VictorJuiz/thesis huggingface dataset:
Quantifying Urban and Agricultural Nonpoint Source Total Phosphorus Fluxes Using Distributed Watershed Models and Bayesian Inference
Keywords: Bayesian
Hydrology
Modelling
Watershed

Despite decades of research, the water quality of many lakes is impaired by excess total phosphorus loading. Four studies were undertaken using watershed models to understand the temporal and spatial variability of diffuse urban and agricultural total phosphorus pollution to Hamilton Harbour, Ontario, Canada. In the first study, a novel Bayesian framework was introduced to apply Spatially Referenced Regressions on Watershed Attributes (SPARROW) to catchments with few long term load monitoring sites but many sporadic monitoring sites. The results included reasonable estimates of whole-basin total phosphorus load and recommendations to optimize future monitoring. In the second study, the static SPARROW model was extended to allow annual time series estimates of watershed loads and the attendant source-sink processes. Results suggest that total phosphorus loads and source areas vary significantly at annual timescales. Further, the total phosphorus export rate of agricultural areas was estimated to be nearly twice that of urban areas. The third study presents a novel Bayesian framework that postulates that the watershed response to precipitation occurs in distinct states, which in turn are characterized by different model parameterizations. This framework is applied to Soil-Water Assessment Tool (SWAT) models of an urban creek (Redhill Creek) and an agricultural creek (Grindstone Creek) near Hamilton. The results suggest that during the limnological growing season (May – September), urban areas are responsible for the bulk of overland flow in both Creeks: In Redhill Creek, between 90% and 98% of all surface runoff, and in Grindstone Creek, between 95% and 99% of all surface runoff. In the fourth chapter, suspended sediment is used as a surrogate for total phosphorus. Despite disagreements regarding sediment source apportionment between three model applications, Bayesian model averaging allows an unambiguous identification of urban land uses as the main source of suspended sediments during the growing season. Taken together, these results suggest that multiple models must be used to arrive at a comprehensive understanding of total phosphorus loading. Further, while urban land uses may not be the primary source of sediment (and total phosphorus) loading annually, their source strength is increased relative to agricultural land uses during the growing season.
Plasmonic Antennas and Arrays for Optical Imaging and Sensing Applications
Keywords: Electromagnetics
Photonics
Plasmonics
Antennas and arrays
Super-resolution

The optics and photonics development is currently driven towards nanometer scales. However, diffraction imposes challenges for this development because it prevents confinement of light below a physical limit, commonly known as the diffraction limit. Several implications of the diffraction limit include that conventional optical microscopes are unable to resolve objects smaller than 250nm, and photonic circuits have a physical dimension on the order of the wavelength. Metals at optical frequencies display collective electron oscillations when excited by photon energy, giving rise to the surface plasmon modes with subdiffractional modal profile at metal-dielectric interfaces. Therefore, metallo-dielectric structures are promising candidates for alleviating the obstacles due to diffraction. This thesis investigates a particular branch of plasmonic structures, namely plasmonic antennas, for the purpose of optical imaging and sensing applications. Plasmonic antennas are known for their ability of dramatic near-field enhancement, as well as effective coupling of free-space radiation with localized energy. Such properties are demonstrated in this thesis through two particular applications. The first one is to utilize the interference of evanescent waves from an array of antennas to achieve near-field subdiffraction focusing, also known as superfocusing, in both one and two dimensions. Such designs could alleviate the tradeoffs in the current near-field scanning optical microscopy by improving the signal throughput and extending the imaging distance. The second application is to achieve more efficient radiation from single-emitters through coupling to a highly directive leaky-wave antenna. In this case, the leaky-wave antenna demonstrates the ability of enhancing the directivity over a very wide spectrum.
Ion Exclusion, pH, and Halogen Activation at the Air-Ice Interface
Keywords: air-ice interface
nitrate
glancing-angle spectroscopy
halogen activation
pH
snow chemistry

