This dissertation offers lessons learned and tools developed as we attempt to apply correlated X-ray scattering (CXS) to non-crystalline proteins in solution. It builds on our previous work of extracting correlation signals from scattering intensities of ensembles of mental nanoparticles, which has led to three-dimensional (3D) structural insights not reflected in azimuthally averaged measurements. In his 1977 paper, Zvi Kam proposed the idea of correlating X-ray photons scattered by an ensemble of randomly oriented particles suspended in solution. He found that if the exposure time is much shorter than the diffusion timescale of Brownian motion, correlations between photons scattered into different angles encode 3D structural information of the particles not accessible via conventional small or wide-angle X-ray scattering. The advent of the X-ray free electron laser (XFEL) renders Kam's idea feasible for non-crystalline solutions of proteins. With femtosecond pulses and extremely high fluences, the XFEL is not only capable of probing ensembles of molecules essentially frozen in time but also delivering a large number of photons per pulse, a capability critical for enhancing angular intensity correlation signals. Meanwhile, probing proteins in solution removes the need for crystallization, allows measurements of mixtures of conformational states under physiological conditions, and broadens opportunities for time-resolved experiments. The body of work in this dissertation draws from scattering data collected with samples containing the G-protein Gi alpha subunit during two separate beam times conducted at the Linac Coherent Light Source. The Gi alpha subunit was chosen for these proof-of-principle experiments because of its important role in the G-protein coupled receptor signaling pathway. This dissertation has taken the first steps in developing and validating CXS as a tool for probing ensembles of biomolecules in solution. These first steps as well as ideas described in this dissertation to improve CXS towards a mature pipeline that yields reliable and detailed structural insights aim to inspire others in the solution scattering community to engage with the unique challenges and rewards of this technique.

This dissertation, "Displaced Landscape: The Art and Life of Ni Zan (1301-1374), " retrieves the lived experiences of underrepresented, displaced people during the Yuan-Ming dynastic transitional era. By using Ni Zan's paintings as counter-memories, my project raises new questions about the dominant mythology surrounding Ni Zan. Mainstream historiographies in China define him as a loyal recluse, which limits understandings of the variety of his life experiences. Instead, my research shows that Ni Zan was an active agent who coped with turbulent times by means of artistic practice, and reconstructs the forgotten local history of the Wu region (today's Suzhou and its surrounding area) in the late Yuan period. My analysis of Ni's paintings shows how art objects can operate as a creative social force and spotlights two previously overlooked potentialities in Chinese painting. First, it can function as, what I term, a "communal space" of memory, communion, or personal/political mourning. Second, it can function as "objectified charisma, " the sedimentation of an individual's virtues and powers into objects embedded with iconic and indexical properties.

Most congestion control algorithms rely on a reactive control system that detects congestion and then marches carefully toward a desired operating point (e.g., by modifying the window size or picking a rate). These algorithms often take hundreds of RTTs to converge—an increasing problem in networks with short-lived flows. Motivated by the need for fast congestion control, this thesis focuses on a different class of congestion control algorithms, called proactive explicit rate-control (PERC) algorithms, which decouple the rate calculation from congestion signals in the network. The switches and NICs exchange control messages to run a distributed algorithm to pick explicit rates for each flow. PERC algorithms proactively schedule flows to be sent at certain explicit rates. They take as input the set of flows and the network link speeds and topology, but not a congestion signal. As a result, they converge faster and their convergence time depends only on fundamental "dependency chains, " essentially couplings between links that carry common flows, that are a property of the traffic matrix and the network topology. We argue that congestion control should converge in a time limited only by fundamental dependency chains. Our main contribution is s-PERC: a new practical distributed proactive scheduling algorithm. It is the first PERC algorithm to provably converge in bounded time without requiring per-flow state or network synchronization. It converges to the exact max-min fair allocation in 6N rounds (where N is the number of links in the longest dependency chain). In simulation and on a P4-programmed FPGA hardware test bed, s-PERC converges an order of magnitude faster than reactive schemes such as TCP, DCTCP, and RCP. Long flows complete in close to the ideal time, while while short-lived flows are prioritized, making it relevant for data centers and wide-area networks (WANs).

