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Open Access Publications from the University of California

UC Santa Barbara Electronic Theses and Dissertations

Stochastic nonzero-sum duopoly games with economic applications

(2019)

We study a class of stochastic duopoly games inspired by the two time-scale feature of many markets. The firms convert their short-term “local” advantage driven by exogenous infinitesimal shocks into a more durable gain through long-term market dominance. As an extension of existing literature, we consider two asymmetric players each of whom adopts timing strategies to increase her profitability and possibly bring negative externality to the rival. In turn, this leads us to more general settings of nonzero-sum games. Characterizing Nash equilibrium as a fixed-point of each player’s best-response to her rival, we construct threshold-type Feedback Nash Equilibrium via best response iteration. Our main contribution is explicitly constructing equilibria for types of duopoly games that represent a wide range of industries. Motivated by the competition among sectors of power generators, we consider a duopoly of producers with finite options to increase their production capacity. We study nonzero-sum games in which two players compete for market dominance via switching controls. We also study mixed switching and impulses games inspired by the vertical competition among the producers and consumers of a commodity. Our analysis quantifies the dynamic competition effects and brings economic insights.

Now That I’ve Seen Their Faces: Contact, Social Justice, and Tourism in Israel/Palestine

(2019)

Tourism is an expanding battleground of the Israeli-Palestinian conflict. Since the founding of the state, Israel’s supporters have used tourism as a mechanism to socialize diaspora Jews and other travelers into supporting Israeli institutions, namely the military. As a counterforce to this mobilization, Palestinians, alongside Jewish/Israeli activists, have also been employing tourism as a method to garner support for justice and human rights in the region. This dissertation examines the power and limits of tourism to engender transnational solidarity.

In a wider sense, this dissertation sheds light on the power of exposure, empathy, and intercultural contact to shift political sympathies and allegiances. I investigate these topics through a case study of Jewish Americans’ experiences on alternative tours to the Occupied Palestinian Territories. Based on an analysis of 87 in-depth interviews, 400 survey responses, as well as three years of participant observation, I use tourism as a lens to examine the barriers to Jewish solidarity with the Palestinian cause. Given the ways that Jewish Americans are typically shielded from Palestinian perspectives and encouraged to support the state of Israel, I use these tours as a microcosmic case to understand what happens when privileged populations are exposed to injustices suffered by marginalized peoples in the Global South.

These tours challenge participants’ stereotypes of Palestinians as dangerous and primitive along with participants’ absolutist ideas of Jewish moral purity and victimhood. In addition to these outcomes, the visceral experience of witnessing Palestinian suffering at the hands of Israeli violence causes many participants to develop greater animosity towards settlers and right-wing segments of Israeli society. I argue that this focus on settlers, as aberrations from Israeli institutions rather than extensions of them, can function to organize participants’ outrage around a population that remains conceptually separate from wider state institutions.

Simultaneously, while participants move past stereotypes of Palestinians as inherently violent, a differential conception of Israeli versus Palestinian violence remains in place. Despite empathetic, emotional reactions to sites such as the checkpoint, participants continue to understand Israeli participation in military violence as involuntary and often necessary. At the same time, participants continue to unequivocally oppose all forms of Palestinian violence. This inconsistency appears rooted in participants’ associations of Israeli violence with a form of social control, and associations of Palestinian violence with disruption and deviance. The persistence of these fundamental currents of privilege and racism within tourists’ ideologies reveals the ways that allegiances to unjust status quos can remain in place, despite increased levels of empathy and intercultural understanding.

My findings demonstrate the ability of tourism and intercultural contact to expand compassion and to mobilize transnational activists. On the other hand, they also reveal the ways that tourism, as a medium for social change, may preserve some of the most fundamental elements of inequality, due to economic forces within the tourism industry. Taken together, these conclusions illuminate how racialized conceptions of the right to violence often go unnoticed and unchallenged in progressive movements for social change. Lastly, through revealing the limits of appealing to those with power through empathy, this dissertation urges movements for social change to prioritize the redistribution of power, rather than focus exclusively on the ideological transformation of those in power.

