Nonpoint source nitrogen runoff from agriculture is a significant contributor to eutrophication in the Chesapeake Bay. The state of Virginia has developed several market and incentive-based water quality credit trading programs to meet federal water quality objectives. In theory, these programs offer a mechanism to achieve environmental goals at least cost. However, in practice these programs face ongoing challenges arising from limited participation by farmers who supply water quality credits and, as a result, often fail to achieve cost efficiency. We build a flexible, accessible, and modular bioeconomic modeling system as a proof-of-concept to evaluate economic-environmental tradeoffs farmers face in an effort to support program participation and achieve environmental goals. We couple a biophysical nitrogen mass-balance model with an agricultural production model and apply the tool to study diverse agroforestry practices. We evaluate the relative efficiency of these practices by empirically estimating a production possibility frontier. We then use our bioeconomic modeling results to define the minimum willingness to accept of farmers, in terms of water quality credit prices, to adopt agroforestry practices that deliver water quality improvements. We extend our model results to estimate water quality credit premiums to compensate risk-averse farmers for undertaking production practices subject to relatively volatile prices in niche fruit markets. We demonstrate that the model generally simulates real-world credit prices, and highlight potential improvements in design for Virginia's trading program. In particular, quality credit trading programs could be more effective and efficient if credits awards reflect heterogeneity in the environmental benefits associated with nuanced land-use alternatives. Our modeling tool offers a framework to support incentive programs that are both economically sound and biophysically grounded. / Master of Science / High levels of nitrogen in the Chesapeake Bay have become an environmental concern for regulatory agencies. A significant portion of nitrogen pollution in the Chesapeake Bay comes from agricultural activities in the Chesapeake Bay watershed. Agricultural nitrogen pollution is not directly regulated at the federal level, so some states have adopted market-based mechanisms to curb emissions. However, some of these programs are seeing less farmer participation than expected. We suggest that part of the low participation rates may be due to program design, and the impact risk plays in farmer decision-making. In an effort to better understand participation in the programs, we develop a method to model these programs’ environmental and economic outcomes. Our method couples a mechanistic model of nitrogen pollution with an agricultural production model and evaluates tradeoffs between economic and environmental values. We find that the modeling method shows promise as a tool for policymakers, researchers, and farmers interested in pollution abatement programs. As a proof-of-concept, we apply the model to a Virginia market-based program and test our low-participation hypotheses. We find that the programs may be more effective if they recognize a greater diversity of farming practices. Our modeling tool offers a framework to support pollution abatement programs that are both economically feasible and environmentally effective.

Electricity is a complex and interesting topic for research and investigation. From a systems level, electricity includes many steps from its generation (power plants) to transmission and distribution to delivery and final use. Within each of these steps are a set of tradeoffs that are region-specific, depending heavily on the types of generation technologies and input fuels used to generate the electricity. These tradeoffs are complex and often not positively correlated to one another, producing a web of information that makes conclusions regarding the net benefit of changes to the electricity generation mix unobvious and difficult to determine using general rules of thumb. As individuals look to change the mix of technologies and fuels used to generate electricity for environmental or economic reasons, this complex web results in a lack of clarity and understanding of the consequences of particular choices. Quantitative tools could provide individuals with clear information and improved understanding of the tradeoffs associated with changes to the electricity mix. Unfortunately, prior to this research, no such tools existed that provided a clear, rigorous, and unbiased quantitative comparison of the region-specific environmental and economic tradeoffs associated with changes to the electricity mix. This research filled this gap by developing a methodology for calculating the environmental and economic impacts of changes to the electricity generation mix for individual regions. This methodology was applied specifically to Texas to develop the Texas Interactive Power Simulator (TIPS), an interactive online tool accessible via the internet. This tool is currently used for direct instruction at The University of Texas at Austin for undergraduate courses. Preliminary data were collected to determine the usefulness of this tool as a classroom aid. These data revealed that a majority of students enjoy using the TIPS tool, felt that they learned about the tradeoffs of electricity generation methods by using TIPS, and wish that there were more learning tools like TIPS available to them. This research also investigated the potential to use energy efficiency to satisfy a portion of the electricity demand that would otherwise be supplied using a generation technology. The methodology and series of decision criteria that were developed with this investigation were used to determine the amount of generation that could reasonably be satisfied with energy efficiency technologies and supportive policies for a particular region of interest, in this case Texas. This methodology was established using the Rosenfeld Effect as a basis for evaluating the energy efficiency potential in a specific region, providing a more realistic maximum energy efficiency value than using theoretical maximum gains based on current best available technology. It was then compared to efficiency potential estimates by the American Council for an Energy-Efficient Economy (ACEEE) and the Public Utility Commission of Texas (PUCT). In this research, I found that Texas is unlikely to realize more than an annual savings of 11% or about 1.5 megawatt-hours per capita compared to 2007 use levels based on nominal energy efficiency approaches. When this potential savings was applied to offset future demand increases in Texas, it was found that new generation capacity would still be needed over the next few decades to meet increasing total electricity demand. I used the economic and environmental tradeoff analysis and energy efficiency limitations methodologies that I established in my research to calculate the economic and environmental tradeoffs of changes to the electricity mix resulting from several scenarios, including federal energy and climate legislation, nuclear renaissance, high wind power growth, and maximizing energy efficiency. The outputs from these scenarios yielded the following observations: 1. Energy efficiency is unlikely to replace more than 11% of total per capita electricity demand in Texas. This level of energy efficiency might reduce total demand in the state, but population growth and its corresponding impacts on state electricity use might outpace the savings from energy efficiency in the long-term. This population growth could result in an overall increase in total annual state electricity use, despite energy efficiency gains. 2. While nuclear power might be environmentally advantageous from the standpoint of total emission of greenhouse gases compared to fossil fuel-fired power plants, it has very high up-front capital costs and is very water-intensive. 3. A federal combined energy efficiency and renewable portfolio standard might require states to install new renewable power generation capacity. In some states, including Texas, the amount of required new generation capacity may be small because of existing state initiatives encouraging renewable generation capacity to be installed in the state and the potential to offset some generation requirements using energy efficiency. / text