"The eastern Bering Sea bottom trawl survey has been conducted annually since 1975 by the Resource Assessment and Conservation Engineering Division of the Alaska Fisheries Science Center, National Marine Fisheries Service. The purpose of this survey is to collect data on the distribution and abundance of crab, groundfish, and other benthic resources in the eastern Bering Sea. These data are used to estimate population abundances for the management of commercially important species in the region. This document includes the time series of results from 1975 to the present. In 2017, 395 total stations (375 standard stations and 20 resampled stations in Bristol Bay) were sampled on the eastern Bering Sea shelf from 4 June to 15 August. In early June, colder bottom temperatures extended into Bristol Bay creating the need to resample 20 stations due to delaying effects of cold water temperature on red king crab reproductive cycle. There was an overall decrease in biomass and abundance in male red king and blue king crab and female red king crab. There was an overall increase in immature female blue king crab and a decrease in mature female biomass and abundance, with no mature female blue king crab being caught in the St. Matthew Island Section. There were overall increases in immature and legal male and immature and mature female biomass and abundance in Chionoecetes bairdi and C. opilio crab, and a decrease in mature male biomass and abundance in Chionoecetes bairdi and C. opilio crab. In addition to the standard eastern Bering Sea survey, in 2017, following the conclusion of the standard survey, 145 stations were sampled in the northern Bering Sea region, encompassing the region south of Bering Strait and including Norton Sound. These stations were sampled between 1 August and 2 September. We report the results of this survey separately from the eastern Bering Sea survey, within the northern Bering Sea section of this report. Blue king crabs occurred largely in the region north of St. Lawrence Isand, in greater densities than were observed for the St. Matthew and Pribilof stocks. Red king crab occurred primarily in Norton Sound, at densities roughly intermediate between those generally observed in the Bristol Bay and Pribilof Districts. Chionoecetes opilio dominated the catch, with the highest densities observed during the 2017 Bering Sea survey occurring to the south of St. Lawrence. Immature male and female opilio were predominate, although mature females were observed, primarily along the western edge of the survey grid, and in the north, near Bering Strait. Morphometrically mature (“large-clawed”) male C. opilio were scattered throughout the survey region, but most prevalent within catch near Bering Strait."--Publisher's website.

Turbulent, dispersed multiphase flows are of critical importance in a wide range of application areas. Experimental investigations are indispensable in the study of such flows, as experiments can provide reliable domain-specific knowledge and/or validation for computational fluid dynamics (CFD) tools. However, only a limited set of experimental techniques currently are available for studying particle-laden flows: pointwise measurements provide high temporal resolution but poor spatial coverage, while laser-based techniques can allow for 2D or 3D measurements, but only in geometrically simple flows. Magnetic Resonance Imaging (MRI) is a powerful tool that can provide fully quantitative, 3D experimental data without the need for optical access. Currently, MRI can provide the time-averaged, 3-component velocity and/or scalar concentration fields in turbulent single-phase flows of arbitrary geometric complexity. In recent years MRI has been applied to the study of single-phase flows across a broad range of problems from the engineering, environmental, and medical arenas. MRI data sets are particularly well suited for validating CFD simulations of complex 3D flows because comprehensive data coverage can be obtained in a relatively short time. The present work describes development, validation, and application of a new diagnostic, wherein MRI is used to obtain the 3D mean volume fraction field for solid microparticles dispersed in a turbulent water flow. The new method is referred to as Magnetic Resonance Particle concentration, or MRP. This technique was designed to maintain the same advantages of existing MRI-based techniques: quantitative data can be obtained in 3D for fully turbulent flow in arbitrarily complex geometries. MRP is based on a linear relationship between the MRI signal decay rate and particle volume fraction (Yablonskiy and Haacke, 1994). The MRP method and underlying physics were validated through several studies, increasing in complexity from a single particle suspended in a gel to a fully turbulent channel flow seeded uniformly with particles. The channel flow case showed that the signal decay rate varied linearly with particle volume fraction, and that the measured proportionality constant was within 5% of the value predicted by the theory of Yablonskiy and Haacke (1994). This good agreement was observed for two fully turbulent Reynolds numbers, 6300 and 12,200, and over most of the measurement domain. However, the measured proportionality constant was lower than expected in the the furthest upstream portion of the channel; several potential reasons for this discrepancy were identified, but none could be proven conclusively at this stage. Following the validation experiments, MRP was applied to three application cases drawn from real-world flows of interest. First, the dispersion of two particle streaks in a model human nasal passage was studied. The results showed that almost all particles reaching the upper portions of the nasal passage (e.g., the olfactory region) entered the nose near the nostril tip, even at high breathing rates where the flow was not laminar. The second case involved MRP concentration measurements for a particle streak in a generic gas turbine blade internal cooling passage. Results in this case provided evidence that small dust-like particles ingested into a cooling passage may behave inertially in the presence of fine flow features, such as the recirculation regions behind ribbed flow turbulators. In the final case, the performance of a particle separator device proposed by Musgrove et al. (2009) was quantified using both MRP and a sample-based analysis performed outside the MRI environment. The two techniques were in agreement regarding the poor overall effectiveness of the separator, and the 3D MRP data were used to examine the particle transport physics and suggest potential design improvements. Taken together, results from the three test cases showed that MRP can provide quantitative, 3D particle concentration data in application-relevant flows, leading to unique insights that would not be possible with existing measurement techniques.

