UC Santa Cruz

Carbon isotopes serve as powerful tools for understanding ocean carbon cycle processes, both past and present. This dissertation investigates two distinct applications of carbon isotopes in the context of ocean alkalinity enhancement (OAE): as tracers of natural geologic carbon and alkalinity release in the past, and as monitoring tools for future carbon dioxide removal. Using a combination of global and regional modeling approaches paired with geochemical data, this work provides new insights into both geologic carbon release during the last deglaciation and verification methods for future ocean-based carbon removal.

In Chapters 1 and 2, I investigate records of anomalously low 14C water in the eastern tropical North Pacific Ocean during the last deglaciation. First, through global carbon cycle modeling constrained by atmospheric CO2 and ∆14C records, I establish that large-scale release of neutralized geologic carbon (up to 2,400 Pg C) could have occurred without significantly disrupting the carbon cycle. Building on this, I develop a regional model of the eastern tropical North Pacific and combine it with new boron isotope (δ11B) data to directly simulate these anomalies, demonstrating that this carbon release must have been neutralized by alkalinity---representing a natural analog for OAE.

In Chapter 3, I shift focus to the present day, examining how carbon isotopes can support modern climate solutions. Using a high-resolution regional model of the California Current System, I evaluate the utility of stable carbon isotopes (δ13C) as a tool for verifying atmospheric CO2 uptake following OAE deployment. This work demonstrates that δ13C provides a diagnostic signal of CO2 removal that persists longer than traditional carbonate measurements, offering a robust verification method for marine carbon dioxide removal.

Together, these chapters advance our understanding of both past ocean carbon cycle processes and future carbon removal strategies, while highlighting the versatility of carbon isotopes as tools for studying natural and engineered perturbations to the marine carbon cycle.

We prove that all triangle Artin groups of the form $A_{2,3,2n}$ where $n>3$ are residually finite. To achieve this, we use the presentation for these groups previously employed by Wu and Ye to establish that each of them splits as a graph of groups. Building on techniques developed by Jankiewicz for other triangular subclasses of Artin groups, we adapt and extend these methods to show residual finiteness in this setting. Additionally, we developed a Python program to assist in specific computations for the case of $A_{2,3,8}$.

This dissertation explores the feasibility of utilizing Silicon Carbide (SiC)Electroluminescence (EL) to estimate current from a SiC MOSFET’s body diode in classical power converter feedback control systems. The study delves into the current and temperature dependencies of SiC EL, demonstrating how light intensity at key wavelengths (390 nm and 500 nm) varies with current and temperature. By maintaining a constant junction temperature, the circuit’s electroluminescence is directly affected by a change in current, while a rise in junction temperature influences the light emission at different wavelengths. The work presents an experimental setup that integrates SiC EL with a closed-loop control system to regulate current in a buck converter. Results from the system demonstrate that SiC EL can be used to predict current, providing a basis for future motor drive torque regulation, speed control, and voltage control in power converters. The dissertation also addresses the challenges of low light intensity and nonlinearity in SiC EL measurements, proposing methods to optimize sensitivity and accuracy using avalanche photodetectors and calibration techniques. Despite limitations, such as the weak emission of SiC EL compared to direct bandgap materials, the research establishes a novel and effective approach for current estimation in power electronics applications, paving the way for improved control systems in power conversion and motor drives.

This thesis focuses on adapting the basic functionality of the Core-Based Tree(CBT) approach to operate efficiently over wireless ad hoc networks. CBT is implemented on top of a recently developed loop-free unicast routing protocol designed for ad hoc net- works called RIPPLE-WiN (for Routing Information Protocol with Probing for Loopless- ness and Efficiency in Wireless Networks), which results in the Wireless Core-Based Tree (WCBT) protocol. WCBT is analyzed together with two archetypes of multicast com- munication, namely emulating multicast routing using unicasting and network flooding. Simulation experiments show that the collision of packet replicas is a major problem for network flooding and WCBT, causing a high level of packet losses. Two mechanisms are proposed to overcome this high packet loss while still benefiting from the ability to forward a multicast packet only once. The first mechanism is Random Forward Delay (RFD) of forwarded multicast packets, and reduces the majority of losses for WCBT and all losses for Network Flooding. The second mechanism is Overhearing, which allows nodes to accept packets not only from their designated parent but also from other nodes. It is shown that combining these two mechanisms with WCBT results in much more efficient use of network resources compared to network flooding while keeping packet losses low.

