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Book
193 pages : 8 illustrations.
Music Library
Book
259 pages (some folded) : illustrations, maps ; 31 cm.
SAL3 (off-campus storage)
Book
1 online resource.
The reentry blackout phenomenon affects most spacecraft entering a dense planetary atmosphere from space, due to a plasma layer that surrounds the spacecraft. This plasma layer is created by the ionization of ambient air due to shock and frictional heating created by the moving reentry vehicle, and, in some cases is further enhanced due to contamination by ablation products. The highly mobile electrons in the plasma cause a strong attenuation of incoming and outgoing electromagnetic waves, including those used for command and control, communication, and telemetry over a period referred to as the ``blackout period''. The blackout period may last up to several minutes, and at reentry speeds that may be of the order of 10 km/s, poses a serious safety hazard for the payload on board the spacecraft, especially for human spaceflight. In this work, we present a method for alleviation of reentry blackout using electric fields in a pulsed fashion. We study the reentry plasma's interaction with electronegative voltage pulses using computer simulations that incorporate models of the plasma's response to the applied electric field and interactions between the plasma sheath and the spacecraft surface. The simulations show how one can create pockets of depleted electron density in the reentry plasma sheath that may be used as ``communication windows'', thereby circumventing reentry blackout. Several parametric sweeps are also performed in order to design a blackout alleviation system. Finally, we present a discussion of experimental efforts to verify the simulation results and conclude with a conceptual design for a reentry communications blackout alleviation system based on the exclusive use of electric fields.
Book
1 online resource.
Cellular state is an old concept. However, scientists have only recently begun the systematic manipulation of cells to characterize and understand the functions of myriad states. As biotechnology advances enable innovative and large-scale measurements on cellular components, new biostatistical tools are required to make sense of the increased data size and complexity, which in turn augment our knowledge of cellular states. In this dissertation, I discuss my contributions to the study of cellular states from the theory and computation angles: 1) modeling and inference of regulatory gene networks with systems of nonlinear deterministic and stochastic differential equations; 2) partition-assisted clustering analysis of high-dimensional single-cell mass cytometry data; and 3) the alignment of subpopulations of cells across cytometry samples by similarity in the associated network structures. These contributions cement a platform that furthers the discussion of cellular states by framing it in both mechanistic and quantitative terms. This platform adds layers of biostatistical knowledge to Biosciences and enhances the discovery of cellular state properties.
Book
1 online resource.
Proteins must achieve their native conformations in order to function and avoid aberrant interactions within the cell. The folded state is formed rapidly for proteins with simple topologies. However, the folding of many large proteins with complex folds is assisted by the diverse array of molecular chaperones. The chaperonins are a unique class of essential protein chaperones found in all domains of life. These complexes are comprised of two 7-9 membered rings that undergo dramatic conforma- tional changes upon ATP binding and hydrolysis. Two classes of chaperonins exist, termed group I and group II. Group I chaperonins exist in bacteria and endosymbiotic organelles, while group II chaperonins are found in all eukaryotes and archaea. Both families promote the folding of substrates in an ATP dependent manner by encapsulating them within discrete central chambers. This thesis focuses on detailing the mechanism of a model group II chaperonin from the archaea Methanococcus maripaludis. Work was performed to define the native folding substrates of the complex as well as to detail the cooperative mechanism that controls all group II chaperonin cycling. A key allosteric interface was identified using a mathematical approach that predicts functionally important residues based on patterns of covariation found in multiple sequence alignments of a protein. Biochemical dissection of mutations at this interface reveal that the chaperonins have evolved to be less cooperative than attainable. Early evidence will be presented that suggests the N- and C-terminal tails of the chaperonin likely serve as coordinators of nucleotide cycling.
Book
1 online resource.