The promise of optical antennas is the ability to tame the light to behave in ways not achievable using traditional optical components. For example, our results here demonstrate that a careful engineering of optical antennas allow the strong, even perfect, absorption of light in ultra-thin geometries, i.e., geometries much thinner than the wavelength of light. Enabled by geometry-sensitive antenna resonances, this absorption behavior can also be realized for a broad selection of colors. A detailed theoretical analysis of the observed perfect absorption phenomenon reveals the role of incoherently interacting degenerate electric and magnetic resonances in overcoming the well-known absorption limit for infinitesimally thin films. With another set of experiments, we show that strongly absorbed optical energy in aluminum nanoantennas can be used to heat them efficiently above their melting temperature and stimulate an explosive exothermic oxidation reaction called melt-dispersion mechanism. Importantly, we see that engineering the specific geometry of the constituent particles allows an unprecedented control of aluminum ignition, both spectrally and spatially, through the fine tuning of the optical antenna resonances.

Reservoir simulation is an important tool for understanding and predicting subsurface flow and reservoir performance. In applications such as production optimization and history matching, thousands of simulation runs may be required. Therefore, proxy methods that can provide approximate solutions in much shorter times can be very useful. Reduced-order modeling (ROM) methods are a particular type of proxy procedure that entail a reduction of the number of unknown variables in the nonlinear equations. This dissertation focuses on two of the most promising proper orthogonal decomposition (POD)-based ROM methods, POD-TPWL and POD-DEIM. A separate (non-ROM) technique to accelerate nonlinear convergence for oil-water problems is presented in the appendix.

