UC Berkeley

Occasions: Poetry with Prose, 1836-1877 begins in an observation: that the reader of texts from this period is always encountering verse when it is least looked for. Misquoted in newspapers, tucked into the busy media environments of the emergent monthlies, deployed in friendship albums, journals, and sentimental novels, and woven into the tissue of the genre we now call literary prose, verse fragments are called on to perform a range of literary and social tasks that are easy for contemporary readings to miss. Though poetry of the period may reach toward the aesthetic independence now associated with “lyricization”, alongside such ambitions Occasions recovers a genre that invites, as often, its own decomposition and requotation. Much of the difficulty lies in how Ralph Waldo Emerson has been read. When foundational, but complex, manifestoes such as “The Poet” are read towards an “ideal” or “imperial” poet, the Transcendentalists’ perennially neglected verse may appear slight or stiff in comparison. But Emerson’s essay dwells also at length in the possibilities of minor verse, granted it be close at hand, just as he advocates in another essay for an “adventitious” poetics of “happy hits”, arguing verse gives “greater delight … in happy quotation than in the poem” (72). It is all these other, less mystified moments of American poetics—intermittent, immanently social, and prone to disappearance—that Occasions traces, among the Transcendentalists, and outward in nineteenth-century social and political life. In the aftermath of the Romantic revolution, “occasional verse” is a cloudy pejorative; Hegel dismisses such pieces as slight, even while recognizing the category may include most lyrics of distinction. To recover a firmer lexicon for Emerson’s “moments” and Henry David Thoreau’s “occasions” of poetry, I return to the rhetorical, commemorative, and oratorical cultures of the early nineteenth century, and to the vivid tradition of Revolutionary and early Republican topical satires derived from them. The influence of these throughlines on Emerson and Thoreau locates the occasion and the verse it produces, as the key to a poetic historiography of crisis and response in which the individual speaker is called toward his moment and audience. With this move, Occasions participates in the gradual erosion of what Rei Terada calls “the autonomy of the lyric object”—and, I contend, the lyric subject—that has anchored much twentieth-century literary theory. Indeed, in Thoreau’s radical appropriations of other’s poetic property, and in the extremes of anonymity he uses verse to create and court, I locate a transhistorical commons and a resistance to the cordon of single authorship. This anonymity, and this ability to make the past present, and vice versa, help clarify the explicitly social affordances that Emerson and Thoreau use verse to propose. One assertion of my readings of Emerson is that “The Poet,” as articulated in the 1844 essay of that name and elsewhere, is shadowed by another model figure of a poet significantly crosshatched by values such as wit, utility, conversation, lightness, and disappearance. Drawing on significant archival research, I suggest the close source of this figure is “Ellery” Channing. Though now a footnote, Channing was in many respects the most prominent exemplum of transcendental poetry in the 1840s; when Emerson withdrew his support of Thoreau’s verse, it was to transfer that endorsement and affection to Channing. I argue that Channing’s poetry and company remain important precisely because of the lightness, the responsiveness to social contexts, and the intermittence that Emerson finds in them. From this angle, the collaborative projects Channing assembles—the unpublished “Country Walks” (compiled from his, Emerson’s, and Thoreau’s journals), and his 1873 biography, Thoreau: The Poet-Naturalist—are striking for the way they figure the analysis of verse (in the former case) and occasional modes such as elegy (in the latter), as the both the materials and the mode of sociality and life narration. Occasions turns last to Thoreau’s inheritance of Emerson’s poetics. Thoreau is initially motivated by the imperial powers of the poet, and his sacralizing function with respect to American geography, as is seen in A Week on the Concord and Merrimack Rivers (1849); however, the sheer proliferation of verse and verse analysis in that text, which overwhelms its putative physical context, exposes the latent tension in Emerson’s system between the visions of the poet as a translator, and as a reorderer of nature. Walden (1854) marks Thoreau’s elaboration of the occasion into an epistemological principle that derives from, and involves poetry, while transcending verse form. I follow finally Walden’s sustained poetomachy with Channing, whose poetry Thoreau frequently and dramatically quotes; the central term of the debate is the cultural value to be accorded to the Poet. I argue that the peak of this debate, in the problem chapter “Baker Farm,” marks Walden’s rejection of ownership of land as of language, and its turn to a poetics of the commons, influentially modeling poems as malleable sites of linguistic reception and barter responsive to specific historical and local needs.

Much prior research has shown that people tend to view themselves as better than others, referred to here as better-than-other (BTO) effects. Much of that research has asked people to compare themselves to the “average” person. In this dissertation, I provide a more nuanced examination of this BTO effect. First, I measured self and peer perceptions in a field study of nine intact social communities, to test whether people hold more positive views of themselves than they do of each of their fellow community members on average. This examination replicated the general BTO effect, but found it to be weaker than effects typically found in prior work. Second, I tested whether the strength of the BTO effect depends on the target of comparison. Specifically, I assessed the social networks within social communities, and found that BTO effects were weaker in comparisons with close friends, and stronger in comparisons with others distant from the individual in the social network. Third, I measured BTO effects specifically along the Big Five personality dimensions, and found BTO effects were strongest for the most evaluative dimensions (i.e., agreeableness, conscientiousness, and openness), and weakest for the less evaluative dimensions (i.e., extraversion and emotional stability). In further exploratory analyses, I also found that closeness in the network interacted with personality judgment domain such that the effect of social closeness on the BTO effect was stronger for the more evaluative Big Five dimensions.

