In the Blink of an Eye: The Truth of Epilepsy

This short documentary was created as a course requirement in HTS 3086 – Sociology of Medicine and Health under the supervision of Dr. Jennifer Singh.Runtime: 07:36 minutesThis documentary explores the illness experience of having epilepsy from the perspective of both the person living with epilepsy and their caregiver (parent)

A Surrogate Machine Learning Method for Real-time Indoor Acoustic Analysis: A Case Study in an Educational Building

Achieving optimal speech intelligibility in educational settings is crucial for effective learning. Designers face challenges due to the diversity of building regulations, which define acoustic comfort in various ways. Objective acoustic parameters such as Definition (D50) and the Speech Transmission Index (STI) are pivotal in assessing acoustic quality tailored to a room’s function, with STI being especially indicative of speech intelligibility. To address the need for quick, accurate predictions of D50 and STI values across classroom areas, this research employs a surrogate machine learning (ML) approach. Our methodology involves simulating acoustic properties in a single educational room using the Pachyderm plugin within Grasshopper to analyze D50 at three key frequencies (125, 1000, and 4000 Hz), as well as the overall STI. We utilize the CatBoost algorithm as a surrogate model to predict the acoustic performance of individual sensors. The effectiveness of our model is assessed using the R2 score, Mean Absolute Error (MAE), and Mean Squared Error (MSE) for individual sensors, along with Pearson correlation for comprehensive sensor analysis. The results demonstrate the high performance and potential of this surrogate ML approach in generating detailed and accurate acoustic heatmaps, thus ensuring enhanced acoustic comfort in educational environments. This method provides a cost-effective and efficient solution for real-time acoustic assessment, paving the way for improved educational building design

Understanding dynamics and distributions of poly(ethylenimine) confined in mesoporous SBA-15 silica and impact on CO2 capture

Solid-supported amines serve as advanced CO2 sorbents, effectively balancing high CO2 uptake and energy-efficient regeneration. These materials enable CO2 capture, even from ultra-dilute sources such as ambient air, which contains approximately 420 ppm CO2. One exemplary sorbent model is poly(ethylenimine) (PEI) in SBA-15. In this sorbent, the physical attributes of PEI dictate its performance. The distribution of PEI determines the extent of amines available for reacting with CO2, determining equilibrium CO2 uptake, while PEI mobility controls the diffusion of CO2 through the PEI-packed pore space, ultimately influencing CO2 uptake rates. This thesis aims to characterize the distribution and motions of PEI confined in SBA-15, utilizing a combination of neutron scattering, solid-state NMR, and molecular dynamics (MD) simulation. First, the effects of different pore wall-PEI interactions are studied, revealing subtle interplays among PEI, solid walls, and wall-grafted chains that result in unique PEI structures and mobilities. Second, the underlying roles of poly(ethylene glycol) (PEG) additives are illuminated, providing insights into the unique behavior observed in CO2 sorption and desorption processes. Finally, the impacts of multiple cycles and the composition of the input gas are investigated, demonstrating that repeated thermal swings and humidity in the input stream lead to changes in PEI properties.Ph.D.Chemical and Biomolecular Engineerin