We first present a new type of large-scale computational screening approach for identifying promising candidate materials for solid state electrolytes for lithium ion batteries that is capable of screening all known lithium containing solids. We first screen 12,831 lithium containing crystalline solids for those with high structural and chemical stability, low electronic conductivity, and low cost. We then develop a data-driven ionic conductivity classification model using logistic regression for identifying which candidate structures are likely to exhibit fast lithium conduction based on experimental measurements reported in the literature. The screening reduces the list of candidate materials from 12,831 down to 21 structures that show promise as electrolytes, few of which have been examined experimentally. We then employ Density Functional Theory (DFT) molecular dynamics simulations to compute ionic conductivity in these promising candidate materials and others, discovering many new crystalline solid materials with DFT-predicted fast single crystal Li ion conductivity at room temperature. When compared to a random search of materials space, we find that a search over the materials identified by our machine learning-based ionic conductivity model is 2.7 times more likely to identify fast Li ion conductors, with at least a 44 times improvement in the log-average of room temperature Li ion conductivity. We then perform an in-depth study of a new solid-state Li-ion electrolyte material emerging from this screening process that is predicted to exhibit extraordinarily fast ionic conductivity, wide electrochemical stability, low cost, and low mass density: materials from the crystalline lithium-boron-sulfur (LBS) system, including Li5B7S13, Li2B2S5 and Li3BS3. We compute the DFT-based single crystal room temperature ionic conductivity of these three phases to be 74, 10, and 2 mS/cm, respectively, and the thermodynamic electrochemical stability window widths of these materials to be 0.50, 0.16, and 0.45 V, respectively. However, we predict that electrolyte materials synthesized from a range of compositions in Li-B-S system may exhibit even wider thermodynamic electrochemical stability windows of 0.6 V and possibly as high as 3 V or greater. We predict the range of optimal boron-to-sulfur ratios for achieving high ionic conductivity over an electrochemical stability window wider than 0.5 V range to be 1:2 to 1:2.5. The Li-B-S phase mixtures within this range of compositions also have low elemental cost of approximately 0.05 USD/m2 per 10 μm thickness with a mass density below 2 g/cc. Finally, we combine our models with existing models of solid electrolyte performance to find new evidence to suggest that optimization of the sulfides for fast ionic conductivity and wide electrochemical stability may be more likely than optimization of the oxides, and that the oft-overlooked chlorides and bromides may be particularly promising families for Li-ion electrolytes. We also find that the nitrides and phosphides appear to be the most promising material families for electrolytes stable against Li-metal anodes. Furthermore, the spread of the existing data in performance space suggests that fast conducting materials that are stable against both Li metal and a > 4V cathode are exceedingly rare, and that a multiple-electrolyte architecture is a more likely path to successfully realizing a solid-state Li metal battery by approximately an order of magnitude or more.

The playwright Eugene O'Neill (1888-1953) produced a body of work—thirty-one full-length plays, and twenty-one one-act plays—that was ambitious in its stylistic innovations, and daring in its thematic concerns. As recounted by historians, biographers, and critics, O'Neill's private life informed his writing process, with his various ailments serving as prompts for the stage. Whenever the theme of addiction appears, as it does in his final plays The Iceman Cometh (1939), Long Day's Journey Into Night (1941), and A Moon for the Misbegotten (1943), scholars note the autobiographical aspect as generative for his artistic output. Painted as a depressive figure whose dysfunctional upbringing made a lifelong impression on him, current literature on O'Neill celebrates the playwright for his distinctly sincere expression of suffering and strife. O'Neill wrote his final plays during a period of renewed interest in addiction science in the nation following World War II. After the failed enterprise of Prohibition, scientists, politicians, and the public instated a new treatment paradigm that placed responsibility on the individual who drinks problematically. This approach helped solidify addiction as a biological—rather than a social or cultural—phenomenon. As a result, the disease model of addiction offered an identity that was receptive to addiction treatment. While the previous century saw the construction of the addict as a person with a weak will, the disease model of addiction constituted the addict's condition as an illness treatable through performative acts. O'Neill's final plays, then, reflect more than the playwright's direct experience with addiction. They reveal the nation's ambivalence towards the concept of addiction as a disease, and with the addict as a sick person. Spectators in post-WWII America labeled O'Neill's final plays as autobiographical not only because he drew from his personal experiences while writing them, but also because such an approach was seen as critical to the addict's recovery. As a result, theater scholars continue to position O'Neill as an artist who utilized the theatre as a transformative tool to address how addiction impacted his own life. Through his compassionate depictions of characters suffering from a disease, O'Neill's late plays showed how recovery depended upon theatrical acts of self-reflection, self-narrative, and self-actualization. They also reflect how spectators saw autobiography as the discursive mode for recovering from addiction. In this sense, I claim that it was not so much that O'Neill's plays were true to life and accepted as such, but that their content on addiction necessitated a search for disclosure in the first place. Rather than explore how the theatre allowed O'Neill to channel his suffering, I instead consider why live performance serves as a durable site for legitimating addiction as an illness. As O'Neill's late plays show, self-disclosure does not lead to an emancipatory experience devoid of coercion. Despite claims made by the medical field and mutual aid groups of its liberatory potential, these acts of recovery encourage self-governance, and trade on the morally inflected narratives about the addict circulating since the nineteenth century. As the first study to consider the collaborative relationship between Eugene O'Neill and the medical field, I consider how the playwright's representation of addicts directly influenced doctors and scientists who engaged with his work from 1939 until the present.