Investigating Electrokinetic and Electrochemical Phenomena in Confined Geometries through Multiphysical Modeling

(2019)

In recent decades, microfluidics and nanofluidics have risen to the forefront of innovation and technological development for a plethora of analytical applications ranging from advanced point-of-care diagnostics and integrated drug delivery systems to multipurpose substance detection. These miniaturized platforms, made possible by emergent microfabrication technologies, often exploit unique features such as increased surface-liquid interactions and small sample volume requirements to efficiently carry out on-chip chemical and/or bioanalytical processes. Moreover, the inherent flexibility of these systems enables a number of processes such as mixing, focusing and separation, visualization and detection, and pumping to be integrated onto a single lab-on-chip platform. However, the physical phenomena that govern these processes tend to be complex and exhibit strong multiphysics coupling, particularly for nanoscale geometries in which finite electric double layers and associated charge-screening effects prevail. Here, numerical simulation offers an avenue for probing the highly coupled nature of electrokinetic and electrochemical effects in confinement, allowing us to elucidate the intricacies of such systems through modeling. By providing an improved fundamental understanding of relevant physical processes, these numerical models enable researchers to optimize existing technologies and develop novel platforms for lab-on-chip applications.

In this work, we discuss the modeling of four separate microfluidic and nanofluidic systems suitable for a wide range of analytical processes. First, we discuss flow visualization in a micromixer device driven by electrothermal flow, with an emphasis on how particle image velocimetry measurements can be used to tune simulation results and better represent 3D flow structures in the physical system. Next, we present a nanofluidic analyte focusing and separation technique which leverages field-effect control via wall-embedded electrodes to locally modulate electric double layer properties and induce ion concentration polarization within the channel. Third, we discuss the dynamics of a nanochannel-confined bipolar electrode system and demonstrate how bipolar electrochemistry provides a flexible platform for mixing, preconcentration, and/or analyte detection. Finally, we introduce a variation of the bipolar electrode system which exploits the nonlinear hydrodynamics associated with induced-charge electroosmotic flow to electrokinetically actuate a peristaltic micropumping mechanism through fluid-structure interactions.

Characterizing Uncertainties in Life Cycle Assessment

(2019)

Life cycle assessment (LCA) aims to support corporate and public policy decisions by quantifying the environmental performance of a product. Understanding uncertainties in LCA results is therefore important for making informed decisions. Monte Carlo simulation (MCS), which uses random samples of the parameters from pre-determined probability distribution, has been widely utilized to characterize uncertainties in LCA. However, as the size of an LCA database grows, running a full MCS is becoming increasingly challenging. Furthermore, uncertainty literature in LCA has focused on life cycle inventory (LCI), while the uncertainties from the remaining steps—including characterization, normalization, and weighting—have not been addressed, despite their perceived relevance in overall uncertainty characterization in LCA.

The objectives of my dissertation are: (1) to develop a new method to improve the computational efficiency of large-scale MCS in LCA, (2) to empirically test the reproducibility of comparative decisions obtained using the method, and (3) to develop and test an analytical method to decompose the overall uncertainty in LCA into its constituents. The new method for uncertainty characterization in LCA involves pre-calculating and storing the distribution profiles of the most widely used LCA database, ecoinvent. Using parallel computing, I have generated the distribution functions for 22 million life cycle inventory (LCI) items of the database. I then tested 20,000 randomly selected comparative LCI cases, and showed that pre-calculated uncertainty values can be used as a proxy for understanding the uncertainty and variability in a comparative LCA study without compromising the ability to reproduce the comparative results.

Another key barrier to conducting uncertainty analysis in LCA occurs in life cycle impact assessment (LCIA), an important step of LCA calculation flowered LCI phase, because characterization models for LCIA do not typically provide uncertainty information for the input parameters, and lack detailed information about the relationships between those inputs. A Pedigree matrix for characterization factor in LCIA was developed to fill in the gap in the uncertainty characterization in LCA. Expert opinions of the use of Pedigree method in estimating uncertainty in LCIA and the Pedigree scores for both LCI and LCIA were collected through an online survey.

Finally, I demonstrated a new method to decompose the overall uncertainties of an LCA result over the contributing factors including those from LCI, characterization, normalization, and weighting, which are the steps involved in LCA calculation. To do so, I adopted the logarithmic mean Divisia index (LMDI) decomposition method into MCS parse out the overall uncertainty into its constituents.

I believe that my research helps improve the efficiency and analytical power of uncertainty analysis in LCA. The findings can be applied to other problems outside of LCA that utilize MCS.

High-Order Sideband Generation for Creating Optical Frequency Combs and Probing Bloch Wavefunctions

(2019)

High-order sideband generation (HSG) is a recently discovered phenomenon in semiconductors simultaneously driven by a weak near infrared (NIR) laser and a strong THz electric field. The NIR field excites electrons out of the valence band into the conduction band, leaving behind holes. The strong THz field can then accelerates the electrons and holes apart before it switches directions to drive the electrons and holes back towards each other. If an electron and a hole recollide, they emit a photon. Since both particles gained energy from the THz field, the emitted photons are typically higher in energy than the original NIR photon. The narrow linewidth of both lasers result in these emitted photons being equally spaced from the NIR photon energy and each other by twice the THz frequency to generate frequency combs. More than 130 orders have been observed.

