor force provided for an abundance of workers needed for a broadly based industrialization, that would also lead to large-scale urbanization as well as a host of changes in this nation’s social fabric. Of note is that the stream of immigrants tended to fill jobs involving demanding physical labor. Such involved the constructing of canals, railroads, buildings, as well as public works. Another portion of Irish immigrants lent their labor services and talents towards urban and civic development; through joining urban police forces, especially. With and through their laboring, Irish immigrants helped to shape the American experience; with a portion of immigrants discovering pathways leading towards social mobility, and therewith securing recognition for their group’s contributions to America’s economic ascent that included both a successful industrialization as well as the formation of major urban centers
Field Canals Improvement Projects Duration Prediction: A Comparative Analysis of Machine Learning Models
No full textThere are several essential elements in project construction management to be studied appropriately, and priority to these elements, such as cost and duration, is predominantly interesting to be investigated. In this research, the duration of field canal improvement projects (DFCIP) was predicted using two relatively new machine learning (ML) models - the Multivariate Adaptive Regression Spline (MARS) and Extreme Learning Machine (ELM). The targeted DFCIP was calculated using other dependent parameters, such as the length of the pipe, years of construction, the geographical zone of the network, the supplied area with water, and finally the actual cost of the field canal initiative. The development of the model was established using a dataset collected from the open-source literature. The modeling results indicated that MARS over the testing phase attained the least root mean square error (RMSE =6.1609), maximum determination coefficient (R2= 0.7448), mean absolute error (MAE=4.28), a mean absolute percentage error (MAPE=0.05169) with Nash- Sutcliffe Efficiency (Nash= 0.7418) and agreement index (MD= 0.7632). On the other hand, the ELM model achieved RMSE value of 6.869, R2 value of 0.6831, MAE value of 4.7804, MAPE value of 0.05759 with Nash = 0.68108, and MD = 0.73402. These metrics indicate the better performance of MARS over ELM in terms of accuracy and best precision of prediction using all the predictors. The research concluded that the proposed methodology provides reliable technology for duration prediction. In addition, the introduced model can be considered for reliable and robust field canal management
Collaborative Anti-Racist Perinatal Care: A Case Study of the Healthy Birth Initiatives–providence Health System Partnership
No full textThis article describes a case study of the partnership between Healthy Birth Initiatives, a community-based organization (CBO) and Black-led public health nurse home visiting program, and the maternal health division of the Providence Health System located in the Pacific Northwest. This study’s purpose was to explore the formation, significance, and impact of this partnership from the perspectives of staff and leadership members from both organizations. We conducted a case study through qualitative interviews with staff, participant observation, and debrief of leadership meetings. We completed a hybrid deductive–inductive thematic analysis of the data, followed by member checking with study participants and other key interest holders. Key facilitators of the CBO–health system partnership included the vital role of leaders in prioritizing the partnership; health system willingness to incorporate new information from the CBO to improve care; and health system utilization of resources to institutionalize changes that emerged from this partnership. Challenges to the CBO–health system partnership included CBO resource limitations; fragmented referral processes and information sharing; and the persistence required to nurture the relationship without formalized roles. This study contributes to the literature by offering staff perspectives on how a CBO–health system partnership formed, successes, early lessons learned, and practical suggestions for how to develop stronger alignment to provide culturally responsive patient-centered care to Black families
Free-Running Ring Oscillators for Crystal-Free Communication Systems: Design, Simulation Challenges, and Frequency Stability Improvements
No full textThis thesis presents techniques for enhancing the frequency stability of ring oscillators (ROs) for crystal-free wireless communication systems. The first major contribution is a tutorial-style study of frequency stability metrics, providing clear definitions, conversions methods, and comparative analysis of commonly used figures of merit such as phase noise, Allan deviation, and jitter. The second contribution addresses the challenges of simulating phase noise in free-running ROs. Key techniques including Periodic Steady-State Noise (PNoise), Harmonic Balance Noise (HBNoise), and transient noise analysis are evaluated in terms of accuracy, convergence behavior, and simulation runtime. Based on these results, practical guidelines are offered for effective simulation of oscillator phase noise. This work lays the groundwork for the design efforts pursued in this thesis and the author\u27s ongoing Ph.D. research.