Our climate is changing. Although the extent and rate of change are still uncertain, it is abundantly clear that the climate of the future will not resemble the climate of the past and will pose significant risks to people around the world. How well people adapt to address these risks will be determined by their adaptive capacity, their ability to instigate and implement change. Understanding and building adaptive capacity may therefore be key to reducing long-term vulnerability to global change. This dissertation clarifies the concept of adaptive capacity, synthesizes the substantial but largely unconnected body of research on adaptive capacity to date, and introduces a new methodological approach to conducting meta-analyses of adaptation science. I express a definition of adaptive capacity in mathematical terms that summarizes current theories on how adaptability is built, ties the concept to related concepts in adaptation, and poses questions about theoretical limits and thresholds for adaptation. I apply computational text analysis and network analysis tools to develop a concept model of adaptive capacity that identifies and organizes 158 determinants of adaptive capacity into 8 categories according to the functional role they play in building capacity. I propose a modular theory of adaptive capacity, in which all eight functional categories are critical but multiple pathways exist to achieve each function. This modular theory reconciles a theoretical debate in the literature and connects insights from existing theories with empirical findings from the field. I propose a new framework, the Adaptive Capacities Framework (ACF), based on the eight functional categories, that enables assessment of adaptive capacity across scales and within multi-scalar systems. Results demonstrate the fragmented nature of adaptive capacity research to date and propose new directions for future research. The dissertation also provides insights for practitioners seeking to prioritize adaptation efforts.

The technological revolution that started with digital electronics more than 50 years ago has pushed the limits of scalability in device fabrication. Device features are currently at the nanometer scale, which requires structures to be built with atomic level accuracy. Advances in nanotechnology have opened up an exciting area of research that allows for molecular level control of device surfaces. Organic molecules can provide the tailorability required to continue the progress of semiconductor technologies. Organic functionalization provides a pathway to control the surface at the molecular level. Fundamental understanding of the adsorption phenomena between organic molecules and the surface is critical to achieve a stable inorganic/organic interface for the creation of hybrid nanostructures. This thesis aims to expand our current toolkit on functionalization to molecules that have multiple functionalities that can react with the surface. The reaction mechanism of these molecules is complex as there are several driving forces that can play a role during adsorption and influence the final reaction products. This thesis covers the adsorption of multifunctional molecules on the Ge(100)-2×1 surface using a combination of experimental and theoretical techniques: Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, and density functional theory calculations. We explored the adsorption of four different molecules: 1,2,3-benzenetriol (C6H6O3); 1,3,5-benzenetriol (C6H6O3); 2-hydroxymethyl-1,3-propanediol (C4H10O3); pyrazine (C4H6N2). These are the first reported studies of these molecules on the Ge(100) surface. In order to understand the extent to which molecular geometry affects surface coverage, a detailed comparison of the adsorption of two triol molecules was carried out: 1,3,5-benzenetriol which has a rigid phenyl backbone and 2-hydroxymethyl-1,3-propanediol with a flexible alkyl backbone. DFT results showed that the rigid backbone exhibits a higher degree of strain, which translates to a loss of exothermicity in the reaction coordinate. Experiments showed that the flexibility of the alkyl backbone provides higher rotational degrees of freedom and an enhancement in surface coverage. In order to understand the effect of intermolecular interactions, the adsorption of 1,2,3-benzenetriol was explored. Interestingly, we found that at high coverage, intramolecular hydrogen bonding in singly and dually adsorbate products breaks to form intermolecular hydrogen bonding with a nearby adsorbate, which provides enhanced stabilization of the surface adduct. This additional stabilization may lower the reactivity of unreacted functional groups even if an empty nearby Ge site is available for reaction. The distribution of products resulted in primarily bidentate adsorbates, leaving an unreacted moiety at the surface. We also found evidence of coverage and temperature effects on adsorbed pyrazine molecules on the Ge surface. It was observed that this molecule adsorbs on Ge through both carbon and nitrogen moieties and the product distribution changes as a function of coverage and temperature. At low coverage, incoming molecules react primarily through C-cycloaddition reactions and N dative bonds. However, as the density of adsorbates increases, new incoming molecules adsorbed primarily through the nitrogen moiety. Furthermore, as the temperature increases, the product distribution changed from primarily non-activated dative bond to activated cycloaddition products. Overall, the studies in this thesis provide new insight into competition and selectivity in adsorption of multifunctional molecules on the Ge(100)-2×1 surface.