Production of jet-range biofuels from 2,3-butanediol

In the transition from our current energy mix still dominated by fossil fuels to an increasingly carbon-neutral energy mix, the energy consumption in the transportation sector is a focal point. In air transport, the most viable current technology for decarbon-ization is through synthetic biofuels. An important route is the jet-fuel synthesis from fermented bio-alcohols using a short-chain olefin intermediate. This thesis examines such a route involving synthesis of jet-range biofuels from 2,3-butanediol (BDO) through a butene intermediate. The thesis first examines the deactivation mechanism of the bifunctional catalysts converting BDO to butene. This is then followed by an exami-nation of the oligomerization of butene with two zeolite catalysts at a range of condi-tions to optimize production in the jet range. The bifunctional catalysts converting 2,3-butanediol to butene are ZSM-5 zeolite (framework type MFI, with 3-D interlinked 10-MR channels) supports with supported copper nanoparticles (Cu/ZSM-5). Zeolites, microporous and crystalline aluminosilicate materials, possess acid sites that can catalyze reactions including the dehydration of al-cohols and hydrocarbon interconversion such as oligomerization and cracking. The con-version from BDO to butene proceeds through the dehydration of the BDO to carbonyl intermediates including methyl ethyl ketone (MEK) and isobutyl aldehyde (IBA). These carbonyl intermediates are then hydrogenated to butanol isomers which undergo another dehydration to produce butene isomers. The zeolite support provides the acid site that catalyze the dehydration steps, but can also produce non-volatile carbonaceous deposits (“coke”). These deposits can induce deactivation of heterogeneous catalysts through routes such as blocking access to pores or active sites. Supported metal catalysts can also deactivate through the growth in size of the metal particles, thus reducing the sur-face area of the active metal. The first chapter of this work examines these two deacti-vation mechanisms among others for the conversion of BDO to butenes and explore de-activation-resistant designs, to achieve longer catalyst lifetimes for butene production from the bio-alcohol. The production of jet-range biofuels from the bio-derived butene involves the ol-igomerization of butene isomers to jet-range olefins before a well-established hydro-genation procedure. The oligomerization of butenes can be performed with a variety of catalysts, but solid acids are preferable because the coke-induced deactivation is less detrimental to the poisoning of metal active sites found in catalysts with active sites such as nickel (II) involving coordination chemistry. Among solid acids, the confine-ment effect zeolite micropores and easier regeneration make zeolites the preferred cata-lyst. The second chapter of this work examines the effects of temperature, pressure, and butene feed rate on the oligomerization of butene catalyzed by HZSM-5 and Hβ zeolites. The chosen temperature, pressure, and feed rate ranges are selected to reflect conditions typical of what can produce mixtures that are close to jet-range. Chapter 1 introduces the fundamentals of zeolite catalysts and supported-metal catalysts, their preparation and applications, the development of Cu/ZSM-5 catalysts, the uses of various acidic zeolites in alkene oligomerization, provides an introduction in catalyst deactivation through coke formation and metal particle growth, and defines the jet fuel specifications this work aims to achieve. Chapter 2 describes the two experimental setups each used for the conversion of 2,3-butanediol to butene and the oligomerization of butene. Chapter 3 examines the deactivation behavior of Cu/ZSM-5 catalysts. The deac-tivation was able to be attributed to mainly a combination of sintering and the reduction in copper site accessibility via coking, with pore filling and support aging found to be negligible. Mesoporous supports from treatment with NaOH and CsOH solutions re-spectively were used to prepare new Cu/ZSM-5 catalysts with superior performance due to more accessible copper and lower coke formation, with the catalyst with CsOH-treated support being the most resistant to deactivation. Chapter 4 explores the effects of temperature, pressure, and butene feed rate on the oligomerization of butene catalyzed by HZSM-5 and Hβ zeolites. The weight-average molecular weight of the oligomer product were found to peak at certain temper-ature and pressure conditions especially at lower feed butene feed rates, due to increas-ing cracking at higher temperatures and lower diffusion rates at higher pressures. Long-term performance at jet-fuel-producing conditions for both catalysts were vary stable given the 3D channel structure of the catalysts and the relatively low coke formation for the long time on stream. Chapter 5 summarizes the conclusions of chapters 3 and 4 and outlines future research directions based on the findings of this work.M.S.Chemical and Biomolecular Engineerin

Leveraging sparsity in deep neural networks for training efficiency, interpretability and generalization

