UCLA

The cornerstone of this dissertation is guayla, a remarkable popular music of the small northeast African nation of Eritrea, its diaspora and northern Ethiopia. Indigenous to Eritrea’s largest ethnic group, the Tigrinya, who make up 50% of the country’s total population of roughly 3-4 million, guayla arose out of traditional music-making and local customs, many of which are still performed today. Guayla has a highly distinctive sound that features soaring vocals, call-and-response singing, and a signature cyclical “five-beat” rhythmic structure that sets everyone dancing at social and political events. Ravinder Rena states that guayla “is [a] powerful and meticulous form of art that brings the emotional integration among the masses. The song is a driving force of an Eritrean society” (Rena 2009). Guayla is particularly present on Eritrean social media and YouTube channels and is a powerful marker of Tigrinya ethnic identity and Eritrean nationhood among the substantial diasporic communities located in North America, Europe, Saudi Arabia, and Australia. Lastly, secular guayla music contrasts with both the prevalence of ancient liturgical music of the Orthodox Tewahedo church which occupies an important place in the cultural history of the country, and the richness of the music of the other eight nationally recognized ethnic groups: Afar, B’dawiyet, Bilen, Kunama, Nara, Rashaida, Saho, and Tigre. The doctoral dissertation “Guayla Nation: Unyielding Tigrinya Music, Dance and Identity in Eritrea” draws from observations, interviews, videos, writings, ephemera, archives, and online testimonials to provide a preliminary ethnographic and historical exploration of Eritrea’s most popular and ubiquitous music legacy.

In the highly-active antiretroviral therapy (HAART) era, sexual minority men (SMM) have achieved high life expectancies. However, they continue to be disproportionately affected by aging-related outcomes (e.g., lung cancer) and low quality of life (e.g., high rates of hospitalization and mortality). Previous literature has shown that overlapping behavioral and psychosocial factors predict HIV infection, low HAART use, and sexual risk behaviors among SMM but whether these factors are linked to adverse health outcomes including hospitalization, lung cancer, and mortality remains unknown. In this dissertation, we used data from the Multicenter AIDS Cohort Study (MACS). The first study included men having sex with men (MSM) who completed at least two assessments between 2004 and 2019 in the Methamphetamine Substudy of the MACS, and we used generalized estimating equation (GEE) models to examine the synergistic effects of psychosocial factors on incident all-cause hospitalization. Our second study involved SMM who underwent at least two assessments in the MACS between 1996 and 2022, and we conducted survival analyses to examine the additive effects of behavioral and psychosocial factors on incident lung cancer. In the third study, we included SMM living with HIV who completed two or more assessments and initiated HAART for at least one year between 1996 and 2022. We examined the predictive capabilities of behavioral and psychosocial factors on all-cause mortality when these factors were added to the Veteran Aging Cohort Study (VACS) Index 2.0, which is a commonly used clinical tool to predict mortality among people living with HIV (PLWH). In our first study, we found synergistic effects of depression, stimulant use, smoking, heroin use, childhood sexual abuse, and intimate partner violence on hospitalization. In our second study, we found a dose-response relationship between the number of behavioral and psychosocial factors (depression, cigarette smoking, heavy alcohol use, and polydrug use) and lung cancer incidence. Our findings from the third study indicated that including depression enhanced the accuracy of the VACS Index 2.0 in predicting mortality. Future interventions should target behavioral and psychosocial factors to reduce healthcare costs and improve the quality of life and overall health among SMM in the US.

This dissertation aims to investigate the thermal behavior of materials and electrodes made from novel synthesis and fabrication methods in lithium-ion batteries and electrochemical capacitors. This dissertation also aims to develop novel characterization techniques with rigorous design and validation. A combination of experimental measurements, numerical simulations, and theoretical analysis are presented.

First, this dissertation compares the thermodynamics behavior and the operando heat generation in lithium-ion battery electrodes made of Ti2Nb2O9 microparticles or nanoparticles synthesized by solid-state or sol-gel methods. Electrochemical testing showed that electrodes made of Ti2Nb2O9 nanoparticles exhibited larger specific capacity, smaller polarization, and better capacity retention at large currents. Potentiometric entropy measurements revealed that both types of electrodes showed similar thermodynamics behavior governed by lithium intercalation in solid solution. However, electrodes made of Ti2Nb2O9 nanoparticles featured smaller overpotential and faster lithium ion transport. In fact, operando isothermal calorimetry during galvanostatic cycling revealed smaller instantaneous and time-averaged irreversible heat generation rates at electrodes made of Ti2Nb2O9 nanoparticles, highlighting their smaller resistive losses and larger electrical conductivity.