Understanding the kinetics of shock-compressed SiO2 is of great importance for mitigating optical damage for high-intensity lasers and for understanding meteoroid impacts. Experimental work has placed some thermodynamic bounds on the formation of high-pressure phases of this material, but the formation kinetics and underlying microscopic mechanisms are yet to be elucidated. In this study, by employing multi-scale molecular dynamics studies of shock-compressed fused silica and quartz, we find that silica transforms into a poor glass former that subsequently exhibits ultrafast crystallization within a few nanoseconds. We also find that, as a result of the formation of such an intermediate disordered phase, the transition between silica polymorphs obeys a homogeneous reconstructive nucleation and grain growth model. We construct a quantitative model of nucleation and grain growth, and compare its predictions with high-pressure silica crystal grain sizes observed in laser-induced damage and meteoroid impact events. Moreover, we have studied the quantum nuclear effects for high-pressure silica crystallization. While quantum nuclear effects play important roles in shock-induced chemical reactions and phase transitions, they are absent in classical atomistic shock simulations. To address this shortcoming, we couple the shock simulation with a colored-noise Langevin thermostat. We find that this semiclassical approach gives shock temperatures as much as 7% higher than classical simulations near the onset of crystallization in silica. We have also studied the impact of this approach on the kinetics of crystallization and the position of high-pressure silica melt line.
Book
1 online resource.
Spins are upset maneuvers in which an asymmetric stall over an airplane's wing causes it to enter a steep downward helical trajectory, often with reduced, annulled, or reversed control surface effectiveness. If these occur at low altitude, there might not be enough airspace to recover before colliding with the ground. Historically, this hazard has been addressed by careful aerodynamic design to suppress or minimize spin tendencies, and by flight crew training. Despite major reductions in accident rates, improvements have stagnated in recent decades, requiring new approaches to the problem. This dissertation proposes a software enabled approach, developing algorithms that can detect spins at an early stage and automatically recover with minimal altitude loss. To enable this study, a high angle of attack aerodynamic model of a typical general aviation aircraft is identified from wind tunnel and flight data. Using this model, the minimal altitude optimal control problem is investigated, and a spin recovery controller is designed. In addition, the relation between arrest delay and altitude loss is quantified, showing that altitude loss grows rapidly within the first turn. Motivated by these results, a methodology for designing spin detection schemes using different sensors is proposed. The methodology is applied to the same general aviation aircraft showing that detection at an early stage of the incipient phase is possible, resulting in as much as a fourfold reduction in altitude loss with respect to recovery from one-turn spins by a human pilot. Finally, the spin detection and recovery system is tested on small-scale UAVs, demonstrating the predicted fourfold altitude loss reduction. The results obtained indicate that such a system could help reduce spin-related accident rates by as much as 45%.
Book
1 online resource.
In this thesis, I explore how bacteria change shape. I used the model bacterial symbiont Sinorhizobium meliloti, which undergoes significant changes in cell shape during its nitrogen-fixing symbiosis with legume plants. Nitrogen is a limiting nutrient in the environment, and nitrogen-containing fertilizers are heavily used in agricultural systems. Organically available (fixed) nitrogen is energetically expensive to produce. Legume plants, in symbiosis with rhizobia soil bacteria, are able to fix nitrogen and do not require nitrogen-containing fertilizers. The model system of Medicago sativa (alfalfa) and the soil bacterium S. meliloti permit laboratory study of this process. As the human population continues to grow, it is essential to understand the conditions that are required for biological nitrogen fixation. M. sativa and S. meliloti initiate symbiosis by exchanging chemical calling cards. S. meliloti cells then invade plant roots. M. sativa forms nodules -- specialized root organs -- to house the bacteria. S. meliloti cells within the nodules differentiate to form nitrogen-fixing bacteroids. These bacteroid cells can be ten times larger and contain twenty times more genomic DNA than free-living cells, and they are often branched in contrast to the rod shaped bacteria found in the soil. The purpose of this dramatic morphological switch is not well understood but appears to be essential for nitrogen fixation. I used different culture media and environmental conditions and examined bacterial mutants to investigate what external factors are involved in S. meliloti free-living cell shape. I tested multiple conditions including microaerobic versus aerobic, salt concentrations in culture media, a range of temperatures, and different carbon sources. Of all these conditions, I discovered that only the presence of phosphate in growth media was strongly correlated to branching and an increase in cell size. This morphology is specifically related to the activity of a phosphate transporter-encoding gene, pstC. This result was confirmed through whole genome sequencing and epistatic expression of mutated and full-length pstC in different laboratory strains of S. meliloti. I hypothesize that an increase in extracellular phosphate concentration may be present within root nodules and may contribute to bacteroid differentiation. More work remains to be done to determine the role of phosphate in legume/rhizobia symbiosis and uncover the mechanism by which phosphate affects cell morphology.