The intensities of the sidebands is typically difficult to simulate theoretically. However, a simple scaling law for the THz frequency and field strength can be used to predict the widths of the frequency combs. This provides greater tunability and control of HSG frequency combs than previously, opening technological applications of these combs. This scaling relation is complicated, however, by the motion of holes as they travel within a complicated Brillouin zone. Berry’s Curvature mixes the Bloch wavefunctions in momentum space, causing a hole to evolve into new states as it is accelerated by the THz field. When an electron recollides with a hole, these different wavefunctions are imprinted onto the intensity and polarization of the emitted sidebands. With careful polarimetry of the sidebands, information can be extracted about the material structure. For example, strain can be introduced to modify the band structure of the material, which significantly alters the measured polarizations of the sidebands. These techniques could lead to all-optical measurements of the band structure, and Berry Curvature of material systems.

In order to apply these techniques to understand novel material systems, HSG must first be observed in these materials. Any new material must be a semiconductor with a band gap near the NIR photon energy where electrons and holes can be created. However, finding systems with suitably small scattering rates and large enough coherence times remains a challenge.

Measuring Biochemical Possibility Spaces in Evolutionary Engineering

(2019)

At the molecular level, artificial selection—controlling the forces of evolution to improve or design new biochemical functions— makes up one of our strongest tools for finding better biocatalysts, pharmaceuticals, and biosensors, as well as for studying the history and process of evolution itself. But fully harnessing evolution requires knowledge of the shape and dynamics of complete evolutionary spaces. Prior to this work, very little research existed comparing the real dynamics of artificial selection to any of the theoretical work that has been written to support it. By updating the classical theory of simple selections towards an engineering focus, and combining this with direct observations of direct evolving populations, my work has shown the first mathematical descriptions of how whole populations evolve during the selection of novel biocatalysts.

This work seeks to address the analysis of evolutionary fitness and chemical activity spaces at several levels. First, we offer a broad-ranging theoretical approach to mapping the distribution of fitness effects in any system under driven selection. Through both simulations and recent experimental data, we show that it is possible to estimate the initial distribution of fitness for nearly any selected population. In addition to potential applications in automated gene engineering, this theoretical solution also makes it possible to approximate the overall distribution of any selectable chemical function across random molecular space, a necessary condition for theoretical optimization of nearly any in vitro selection.

Zooming in, we next develop tools to view an entire population of active catalysts and how it dynamically changes over the course of an entire selection. Working with a model selection for de novo RNA triphosphorylation catalysts, we develop a new high-throughput method to measure many active catalysts in parallel, building the first portrait of how tens of thousands of different functional molecules enrich or disappear over the course of an entire artificial selection. New heuristics for assessing the effectiveness of various activity- estimation methods allowed us to efficiently identify highly active ribozymes, as well as estimating catalytic activity without performing any additional experiments. We also present the first picture of non-ideality during a real selection, demonstrating that stochastic effects can be a powerful and quantifiable confounding factor on predicted selection dynamics. Finally, this analysis allows us to build the highest-resolution extant picture of a biocatalyst activity distribution, showing a catalytic activity that is log-normal, consistent with a mechanism for the emergence of activity as the product of many independent contributions.

Finally, we design our own model selection to investigate the evolution of a theoretical aminoacylase RNA whose existence may have been crucial to the origin of the genetic code. Using this system, we have developed techniques for Sequencing to determine Catalytic Activity Paired with Evolution (SCAPE), a comprehensive workflow that allows complete mapping of large, dynamic landscape of chemical activity. By measuring catalytic activity of millions of evolved biomolecules simultaneously, we pair kinetic variations with genetic sequence at single nucleotide resolution, building the first complete map of all evolutionary pathways to an engineered function from anywhere in genetic space. The resulting map contains approximately six orders of magnitude more data than any previously- measured landscape of catalytic data, and suggests features of genetic epistasis and evolutionary ruggedness may be remarkably consistent across many unrelated biocatalysts with similar function. Our methods and results suggest general applicability to more complicated systems, as a viable alternative to the heuristic methods typically used to evaluate molecular selections, as well as validating a suite of capable tools for quantifying and optimizing the emergence of a wide range of evolvable biocatalytic functions.