Computational models of cognitive processes have provided deep insights into specific mechanisms of human learning and decision-making. A natural corollary of research into the typical functioning of such mechanisms is to investigate how mental disorders might cause impairment of the underlying algorithmic processes. This line of research, frequently referred to as computational psychiatry, seeks to contribute to more personalized diagnoses and treatments of mental disorders by characterizing behavioral and computational profiles of psychopathology. In the current dissertation we focus on the role of latent dimensions of internalizing psychopathology in computational processes of learning and decision-making to provide insight into dissociable dimensional phenotypes. Chapter 1 of the dissertation provides a primer to computational characterizations of learning and decision-making, including recent and foundational research results in computational psychiatry which motivate the subsequent original research questions. In Chapter 2, we present an investigation of effortful decision-making in reward pursuit and threat avoidance. We use a detailed dimensional profile of depressive and anxious symptoms to characterize unique mechanistic impairments across frequently comorbid symptoms, with individual symptoms of depression relating to specific differences in effortful reward pursuit processes, while a dimensional characterization of anxiety relates to multiple mechanistic differences in effortful threat avoidance. In Chapter 3, we extend the space of psychopathological inquiry to include subclinical levels of mania in an investigation of decision-making under ambiguity in reward pursuit, loss avoidance, and threat avoidance paradigms. Here, individual differences in loss avoidance decisions under ambiguity show opposing effects of anhedonic depression and physiological anxiety in risk sensitivity, with depression showing increased risk sensitivity and anxiety showing reduced nonlinear risk valuation. Ambiguous decisions in threat avoidance reveal a significant dissociation between anhedonic depression and hypomania, with higher levels of hypomania associated with significantly less risk sensitivity in threat avoidance than anhedonic depression. We also find suggestive, although not concrete, evidence that sensitivity to threat magnitude may relate to comorbid variance between anxiety and depression, and we recommend additional work in this area of investigation. In Chapter 4, we present a detailed investigation of dimensional characterizations of anxiety in reinforcement learning impairment, using an experimental paradigm which differentially manipulates the relative load of working memory versus learning systems across task. Here we find that physiological, but not cognitive, anxiety is related to significant learning impairments which are contributed to both by slower learning processes and by increased rate of working memory decay. These results may have implications for a wide variety of extant reinforcement learning modeling studies in computational psychiatry by emphasizing the importance of an algorithmic account of supporting working memory processes in such investigations. Chapter 5 provides a discussion of the results in the framework of dissociating unique profiles of dimensions of internalizing psychopathology. As additional insights into the relationships of latent dimensions of psychopathology with algorithms of cognition are gained, the field as a whole is enabled to approach the idea of replicable computational phenotypes of psychopathology. The aim of this dissertation is not to operationalize such phenotypes at present, but rather to contribute to the growing body of knowledge within the field to enable and inspire more detailed investigations and replications of the findings contained herein.

The ongoing urbanization process is swiftly giving rise to megaregions, reshaping the urbanlandscape. Travel constitutes a vital aspect of urban life; hence, a comprehensive understanding of traffic dynamics is crucial for the efficient management of cities. Traffic dynamics refer to the patterns of movement exhibited by people and vehicles within a transportation system. These dynamics are typically characterized in terms of flow and speed, providing crucial insights into the functioning of the urban transportation system.

Traffic simulators are extensively used to analyze traffic dynamics in cities and regions.However, many traffic simulators used in regional studies have limitations. One primary limitation is the substantial data and computational requirements necessary to model a large urban region with high fidelity and speed. To tackle this challenge in traffic simulators, many researchers reduce the size of the road network, including only major arterials and highways; model a subset of the population; and/or aggregate travel demand to a smaller subset of network nodes, aiming to obtain rough estimates of travel volumes. However, the downsizing of the road network and the reduction/consolidation of demand lead to alterations in the characteristics and performance of the network. This approach can result in highly inaccurate predictions that fail to capture the actual dynamics and behavior of traffic. It can provide misleading information to agencies regarding the necessary investments for constructing, maintaining, or improving their roadway infrastructure, as well as decisions related to traffic management and incident response plans.

To address these gaps, this dissertation provides a detailed characterization of large-scaleregional traffic dynamics across diverse scenarios using a high-performance traffic simulator. It contributes to and leverages a scalable high-performance mesoscopic traffic simulation platform named Mobiliti, which incorporates various routing strategies to model real-world traffic conditions with high fidelity and speed. In the case of San Francisco Bay Area, Mobiliti can simulate 19 million vehicle trips on a road network with approximately 0.5 million nodes and 1 million links, representing 7 million drivers and 4 million truck trips in less than three minutes. Subsequently, using data analytics and machine learning models, we identify traffic patterns and characterize cities based on transportation-oriented typologies for large metropolitan regions.