Although the air-ice interface is atmospherically important, it is difficult to model accurately because exclusion and precipitation of solutes during freezing, deposition of atmospheric species, and heterogeneous/photochemical processes all affect its properties. In this thesis, glancing-angle spectroscopic methods were developed to study ice surfaces. Glancing-angle Raman spectroscopy showed that nitrate is not strongly excluded to the ice surface during freezing, in contradiction with expectations based on equilibrium thermodynamics. Glancing-angle laser-induced fluorescence showed that hydronium ions are not strongly excluded when dilute acidic solutions (HNO3 or HCl) are frozen. These results suggest that solutes are not universally excluded and that care should be taken in modelling surface concentrations on ice. Deposition of HCl(g) was found to result in different pH responses at the "pure" vs. "salty" ice surfaces. Changes at the "salty" ice surface were consistent with the existence of a brine layer at the air-ice interface while changes at the "pure" ice surface were distinctly different, indicating that it may not be appropriate to model it as a cold, liquid layer. Significantly, results also suggest that the sea ice surface is buffered against pH changes, with important implications for interpreting pH-dependent chemistry. The conversion of sea-salt derived halides to reactive halogen species can lead to dramatic changes in the oxidative capacity of the overlying atmosphere. At ambient pH and naturally occurring halide concentrations, the dark ozonation of NaBr and NaI solutions was found to proceed more quickly on frozen vs. aqueous substrates, consistent with a freeze-concentration enhancement in halide concentration at the surface. A photochemical mechanism for halogen release from artificial saline snow was evidenced. The presence of ozone and light in the actinic region leads to accelerated production of Br2 and BrCl and the release of Cl2, in a process enhanced by high surface area, acidity and additional gas phase Br2. The results provide strong evidence for snowpack "halogen explosion" chemistry in which gas phase halogens are recycled back into a concentrated brine layer at the snow grain surface.
Hedging Cost Analysis of Put Option with Applications to Variable Annuities
Keywords: Variable Annuities
Hedging Cost Analysis
Semi-Analytic Algorithms

Variable annuities (VA) are equity-linked annuity contracts which provide the opportunity for policy-holders to benefit from financial markets appreciation and at the mean time provide protection from the downside risks of the markets. They have been overshadowing traditional fixed annuities to become the leading form of protected investment worldwide. However, the embedded guarantees in VA can bring significant downside risks to the insurer and need to be hedged. Among different hedging strategies, move-based discrete hedging strategies are widely adopted in practice but the cost analysis for move-based discrete hedging strategies are mathematically complex. In this thesis, we first examine various move-based hedging strategies and show that a two-sided underlier-based hedging strategy is desirable for the return of premium guarantee. Then we assume a GBM model for the sub-account to develop a semi-analytic framework for the hedging cost analysis of this strategy and thereby propose a modified "Percentile Premium Principle", which imposes a significant "loading" on top of the regular charge to cover the costs arising from the discrete re-balances under the two-sided underlier-based strategy. We apply the modified "Percentile Premium Principle" to the pricing of various VA designs, including GMMB, annual ratchet VA, structured product based VA with both buffered and contingent protection. Finally, we advance the algorithm towards a more general model-GBM with regime switching-to allow a better representation of the VA sub-account.
Essays on Learning and Strategic Investment
Keywords: strategic experimentation
learning

The first chapter studies the strategic timing of irreversible investments when returns depend on an uncertain state of the world. Agents learn about the state through privately observed signals, as well as from each other's actions and experience. In this environment there is the possibility of learning feedback in which an agent's present action affects how much she can learn from the other agent's experience in the future. I characterize symmetric mixed-strategy equilibria, and show that private information mitigates free-riding and increases efficiency if the prior belief about the state is not too low, but that it may lead to inefficient over-investment otherwise. The second chapter examines the effect of trade opportunities on a seller's incentive to acquire information through experimentation. I characterize the unique equilibrium outcome, and discuss the effects of variations in the information structure on the probability of trade. The main result is that more accurate information for the buyer can reduce social welfare. Efficiency requires that the buyer offers a price that the seller always accepts and that the seller experiments when it is socially optimal to do so. When the buyer receives an informative signal about positive experimentation outcomes, the absence of such a signal can induce the buyer to purchase the good with low but known quality at a low price. If the buyer receives an informative signal about negative experimentation outcomes, the seller might not experiment so as to avoid the risk of generating an outcome that could trigger the buyer to reduce her offer. The third chapter analyzes the contracting problem of a principal who delegates research to two independently experimenting agents. The features of the optimal contract depend on the principal's preferences over the agents' successes. If successes are substitutes, the first agent to produce a success receives the greatest reward. The competition for the first success benefits the principal because it reduces the agents' incentive to delay their effort. In contrast, when successes are complements, the reward for the second success is greater which results in a second mover advantage that encourages agents to delay effort.
Species Distribution Modeling: Implications of Modeling Approaches, Biotic Effects, Sample Size, and Detection Limit
Keywords: species distribution model
biotic interactions
sample size
detection limit
model generality
model sensitivity
linear discriminant analysis
multiple logistic regression
random forests
artificial neural networks
ensemble model
Bythotrephes longimanus
CAISN