Machine Learning for Addressing Data Deficiencies in Life Cycle Assessment

(2019)

Life Cycle Assessment (LCA) is a tool that can be used to assess the impacts of chemicals over the entire life cycle. As the large number of new chemicals being invented every day, the costs and time needed to collect necessary data for LCA studies pose a challenge to LCA practitioners, as the speed of LCA studies cannot keep up with the speed of new chemical development. In practice, therefore, LCAs are conducted in the presence of data gaps and proxy values, limiting the relevance and quality of the results. As the techniques of machine learning evolves, a new opportunity to improve on data deficiencies and on the quality of LCA emerged. This dissertation is an attempt to harness the power of machine learning techniques to address the data deficiencies in LCA. It consists of four chapters: (1) Introduction. (2) Rapid life-cycle impact screening for decision-support using artificial neural networks. (3) Species Sensitivity Distributions Derived for a Large Number of Chemicals Using Artificial Neural Networks. IV. (4) Reducing the Uncertainty of the Characterization Factors in USEtox by Machine Learning – A Case Study for Aquatic Ecotoxicity. Each chapter is elaborated briefly below.

The first chapter is the general introduction. The second chapter aims to demonstrate the method of estimating the characterized results using Artificial Neural Networks (ANNs). Due to the lack of necessary data, very limited amount of characterized results for organic chemicals exist. In this chapter, I developed ANNs to estimate the characterized results of chemicals. Using molecular structure information as an input, I trained multilayer ANNs for the characterized results of chemicals on six impact categories: (1) global warming. (2) acidification. (3) cumulative energy demand. (4) human health. (5) ecosystem quality. (6) eco-indicator 99. The application domain (AD) of the model was estimated for each impact category within which the model exhibits higher reliability. As a result, the ANN models for acidification, human health, and eco-indicator 99 showed relatively higher performances with R2 values of 0.73, 0.71, and 0.87, respectively. This chapter indicates that ANN models can serve as an initial screening tool for estimating life-cycle impacts of chemicals for certain impact categories in the absence of more reliable information.

The second chapter aims to estimate the ecotoxicological impact of chemicals using machine learning models. In chemical impact assessment, the overall ecotoxicological impact of a chemical to ecosystem, also known as the Effect Factor (EFs), is derived from the toxicity to multiple species through Species Sensitivity Distribution (SSDs). In the third chapter, I turned to estimate the chemical toxicities to several aquatic species with machine learning models, and then use them to build SSD, and to estimate the EF of organic chemicals. Over 2,000 experimental toxicity data were collected for 8 aquatic species from 20 sources, and an ANN model for each of the species was trained to estimate the Lethal Concentration (LC50) based on molecular structure. The 8 ANN models showed R2 scores of 0.54 to 0.75 (average 0.67, medium 0.69) on testing data. The toxicity values predicted by the ANN models were then used to fit SSDs using bootstrapping method. At the end, the models were applied to generate SSDs for 8,424 chemicals in the ToX21 database.

The last chapter of this dissertation aims to reduce the uncertainty of an existing chemical fate model using machine learning techniques. Fate Factor (FF), which accounts the persistence of chemicals in environmental compartments, is an intermediate input in to calculate the characterized results of life cycle impact assessment. The most widely used tool to calculate chemical FFs: USEtox, requires several chemical properties as inputs, including: octanol-water partitioning coefficient (Kow) and vapor pressure at 25 ℃ (Pvap25). When those chemical properties are missing, USEtox provides proxy methods to estimate them. In the fourth chapter, I seek to answer the question that whether replacing the current proxy methods with machine learning models are always improving the accuracy of FFs. The contribution of each chemical property to the FFs was evaluated. And ANN-based predictive models were developed to predict these chemical properties. The uncertainty of the current proxy methods in the USEtox’s FF model and the newly developed ANN models were compared. New FFs for the chemicals in the ToX21 database were calculated using the best predictive model when experimental properties were unknown. The EFs generated by the models in the second chapter were estimated. Lastly, more than 300 new CFs with good prediction confidence for the organic chemicals in the ToX21 database were calculated. These CFs are new to the field of LCA and can be used to reduce the uncertainty of LCA studies when the measured data isn’t available.

