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UC Riverside Electronic Theses and Dissertations

Cover page of Ecological Factors Shaping Nosema and Crithidia Prevalence in Coastal Sage Scrub Bee and Ant Communities

Ecological Factors Shaping Nosema and Crithidia Prevalence in Coastal Sage Scrub Bee and Ant Communities


Pathogens are one of the major factors attributed to bee species declines. Flowers can serve as hubs for bee pathogens and shared floral resources mediate the acquisition of pathogens from infected to healthy hosts. While previous research places emphasis on pathogen spillover from managed bees to wild bee species, there is a paucity of knowledge as to the role of non-bee floral visitors, such as ants, in shaping pathogen prevalence for bees. Here, we document the prevalence of two common bee pathogens, Nosema spp. and Crithidia spp., among honey bees, native bees, and ants at an ecological reserve in Southern California encompassing threatened coastal sage scrub habitat to understand how ecological factors, such as space and time, shape host-parasite interactions for these insect taxa. Nosema and Crithidia were detected in honey bees, native bees, and ants. Surprisingly, both pathogens were highly prevalent in ants. The interaction of sampling period x insect taxa had the most pronounced effects in influencing prevalence for both pathogens. Plant-host associations did not appear to be important in shaping pathogen prevalence for honey bees and native bees. This is the first study, to our knowledge, to detect Nosema and Crithidia in ants and several native bee species. We discuss two potential mechanisms, shared floral resources and consumption of infected prey, that may explain pathogen transmission between bees and ants. Ants may serve as a previously undescribed reservoir for Nosema and Crithidia and future research should define the potential for pathogen spillback from ants back into bee populations.

Fully Distributed Active Joint Localization and Target Tracking Algorithms Design for Multi-Robot System


In this thesis, we study the problem of multi-robot active joint localization and target tracking (AJLATT), where a team of robots mounted with sensors of limited field of view actively estimate their own and the target's states cooperatively. Each robot designs its motion strategy to gain better estimation performance while avoiding collisions by using only the information from itself and its one-hop communicating neighbors.

By leveraging the framework of joint localization and target tracking (JLATT) presented in our previous work, we propose two fully distributed algorithms that help each robot design motion strategies to achieve better localization and target tracking performance. These two algorithms are designed from, respectively, the control and optimization perspectives.

The control-based algorithm is designed by incorporating the estimated target's and robots' states and their uncertainties as well as collision avoidance in the control policy.

The optimization-based algorithm minimizes an objective function involving both the target's and robots' estimation uncertainties and a potential function that helps each robot avoid collision and maintain communication connectivity when the robot is planning its motion.

Monte Carlo simulations demonstrate our algorithms' feasibility to solve the AJLATT problem, and performance comparison between these two algorithms is given.

Cover page of A Near-Infrared Look at Bulgeless and Dwarf Galaxies: An Investigation of Obscured AGN and Their Outflows

A Near-Infrared Look at Bulgeless and Dwarf Galaxies: An Investigation of Obscured AGN and Their Outflows


It is now widely accepted that supermassive black holes (SMBHs) lie at the center of most massive galaxies. While SMBHs in these hosts have been well documented and studied, those in lower mass hosts, such as dwarf and spiral galaxies without a central bulge component (i.e. bulgeless galaxies), have remained quite elusive and insufficiently studied. Identifying and examining exactly how active galactic nuclei (AGN) co-evolve and influence bulgeless and dwarf host galaxies is the foundation of my dissertation research. These galaxies can be used as local analogs of the infant Universe (gas rich, low metallicity, rapidly growing) and their BH number density and occupation fraction can provide important constraints on the BH seed population. However, these AGN are difficult to study since they are energetically weak or heavily obscured. The IR provides a unique and compelling avenue to study these AGN since it is not as severely affected by dust obscuration as optical observations. As such, my research utilizes IR selection techniques and observations to identify a new population of obscured AGN that have been overlooked by large optical surveys. Much of my work has focused on coronal lines (CLs), outflows, and analyzing the impacts of AGN on their hosts. In my publications, we confirm AGN activity in a sample of dwarf and bulgeless galaxies through the presence of CL and hidden broad-line emission. In many of these, we find fast, kpc outflows that are likely driven by the AGN. We also find that the outflowing gas have sufficient escape velocities to allow the gas to enrich the circumgalactic medium. As a result, these outflows have the potential to suppress star formation and could play a key role in dwarf and bulgeless galaxy evolution. Additionally, these observations can help deliver key constraints to improve the theoretical feedback models of dwarf and bulgeless galaxies.