Leveraging Mobiliti alongside real-world data sources, we delve into various facets of regionaltraffic dynamics, covering both novel and critical areas. Our investigation began by examining dynamic routing, a prevalent feature introduced by the widespread adoption of navigation apps. This feature introduces an additional layer of traffic control, thereby altering traffic dynamics on streets. Through modeling the varying penetrations of dynamic routing, we quantified its effects on the San Francisco Bay Area using metrics such as Vehicle Miles Traveled (VMT), Vehicle Hours of Delay (VHD), affected trips, and its impact on local roads, among others. Next, we recognized that different types of traffic routing, for example, prioritizing time savings versus prioritizing fuel efficiency, influence traffic dynamics in distinct ways. These dynamics, in turn, shape our cities and significantly impact quality of life. Consequently, we developed a framework to analyze these complex dynamics. We evaluated the impact of different routing strategies across multiple dimensions, examining their effects on neighborhoods, safety, environment, and more. Thirdly, network resilience is a critical factor in the San Francisco Bay Area, known for its interconnected bridges. Incidents in this region have the potential to disrupt multiple cities, impacting productivity and energy efficiency. Therefore, we focused on examining the vulnerability of the transportation system to such events and the cascading effects they may cause. A deeper understanding of these dynamics can assist in effective event response planning. The final two studies focus on cities within metropolitan regions. We begin by analyzing street network structures of cities and then proceed to incorporate additional transportation dimensions such as travel demand, traffic flow, and infrastructure. By employing clustering techniques, we identify city typologies and their respective characteristics. This analysis offers us the opportunity to reflect on past urban development efforts, learn from one another, and envision our future city-building objectives.

In total, the specific contributions of this dissertation are:

1. Analyzed the effects of dynamic routing and its varying penetration rates across avast metropolitan region using large-scale discrete event simulations, demonstrating its substantial influence on mobility metrics at a regional scale. Previous studies were constrained by geographic scale and a limited number of simulation runs, thus failing to capture the full impacts of dynamic routing. 2. Developed a novel multi-themed analytical framework called the Socially-Aware Evaluation Framework for Transportation (SAEF), aiding in comprehending how traffic routing and resulting dynamics impact cities within a region. Our framework’s indicators are carefully chosen to detect system changes when routing strategies are altered, with a focus on neighborhood-related indicators, often overlooked in existing frameworks. 3. Enhanced the evaluation of large-scale network disruptions by modeling dynamic route choices for travelers within a full-scale urban network, encompassing an entire day’s demand. Our method more realistically captures drivers’ behavior during incidents and we are able to capture the full impacts of incidents at scale, thus enabling the creation of better traffic management and response strategies. 4. Created city typologies for all cities within a metropolitan region based on street network structure, which can provide valuable insights into how drivers experience a city based on its street layout. To aid in this classification, we introduced a new metric for categorizing intersections that distinguishes between various types of 3-way and 4-way intersections based on geometric angles. By incorporating this geometric metric alongside existing centrality metrics, we have achieved better differentiation for improved typology generation, capturing the nuances between grid and other street typologies more effectively. 5. Developed transportation-oriented city typologies based on various dimensions including traffic flow, trip demand, multi-modal network, land use, and road network. These typologies serve as a foundation for facilitating the effective exchange of policies and resources, relying on a thorough understanding of traffic characteristics. We integrated metrics related to trip demand and traffic flow alongside commonly used metrics from road network, multi-modal network, and land use. This integration is crucial for capturing the travel behavior and traffic dynamics of cities, enabling the generation of meaningful and comprehensive typologies.

Each of the items mentioned above is described in more detail in the following paragraphs.

In the second chapter, we examined dynamic routing and its impact on large urban areasusing the Mobiliti traffic simulator. Over the last few decades, navigation apps have introduced a new level of traffic control and warranting study as they become pervasive and dictate street traffic flows. Previous work on dynamic routing has been constrained by limited geographic scale and a small number of simulation experiment runs, often requiring hours to complete a single simulation. This limitation poses a bottleneck for running multiple simulations and testing various what-if scenarios to identify the full range of rerouting impacts. We address this gap by utilizing high-performance parallel computing, large urban scale simulator Mobiliti, which can run a single simulation for the entire San Francisco Bay area in less than 10 minutes. We ran multiple simulations with varying penetration rates, revealing diminishing benefits of rerouting after a 70% penetration rate. We also found that dynamic rerouting effectively reallocates vehicle flows from heavily utilized highways and arterials to less congested neighborhood links, reducing overall system delay. Interestingly, the increased traffic volume on local roads does not always lead to congestion, as many links do not reach congested levels despite the increased flow. In summary, our analysis demonstrates, for the first time, the effects of varying penetration rates on traffic dynamics at a regional scale.