When we develop and use species distribution models to predict species' current or potential distributions, we are faced with the trade-offs between model generality, precision, and realism. It is important to know how to improve and validate model generality while maintaining good model precision and realism. However, it is difficult for ecologists to evaluate species distribution models using field-sampled data alone because the true species response function to environmental or ecological factors is unknown. Species distribution models should be able to approximate the true characteristics and distributions of species if ecologists want to use them as reliable tools. Simulated data provide the advantage of being able to know the true species-environment relationships and control the causal factors of interest to obtain insights into the effects of these factors on model performance. I used a case study on Bythotrephes longimanus distributions from several hundred Ontario lakes and a simulation study to explore the effects on model performance caused by several factors: the choice of predictor variables, the model evaluation methods, the quantity and quality of the data used for developing models, and the strengths and weaknesses of different species distribution models. Linear discriminant analysis, multiple logistic regression, random forests, and artificial neural networks were compared in both studies. Results based on field data sampled from lakes indicated that the predictive performance of the four models was more variable when developed on abiotic (physical and chemical) conditions alone, whereas the generality of these models improved when including biotic (relevant species) information. When using simulated data, although the overall performance of random forests and artificial neural networks was better than linear discriminant analysis and multiple logistic regression, linear discriminant analysis and multiple logistic regression had relatively good and stable model sensitivity at different sample size and detection limit levels, which may be useful for predicting species presences when data are limited. Random forests performed consistently well at different sample size levels, but was more sensitive to high detection limit. The performance of artificial neural networks was affected by both sample size and detection limit, and it was more sensitive to small sample size.
Providing Smoking Cessation Interventions: A Survey of Nurses in Primary Health Care Settings in Ontario, Canada
Keywords: Smoking Cessation
Primary Health Care Nurses

Globally tobacco use and exposure to tobacco smoke represent some of the greatest risk factors for mortality. Best practice guidelines and standards of practice support nurses' provision of smoking cessation interventions. Nurses employed in primary health care settings interact with large numbers of people who smoke, and have the potential to significantly reduce tobacco use in the population. Evidence shows that nurses do not consistently implement smoking cessation interventions. The purpose of this cross-sectional study was to describe nurses' perceptions of factors that influence their intentions related to providing smoking cessation interventions in primary health care settings. A conceptual framework derived from the Theory of Planned Behavior and relevant empirical literature guided the study. A questionnaire measuring the concepts of interest was mailed to a random sample of Registered Nurses and Nurse Practitioners in Ontario. Responses of 237 eligible participants were available for analysis. Multiple regression analyses were used to examine the hypothesized relationships between nurses' attitudes, subjective norms and perceived behavioural control, and their intention to implement smoking cessation interventions, and the association between intention and practice related to smoking cessation. The Theory of Planned Behavior concepts explained up to 48.5% of variance in behavioural intention. Perceived behavioural control was most strongly associated with intention to provide smoking cessation interventions. Behavioural intention was correlated with smoking cessation practice. Analysis of responses to open-ended questions identified factors that facilitated (wish to improve patients' health, organizational support, access to resources, a perception of patient readiness to quit, and training in smoking cessation) and hindered (lack of time, lack of patient readiness, lack of support and resources, and lack of knowledge) nurses' provision of smoking cessation interventions. Overall, the study results suggest that nursing intention to engage in smoking cessation practices in primary health care settings was associated with organizational factors. Further research is required to explore how primary health care organizations can support nurses so that they fully realize their role in reducing the impact of tobacco use on the health of the people in Ontario.
The Theta Correspondence and Periods of Automorphic Forms
Keywords: Theta correspondence
Automorphic forms