Design and Optimization of a Composite Heat Spreader to Improve the Thermal Management of Three-Dimensional Integrated Circuits


The present study documents the optimal distribution of a limited amount of high thermal conductivity material to enhance the heat removal of 3D integrated circuits, ICs, numerically. The structure of the heat spreader is designed as a composite of high thermal conductivity (Boron Arsenide) and moderate thermal conductivity (copper) materials. The volume ratio of high conductivity inserts to the total volume of the spreader is fixed. For the configuration of the inserts, two categories are considered, namely ring type and blade type. For the former, various patterns of the single and double ring inserts are studied; while for the latter, three main configurations including radial, one level of pairing, and two levels of pairing are examined. To examine the impact of adding high conductivity inserts on the cooling performance of the heat spreader, a detailed analysis is performed to find the optimal geometry for each category. An approach is implemented to find the structures corresponding to the lowest maximum temperature of the 3D IC while the ratio of the Boron Arsenide volume to the whole heat spreader volume is fixed. Four different boundary conditions are examined to seek their impact on the optimal configuration of the inserts. For the double ring insert layout, the optimal distribution of the high-conductivity material between the inner and outer rings is found. The results show that for the optimal conditions, the maximum temperature of the 3D IC is reduced up to 10%. For the blade inserts, the results show that for the constant temperature, variable temperature, and convection heat transfer boundary conditions at optimal conditions, the maximum temperature of the whole structure can be reduced to 13.7, 11.9, and 13.9%, respectively; while the size of the heat sink and heat spreader is mitigated by 200%.

Pathways to Populism: Economics, Culture, and Ideological Convergence


This dissertation proposes a variation in motivations for voting for left and right populist parties, respectively. It argues voting for both types of populist parties is motivate by disaffection with government policies and perceived ideological convergence - the perception that mainstream parties are essentially ideologically interchangeable on issues relevant to them. Where the pathways to populist voting diverge, however, is argued to be based on the issue type for which the voter has become disaffected. It is argued left populist voters are disaffected with the economy, while right populist voters are disaffected by cultural policies (e.g. immigration). The respective populist party types are argued to own these issue spaces, based on the frequency and fervency with which they address them, giving them authority on the matter. The dissertation explores these claims through the use of a mixed-methods design. The first part of the dissertation explores the topic through statistical analysis. The association between ideological convergence, government failure on cultural issues and right populist voting finds positive support. The association between ideological convergence, government failure on economic issues and left populist voting does not find support. This result was likely due to a lack of data and cases – something which can be remedied with more of both in the future. Case studies of the Front National in France (right populism) and Podemos in Spain (left populism) are then conducted. The French case study tests the mechanisms suggested by the theory of the dissertation to ensure that the positive association of the statistical analysis was due to the hypothesized factors. The Spanish case study test the mechanisms suggested by the theory of the dissertation to offer evidence that the relationship is functioning as hypothesized, despite the null findings of the left populism statistical model. The dissertation concludes by discussing its findings and contributions.

A History of California Anti-Miscegenation Law: Legalizing White Supremacy


California has often been viewed as a rather lenient and progressive state in terms of being accepting of people and beliefs. However, the exclusionary and restrictive legal history of California has also been around since the addition of California into the Union. These laws were used to exclude and restrict non-white racialized people in a multitude of ways. Some of these included laws about citizenship, property, immigration, and marriage. All of these restrictive laws support and influence each other by being focused on one main goal, upholding white supremacy through the legal system. These laws all worked together and were important parts of supporting white supremacy but the focus of this thesis will be on anti-miscegenation laws in California from the beginning of California to the mid-twentieth century. Anti-miscegenation laws in California were both similar and different from the typical law found in the United States. The laws were more restrictive than many anti-miscegenation laws found throughout the United States as they excluded more groups; however, California was also less restrictive in terms of the punishments and voiding of marriages. Since California was both more restrictive and less restrictive than many states it makes for an interesting case study. Yet, there has not been much focus on California except for the groundbreaking court case of Perez v. Sharp (1948). So, this thesis focuses on not only the court case but also how California gained their anti-miscegenation law in the first place and how there was a struggle over its support even after the laws were declared unconstitutional within the state.