In the third chapter, we present an analytical framework called Socially- Aware EvaluationFramework for Transportation (SAEF), which assists in understanding how traffic routing and the resultant dynamics affect cities across a region across multiple dimensions. With the proliferation of real-time navigation routing apps, traffic dynamics in urban environments have changed, resulting in undesired effects that compromise safety and neighborhood health. Therefore, understanding these disparities in traffic distribution across various dimensions is crucial for decision-makers. While previous studies have created frameworks to assess the effects of wide-ranging transportation infrastructure changes or the adoption of smart city technologies, none have established a framework with indicators specific enough to capture the impacts of various traffic routing strategies on cities. Furthermore, existing frameworks and metrics lack translatability to identify the impacts of routing strategies, as crucial dimensions like safety or neighborhood considerations are not adequately addressed. Therefore in this work, our first contribution is developing a framework with a set of themes and indicators that can capture the impact of traffic routing holistically. We identified relevant indicators from the literature, organizing them into four themes: neighborhood, safety, mobility, and environment. When necessary, we developed new methodologies to calculate these indicators. A second contribution is the application of SAEF framework to four cities in the Bay area in the context of three different routing strategies - user equilibrium travel time, system optimal travel time, and system optimal fuel. The four cities were compared to understand how city structure and urban form play a role in the distribution of traffic dynamics. The results demonstrate that many neighborhood impacts, such as traffic load on residential streets and around minority schools, degraded with the system-optimal travel time and fuel routing in comparison to the user-equilibrium travel time routing. The findings also show that all routing strategies subject the city’s disadvantaged neighborhoods to disproportionate traffic exposure. With the widespread adoption of navigation apps, our intent with this work is to provide an evaluation framework that enables reflection on the consequences of traffic routing, allowing city planners to recognize the trade-offs and potential unintended consequences.

In the fourth chapter, we offer a set of evaluation tools designed to measure the impact of significanttransportation disruptions on a regional scale. We illustrate the application of these tools through a case study involving the closure of the Richmond-San Rafael Bridge in the San Francisco Bay Area. Evaluating the dynamics of transportation networks in the context of events can inform disaster plans and aid in traffic management strategies in preparation for or during an event. Existing research on road network disruptions often relies on short time frames and small-scale models, largely due to computational limitations that hinder the widespread adoption of large-scale urban simulation models. Consequently, smaller-scale micro-simulation models are commonly preferred for designing response plans, typically targeting selected highways and major arterials in close proximity to incidents. However, these studies face three key limitations. Firstly, they often rely on user-equilibrium assumptions for route choice, which fail to adequately reflect realistic driver behavior during incidents. Secondly, they use reduced road network representations due to computational constraints, typically focusing on small areas surrounding closures. Thirdly, they frequently extrapolate findings from peak periods to estimate daily impacts, potentially overestimating congestion due to differences in traffic dynamics between peak and non-peak periods. To address these gaps, our study employs a large-scale, mesoscopic simulation model with dynamic routing capability. This model enables us to simulate a full-scale urban network with an entire day’s demand, allowing for a comprehensive assessment of the regional traffic impact of the incident. Our findings indicate that the region experienced an additional 14,000 vehicle hours of delay and 600,000 vehicle miles due to the bridge closure. Furthermore, the median traffic volume on neighborhood streets in San Francisco, Vallejo, and San Rafael increased by more than 10%, highlighting the role of local roads in accommodating the traffic overflow, a factor often overlooked in prior studies. With large-scale modeling of a critical network disruption using dynamic rerouting capability, complete road network, and full demand, we provide valuable insights into the response dynamics of this specific event. In doing so, we demonstrate the value of such regional analyses to incident and disaster planning.

In the fifth chapter, we developed typologies to classify cities within a metropolitan area accordingto their street network characteristics. Spatial networks such as streets and transit lines influence urban dynamics and travel behavior. Analysing these patterns can also help identify how drivers experience city streets and understand the unique characteristics and challenges present in each urban environment. While previous studies have investigated global network patterns for cities, they have often overlooked detailed characterizations within a single large urban region. Additionally, most existing research uses metrics like degree, centrality, orientation etc., and misses the nuances of street networks at the intersection level, such as geometric angles formed by links at intersections, which could offer a more refined feature for characterization. To address these gaps, this study examines 94 cities in the San Francisco Bay Area, taking into account diverse road network features. We introduce a novel metric for classifying intersections, distinguishing between various types of 3-way, and 4-way intersections based on the angles formed at the intersections. Through the application of clustering techniques in machine learning, we have identified three distinct typologies - grid, orthogonal, and organic cities - within San Francisco Bay Area. Gridded cities are distinguished by their dense network of right-angled four-way and three-way intersections. These cities exhibit a compact layout with smaller link lengths and slower traffic speeds. On the other hand, orthogonal cities exhibit a street network configuration characterized by right-angled three-way intersections and longer street lengths. Organic cities represent a third typology, characterized by their irregular and non-grid-like street network. These cities feature long links with numerous dead ends and winding, circuitous roads. Our findings indicate that the integration of the new metric has improved our ability to distinguish between different types of cities, complementing the existing metrics. In gridded cities, the introduction of the new metric enhances the recognition of grid patterns by explicitly considering 90-degree intersection angles. Conversely, for non-gridded cities, a notable advancement is the ability to differentiate between various types of degree 3 nodes (3-way intersections). While many cities have a significant number of degree 3 nodes, the arrangement of these intersections can vary greatly due to angle variations, resulting in either 90 degree T intersections or non-T intersections. Our study showcases the effectiveness of the new metric in capturing these distinctions, facilitating the classification of cities with a high proportion of T intersections into orthogonal cities and those with non-T intersections into organic cities. The significance of this differentiation extends to how drivers navigate and experience intersections and streets within cities. Based on the angles, turns, and curves of the road network, driving experiences vary significantly. Therefore, understanding these nuances is crucial for optimizing traffic flow, enhancing road safety, and improving overall driving experiences for motorists.