Similarly, this dissertation compares NMC622 lithium-ion battery electrodes fabricated using a novel 3D printing process or the conventional 2D tape casting process. Potentiometric entropy measurements revealed that their thermodynamics behavior were identical and consisted of lithium deintercalation in solid solution. However, operando isothermal calorimetry indicated that the 3D printed electrodes featured larger specific capacity and better rate performance, attributed to their larger electrode/electrolyte interfacial surface area and electrical conductivity as well as their faster lithium ion transport. Therefore, the instantaneous heat generation rates were smaller in 3D printed electrodes, reducing the overall specific electrical energy and thermal energy dissipation per unit charge stored.

Furthermore, this dissertation proposes a novel and fast microcalorimetry electrothermal impedance spectroscopy (ETIS) method based on heat generation rate measurements at each electrode of a lithium-ion battery cell. This new method is capable of retrieving the open-circuit voltage, the entropic potential, and the partial entropy changes at each electrode from measurements at a single temperature. It also shortens the measurement duration to a few hours compared to several days using the galvanostatic intermittent titration technique (GITT). This novel microcalorimetry ETIS method was first validated with numerical simulations and then experimentally demonstrated on PNb9O25 or TiNb2O7 battery cells.

Finally, this dissertation validates the step potential electrochemical spectroscopy (SPECS) method and refines the associated analysis capable of differentiating the contributions of electrical double layer formation and Faradaic reactions to the total charge storage in threedimensional porous pseudocapacitive electrodes. The modified Poisson-Nernst-Planck model coupled with the Frumkin-Butler-Volmer theory were used to numerically reproduce experimental data obtained from the SPECS method accounting for interfacial, transport, and electrochemical phenomena. The fitting analysis of the SPECS method was modified for the Faradaic current. The new model can accurately predict the individual contributions of EDL formation and Faradaic reactions to the total current. Moreover, the contributions of EDL formation at the electrode surface or at the electrode/electrolyte interface within the porous electrode can be identified. Similarly, the Faradaic reactions due to surface-controlled or diffusion-controlled mechanisms can be distinguished.

Temperature reconstructions of past oceanic conditions and climates are vital to our understanding of the earth system. Foraminiferal tests are one of the most widely used archives of past climate. Several studies have previously reported that the clumped isotope composition (Δ47) of core-top foraminifera record seawater temperatures, and this proxy is increasingly being applied to foraminifera. In this thesis, I examine multiple aspects of foraminiferal clumped isotope thermometry that are less well constrained and apply this proxy to early Cenozoic samples for temperature reconstructions in a hothouse climate. The first chapter presents an investigation of thermal and non-thermal effects on core-top foraminifera that includes both new data and a meta-analysis of published data. It shows that foraminiferal Δ47-temperature regressions are indistinguishable from inorganic calcite. It also reports possible effects of bottom water carbonate saturation on both planktic and benthic foraminifera. The second chapter is an investigation of the potential impacts of dissolution on foraminiferal Δ47. We report data for multiple species from a core-top transect at the Ontong Java Plateau and from dissolution impacts. It shows evidence that in some species, dissolution elevates Δ47, and biases Δ47-temperatures to colder values. Multiple mechanisms for dissolution impacts on ∆47 and correction methods are explored. The third chapter applies clumped isotope paleothermometry to constrain temperatures across the Paleocene-Eocene Thermal Maximum (PETM) in the South Atlantic. We report planktic foraminiferal and fine fraction isotopic data and derive sea surface temperature and seawater composition estimates and compare this record to published proxy-based temperature estimates and climate model predictions. Chapter 4 investigates clumped isotope changes across the PETM but over a suite of global oceanic sites. Non-thermal influences, especially recrystallization and dissolution, contribute variably to each site, and this chapter systematically explores the possible impacts of each of these factors to the interpretation of clumped isotope temperature estimates, and comparisons with other proxy reconstructions and model temperatures. These reconstructions are used to evaluate equator-to-pole temperature and seawater δ18O gradients, and climate sensitivities to forcing, and could be used for model parameterization.