A fundamental challenge faced by all organisms is the risk of infection by pathogens that can significantly reduce their fitness. The evolutionary dynamic between hosts and pathogens is expected to be a coevolutionary cycle, as pathogens evolve by increasing their level of virulence and hosts respond by increasing their level of resistance. The factors that influence the dynamics of adaptation by pathogen and host in response to one another are not well understood. Social insects live in dense colonies in high-pathogen soil environments, making them an ideal model system to study the factors influencing the evolution of pathogen resistance. In this thesis work, I investigated several alternative hypotheses to explain patterns of host resistance to entomopathogenic fungi in the harvester ant genus Pogonomyrmex: that high resistance is associated with high environmental pathogen loads, that local adaptation leads to increased resistance to coevolved pathogen populations, that life history tradeoffs increase allocation to resistance in harsher environments, and that increased genetic diversity caused by interspecific hybridization enhances inherent resistance. First, I characterized patterns of spatial variation in abundance and diversity of fungal pathogens among habitats of Pogonomyrmex species. I found 17 genera of fungi in the soil, six of which were entomopathogenic. Lower precipitation habitats, where P. rugosus occurs, had the lowest diversity, while the highest was experienced by the H lineage, one of two hybrid populations. When actual infection rates of field-caught workers were compared, the mesic-habitat P. barbatus was infected significantly more often. These results suggest that habitat does plays a role in fungal diversity, and that species are exposed to more entomopathogens may be more likely to get infected. Second, I tested experimentally whether hybridization and or habitat differences play a role in pathogen resistance by testing the effect of soil type and species identity on infection rates in pupae of the two species and their hybrids. This experiment showed P. rugosus ants had the highest inherent resistance to infection, supporting the life history tradeoff hypothesis. This suggest that Pogonomyrmex ants species are allocating their resources differently according to their environment, with more stressful environment leading to less investment in reproduction and more in protection against pathogens. Overall our study shows that environment plays a role in differences in infection risk, while genetic effects such as hybridization may not play a role in pathogen resistance.