In the sixth chapter, we expanded upon our previous city characterization work focused onnetwork structure by incorporating multiple transportation dimensions. As cities evolve and face shared challenges, the development of city typologies, rooted in a comprehensive understanding of traffic characteristics, becomes crucial for facilitating the effective exchange of policies and resources among them. Prior work on transportation based city typologies often fails to provide characterizations specific to a single extensive urban area, as it predominantly focuses on cities globally. Furthermore, these studies frequently overlook essential dimensions such as trip demand and traffic flow in their characterizations, despite their significant impact on street behavior and traffic dynamics. Therefore in this study, we develop a transportation-focused characterization for all cities within a large urban region, specifically the San Francisco Bay Area, California. We incorporate over 40 metrics across five transportation dimensions: road network, trip demand, traffic flow, multi-modal network, and land use. Using factor analysis and unsupervised machine learning clustering methods, we identified eight distinct typologies for the Bay Area: Live Work; Job and Activity Magnets; Anchor Cities; Multi-modal; Hyper-connected; Low-density residential; Mediumdensity Residential; Mixed-use residential. The results revealed that many clusters were characterized by features from travel demand and traffic flow dimensions, thus signifying their importance in typology generation. These typologies can serve as a basis to create discourse among Bay Area cities and determine if, through success/failure experiences, common strategies can be formed.

In total, the analytical framework and methods outlined in this dissertation provide detailedand nuanced insights into regional traffic dynamics, surpassing existing literature. By utilizing and contributing to the Mobiliti simulator, we modeled large urban areas with high fidelity and speed, enabling the testing of multiple “what if”scenarios for large metropolitan regions. Our investigation of dynamic routing and its varying penetration rate in Chapter 2 represents the first large-scale regional study examining the impact of real-time traffic routing. Furthermore, the SAEF framework presented in Chapter 3 of this dissertation represents the first analytical framework that captures the impact of traffic routing holistically. With the widespread adoption of navigation apps, this framework enables reflection on the consequences of traffic routing, allowing city planners to recognize the trade-offs and potential unintended consequences. The large-scale network disruption evaluated in Chapter 4 provides a suite of analytical tools for assessing disruptions at both regional and local levels. These tools enable the creation of enhanced traffic management and response strategies by capturing driver behavior more realistically. The typologies developed in Chapters 5 and 6 provide a comprehensive understanding of cities in a region, considering both network structure and overall transportation dimensions. The new metric introduced in Chapter 5 aids in quantifying the network more precisely, while the comprehensive use of various metrics from different transportation dimensions, particularly trip demand and traffic flow, facilitates a more thorough characterization of cities in Chapter 6. The identified typologies can catalyze dialogue among San Francisco Bay Area cities, facilitating the exploration of common strategies derived from shared experiences of success or failure. Ultimately, the findings presented in this dissertation contribute not only to enriching academic discourse on transportation dynamics but also carry practical implications for policymakers. They furnish invaluable guidance for crafting more effective and nuanced traffic management strategies for cities and large metropolitan regions, thereby shaping the future of urban mobility with precision and foresight.

The increasing complexity of global challenges demands a STEM-enriched approach to learning for all students, regardless of their future career paths. Challenge-Based Learning (CBL) is a pedagogical method to foster a STEM-enriched education, engaging students in the design of societally impactful, interdisciplinary solutions. To investigate the potential of CBL, specifically in the context of Undergraduate STEM Education (USE), it is crucial to assess students’ affective development such as their attitudes, beliefs, and self-perceptions related to STEM. This dissertation explores the impact of CBL on student affect through three interconnected studies centered on a large-enrollment Bioinspired Design course. Chapter 1 explores overall growth in measures of science connection—Science Identity (SciID), Science Self-Efficacy (Eff), and Internalization of Scientific Community Values (Val)—using the Tripartite Integration Model of Social Influence (TIMSI) framework. Results demonstrated significant pre/post increases in SciID and Eff across five semesters, with Val remaining stable. Item level analyses revealed specific impacts of CBL activities on these affective measures, particularly in developing students’ confidence in creating novel technologies. Chapter 2 investigates the equity of these affective growth outcomes across seven demographic variables. Results indicated that the observed increases in science connection were largely equitable across diverse student populations, with differences in SciID development based on STEM major status and class status. Chapter 3 introduces and validates a novel affective construct: Innovation Skills self-efficacy. Developed using the Berkeley Evaluation & Assessment Research (BEAR) Assessment System, this construct provides a more targeted measure of self-efficacy aligned with the Innovation Skills needed for the future STEM-enriched workforce. Results showed approximately one standard deviation of pre/post growth, with a large effect size in the context of educational interventions. Collectively, this dissertation showcases the potential of CBL approaches in USE to foster equitable development of science connection and Innovation Skills self-efficacy across diverse student populations through comprehensive, psychometrically robust assessments of student affect. This research underscores the importance of holistic approaches to STEM education that cultivate not only knowledge and skills, but also the attitudes and beliefs necessary for success in the known and unknown STEM-enriched careers of the future.