Ecological implications of copper-based nanoparticles in aquatic complex matrices: Fate, behavior, and toxicity assessment

(2019)

Engineered nanomaterials (ENMs) are likely to undergo some degree of modification when released into the environment, which can influence the fate, behavior, and toxicity of nanoparticles (NPs). The environmental factors in natural aquatic ecosystems, such as water chemistry, hydrology, disturbance, and biotic interactions, can transform or “age” toxic chemicals through physical, chemical, and biological processes, including aggregation and disaggregation, adsorption, redox reaction, dissolution, complexation and biotransformation. The extent of aging can vary considerably over time and within a single or a number of water bodies, such as a river that flows into the ocean through an estuary. However, the ecological effects of NPs under realistic environmental exposure scenarios are not yet fully understood. Adding to these challenges are problems arising from traditional ecotoxicological risk assessments, which are inevitably hampered by narrow subsets of relevant species, toxicants, exposure conditions, and levels of impact. The present work examined the toxicity of copper-based nanoparticles (CBNPs), which frequently enter natural aquatic ecosystems due to their increasing application in consumer products, by assessing their impact on marine phytoplankton and estuarine amphipods, organisms that are central to aquatic ecosystems. Standard toxicological methods were used, along with physiological measurements, studies of fate and transport, and mechanistic biological models based on Dynamic Energy Budget (DEB) theory. The aim of the work was to understand 1) the influence of aging processes on CBNPs under environmentally relevant test conditions, 2) the impact of aged CBNPs on a marine phytoplankton population, 3) the potential impact of CBNPs on non-target estuarine organisms, and 4) the potential for detecting and predicting the toxic effects of CBNPs on an individual, to generate model estimates of effects on populations and communities. CBNPs were found to be toxic to benthic estuarine organisms at concentrations of CBNPs already found in the natural environment. However, sublethal toxicity may not be detected by traditional ecotoxicological tests. Additionally, aging was found to influence the fate and transport of CBNPs through oxidation, aggregation, and dissolution processes, increasing Cu toxic ion bioavailability to pelagic organisms over time. While current studies increasingly consider more realistic environmental exposure scenarios, this work, as well as that of other researchers, suggests that CBNPs behave differently under prolonged environmental exposure, and nanoecotoxicological research should focus on sublethal impacts, integrated with mechanistic biological models and based on Dynamic Energy Budget (DEB) theory.

Role of Monomer Sequence in Polymer Coatings and Self-Assembly

(2019)

Polymeric materials that incorporate multiple functionalities are crucial in a variety of applications, from adhesives and membranes to thermoplastic elastomers and electrolytes. Control over the length scale of each component is key to designing the structure and resulting properties, driving efforts for greater control in copolymer systems. Controlling comonomer sequence is an attractive tool to reach this goal, as the length scales of assembly can be set by tuning the size and connectivity of different chemistries. However, materials systems that bridge the sequence-specificity of biopolymers and robustness of synthetic polymers are needed to experimentally understand the role of comonomer sequence in multicomponent polymer materials. This work utilizes versatile and scalable polypeptoid chemistry to install sequence-defined chains into traditional polymer systems, focusing on two potential applications. First, the roles of polymer sequence and functionality are investigated in a modular surface-active coating, achieving optimal marine antifouling and fouling release properties with finer length scales of amphiphilicity. Second, the role of comonomer sequence is investigated in self-assembling diblock copolymers, forming lamellae with tunable thermal and morphological properties based on sequence. The findings in this work emphasize the utility of comonomer sequence as a design tool to target both surface and bulk properties of multicomponent polymer materials.

Heterogeneous Silicon/III-V Photonic Integration for Ultralow Noise Semiconductor Lasers

(2019)

Low noise lasers, with spectral linewidth of the kHz level and below, are in demand by an increasing number of applications, such as coherent communications, LIDAR, and optical sensing. Such a low noise level is currently available only in solid-state lasers, fiber-based lasers, and external cavity lasers. These lasers are typically bulky, expensive and not scalable for mass production. Semiconductor diode lasers, although attractive for their low form factor, mass producibility and compatibility to integrated circuits, are notorious for their low coherence with typical linewidths over several MHz.

Heterogeneous silicon/III-V photonics integration opens a path to understand and develop low noise semiconductor lasers. By incorporating low loss high-Q silicon waveguide resonators as integral/extended parts of the Si/III-V laser cavity, we have demonstrated that it is possible to reduce the quantum noise in semiconductor lasers.

In this thesis, we discuss our attempt and success in pushing the noise level of the heterogeneously integrated Si/III-V lasers to record low levels using ring resonator coupled cavity lasers. The first generation of our lasers achieved Lorentzian linewidth in the kHz level. The second-generation lasers, with a new waveguide architecture for ultralow loss and novel cavity designs on silicon, have reached down to ultralow spectral linewidth of 100s-Hz level. Some of the fabricated lasers also possess an ultrawide wavelength tuning range of 120 nm across three optical communication bands (S+C+L). This unprecedented performance shows the potential for heterogeneous silicon photonics to reshape the future of semiconductor lasers.