Advancements in genome sequencing technologies, together with technologies measuring other modalities, have been redefining the field of human genetics. With the increased availability of data and computational resources, researchers are now able to capture complex genetic interactions that were once difficult or impossible to measure.

In the past decade, growing discoveries in genome-wide association studies (GWAS) have been significantly driven by the increasing availability of high-quality sequencing data and the rapid expansion of datasets. Most existing works focused on modeling additive genetic effects while largely overlooking the contribution of more complex genetic interactive effects, also known as epistasis, on outcome traits. Such a phenomenon is partially influenced by earlier theoretical work, challenges in effective modeling under extremely high-dimensional space, and the limited measurement available then.

While an increasing body of literature suggests that additive models explain most heritability at the population level in human genetics, understanding genetic interactions holds significant potential to bridge the gap between statistical genetics and underlying biological mechanisms. This understanding can enhance the development of precision medicine and personalized health, ultimately linking genetic research to individual-level applications.

The first challenge in understanding genetic nonlinearity lies in the high-dimensional nature of the data, where the largest sample size is often no greater than the number of sequenced genetic features. Consequently, it becomes necessary to constrain the search space and focus on studying specific types of interactions.

In this thesis, I present three research projects surrounding this topic. The first project investigates efficient modeling of genetic quadratic interactions within a localized context. Building on this, the second project explores higher-order interactive relationships. Finally, the third project broadens the scope of interactive features, modeling interactions between a target variant and other variants across the whole genome scale.

Road networks support economic activities, emergency services, and daily operations. However, those located in complex terrains, such as hillside regions, are particularly vulnerable to natural hazards such as earthquakes, landslides, and wildfires, which can severely disrupt connectivity and cause significant social and economic impacts. As climate change accelerates the frequency and intensity of these hazards, there is a growing need for scalable, data-driven approaches to enhance the resilience of these networks. Traditional methods for planning and retrofitting infrastructure fall short in addressing the complexities posed by both terrain and hazard exposure, particularly in large-scale networks.This dissertation aims to develop a comprehensive, data-driven framework with multiple modular components to improve the resilience and capacity of transportation networks in vulnerable regions. It focuses on introducing scalable algorithms for ranking road criticality, integrating hyperlocal data into capacity expansion planning, and optimizing retrofitting strategies to ensure network functionality during and after disaster events. Additionally, the work incorporates an equity dimension in retrofitting strategies to ensure fair resource distribution across different socioeconomic groups. To achieve these objectives, this dissertation presents multiple frameworks that develop and apply advanced machine learning and optimization techniques. First, it introduces a Graph Neural Network (GNN) for ranking road segments based on their criticality. This approach provides a more efficient alternative to conventional methods, significantly reducing the computational time needed to rank road segments while maintaining high accuracy. This capability enables rapid decision-making in both everyday operations and during disruptions. Second, the dissertation focuses on integrating hyperlocal data into capacity expansion planning, utilizing data gathered through mobile mapping systems equipped with LiDAR and vision-based technologies. By incorporating this high-resolution data, the framework allows for more accurate capital allocation, especially in hillside regions where terrain complexity poses a challenge for traditional methods. Third, the dissertation addresses the challenge of optimizing earthquake resilience in large-scale networks. A probabilistic framework is developed that combines a Siamese Graph Convolutional Network with a Genetic Algorithm. This approach evaluates various retrofitting strategies and identifies optimal solutions that balance budget constraints while minimizing welfare losses for low-income populations, ensuring a more equitable distribution of resources. Lastly, a novel framework is developed for managing wildfire risks to road networks using Generative Adversarial Networks (GANs). It generates high-fidelity synthetic weather scenarios that influence wildfire ignition and spread, creating a stochastic catalog of events to simulate fire progression and its impacts on road networks. This information feeds into an optimization model that identifies critical road segments for retrofitting, optimizing investments to maximize resilience. The methodologies were applied to transportation networks, mainly focusing on the hillside regions of Los Angeles, showcasing the effectiveness of each approach. The proposed GNN models improved the speed of road criticality rankings, while maintaining high accuracy and enabled large-scale retrofitting optimization. Hyperlocal data integration proved essential for precise capacity expansion, while the earthquake and wildfire resilience frameworks optimized retrofitting strategies, significantly increasing network resilience even with small investments. The main contribution of this dissertation is the development of scalable, data-driven methods for enhancing the resilience of transportation networks. By integrating advanced machine learning models and hyperlocal data, the proposed approaches provide a robust toolkit for policymakers and infrastructure planners to ensure that transportation networks can better withstand and recover from disruptions. These contributions form a strong foundation for resilient and more equitable infrastructure planning in regions susceptible to natural hazards.