Wind energy is the leading renewable technology in the U.S., generating over 10% of utility-scale electricity in recent years. Rapid growth in wind energy installations has made modeling and prediction of atmospheric boundary layer (ABL) wind speeds and the associated turbulence critical for wind turbine siting, resource assessment, and operational power forecasting. A number of modeling challenges currently exist, such as representing the impact of terrain on wind turbine wakes and capturing small-scale turbulence in stably-stratified conditions. Many low-fidelity wind turbine simulation methods fail to incorporate topography and struggle to account for dynamic flow behavior. In this dissertation, results are presented using high-fidelity large-eddy simulation (LES), which captures the dynamic and turbulent behavior of ABL winds, providing a framework to simulate a wide variety of turbulent atmospheric phenomena with a wind turbine parameterization to understand turbine-airflow interactions.

First, high-resolution simulations of the 2017 Perdigão field campaign in Portugal are conducted. The Perdigão site consists of two parallel ridges with a wind turbine located on top of one of the ridges. Both convective and stable atmospheric conditions are simulated to understand how the wind turbine wake behaves in complex terrain in two representative flow regimes. For the convective case study, flow recirculation in the lee of the ridge occurred, thus deflecting the wake upwards. For the stable case study, the wake deflected downwards following the terrain due to a mountain wave that occurred. The vertical behavior of the wind turbine wake can be detrimental to downwind turbines; however, this vertical behavior is not accounted for in current wind farm design wake models. These case studies demonstrate the dependence of the wind turbine wake behavior on terrain-induced flow phenomena, which, in-turn, depend on the thermal stratification of the atmosphere.

The stable case study from Perdigão is then studied in more depth to better understand both the ambient and wind turbine wake turbulence characteristics. Novel derived measurements of the turbulence dissipation rate are available from the field campaign, providing an opportunity to further examine the spatial structure of turbulence predicted by the model. Additionally, in this study, the dynamic reconstruction model (DRM) LES turbulence closure is used to better represent smaller-scale turbulence. The DRM closure more accurately predicts turbulence metrics, including the turbulence dissipation rate, most notably upwind of the major topographic features. After the flow passes over the first ridge, the differences between the DRM and a standard eddy-viscosity closure are small close to the surface, although the DRM closure does better predict the turbulence dissipation rate in the upper atmosphere in this region. Because the DRM closure is not a standard eddy-viscosity closure, negative turbulence dissipation rate or the backscatter of energy from smaller scales to larger scales is predicted; however, backscatter cannot be derived from Perdigão measurements due to the experimental setup and analysis methods used, thus leaving validation of this aspect for future work.

Next, a range of idealized stable boundary layer (SBL) conditions are modeled in support of the American Wake Experiment (AWAKEN) field campaign to address: (1) the effect of wind turbines on SBL development, and (2) the effect of intermittent turbulence on wind farm performance. In weak SBL conditions, turbulence is continuous and easier to simulate. With the intermittent turbulence that occurs in strongly stable conditions, only the DRM closure can resolve realistic turbulence. For all SBL conditions simulated, the wind farm significantly impacts wind speeds and thermal structure well downwind (greater than 30 rotor diameters or 2.4 km) of the farm. Wind speeds in the wakes are reduced, and the increased mixing as a result of the wakes weakens the stable stratification in the boundary layer.

Finally, simulations are performed of a real case study of intermittent turbulence observed during the AWAKEN field campaign. The intermittent turbulence event is determined to be driven by a nocturnal mesoscale convective system (MCS). The MCS results in a cold pool, which radiates outwards as a density current. This density current perturbs the SBL, thus inducing gravity waves. The structure of the simulated gravity waves is found to be especially sensitive to the parameterization of cloud and precipitation processes (microphysics). The gravity waves have very strong effects on the flow in the upper atmosphere; however, closer to the surface where there is additional ambient turbulence and turbulence generated by wakes, the effect of the waves is more nuanced. Notably, the waves induce local wind direction variation, which leads to fluctuations in the power output as various turbines within the farm are subjected to the wakes of nearby turbines.

The findings presented in this dissertation provide insight into wind farm performance in a broad range of atmospheric conditions by incorporating both terrain effects and thermal stratification. Specifically, these conditions include dynamic turbulent phenomena that current wind farm design tools are unable to capture. The advances in this dissertation related to high-resolution LES reveal novel and complex relationships between wind turbines and the atmosphere that can significantly improve wind farm power predictions at large.