It is estimated that 2.4% of the US population has an artificial hip or knee as of 2010, and the prevalence of metallic implants continues to grow. Current imaging evaluation of complications near implants relies on X-Ray, which has limited contrast in soft tissues and thus limited sensitivity for early disease. Magnetic Resonance Imaging (MRI) is a safe medical imaging modality known for its excellent soft tissue contrast, which could be a useful tool for accurate, early and non-invasive assessment of complications. However, severe magnetic field variations induced by metal often render conventional MRI techniques non-diagnostic near the implants. Multi-spectral imaging (MSI) techniques resolve metal-induced field perturbations, but they suffer from long scan times that delay their widespread clinical adoption, and residual frequency-encoding artifacts that cause resolution loss close to the metal. This dissertation focuses on techniques to improve MRI near metal in terms of scan efficiency, artifact correction and delineation of implants. First, a signal model of MSI was introduced to compactly represent the signal distribution in the spectral dimension and enable accelerations of MSI scans. The model-based reconstruction was demonstrated to provide 3-fold acceleration beyond conventional acceleration techniques including parallel imaging and partial Fourier reconstruction. Next, a deep-learning-based reconstruction was presented to reduce the reconstruction times of optimization-based reconstruction and improve the reconstructed image quality of accelerated imaging near metal. Then, the frequency-encoding artifacts induced by metal, including signal hyper-intensities, signal oscillations and resolution loss, were analyzed and alternating-gradient MSI acquisitions were introduced to correct these artifacts. Finally, a method for susceptibility mapping inside signal voids was presented to delineate the geometry and material of metallic implants.

Photochemistry studies chemical reactions caused by absorption of light. Developing theoretical and computational tools for photochemistry will not only help better understand photochemical processes such as photosynthesis and vision, but can also provide guidelines about how molecular photodevices can be better designed. Therefore, the goal of my graduate research is to develop a set of computational tools for studying photochemical processes. Physical systems have a hierarchical structure, i.e. basic particles like nuclei and electrons interact leading to the formation of molecules, and molecules interact and change conformations giving rise to chemical reactions. Naturally, the corresponding theoretical methods should also follow this hierarchy. At the bottom level, we need molecular integrals to describe different types of interactions between basic particles. I introduced the automated code engine (ACE) that generates optimized codes for computing integrals on the graphical processing units, and developed several variants of tensor hyper-contraction (THC) approximations. ACE reduces the computational prefactor of integral evaluations whereas THC reduces the formal scaling. On top of the integrals, we then need electronic structure methods to describe the energies and forces for a molecule at any given nuclear configuration; including electron correlation is the key to having an accurate description. Here, I first developed single reference THC-MP2 to capture the dynamic correlations, and then developed multi-reference THC-CASPT2 method to incorporate static correlations simultaneously. These methods were later generalized to THC-MSPT2 to enable descriptions for excited states and conical intersections, both are critical for photochemistry. Finally, given the electronic structure methods, we then need methods to explore the potential energy surfaces. In particular, critical point search methods locate the important configurations (e.g. Franck-Condon point, conical intersections), while molecular dynamics methods generate trajectories describing how the molecules move and interact with each other. By interfacing the electronic structure methods that I developed with the geomeTRIC geometry optimizer and G-AIMS non-adiabatic dynamics framework, a complete toolbox for understanding photochemistry is provided.