The difference in evolutionary interests of males and females can select for traits that favour an individual??s fitness at the expense of their mate??s lifetime fitness. Despite the widespread occurrence of this sexual conflict over reproductive interactions, however, research to date has largely focused on the fitness costs imposed on females by manipulative males. Empirical evidence is particularly sparse for how mating can also be costly for males, the genetic structure of traits involved in reproductive interactions, and how sexual conflict can modify sexual selection in general. My aim was to explore the broader evolutionary consequence of sexual conflict and male-female interactions. In the nuptial-feeding Australian ground cricket, Pteronemobius sp., I used an experimental evolution approach to explore how diet and sexual conflict interact to determine the costs of mating. In the Australian black field cricket, Teleogryllus commodus, I used molecular and quantitative genetic approaches to characterise the fitness consequences and genetic basis of spermatophore attachment, a trait at the centre of inter-locus sexual conflict, and then related this to both condition and male attractiveness. Finally, in T. commodus, I quantified how sexual conflict alters the sexual selection acting on male sexual traits and how this in turn shapes genetic architecture and the persistence of additive genetic variance. My results demonstrate the complex nature of reproductive interactions between males and females. Importantly, I show that diet can mediate the expression of sexual conflict in a mating system and shape the evolution of male life-span. I also show that reproductive interactions influence the fitness benefits that both male and females obtain from mating in ways that are not predicted by current theory and that much of the potential for such traits to co-evolve is via a common genetic association with condition. Finally, I demonstrate that sexual conflict can profoundly modify the process and outcome of sexual selection, thereby influencing how additive genetic variation is maintained in a suite of male sexual traits. These results highlight the need for a greater integration of sexual conflict and sexual selection theory as the evolutionary potential and significance of sexual conflict may currently be underestimated.

The difference in evolutionary interests of males and females can select for traits that favour an individual??s fitness at the expense of their mate??s lifetime fitness. Despite the widespread occurrence of this sexual conflict over reproductive interactions, however, research to date has largely focused on the fitness costs imposed on females by manipulative males. Empirical evidence is particularly sparse for how mating can also be costly for males, the genetic structure of traits involved in reproductive interactions, and how sexual conflict can modify sexual selection in general. My aim was to explore the broader evolutionary consequence of sexual conflict and male-female interactions. In the nuptial-feeding Australian ground cricket, Pteronemobius sp., I used an experimental evolution approach to explore how diet and sexual conflict interact to determine the costs of mating. In the Australian black field cricket, Teleogryllus commodus, I used molecular and quantitative genetic approaches to characterise the fitness consequences and genetic basis of spermatophore attachment, a trait at the centre of inter-locus sexual conflict, and then related this to both condition and male attractiveness. Finally, in T. commodus, I quantified how sexual conflict alters the sexual selection acting on male sexual traits and how this in turn shapes genetic architecture and the persistence of additive genetic variance. My results demonstrate the complex nature of reproductive interactions between males and females. Importantly, I show that diet can mediate the expression of sexual conflict in a mating system and shape the evolution of male life-span. I also show that reproductive interactions influence the fitness benefits that both male and females obtain from mating in ways that are not predicted by current theory and that much of the potential for such traits to co-evolve is via a common genetic association with condition. Finally, I demonstrate that sexual conflict can profoundly modify the process and outcome of sexual selection, thereby influencing how additive genetic variation is maintained in a suite of male sexual traits. These results highlight the need for a greater integration of sexual conflict and sexual selection theory as the evolutionary potential and significance of sexual conflict may currently be underestimated.