Sparse neural networks (Sparse NNs) are characterized by having fewer connections between consecutive layers compared to traditional fully connected, or dense NNs. Historically, sparsity has been studied post-training to enhance inference efficiency and as a regularization mechanism to improve generalization. However, additional benefits beyond these areas remain underexplored. In this thesis, we investigate sparse NNs, various sparsity patterns, and their broader benefits, including improved training efficiency, enhanced interpretability, and stronger generalization. First, we introduce PHEW (Path with Higher Edge-Weights), a novel method for identifying sparse sub-networks within dense NNs at initialization, without using any training data. PHEW is a probabilistic network formation method based on biased random walks, relying solely on the initial weights of the NN. Importantly, PHEW does not make any task-specific assumptions; instead, it exploits structural properties inherent in dense NNs that promote faster convergence and better generalization. By identifying effective sparse sub-networks at initialization, PHEW reduces the computational burden of training dense NNs and consistently outperforms other state-of-the-art methods. Second, we propose Neural Sculpting, a technique to uncover the underlying hierarchically modular task structure within NNs. Many real-world tasks exhibit hierarchical modularity, where complex target functions can be decomposed into simpler sub-functions arranged in a hierarchy. We pose the following question: given a sufficiently deep NN, how can we uncover the task’s hierarchical structure? Neural Sculpting uses an iterative process of pruning both units and edges during training, followed by network analysis to detect functional modules and infer hierarchical relationships between them. This method enhances the interpretability of NNs by guiding them to reflect the task’s inherent hierarchical and modular structure through pruning, and subsequently revealing that structure through network analysis. Finally, we leverage structural information about the task’s hierarchical modularity to enhance NN performance by aligning the architecture at initialization with the task’s structure. Specifically, we investigate how modular NNs can outperform dense NNs by systematically varying the degree of structural knowledge incorporated at initialization. We compare architectures ranging from monolithic dense NNs, which assume no prior knowledge, to hierarchically modular NNs with shared modules, which leverage sparsity, modularity, and module reusability. Incorporating modularity and module reuse significantly enhances learning efficiency and generalization, particularly in data-scarce scenarios, where hierarchically modular NNs excel by promoting functional specialization and reducing redundancy. These findings reveal that task-specific architectural biases can lead to more efficient, interpretable, and effective learning systems. In conclusion, this thesis demonstrates that sparse NNs offer not only enhanced training and inference efficiency but also superior interpretability and generalization capabilities. These findings have broad implications for NN design across various domains, particularly in data-scarce scenarios or applications where understanding the underlying task structure is essential. Future work may focus on refining these methodologies and extending their applicability to more complex, real-world tasks and larger-scale architectures.Ph.D.Machine Learnin

Aluminumnano-Layersuperconducting Thin Films

The fascinating properties of superconductors have led to widespread use in applications such as quantum computing, MRI machines, and particle accelerators. However, understanding how factors like material thickness influence these properties remains a key challenge in the field. We focus on investigating the relationship between the thickness of aluminum nanolayers in superconductors and their corresponding resistivity and critical temperature. Aluminum, being a well-characterized low-temperature superconductor, provides an ideal model system for exploring these effects. By systematically varying the thickness of the aluminum films and conducting comprehensive microstructural and electrical characterization, this study aims to elucidate the fundamental mechanisms that govern superconductivity in thin films. The research presented in this thesis not only contributes to the fundamental understanding of superconductivity but also has practical implications for the design and optimization of superconducting materials for technological applications. By advancing our knowledge of how thickness and microstructure affect the superconducting properties of aluminum-based films, this work lays the groundwork for future innovations in both low and high-temperature superconducting technologies.M.S.Materials Science and Engineerin

Examining dexterous motor control in children born with a below elbow deficiency

Presented on March 10, 2025 at 11:15 a.m. in the Petit Biotechnology Building, Room 1128.Wilsaan Joiner is a professor in the Department of Neurobiology, Physiology and Behavior at the University of California, Davis.64:40 minutesDr. Wilsaan Joiner's laboratory studies how we use different sources of information to aid behavior, ranging from visual perception to movement planning and updating. Specifically, we are interested in how external and internally-generated sensory information is integrated in healthy individuals, in comparison to certain disease and impaired populations (e.g., Schizophrenia and upper extremity amputees). Achieving this understanding may lead to better methods for diagnosing and treating impairments of the nervous system

The Future in the Past

Discussion portion of Lost in the Stacks, episode 626. Features the show hosts discussing the article "Yahoo! and the Abdication of Judgment", by Laura Cohen, published in 2001 in the magazine American Libraries. Hosts discuss how librarians feared or embraced new online tools and platforms at the dawn of the internet, and how this article reflected (or diverged) from common thinking about libraries at the time.Discussion portion of Lost in the Stacks, episode 626. Features the show hosts discussing the article "Yahoo! and the Abdication of Judgment", by Laura Cohen, published in 2001 in the magazine American Libraries. Hosts discuss how librarians feared or embraced new online tools and platforms at the dawn of the internet, and how this article reflected (or diverged) from common thinking about libraries at the time

Concussions and CTE

This short documentary was created as a course requirement in HTS 3086 – Sociology of Medicine and Health under the supervision of Dr. Jennifer Singh.Runtime: 08:45 minutesThis documentary explores the illness experience of concussion experienced during college and professional football