The study of periods of automorphic forms using the theta correspondence and the Weil representation was initiated by Waldspurger and his work relating Fourier coefficients of modular forms of half-integral weight, periods over tori of modular forms of integral weight and special values of L-functions attached to these modular forms. In this thesis, we show that there are general relations among periods of automorphic forms on groups related by the theta correspondence. For example, if G is a symplectic group and H is an orthogonal group over a number field k, these relations are identities equating Fourier coefficients of cuspidal automorphic forms on G (relative to the Siegel parabolic subgroup) and periods of cuspidal automorphic forms on H over orthogonal subgroups. These identities are quite formal and follow from the basic properties of theta functions and the Weil representation; further study is required to show how they compare to the results of Waldspurger. The second part of this thesis shows that, under some restrictions, the identities alluded to above are the result of a comparison of nonstandard relative traces formulas. In this comparison, the relative trace formula for H is standard however the relative trace formula for G is novel in that it involves the trace of an operator built from theta functions. The final part of this thesis explores some preliminary results on local height pairings of special cycles on the p-adic upper half plane following the work of Kudla and Rapoport. These calculations should appear as the local factors of arithmetic orbital integrals in an arithmetic relative trace formula built from arithmetic theta functions as in the work of Kudla, Rapoport and Yang. Further study is required to use this approach to relate Fourier coefficients of modular forms of half-integral weight and arithmetic degrees of cycles on Shimura curves (which are the analogues in the arithmetic situation of the periods of automorphic forms over orthogonal subgroups).
Authority, Justice, and Public Law: A Unified Theory
Keywords: public law
authority
justice
rights
constitutional law
legal theory

In articulating the juridical relationship between the individual and the state, a theory of public law must confront a fundamental problem. The practice of public law involves appeals to ideas of both authority and justice, but these ideas appear to be antagonistic rather than complementary. On the one hand, persons must act in conformity with legal obligations enacted through the contingent exercise of public authority. On the other, persons must act in conformity with timeless ideals of public justice. The theoretical puzzle at the core of public law stems from the incompatibility of these convictions. Because enacted laws are often unjust and just laws are rarely enacted, persons often find themselves simultaneously pulled in one direction by the demands of public authority and pulled in another by the demands of public justice. To escape this tension, the leading theories invariably fragment their subject matter by reducing the whole of public law to one of its aspects, authority in abstraction from justice or justice in abstraction from authority. The purpose of this project is to articulate a unified theory of public law that integrates the distinctive claims of authority and justice into a common framework. My central claim is that once authority and justice are appropriately conceived and justified, they are neither antithetical virtues of opposing theoretical frameworks nor isolated notions. Instead, authority and justice are the mutually implicating principles of a legal system: the right of rulers to exercise public authority is always accompanied by a duty to govern justly; the right of the ruled to just governance presupposes the presence of publicly authoritative institutions. By setting out the character and interrelation of the fundamental components of a legal system, the unified theory illuminates the general practice of public law from the legal systems of the ancient world to the inner workings of modern constitutional states.
Multi-state Bayesian Process Control
Keywords: real-time monitoring
Markov decision processes
Statistical process control
decision-making under uncertainty

Bayesian process control is a statistical process control (SPC) scheme that uses the posterior state probabilities as the control statistic. The key issue is to decide when to restore the process based on real-time observations. Such problems have been extensively studied in the framework of partially observable Markov decision processes (POMDP), with particular emphasis on the structure of optimal control policy. Almost all existing structural results on the optimal policies are limited to the two-state processes, where the class of control-limit policy is optimal. However, the two-state model is a gross simplification, as real production processes almost always involve multiple states. For example, a machine in the production system often has multiple failure modes differing in their effects; the deterioration process can often be divided into multiple stages with different degradation levels; the condition of a complex multi-unit system also requires a multi-state representation. We investigate the optimal control policies for multi-state processes with fixed sampling scheme, in which information about the process is represented by a belief vector within a high dimensional probability simplex. It is well known that obtaining structural results for such high-dimensional POMDP is challenging. Firstly, we prove that for an infinite-horizon process subject to multiple competing assignable causes, a so-called conditional control limit policy is optimal. The optimal policy divides the belief space into two individually connected regions, which have analytical bounds. Next, we address a finite-horizon process with at least one absorbing state and show that a structured optimal policy can be established by transforming the belief space into a polar coordinate system, where a so-called polar control limit policy is optimal. Our model is general enough to include many existing models in the literature as special cases. The structural results also lead to significantly efficient algorithms for computing the optimal policies. In addition, we characterize the condition for some out-of-control state to be more desirable than the in-control state. The existence of such counterintuitive situation indicates that multi-state process control is drastically different from the two-state case.