Treating water in order to reduce human and environmental risks requires the use of electricity and chemicals, the generation of which creates emissions of air pollutants such as NOx, SO2, PM2.5, and CO2. Emissions of air pollutants establishes a health and environmental risk tradeoff between air and water pollution. Addressing air-water tradeoffs by adopting a one environment framework requires new methods for quantifying these tradeoffs, new technologies to minimize air-water tradeoffs, and new tools for decision makers to incorporate these tradeoffs into compliance decisions. In my thesis, I develop methods for quantifying damages from air emissions associated with water treatment; assess the feasibility of forward osmosis (FO), a technology which holds the promise to avoid air-water tradeoffs; and create a tool to holistically assess compliance with air and water emission standards for coal-fired power plants (CFPPs). I start my thesis by creating a method to quantify the damages caused by the air emissions that resulting from the treatment of drinking water (Chapter 2), municipal wastewater (Chapter 3), and flue gas desulfurization (FGD) wastewater (Chapter 4). These studies use life-cycle models of energy and chemical consumption for individual water treatment unit processes in order to estimate embedded emissions of criteria air pollutants and greenhouse gasses per cubic meter of treated water. Damages from these additional air emissions are assessed and incorporated into benefit-cost analyses. I find that for drinking water rules, the net benefit of currently implemented rules remains positive but the promises of net benefits for some proposed rules are conditional on the compliance technology that is selected. For municipal wastewater, I find that while there are ~$240 million (in 2012 USD) benefits in air emission reduction from installing biogas-fueled electricity generation nationwide, there are several states where biogas-fueled electricity creates more air emissions than it displaces. For FGD wastewater treatment, I find that complying with the effluent limitation guidelines has an expected ratio of benefits to cost of1.7-1.8, with damages concentrated in regions with large chemical manufacturing industries or electricity grids that are heavily reliant on coal. In the next part of the thesis, I assess the techno-economic feasibility of power plant waste heat driven FO to reduce the air emissions associated with FGD wastewater treatment. In Chapter 5, I assess the quantity, quality and the spatial and temporal availability of waste heat from US coal, nuclear, and natural gas power plants. I find that while 18.9 billion GJ of potentially recoverable waste heat is discharged into the environment, only 900 million GJ of that heat is from the flue gas and is at a temperature high enough to drive water purification using forward osmosis (FO). In Chapter 6, I build a model of FO to assess its thermal energy consumption and find that the 900 million GJ of waste heat produced at coal and natural gas power plants is sufficient to meet their boiler feedwater and FGD wastewater treatment needs. In Chapter 7, I incorporate cost into the energy consumption model of FO, and conclude that treatment of FGD and gasification wastewater using waste heat driven FO is economically competitive with mechanical vapor recompression. In Chapter 8, I create an energy-balance model of a CFPP and nine environmental control technologies for compliance with FGD wastewater and carbon capture regulations. I use this model to maximize plant revenue at the National Energy Technology Laboratory’s 550 MW model CFPP without carbon capture. I find that revenue is maximized by using residual heat for water treatment or carbon capture. If both carbon capture and zero liquid discharge water treatment regulatory standards are in place, I conclude that the plant maximizes revenue by allocating residual heat and steam to amine-based carbon capture and electricity to mechanical vapor recompression for FGD wastewater treatment.

This thesis makes fundamental computational and statistical advances in testing and estimation, making critical progress in theory and application of classical statistical methods like classification, regression and hypothesis testing, and understanding the relationships between them. Our work connects multiple fields in often counter-intuitive and surprising ways, leading to new theory, new algorithms, and new insights, and ultimately to a cross-fertilization of varied fields like optimization, statistics and machine learning. The first of three thrusts has to do with active learning, a form of sequential learning from feedback-driven queries that often has a provable statistical advantage over passive learning. We unify concepts from two seemingly different areas—active learning and stochastic firstorder optimization. We use this unified view to develop new lower bounds for stochastic optimization using tools from active learning and new algorithms for active learning using ideas from optimization. We also study the effect of feature noise, or errors-in-variables, on the ability to actively learn. The second thrust deals with the development and analysis of new convex optimization algorithms for classification and regression problems. We provide geometrical and convex analytical insights into the role of the margin in margin-based classification, and develop new greedy primal-dual algorithms for non-linear classification. We also develop a unified proof for convergence rates of randomized algorithms for the ordinary least squares and ridge regression problems in a variety of settings, with the purpose of investigating which algorithm should be utilized in different settings. Lastly, we develop fast state-of-the-art numerically stable algorithms for an important univariate regression problem called trend filtering with a wide variety of practical extensions. The last thrust involves a series of practical and theoretical advances in nonparametric hypothesis testing. We show that a smoothedWasserstein distance allows us to connect many vast families of univariate and multivariate two sample tests. We clearly demonstrate the decreasing power of the families of kernel-based and distance-based two-sample tests and independence tests with increasing dimensionality, challenging existing folklore that they work well in high dimensions. Surprisingly, we show that these tests are automatically adaptive to simple alternatives and achieve the same power as other direct tests for detecting mean differences. We discover a computation-statistics tradeoff, where computationally more expensive two-sample tests have a provable statistical advantage over cheaper tests. We also demonstrate the practical advantage of using Stein shrinkage for kernel independence testing at small sample sizes. Lastly, we develop a novel algorithmic scheme for performing sequential multivariate nonparametric hypothesis testing using the martingale law of the iterated logarithm to near-optimally control both type-1 and type-2 errors. One perspective connecting everything in this thesis involves the closely related and fundamental problems of linear regression and classification. Every contribution in this thesis, from active learning to optimization algorithms, to the role of the margin, to nonparametric testing fits in this picture. An underlying theme that repeats itself in this thesis, is the computational and/or statistical advantages of sequential schemes with feedback. This arises in our work through comparing active with passive learning, through iterative algorithms for solving linear systems instead of direct matrix inversions, and through comparing the power of sequential and batch hypothesis tests.