Characterization and Design of Ignition and Energy Release Pathways in Energetic Nanocomposites


Solid propellants are a class of energetic (combustible) materials that undergo a rapid exothermic chemistry when an activation barrier is overcome. Applications for such energetic materials can be found in space exploration technologies, automobile airbags, material synthesis, ordinance and rare-earth mining. To broaden their applications and capability, various studies have been conducted to safely and controllably release their stored potential energy. These studies have mainly focused on manufacturing techniques to tailor the properties of energetic materials to release their stored potential energy in a predictable fashion. Other studies have focused on the use of electromagnetic stimulation in ultraviolet (UV), visible (vis) and near-infrared (NIR) regions to control ignition and combustion of propellants in-operando; however, the UV-NIR electromagnetic stimulation was observed to be limited to surface level absorption due to the inherent high photon attenuations of the solid propellants. In contrast to UV-NIR electromagnetic radiation, microwave radiation has shown to display rapid, selective and volumetric heating to control and modulate energy release pathways in solid propellants.

In this work, we first explore mechanisms that control the ignition of energetic materials at microwave frequencies. High-speed color camera imaging, infrared pyrometry, temperature jump (T-jump) ignition and differential scanning colorimetry methods are used to understand the mechanisms driving ignition in 3D printed nanoscale titanium (nTi)/ polyvinylidene fluoride (PVDF) films. This work is further expanded to engineer material systems with controllable ignition under stimulation at 2.45 GHz, where manganese oxide (MnOx) was studied as an oxidizing microwave agent. Beyond research on microwave ignition, the effect of microwave heating during in-operando combustion of reactive materials is investigated. First, heat transfer mechanisms influencing the combustion of energetic materials were experimentally studied via microscope imaging and pyrometry tools. Subsequently, infrared thermometry and color ratio pyrometry were employed to study microwave heating rate, flame front and propagation velocity of gasless Al-Zr-C composites. The research explores mechanisms that drive and impede response of solid propellants to microwave energy prior to ignition as well as throughout rapid energy release.

Cover page of Using Hsp40 Affinity to Profile Destabilized Proteomes

Using Hsp40 Affinity to Profile Destabilized Proteomes


Proteins in their native or optimal conformational states can perform essential cellularfunctions to assist in the stability of the proteome and the overall health of the cell. However, if a protein misfolds, it instead can gain a new toxic function. Misfolded proteins can aggregate into more stable toxic precursors that can cause cellular degradation and ultimately induce health disorders such as Parkinson’s Disease, Alzheimer’s Disease, and Huntington’s Disease. Cellular stresses are significant factors in protein misfolding. These stimuli can change protein structure through several mechanisms such as covalent modification of cysteines or oxidation of methionine residues. The immediate consequence of posttranslational modifications of a protein from stress is difficulty in refolding. The long term consequence is that these altered proteins can instead gain new toxic functions as they proceed to aggregate. Therefore, changes to protein structures induced by stresses can create cellular havoc as the cell scrambles to recover from the affected misfolded proteins. Several analytical assays can measure the effects of stress on protein stability. viii Flourescence assays show direct changes to a protein based on gain or loss of signal. Mass Spectrometry surveys entire proteomes by measuring a protein’s ability to react after exposure to a cellular stress. Herein, we describe a quantitative proteomics approach that measures proteins binding to Hsp40 chaperone DNAJB8, a protein quality control factor designed to recognize misfolded proteins. We aimed to use the chaperone to identify destabilized proteins and deduce toxic cellular mechanisms arising from environmental stresses. We found several ribosomal proteins such as TAR DNA-Binding protein (TDP-43) and Pyruvate Dehydrogenase E1 subunit (PDHA1) misfolded after arsenite exposure. Later, we found potential biomarkers in GAPDH and PARK7 involved in driving the cellular toxicity of propachlor. Led by limited proteolysis, we conducted several validation experiments to show that DNAJ8 recognized significantly destabilized proteins after exposure to environmental toxins. In total, we hope to show the effectiveness of this approach in exploring how environmental toxins can impact cellular proteostasis and identifying the resulting susceptible proteome.