Book
1 online resource.
Faults are one of the building-blocks for subsurface modeling studies. Incomplete observations of subsurface fault networks lead to uncertainty pertaining to location, geometry and existence of faults. In practice, gaps in incomplete fault network observations are filled based on tectonic knowledge and structural interpreter's intuition pertaining to fault relationships. Modeling fault network uncertainty with realistic models that represent tectonic knowledge is still a challenge. Although methods that address specific sources of fault network uncertainty and complexities of fault modeling exists, a unifying framework is still lacking. In this paper, a rigorous approach to quantify fault network uncertainty is proposed. Fault pattern and intensity information pertaining to fault networks are expressed by means of a marked point process, namely a marked Strauss point process. Fault network information represented using marked Strauss point process is constrained to fault surface observations (complete or partial) within a Bayesian framework. A structural prior model is defined to quantitatively express fault patterns, geometries and relationships within the Bayesian framework. Structural relationships between faults, in particular age-based fault abutting relations, are represented with an implicit, level-set based approach to represent abutting relations between fault surfaces. A Markov Chain Monte Carlo sampler is used to sample posterior fault network realizations that reflect tectonic knowledge and match fault observations. We apply the methodology to a field study from Nankai Trough and Kumano Basin. In this illustrative study, the target for uncertainty quantification is a deep site with attenuated seismic data with only partially visible faults and many faults missing from the survey or interpretation. A structural prior model is built from shallow analog sites that are believed to have undergone similar tectonics compared to the site of study. Fault network uncertainty for the field is quantified with fault network realizations that are conditioned to structural rules, tectonic information and partially observed fault surfaces. The proposed methodology generates realistic fault network models conditioned to data and a conceptual model of the underlying tectonics. In the second part of the thesis, an approach to incorporate fault network uncertainty in fluid flow problems is proposed. The main challenge is creating multiple structural frameworks and creating deformable grids prior to fluid flow simulation. Proxy-based workflow are presented in order to choose fault network realizations that result in most dissimilar fluid-flow responses. Thus, the computational load of evaluating exhaustive set of fault network realizations is reduced to a select few. The proxy-based approach is illustrated using a field case.
Book
1 online resource.
Since the turn of the 21st century, the international aid community has embraced an unprecedented focus on education in situations of conflict and emergency. This dissertation argues that this growing global focus indicates a dramatic shift in how the world responds to humanitarian crises and how it envisions the role of education. It points to an earlier world in which humanitarian and development domains were more strictly divided and where education, though integral part of development, was not seen as a necessary social service to be delivered in times of humanitarian emergency. In three articles, the dissertation examines the factors that have facilitated today's unprecedented global mobilization around education in crisis settings, studies the striking expansion of a global network that has been integral to this mobilization, and investigates how global specialists experience their work in this emergent professional field.
Book
447 pages ; 24 cm.
SAL3 (off-campus storage)
Book
1 online resource.
In this dissertation, we describe, implement, and test wavefunctions that describe short-range, so called dynamic correlation effects through the use of explicitly correlated geminal pair functions. We present both a single-configuration explicitly correlated analogue of a Hartree-Fock wavefunction, and a multi- configurational variant, a Geminal-augmented Complete Active Space Self Consistent Field method. These methods are designed to efficiently include dynamic correlation effects during variational optimization of the wavefunction, rather than resorting to a post hoc perturbation description. We test both methods on a number of model systems and show that the geminal-augmented wavefunction is capable of describing short-range correlation effects. In addition, we present a fully exact implementation of the algorithms necessary to evaluate the many-electron integrals that arise in explicitly correlated wavefunctions.