This thesis presents a unified classification of semisimple Lie algebras and Kac-Moodyalgebras through their shared foundation in Cartan matrices and Dynkin diagrams. Motivated by the systematic classification of finite-dimensional Lie theory and inspired by Professor Chongying Dong’s insight, this work systematically explores the algebraic and geometric frameworks underpinning both classifications. For semisimple Lie algebras, we establish the classification via root systems and Dynkin diagrams, emphasizing Cartan’s criterion and the Killing form. Extending these principles, Kac-Moody algebras are constructed through generalized Cartan matrices, revealing infinite-dimensional symmetries critical to modern theoretical physics. By bridging finite and infinite dimensions, this thesis highlights applications in string theory, conformal field theory, and quantum gravity, while demonstrating how combinatorial tools like Dynkin diagrams unify disparate algebraic structures. The representation theories of both algebras are examined, culminating in the Weyl and Weyl-Kac character formulas, which underpin physical systems from atomic spectra to vertex operator algebras.

Polytemporal music presents opportunities for the discovery of new rhythmic expression. The juxtaposition of unrelated tempi produces a spectrum of simultaneity and “temporal dissonance” (Reynolds, 1984; Thomas, 2000). Yet, its development and execution are accompanied by significant challenges, beginning with its difficulty for humans to perform. To pursue a new approach in the study of polytemporality, I mapped the concepts within “coupled oscillators” (Pikovsky et al., 2001; Strogatz, 2012) to the concurrent performance of independent musical tempi. I developed synchronization strategies to equalize loop lengths, align playback speeds, and coordinate rhythmic patterns in an iterative compositional development process, employing elements of user interface mechanics and game design. The four polytemporal games in this collection invite audience members to play with this complex musical topic in an interactive, browser-based environment. This document describes the composed musical works, details their foundational concepts and technologies, and discusses experiences in, and strategies for, creating them.

Since the beginning of the modern telescope, astronomers have thought of new surveys and methods to study astrophysical phenomena. In this dissertation, I present the Swope Supernova Survey, a low-redshift photometric survey at Las Campanas Observatory, Chile, detailing its motivation, methodology, and significant contributions to transient astrophysics. I also highlight my vital contributions to the survey and science enabled. Since its inception in 2016, the survey has established itself as a critical resource for the study of transients below +30◦ declination, covering a wide wavelength range (u to i band), precise calibration, and high observing cadences. I specifically focus on the first Type Ia Supernova (SN Ia) data release, an effort that I led to provide over 100 high-cadence light curves in five photometric bands. This dataset enhances low-redshift SN Ia samples and opens the path for future work that will significantly contribute to SN cosmology. Finally, I introduce a novel parametrization of i-band light-curve diversity. I present the ∆m1 − ∆m2 parameter, which captures differences between the data and model at the i-band secondary maximum and minimum. Strong correlations are identified between this parameter and key spectral features, such as Ca II pEW0 and Si II v0, highlighting the role of spectral variations in shaping i-band light curves. This work also shows how these variations impact SN Ia composite spectra and synthetic photometry, revealing limitations in the widely used SALT3 SN Ia model. This dissertation highlights the importance of combining photometric and spectroscopic analyses to advance our understanding of SNe Ia, further exploring connections between SN Ia spectral features, i-band light-curve morphology and diversity, physical processes, environmental dependencies, and the accuracy of SNe Ia as precise cosmological distance indicators.

Power flow (PF) analysis is critical to power system operation and planning. Nowadays, renewable energy power generation has been widely installed in power grids because they are environmentally friendly. The high penetration of renewable energy brings significant fluctuations to the power system states. Probabilistic power flow (PPF) analysis aims to characterize the probability properties of voltage phasors with stochastic power injections.