Cover page of Data-Driven Integration of Renewable Energy in Smart Grid

Data-Driven Integration of Renewable Energy in Smart Grid


Renewable energy is an environment-friendly and economically attractive source of electricity generation. However, substantial grid integration of renewable energy is challenging as the power generation from renewables is weather-dependent, highly intermittent, and uncontrollable. To address these challenges, we exploited machine learning and data analytic techniques to develop frameworks and algorithms for integrating renewables into the grid.

Distribution grid planning, control, and optimization require accurate estimation of solar photovoltaic generation and electric load in the system. Most small residential solar PV systems are installed behind-the-meter making only the net load readings available to the utilities. We developed an unsupervised framework for estimating solar PV generation of individual customers by disaggregating the net load readings. Next, we developed an unsupervised framework for joint disaggregation of the net load readings of a group of customers. Our algorithms synergistically combined a physical PV system performance model for individual solar PV generation estimation with a statistical model for load estimation.

High solar PV penetration in the distribution grids gives rise to frequent voltage fluctuations due to the intermittent nature of solar PV production. The slow operating conventional voltage regulating devices, therefore, need to be supplemented with fast operating real and reactive power control of smart inverters. Complete and accurate information about distribution network topology and line parameters needed for traditional model-based Volt-Var optimization methods is often unavailable. To tackle these challenges, we developed a two timescale Volt-Var control framework with model-based slow timescale control and a reinforcement learning-based fast timescale smart inverter control. The proposed framework does not rely on any distribution network secondary feeder information but requires primary feeder information. Next, we proposed a completely model-free reinforcement learning-based two timescale Volt-Var control framework that does not rely on any distribution network primary or secondary feeder topology or parameter information.

Natural and anthropogenic aerosols have a great influence on meteorological variables which in turn impact the reservoir inflow and ultimately hydropower generation. We developed a comprehensive framework to quantify the impact of aerosols on reservoir inflow by integrating the physical Weather Research and Forecasting Model with chemistry (WRF-Chem) and a statistical dynamic regression model. We quantified the impact of aerosols on hydropower generation and revenue by incorporating the hydropower operation optimization toolbox into the framework.

Lastly, we developed a data-driven framework for the predictive maintenance of distribution transformers to increase the reliability of the distribution system. We utilized readily available data such as the transformers' specification, loading, location, and weather.

Towards Robust Deep Neural Network Architectures for Malware Classification


Modern commercial antivirus systems increasingly rely on machine learning to keep up with the rampant inflation of new malware. However, it is well-known that machine learning models are vulnerable to adversarial examples. Previous works have shown that ML malware classifiers are fragile to the white-box adversarial attacks. However, ML models used in commercial antivirus products are usually not available to attackers and only return hard classification labels. Therefore, it is more practical to evaluate the robustness of ML models and real-world AVs in a pure black-box manner. Since existing state-of-the-art malware classifications are quite vulnerable to our attacks, the next question is how to create a new architecture to make the malware classifiers more robust against different kinds of adversarial attacks, including Benign content appending, content relocation, and code randomization. Finally, memory-only malware has become more and more popular in recent years. Since they are not written on disks, it becomes important to recognize their presence in memory. Moreover, these malware samples may hide their process information in the system, we need a way to identify them fast and robustly.This dissertation addresses these problems by presenting insights, methods, and techniques on how to perform attacks and defenses on malware classification. Firstly, a black-box Reinforcement Learning based framework called MAB-Malware is developed to generate adversarial examples for PE malware classifiers and AV engines. It has a much higher evasion rate than other off-the-shelf frameworks. Secondly, a selective hierarchical BERT-based new classification architecture is proposed to automatically select malicious functions for malware classification that is robust against different attacks. Thirdly, a graph-based deep learning approach is presented to automatically generate abstract representations for kernel objects, with which we could recognize the objects from raw memory dumps in a fast and robust way.