Book
268 pages : 17 illustrations ; 23 cm.
Green Library
Book
735 pages : many illustrations (partly color) ; 31 cm.
Art & Architecture Library (Bowes)
Book
1 online resource.
As global surface temperatures rise due to human-induced greenhouse gas emissions, increases in atmospheric moisture and changes in atmospheric circulation result in altered precipitation patterns. The proportion of total precipitation that falls in extreme events is increasing in many places around the world, including the United States (U.S.) Midwest, a globally important region of agricultural production. Agriculture is a major source of reactive nitrogen (N) inputs to the environment, where N can have a range of detrimental consequences. In particular, N leached from agriculture contributes to widespread eutrophication of estuaries and coastal ecosystems, and contamination of groundwater where it poses threats to human health. The implications of more extreme rainfall patterns for N cycling and losses in agricultural systems are uncertain. In this dissertation, I evaluate the effects of more extreme rainfall patterns on N leaching from field crop systems in the U.S. Midwest, and the extent to which the N leaching response depends on cropping system management. In Chapter 2, I use a short-term laboratory incubation to evaluate the effects of larger, less frequent water additions on N cycling in and potential N leaching from agricultural soils under conventional, no-till, and biologically-based management. I found that larger, less frequent water additions increased total N leaching compared to smaller, less frequent water additions, and that differences in N leaching were mainly explained by differences in drainage following water additions. Soils from biologically-based cropping systems exhibited greater potential N leaching than soils from conventional and no-till cropping systems, likely due to higher concentrations of labile organic matter and rates of N turnover. Chapters 3 and 4 are based on a 234-day manipulative rainfall experiment conducted at the Kellogg Biological Station Long Term Ecological Research site in southwest Michigan, in which extreme rainfall patterns were imposed in conventional and no-till cropping systems. Chapter 3 evaluates the effects of extreme rainfall patterns on deep drainage and soil water content. In contrast to predictions by other researchers, I found no evidence that extreme rainfall patterns increase surface soil dryness or variability in surface soil water content. I also found that extreme rainfall patterns increased water storage in deep soil layers as well as water flux beyond the root zone. Chapter 4 builds on Chapter 3, combining simulations of water flux with observations of ecosystem N dynamics to evaluate the effects of extreme rainfall patterns on nitrate leaching. In conventional cropping systems, extreme rainfall patterns increased nitrate leaching, while in no-till cropping systems, extreme rainfall patterns had no effect on nitrate leaching. Extreme rainfall patterns also increased net N mineralization and inorganic N concentrations in surface soils in both cropping systems, consistent with increased nitrate leaching under extreme rainfall patterns in the conventional cropping system only. I suggest that the difference in the response of nitrate leaching to extreme rainfall patterns observed between the conventional and no-till cropping systems may be related to hydrological flow paths, with greater macropore or bypass flow in no-till soils. Together, these findings suggest that altered rainfall patterns driven by climate change may exacerbate nitrate leaching from agricultural systems. Moreover, cropping system management (e.g. conventional vs. no-till vs. biologically-based) appears to play a complex but important role in determining the extent of the effect.
Book
1 online resource.
We introduce an online learning platform that scales collaborative learning. We study the impact of team formation and the team formation process in massive open online classes. We observe that learners prefers team members with similar location, age range and education level. We also observe that team members in more successful teams have diverse skill sets. We model the team formation process as a cooperative game and prove the existence of stable team allocations. We propose a polynomial-time algorithm that finds a stable team allocation for a certain class of utility functions. We use this algorithm to recommend teams to learners. We show that team recommendations increase the percentage of learners who finish the class.
Book
1 online resource.