Exploiting the impressive capability of neural networks (NNs) in complex function approximation, we utilize the NN as a rapid PF solver in real-time applications. Motivated by residual learning, the first work proposes a new NN structure based on the physical characteristics of PF equations. Specifically, we add a linear layer between the input and the output to the multilayer perceptron (MLP) structure. We design three schemes to initialize the NN weights for the shortcut connection layer based on the linearized PF equations. Numerical results show that the proposed approach outperforms existing NN frameworks in estimation accuracy and training convergence. However, the branch flow estimation accuracy of the NN-based methods on some benchmark systems is lower than the linearized PF-based method. The inherent reason is that the NN outputs are voltage angles instead of voltage angle differences, while the latter determines the branch flows. To further improve the branch flow estimates, the second work separates the training of voltage magnitudes and phase angles due to their different properties. We incorporate the errors of voltage angle differences into the training loss function.

Based on PF equations, optimal power flow (OPF) analysis minimizes the total generation cost while subject to other operational constraints. To help the independent system operator (ISO) clear the real-time energy market, we develop an unsupervised learning-based framework to solve the OPF problem rapidly. We employ a modified augmented Lagrangian function as the training loss. The multipliers are updated dynamically during the training process based on the degree of constraint violation. Numerical results show that the dynamic updates of the penalty weight coefficient improve the feasibility of solutions compared to the fixed pre-assigned coefficient.

To ensure the PF balance, the NN predicts a subset of decision variables, and the remaining variables are obtained by a subsequent PF solver. However, the variable splitting scheme introduces heavy computation complexity when it comes to computing gradients in backpropagation. Hence, in the fourth work, we aim to reduce the total computational time of the NN to enable a daily update of the NN. We propose a physics-informed gradient estimation method based on a semi-supervised learning framework. We employ ridge regression to obtain pseudo-optimal solutions and build a hybrid dataset. We propose a batch-mean gradient estimation method based on the linearized Jacobian model to speed up the training process. Numerical results show that the proposed gradient estimation method achieves a similar convergence rate as the ground truth Jacobian. Moreover, the proposed method rapidly obtains near-optimal solutions, which is appealing in real-time applications.

A search for the Standard Model Higgs boson produced in association with a high energy photon is performed using 133 fb−1 of pp collision data collected at √s = 13 TeV with the ATLAS detector at the Large Hadron Collider at CERN. The H + photon final state is particularly promising to study because the photon requirement reduces the multijet background, and the bb final state is the dominant decay mode of the Higgs boson. Event selection requirements isolate vector boson fusion Higgs production, the dominant production mode in this channel. Several improvements enhance the search sensitivity compared to previous measurements, including better background modeling and characterization, use of a dense neural network classifier, and an updated signal extraction strategy adopting a binned-likelihood fit directly to the classifier discriminant. These advancements result in a Higgs boson signal strength measured as 0.2 ± 0.7 relative to the Standard Model prediction. This corresponds to an observed significance of 0.3 standard deviations, compared to 1.5 standard deviations expected signal significance.

During protein synthesis, the ribosome must move the tRNAs and mRNA together in single codon steps after the addition of each amino acid to the polypeptide. This process of translocation is catalyzed by the GTPase elongation factor G in prokaryotes. EF-G hydrolyzes GTP during each round of translocation, yet the purpose of this energy expenditure is unclear. Here, we first ask how inhibiting GTP hydrolysis using GTP analogs and a mutant form of EF-G impacts the structural rearrangements of the ribosome that take place during translocation, monitored with Förster resonance energy transfer (FRET). We find that hydrolysis is required only for reverse rotation of the 30S head domain, an event that occurs late in translocation after the tRNAs and mRNA have completed the bulk of their movement. We then investigate the specific role and timing of phosphate (Pi) release from EF-G, which is delayed relative to hydrolysis and is the step responsible for the bulk of the energy derived from hydrolyzing GTP. We determine the timing of Pi release relative to the structural rearrangements of the ribosome by monitoring structural dynamics with FRET and in parallel observing the kinetics of Pi release with a fluorescence-based reporter. We find that Pi release occurs after forward head rotation and, surprisingly, is coupled to reverse intersubunit rotation. Further, we show that both Pi release and EF-G dissociation are required for reverse head rotation. To account for these findings, we propose that delayed Pi release prevents premature dissociation of EF-G; this ensures that the codon-anticodon duplex is stabilized by EF-G throughout its movement to prevent a frameshift. We conclude that the GTPase function of EF-G, rather than driving tRNA movement, is crucial for enforcing accuracy during translocation. This function may well extend to other translational GTPases such as IF2 and EF-Tu, which also exhibit delayed Pi release and have critical roles in enforcing accuracy during different steps of protein synthesis.