Slow electron velocity-map imaging (SEVI), a high-resolution variant of anion photoelectron spectroscopy, has proven to be a powerful and versatile spectroscopic technique capable of measuring the vibronic structure of a wide range of molecular and cluster species with exquisite detail. The extension of SEVI to study anions cooled to their ground vibronic state (cryo-SEVI) through the addition of a cryogenic ion trap dramatically improved spectral resolution and enabled larger species to be probed. This, however, also greatly limits the number of neutral states accessible via photodetachment. To overcome this, anions are resonantly excited to a selected vibrational state using infrared (IR) radiation prior to photodetachment. This new technique, dubbed IR cryo-SEVI, offers the potential to probe the vibrational structure of both anionic and neutral species to an even greater extent.The cryo-SEVI spectrum without vibrational pre-excitation was collected for the acetyl anion (CH3CO¯). This spectrum reveals a significant vibrational progression along the CCO bending mode with transitions up to Δv=11 clearly observed. The measured electron affinity for the acetyl radical is also used to calculate a refined value for the gas-phase acidity of acetaldehyde (CH3CHO). Cryo-SEVI with vibrational pre-excitation was first applied to the hydroxide anion (OH¯) by exciting the well-characterized R(0) rovibrational transition of the anion. The large rotational constant of the diatomic paired with the high resolution of cryo-SEVI enabled the rotational fine structure to be fully resolved. Upon excitation, depletion of the ground state features is observed along with new features that appear corresponding to transitions from vibrationally excited anions. Additionally, the IR absorption profile of the anion was measured by monitoring the growth of new features as the IR energy is varied, precluding the need for messenger-tagged species that perturb vibrational frequencies. IR cryo-SEVI was extended to polyatomic systems with the vinoxide anion (CH2CHO¯) excited along the CO (ν4) and lone CH (ν3) stretching modes. Excitation of the lower frequency ν4 fundamental results in photoelectron spectra that can be fully explained within the harmonic approximation. Excitation of the higher frequency ν3 fundamental, however, results in a more complicated spectrum consisting of several unexpected transitions. Theoretical considerations reveal the ν3 fundamental is anharmonically coupled to nearby vibrational states in both the anion and neutral manifold, leading to newly allowed transitions appearing in the spectrum. The nitrate radical (NO3) has been the focus of several theoretical and experimental investigations owing to its complex electronic structure arising from strong vibronic interactions between electronic states. IR cryo-SEVI is used to definitively settle a decades-long controversy over the fundamental frequency of the degenerate stretching mode (ν3), wherein the ν3 and 2ν3 vibrational states of the NO3¯ anion are accessed prior to detachment. Through comparison to theory, assignment of transitions from the 2ν3 state allow for unambiguous determination of the neutral ν3 frequency. Vinylidene (H2CC), a high-energy isomer of acetylene, represents a model system to study how isomerization affects the vibrational structure of molecular species. The small barrier for isomerization to acetylene (HCCH) as well as the asymmetric shape of the potential energy surface allows for low-lying vibrational states of vinylidene to interact with highly excited HCCH states, resulting in a complex vibrational structure. Cryo-SEVI spectra collected with and without vibrational pre-excitation probe vinylidene’s complicated structure with unprecedented detail, giving insight into which vibrational modes drive the isomerization reaction. The high-resolution of cryo-SEVI also reveals the nature of vinylidene-acetylene coupling in the energy region surrounding the predicted isomerization barrier height.

Chapter 1. The relevant background to the project is communicated in addition to the project hypothesis and strategy. Tetrylene-f-element bonded complexes are introduced as compounds of nearly unexplored chemical reactivity and bonding character. The strategy of combining tetrylenes with coordinatively unsaturated f-element precursors is briefly described.

Chapter 2. Novel uranium-tetrylene bonded complexes are synthesized by utilization of amidinate-supported silylenes. The solid- and solution-state structures of these compounds are examined by X-ray crystallography, absorption spectroscopy, nuclear magnetic resonance spectroscopy, and variable-temperature magnetometry. The nature of the uranium-silicon interactions is further elucidated by density functional theory methods.

Chapter 3. The reactivity of f-element-silylene complexes toward hydrogen gas is reported. Despite showing little evidence for bonding in solution, a uranium-silylene complex rapidly activates hydrogen to yield a dihydrosilane product. Lanthanide analogues to the uranium-silylene complex are much less efficient catalysts, while common main group Lewis acids show no catalytic activity. The mechanisms of both the actinide- and lanthanide-catalyzed reactions are deconvoluted through isotope labeling studies and kinetic and theoretical modeling. Investigation of the uranium-catalyzed pathway reveals that dihydrogen complexation by uranium is accessible and may underpin the particular efficiency of this catalyst.