We study the problem of interpolating all values of a discrete signal f of length N when d< N values are known, especially in the case when the Fourier transform of the signal is zero outside some prescribed index set J; these comprise the (generalized) bandlimited spaces B^J. The sampling pattern for f is specified by an index set I, and is said to be a universal sampling set if samples in the locations I can be used to interpolate signals from B^J for any J. When N is a prime power we give several characterizations of universal sampling sets, some structure theorems for such sets, an algorithm for their construction, and a formula that counts them. There are also natural applications to additive uncertainty principles. Universal sets are related to the invertible, but possibly poorly conditioned submatrices of a discrete Fourier transform matrix. At the other extreme, we study the problem of finding unitary submatrices of the discrete Fourier transform matrix. This problem is related to a diverse set of questions on idempotents on Z_N, tiling Z_N, difference graphs and maximal cliques. Each of these is related to the problem of interpolating a discrete bandlimited signal using an orthogonal basis.
Book
1 online resource.
Advancements in next-generation low-cost, high-throughput DNA sequencing technologies have made it possible to sequence a large number of human genomes. To date, at least tens of thousands of individuals have been whole genome sequenced. Even more large-scale population sequencing projects are actively underway or will be launched in the foreseeable future. This vast amount of genomic data undoubtedly advances the characterization of human genome variation and supports disease studies across diverse cohorts. However, the challenging problem of how to efficiently and precisely determine individual-level genomic differences from this huge amount of sequencing data exists. This natural first step of leveraging sequencing data for genomic analyses can be computational intensive, while the quality of the restored genomes influences a wide variety of downstream applications, such as association studies, personalized medicine, and population genomics. In this dissertation I present computational methods to approach this fundamental problem in the context of ever-increasing sequencing data volume and demonstrate the effectiveness and efficiency of these methods using real data from latest population sequencing projects. First, I present a new method that maps reads of newly sequenced human genome to a large collection of genomes, aiming to reduce the inherent biases induced by aligning to any single reference genome. Second, I introduce an approach, named Reveel, for single nucleotide variant calling and genotype calling of large cohorts that have been sequenced at a low coverage, that aims for computational efficiency as well as accuracy in capturing linkage disequilibrium patterns present in rare haplotypes. Third, on the basis of the Reveel framework I present a reference-based approach that effectively incorporates genotypes from completed projects to improve the genotyping quality of new datasets while maintaining low computational costs. Finally, I demonstrate an application of genotype information for improving the efficiency of identity-by-descent detection from a large cohort.
Book
1 online resource.
In order to facilitate the rapid adoption of renewable energy, it is necessary to identify low cost renewable-based systems to compete with fossil-based electricity generation. In this work, we use computational optimization to explore the integration of solar thermal energy into new or existing fossil energy systems. We use combined design and operations optimization to incorporate the time-varying aspects of solar thermal operation into the design decision. We explore two distinct systems. The first is a carbon capture retrofit to a coal-fired power plant, where we consider supplying the necessary auxiliary heat for carbon capture from both solar thermal and natural gas systems. The second is an integrated solar combined cycle, where solar and natural gas resources are used together for electricity production. These systems are both modeled with the Hybrid Power Plant Optimization (HyPPO) model (expanded in this work), which is a flexible model that uses modular representations of different interacting systems. These systems include gas turbines, multi-pressure heat recovery steam generators, steam turbines, and solar thermal fields, where interactions are modeled through mass and energy balances. In each of the power plant designs considered, we use HyPPO to perform optimizations under a variety of economic scenarios in order to assess the viability of solar thermal integration in different potential markets. Post combustion carbon capture using amine-based solvents, considered in the first part of this work, requires a significant thermal load. In a traditional retrofit carbon capture system, the efficiency penalty associated with extracting steam to meet this thermal demand is high. Consequently, we explore supplying this thermal load with an auxiliary power plant using either solar thermal energy, natural gas energy, or both. The solar thermal field considered is a low-temperature, enclosed-trough solar thermal system originally designed to provide steam for enhanced oil recovery. We examine a power plant that is limited by a CO2 emissions constraint of 499 kg/MWh, which is consistent with state-level legislation in multiple locations throughout the United