Conservation and restoration of quaking aspen in the western United States requires an understanding of how and when aspen clones became established, how clones adapt to environmental challenges, and how individual clones interact within stands. I used molecular tools to identify individual clones in a natural population of aspen in southern Utah and detected high and low levels of clonal diversity within stands. Stands with high clonal diversity were located in areas with a more frequent fire history, indicating that fires may have prepared sites for seed germination and establishment over time. Conversely, areas of low clonal diversity corresponded to areas with less frequent fire. The same molecular tools were then used to investigate clonal interactions/succession over relatively recent time. For this portion of the study I sampled small, medium, and large aspen ramets (stems) at 25 subplots within spatially separated one-hectare plots, and mapped the clonal identities. I found that approximately 25% of the clones appeared to be spreading into adjacent clones, while 75% of the clones had a stationary pattern. In the final portion of the study, I again used molecular tools to identify aspen clones and investigated tradeoffs between growth and defense chemistry in mature, naturally-occurring trees. Growth was estimated using a ten-year basal area increment, and the percent dry weight of salicortin, tremulacin, and condensed tannins was measured in the same trees. Overall I discovered evidence for a tradeoff between growth and salicortin/tremulacin, and a marginally significant but positive relationship between growth and condensed tannins.

<p>An ever-increasing number of Internet applications, such as content and software distribution, distance learning, multimedia streaming, teleconferencing, and collaborative workspaces, need efficient and secure multicast communication. However, efficiency and security are competing requirements and balancing them to meet the application needs is still an open issue.</p><p>In this thesis we study the efficiency versus security requirements tradeoffs in group key management for multicast communication. The efficiency is in terms of minimizing the group rekeying cost and the key storage cost, while security is in terms of achieving backward secrecy, forward secrecy, and resistance to collusion.</p><p>We propose two new group key management schemes that balance the efficiency versus resistance to collusion. The first scheme is a flexible category-based scheme, and addresses applications where a user categorization can be done based on the user accessibility to the multicast channel. As shown by the evaluation, this scheme has a low rekeying cost and a low key storage cost for the controller, but, in certain cases, it requires a high key storage cost for the users. In an extension to the basic scheme we alleviate this latter problem.</p><p>For applications where the user categorization is not feasible, we devise a cluster-based group key management. In this scheme the resistance to collusion is measured by an integer parameter. The communication and the storage requirements for the controller depend on this parameter too, and they decrease as the resistance to collusion is relaxed. The results of the analytical evaluation show that our scheme allows a fine-tuning of security versus efficiency requirements at runtime, which is not possible with the previous group key management schemes.</p> / Report code: LiU-TEK-LIC-2003:53.

Advances in configurable logic technology have permitted the development of low-cost, high-speed configurable devices, allowing one or more soft processor cores to be introduced into a configurable computing system. Soft processor cores offer logic-area savings and reduced configuration times when compared to the hardware-only implementations typically used for application specific acceleration. Programs for a soft processor core are small and simple compared to the design of a hardware core, but can leverage custom hardware within the processor core to provide greater acceleration for specific applications. This thesis presents several configurable system models, and implements one such model on a Nios Embedded Processor Development Board. A software programmable and hardware configurable lightweight processor core known as the FAST CPU is introduced. The configurable system implementation attaches several FAST CPUs to a standard Nios processor to create a system for experimentation with application specific acceleration. This system incorporating the FAST CPUs was tested for bus utilization behaviour, computing performance, and execution times for a minheap application. Experimental results are compared to the performance of a software-only solution, and also with previous research results. Experimental results verify that the theory and models used to predict bus utilization are correct. Performance testing shows that the FAST CPU is approximately 25% slower than a general purpose processor, which is expected. The FAST CPU, however, is 31% smaller in terms of logic area than the general purpose processor, and is 8% smaller than the design of a hardware-only implementation of a minheap for application specific acceleration. The results verify that it is possible to move functionality from a general purpose processor to a lightweight processor, and further, to realize an increase in performance when a task is parallelized across multiple FAST CPUs. The experimentation uses a procedure by which a set of equations can be derived for predicting bus utilization and deriving a cost-benefit curve for a coprocessing entity. They are applied to a specific system in this research, but the methods are generalizable to any coprocessing entity.