The chapters of this dissertation encompass two main projects. First is the study of ions and molecules at buried interfaces through further development of second-order nonlinear scattering spectroscopies. The goal of these studies, documented in Chapters 3-5, is to develop a more acute understanding of the role of solid-water interfaces for ocean chemistry, water purification, drug delivery, catalysis, and environmental sensors. The second project is the investigation of the liquid state of carbon using X-ray free electron laser scattering and is discussed in Chapter 6.Chapter 1 provides a brief history of ion adsorption to the air-water interface, describing how the field of nonlinear spectroscopy has aided in the development of a general theorem, viz. that highly polarizable, weakly solvated ions have a propensity for the interface. I discuss how the techniques of Second Harmonic Generation have developed to study colloidal, buried interfaces, extending our understanding of adsorption of ions and molecules to solid-water interfaces. Finally, the investigation of the liquid state of carbon is motivated. Chapter 2 discusses the methods used for the studies in this dissertation, starting with a general description of second-order nonlinear spectroscopy. Before detailing Second Harmonic Scattering (SHS), a review of Second Harmonic Generation is provided. The advancements in SHS techniques utilized in the remaining chapters, viz. competitive adsorption, angle-resolved SHS (AR-SHS), and polarization-resolved SHS are chronicled. Additionally, I include an introduction to Resonant Inelastic X-ray scattering (RIXS) for the purpose of probing the liquid carbon. Chapter 3 reports a more robust picture of the adsorption mechanism for dye molecules to charged polystyrene interfaces by investigating the temperature-dependence of the SH signal. The enthalpy and entropy contributions to the free energy were separated. Despite the small changes in ΔGads across the charged polystyrene surfaces, the sign and magnitude of ΔHads changed as a function of temperature. We find that the sign of ΔHads is affected more by the charge of the surface than by the charge of the adsorbate, and attribute the sign change to different mechanisms for adsorption to charged polystyrene surfaces. Chapter 4 extends our studies to porous colloidal interfaces, e.g. porous silica and metal organic framework (MOF) nanocrystals. We employ AR-SHS and polarization-resolved 1 measurements to elucidate the probable reorientation of malachite green dye as a function of concentration. I anticipate that the work in this chapter serves as preliminary data for the development of theories that explicitly describe a buried rough or porous interface. Chapter 5 revisits the use of a competitive adsorption model for comparing the adsorption energy of non-resonant molecules to silica nanoparticles (SNP) and polystyrene beads (PSB). I find that employing the competitive displacement model with our SHS measurements permits resolving the difference in ΔGads for caffeine on SNP and PSB, which amounts to ~1 kcal/mol. Chapter 6 details time-resolved RIXS and XES measurements of ultrananocrystalline diamond (UNCD) and amorphous carbon (a-C) to directly probe changes in the electronic structure of the samples following laser irradiation. However, we find no evidence of changes to the electronic structure and attribute decreases in the time-resolved intensities for a-C and UNCD to transition blocking, subsequent relaxation, and eventual ablation of the samples. A rich comparison of experimental parameters used in a multitude of X-ray spectroscopy techniques is discussed.

This dissertation puts land at the center of the American state formation to analyze the emergence of the American administrative and developmental state. As the first nation to emerge from revolt against colonial rule, in the United States empire and republicanism collided to produce a settlers’ republic. Therein, it was through the work of acquiring, surveying, and selling land; promoting infrastructure projects such as canals and railways; and managing western territories that a people wary of centralized authority paradoxically found themselves building an expansive, bureaucratized, and increasingly developmental American state. Significantly, with so much of the activity of the early American state directed towards the acquisition, settlement, and incorporation of land, territory emerged as the orienting object of government. Territory was reimagined a space to be governed—to be improved, economized, and developed in the nation’s interest. It was in pursuing settlers’ visions of an empire for liberty that early Americans found themselves building the administrative institutions and ways of relating government to territory, economy, and society now pervasive in today’s nation-states.

I make this argument in a series of studies. In the first, I construct a novel dataset characterizing antebellum Congressional debate activity from 1789 to 1861 (N=12,658), to demonstrate that the majority of early Congressional activity was concerned with acquiring, defending, and transforming land. Theorizing this centrality of land requires considering how modern state formation involved not only the expansion and bureaucratization of state administrative capacities, but also a transition from expansive empires of difference to territorially, economically, and socially contiguous nation-states. In the United States, this transition occurred via settler colonial practices of territorial incorporation such as, in the antebellum period, the integration of new states on equal political footing, and the construction of transportation and communication infrastructures.

In the second study, I examine the period in the United States before it was taken-for-granted that government should promote infrastructure projects such as roads, canals, and railways to stimulate what is now called economic development. I demonstrate how the public lands—the broad swathes of land in the national domain for which title had not been transferred to private owners—were repeatedly called upon as a fiscal resource, thereby allowing early American state builders the flexibility to experiment with various institutional arrangements and new governmental rationalities to justify government support of infrastructures. Crucially, to mobilize the public lands in this way, early American state builders relied on assumptions of Native dispossession and erasure. These assumptions lowered the perceived costs of mobilizing the public lands as a fiscal resource, and institutionalized processes of Indigenous dispossession and erasure in American political and economic development.

In a third study, I analyze the legislative debates leading up to the authorization of the first transcontinental railroad as a window into how federalism complicated territorial expansion and development. I theorize federalism as the outgrowth of empire to demonstrate how federalism territorialized understandings of sovereignty, thereby rendering decisions over territorial expansion and development politically destabilizing in ways that they would not be under empire. I analyze the debates leading up to the passage of the 1862 Pacific Railway Act to examine how federalism complicated territorial development in practice. The fact that the railroad could not be approved until after the outbreak of the Civil War, which removed Southern sectional interests from Congress, illustrates the limitations of federalist systems to make decisions over land. Because federalism allows legislators to sidestep conflict by excluding dissenting positions via the re-drawing or strategic selection of political boundaries, federalism does not incentivize meaningful compromise over questions of territorial development.

I conclude by sketching the evolution of the United States’ settler political economy. I discuss how the various efforts to manage land examined in detail above were situated in the United States’ evolving land policy, administrative capacities, and governmental rationalities. From the General Land Office to the Department of Interior, it was to meet the demands of managing vast territories that some of the United States’ earliest and most technocratic administrative agencies were established. And it was from the bird’s eye view of those agencies that early American state builders experimented with ways of promoting and optimizing public welfare using land policy that echo the economizing reasoning of today.

Analyzing the United States as a settlers’ republic makes visible the trade-offs and exclusions constitutive of American state formation and establishes the central role that land played in American political and economic development. By doing so, this dissertation points to the horizon needing to be transformed therein to perfect democratic government and reimagine relationships between land, government, and people more generally.