States. We optimize the nonlinear, time-dependent operations over a series of days representative of a year, along with large-scale system design choices. The decision to use solar thermal, natural gas, or both was found to be sensitive to economic conditions -- particularly the electricity clearing price, natural gas price, and discount rate. Designs utilizing solar thermal energy are found to be profitable in a variety of economic scenarios, including high electricity clearing prices and low discount rates. The second power plant type examined is an integrated solar combined cycle (ISCC), which combines thermal streams from both a natural gas and a solar thermal system to produce electricity. In order to explore ISCC designs using computational optimization, we expand HyPPO to include a proxy model of a solar thermal field that, when combined with the heat recovery steam generator and natural gas turbine modules, allows for fast ISCC simulations. We use this model to explore the optimal operations of an ISCC. In order to explore ISCC design variables, which include heat exchange element sizes, turbine sizes, system pressures, and the integration strategy between solar thermal and natural gas streams within the heat recovery steam generator, we develop a computationally efficient reduced-form model. We use this reduced-form model for combined design and operations optimization, and find that a preferred integration strategy is to use the solar thermal system to supplement the low-pressure water stream preheating and evaporation. By applying bi-objective optimization, we explore the tradeoff between ISCC economics (net present value) and CO2 emissions intensity. We then quantify, under a variety of economic scenarios, the cost of CO2 avoided. The results for the cost of CO2 avoided suggest that our optimal ISCC designs may be competitive with other approaches for low-carbon electricity generation, such as carbon capture and storage.
Book
1 online resource.
In my three-article dissertation, Concerning the Other: Empathic Discourse in Worldwide, National, and Student-Authored Textbook Historical Narratives, I explore how textbook authors empathize with marginalized groups. My data includes approximately 1,000 textbooks published from 1910 to 2010 from over 100 countries around the world, 50 U.S. textbooks published from 1860 to 2015, and over 100 digital history textbook chapters produced by approximately 250 students in three different U.S. high school settings. My first study begins at the global level. I use descriptive statistics to analyze the extent to which textbook authors empathize with discuss minorities, women, immigrants and workers. I measure where and when textbooks mention these marginalized groups as having rights and experiencing discrimination or oppression, themes which some previous scholars conducting cross-national research have called the valorization of diversity (Ramirez, Bromley, and Russell 2009). While previous cross-national research documents a linear expansion of all these variables from the mid-20th century onwards, I find that this expansion was preceded by an earlier expansionist wave beginning in the 1920s, which was followed by a receding wave during the mid-20th century before slowly rising again thereafter. I posit that the first wave can be explained by incipient global rights discourse that emerged in the aftermath of World War I and retracted after World War II as a reaction against Communism. For my next study, I shift to the national level and qualitatively examine the differing ways U.S. textbook authors compassionately discuss dominant groups vis-à-vis marginalized groups. I analyze how these authors construct historical narratives that engage a reader, such as their use of active versus passive writing style. Although textbook authors, in general, write in an increasingly distant manner over time, I find they are more likely to write about the suffering of minorities in a distancing way compared to dominant populations. I argue that textbook authors, traumatized by the Civil War in which white Americans committed violence against other white Americans, used alter their use of linguistic valence to highlight the suffering of Whites and downplay hardships experienced by Blacks and Native Americans in order to cement white unity among their readers. I use a similar methodology for my final study, where I narrow in to the local level to analyze students as the agents of historical production. Visiting two public high schools in the Bay Area of California and one private high school in North Carolina, I examine the extent to which high school students empathically portray the experiences of different marginalized groups when given free reign to create their own digital history textbook chapters. Although I find that students use the affordances of digital technology to create empathic narratives in novel ways, students still internalize the distancing writing convention of traditional textbooks authors while similarly being more likely to write empathically about dominant elites as well as groups in which they identify. Overall, I hope my research demonstrates to textbook writers and social studies teachers alike that history textbook authors can unconsciously entrench a lack of concern for the experiences of marginalized groups through both their writing content and style, and that writing styles can be just as important as content in influencing how readers might empathize with both the historical and contemporary experiences of marginalized groups.