An explosion in information technology during the past 50 years has placed the people of today within a data-driven digital world. Despite many information processes being transferred to the digital domain, it remains an indisputable fact that the information within physical properties are analog signals. As such, the analog-to-digital-converter (ADC) acts as a gateway between the two realms and its performance dictates the overall capability of an entire receiver system. Furthermore, although many different kinds of ADC architectures have been developed for different applications, the question of how to drive the ADCs still remains unanswered.

This work investigates a novel approach to achieving both high speed and high resolution analog-to-digital conversion. We leverage an optical system to perform low-jitter sampling and transfer that signal to the electronic domain by means of a photodiode. An electronic front-end is designed to interface with an array of commercial-off-the-shelf (COTS) pipelined ADCs to achieve an effective sampling rate of 10GS/s with 14 bits. The front-end also demonstrates its ability to drive the input of a COTS ADC. The targeted linearity for the entire system is an excess of 10 effective bits. The front-end is fabricated in TSMC 28nm CMOS technology and integrated with the photodiode, custom ADC board, and COTS FPGA board.

The ability to write is critical to academic and professional success. Yet, writing remains a neglected area of instructional focus in secondary schools. One significant challenge in teaching writing—both as a subject and as an activity—is the variability in teachers’ conceptions of writing and knowledge of how to teach it. While research on writing pedagogy has been conducted in public schools, little attention has been given to understanding writing instructional practices within private schools. This qualitative study examined how instructional leaders at private secondary schools develop and implement writing curricula and explored what they reported learning through the process. A phenomenographic research design was used to analyze variations in five participants’ experiences and perspectives. Data were collected through two semi-structured, one-on-one interviews. Analysis revealed two key findings: (1) the development of writing curricula is an iterative process characterized by incremental change, and (2) curriculum development serves as a vehicle for both personal and professional growth. These findings provide insight into the complexities of writing curriculum development in private schools and emphasize the influence of competing factors on fostering curriculum innovation and professional development.

This thesis investigates the use of deep learning and machine learning models for high-frequency stock price prediction and inference. By using models such as Long Short-Term Memory (LSTM) networks, Convolutional LSTMs (CLSTM), and Transformer architectures, this work evaluates the predictive performance of these models in both single-step and multi-step stock price prediction tasks. The models are trained on various datasets, including those with technical indicators, sentiment analysis, and the US Dollar Index, along with Fourier-transformed features for improved feature engineering. The results demonstrate that Transformer-based models, particularly those added with convolutional layers, outperform LSTM-based models in capturing long-term dependencies and making accurate predictions over extended time periods. Additionally, the Fourier-transformed features enhances overall models performance by revealing underlying periodic patterns in stock prices. This research contributes to the growing literature on stock price prediction and inference by offering insights into model architectures and feature engineering techniques that improve the accuracy of financial forecasting.

JWST has begun to reveal the nature of the small-scale structures of our Universe at unprecedented distances, probing an era of galaxy formation with remarkably different properties. This has offered the opportunity for novel tests of our models of cosmology and galaxy formation. In this thesis, I focused on a defining characteristic of ΛCDM structure formation, the streaming velocity, which has a variety of impacts on small-scale structure. Specifically, I developed models of and observational predictions for a new class of small-scale structures called SIGOs derived from this mechanism, with the aim of enabling future detections of these objects that would place constraints on alternative cosmologies. I also showed that--in contrast to previous expectations--the streaming velocity enhances star formation on a per-galaxy basis in the dwarf regime at high redshifts. Taken together, I showed that the streaming velocity has significant influence on high-redshift small-scale structure formation, and may serve as an important signal of our standard cosmology.