The core design contribution is a Digitally Controlled Ring Oscillator (DCRO) that employs current-starved inverter delay cells with binary-weighted transistor DACs. These DACs enable real-time adjustment of effective transistor widths via digital control words, allowing dynamic symmetry tuning of individual delay stages to mitigate flicker noise upconversion without requiring a large number of analog I/Os. A prototype oscillator, as well as an RF front-end for its testing, has been submitted for fabrication in Intel\u27s 16 nm FinFET process, with silicon return expected in Q4 2025. Looking ahead, two system-level control strategies are proposed to maintain both short-term phase noise performance and long-term frequency stability without a crystal reference. The first uses a charge-pump-based feedback loop driven by the received RF signal but is limited by poor channel selectivity. The second is a dual-mode architecture comprising: (1) a calibration mode using an external PLL to sweep across temperature and voltage corners, generating a multi-dimensional lookup table (LUT) of control words, and (2) a free-running mode where the PLL is disabled, and the DCRO is stabilized using on-chip LUT-based control. Together, the proposed DCRO architecture and control strategies aim to enable a digitally tunable, low-power, inductor-free solution for next-generation crystal-free wireless communication systems
Probabilistic Intervals Around Population Forecasts: a New Approach with a Subnational Example Using Washington State Counties
No full textPopulation forecasts produced by governments at all levels are used in the public sector, the private sector, and by researchers. They have been primarily produced using deterministic methods. This paper shows how a method for producing measures of uncertainty can be applied to existing subnational population forecasts while meeting several important criteria, including the concept of utility. The paper includes an assessment of the efficacy of the method by: (1) examining the change in uncertainty intervals it produces by population size and population growth rate; and (2) comparing the width and temporal change of the uncertainty intervals it produces to the width and temporal change of uncertainty intervals produced by a Bayesian approach. The approach follows the logic of the Espenshade-Tayman method for producing confidence intervals in conjunction with ARIMA equations to construct a probabilistic interval around the total populations forecasted from the Cohort Component Method, the typical approach used by demographers. The paper finds that population size and population growth rate are related to the width of the forecast intervals, with size being the stronger predictor, and the intervals from the proposed method are not dissimilar to those produced by a Bayesian approach. This approach appears to be well-suited for generating probabilistic population forecasts in the United States and elsewhere where these forecasts are routinely produced. It has a higher level of utility, is simpler, and is more accessible to those tasked with producing measures of uncertainty around population forecasts
2025 Commencement College of the Arts Ceremony Video
No full textVideo presentation of the PSU College of the Arts Commencement ceremony, held June 13, 2025, at the Viking Pavilion at Portland State University.https://pdxscholar.library.pdx.edu/commencement_2020s/1011/thumbnail.jp
2025 Commencement School of Business Ceremony (Undergraduate) Video
No full textVideo presentation of the PSU School of Business Commencement ceremony, held June 14, 2025, at the Viking Pavilion at Portland State University.
This ceremony honors undergraduate students in the School of Business.https://pdxscholar.library.pdx.edu/commencement_2020s/1015/thumbnail.jp
Understanding the Role of Sentiment and Emotion for Predicting Forced Displacement
No full textDigital trace data play an important role determining where and when people will move during migration crises because of their detailed temporal and spatial granularity. Yet, identifying variables that reliably serve as early indicators of movement remains a challenging task. Within this context, we conduct an in-depth analysis of two types of variables that can be constructed from social media data – sentiment and emotion. Sentiment is conceptually broad and easier to detect from social media posts, while emotion is conceptually nuanced and more difficult to determine. We investigate the potential of both sentiment and emotion of Twitter/X posts as indirect indicators of border crossings from Ukraine by comparing sentiment and emotion to one another at different temporal scales (daily, weekly, monthly), and relating each of them to border crossing counts using a lead-lag analysis. We find that sentiment is a better early warning indicator across temporal scales. We then extend our analysis to consider two other displacement case studies, Sudan and Venezuela, to see how transferable these results are. Finally, there are different approaches for measuring sentiment and emotion, each with different levels of explainability and computational cost (lexicon-based, classic machine learning, and deep learning). We consider all these variations in the three languages associated with our case studies and find that deep learning using Pretrained Language Models, such as mBERT and BETO, performs significantly better than more interpretable approaches, despite relatively small training data sets. In summary, we conclude that migration scholars generally fare better with “simple but broad” as opposed to “nuanced but complex” signals and also find that as the temporal resolution decreases, these types of signals are not well correlated and therefore, may be less useful for determining when people will move during times of crisis
Just-In-Time Adaptive Intervention to Improve HIV Prevention and Substance Use in Youth Experiencing Homelessness (MY-RIDE): Protocol Fora Randomized Controlled Trial
No full textBackground: Youth who are experiencing homelessness face a higher risk of HIV infection compared to their housed peers, and suicide and overdose remain the leading causes of death among homeless youth. Just-in-Time Adaptive Interventions (JITAIs) are gaining momentum for HIV prevention and substance use research. Yet, most interventions for homeless youth have not addressed modifiable real-time factors. Objective: This paper describes the development and implementation of a randomized attention-controlled trial to assess the efficacy of motivating youth to reduce infections, disconnections, and emotional dysregulation (MY-RIDE), a JITAI to improve HIV prevention and substance use in homeless youth. Methods: This study will enroll 320 homeless youth aged 18-25 years. The intervention was co-designed with homeless youth using the Information-Motivation-Behavioral Skills Model and consists of an individual nurse-led session about HIV prevention and 3 months of a JITAI with personalized messaging delivered by phone in real time in response to one’s current level of risk. Participants also had access to an on-demand nurse helpline through the app. Results: Institutional review board approval was obtained in the summer of 2024. Recruitment began in the fall of 2024 at shelters, drop-in centers, and other organizations that serve homeless youth. Participants complete a baseline survey and HIV/sexually transmitted infection (STI) testing and are provided with a smartphone with the intervention app. Follow-up surveys and HIV/STI testing are conducted at immediate, 3-, 6-, and 12-month time points post intervention to assess uptake of HIV prevention strategies and substance use reduction. A total of 192 are enrolled to date. Conclusions: The results of this study will determine whether MY-RIDE increases HIV prevention strategies and decreases substance use when compared to homeless youth in the attention control group. We will also evaluate if MY-RIDE impacts protective factors such as willingness to take pre-exposure prophylaxis medication and use of mental health and substance use services, and antecedents of risk such as stress, substance use urge, and substance use