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Affine term structure models (ATSMs) are one set of popular models for yield curve modeling. Given that the models forecast yields based on the speed of mean reversion, under what circumstances can we distinguish one ATSM from another? The objective of my dissertation is to quantify the benefit of knowing the “true” model as well as the cost of being wrong when choosing between ATSMs. In particular, I detail the power of out-of-sample forecasts to statistically distinguish one ATSM from another given that we only know the data are generated from an ATSM and are observed without errors. My study analyzes the power and size of affine term structure models (ATSMs) by evaluating their relative out-of-sample performance. Essay one focuses on the study of the oneactor ATSMs. I find that the model’s predictive ability is closely related to the bias of mean reversion estimates no matter what the true model is. The smaller the bias of the estimate of the mean reversion speed, the better the out-of-sample forecasts. In addition, my finding shows that the models' forecasting accuracy can be improved, in contrast, the power to distinguish between different ATSMs will be reduced if the data are simulated from a high mean reversion process with a large sample size and with a high sampling frequency. In the second essay, I extend the question of interest to the multiactor ATSMs. My finding shows that adding more factors in the ATSMs does not improve models' predictive ability. But it increases the models' power to distinguish between each other. The multiactor ATSMs with larger sample size and longer time span will have more predictive ability and stronger power to differentiate between models.

Former researches provide evidence that invasive species could alter ecosystem’s components, threaten native species and cause economic losses in southern forest lands. The objective of the project is to explore significant driving factors and develop geospatial models for monitoring, predicting and mapping the extent and conditions of major invasive species. In the study area, 16 invasive species were classified into four groups: regionally spreading species, regionally establishing species, locally spreading species and regionally colonizing species by population size and spatial characteristics. According to local Moran’s I, spatial autocorrelation existed in 16 invasive species. Autologistic model and simultaneous autoregressive model were employed to explore the relationships between spatial distribution and a set of indentified variables for Chinese privet, kudzu, Nepalese browntop and tallow tree at plot and county levels. The project showed that human-caused disturbances and forest types were significantly related to the spatial distribution of four invasive species in different scales.

Irrigation water allocation is essential to the management of agricultural water use in irrigation districts. Many irrigation optimization models were proposed from previous studies to provide decision support for water managers. In order to capture the complex nonlinear relationships and meet different water demands, more advanced multi-objective nonlinear programming models were developed in the past decade. However, it is still a challenging task to address varies uncertainties associated with irrigation optimization. Fuzzy programming, interval programming, and chance-constrained programming can be used to quantify uncertainties in simplified formats, but none of them can represent complex uncertainty in a composite format. In this thesis, a cloud-based dual objective nonlinear programming (CDONP) model is developed by implementing a cloud modeling method in an irrigation model to address the uncertainties of reference evapotranspiration (ET0) and surface water availability (SWA). The cloud modeling method is used to generate 2,000 data samples from historical data. The results show that the generated samples are consistent with historical data. Optimized allocation schemes are provided, and the performance of the CDONP model are discussed. This is the first Canadian study that used the cloud modeling method in irrigation water allocation. This method provides a solution to quantify composite uncertainties based on limited data, which represents a unique contribution to irrigation water allocation modeling. This study provides valuable decision support for agriculture management to improve water use efficiency. / Thesis / Master of Applied Science (MASc)

The focus of this paper is based in comparative law between four countries. Denmark, Finland, Norway and Sweden. Specifically how each of them handle workplace representation, both within the field of safety/wellbeing of the employees and the field of union work. The findings within this paper is that each country have very similar rules regarding safety officers, skyddsombud in Swedish. However each country have taken their own path in the field of unions. Sweden and Finland choose to regulate the relationships between the union and employer representatives while Norway and Denmark leave it to the two to get along without interference. Denmark, Finland and Norway have basic collective agreements, while Sweden only regulate through law or the collective agreement each workplace agrees to themselves. Finland uniquely creates ways for the employees to circumvent the unions.

Surrogate models are indispensable in the analysis of engineering systems. The quality of surrogate models is determined by the data quality and the model class but achieving a high standard of them is challenging in complex engineering systems. Heterogeneity, implicit constraints, and extreme events are typical examples of the factors that complicate systems, yet they have been underestimated or disregarded in machine learning. This dissertation is dedicated to tackling the challenges in surrogate modeling of complex engineering systems by developing the following machine learning methodologies. (i) Partitioned active learning partitions the design space according to heterogeneity in response features, thereby exploiting localized models to measure the informativeness of unlabeled data. (ii) For the systems with implicit constraints, failure-averse active learning incorporates constraint outputs to estimate the safe region and avoid undesirable failures in learning the target function. (iii) The multi-output extreme spatial learning enables modeling and simulating extreme events in composite fuselage assembly. The proposed methods were applied to real-world case studies and outperformed benchmark methods. / Doctor of Philosophy / Data-driven decisions are ubiquitous in the engineering domain, in which data-driven models are fundamental. Active learning is a subdomain in machine learning that enables data-efficient modeling, and extreme spatial modeling is suitable for analyzing rare events. Although they are superb techniques for data-driven modeling, existing methods thereof cannot effectively address modern engineering systems complicated by heterogeneity, implicit constraints, and rare events. This dissertation is dedicated to advancing active learning and extreme spatial modeling for complex engineering systems by proposing three methodologies. The first method is partitioned active learning that efficiently learns systems, changing their behaviors, by localizing the information measurement. Second, failure-averse active learning is established to learn systems subject to implicit constraints, which cannot be analytically solved, and to minimize constraint violations. Lastly, the multi-output extreme spatial model is developed to model and simulate rare events that are associated with extremely large values in the aircraft manufacturing system. The proposed methods overcome the limitations of existing methods and outperform benchmark methods in the case studies.

Background: The selection and prioritizing of requirements is the most difficult challenge insoftware development. Prioritizing requirements is a difficult task. Due to the importance of thepriority of requirements, many methods have been developed on how to prioritize requirements.However, with the increase of software modules and the expansion of software platforms, thesingle requirement prioritization method can no longer match the increase in the number ofrequirements. Little is know in how to find and develop integrated requirement prioritizationmethod. Objectives: The main purpose of this research is to explore the main challenges and successcriteria that practitioners consider when determining the priority of product requirements. Builda good requirement prioritization model to tackle these challenges. And evaluate the strengthsand limitations of this model. Method: We conducted a questionnaire survey to learn more about the major problems andsuccess criteria for prioritizing product requirements. After that, we presented a model thatcombined the KANO model and Analytic Hierarchy Process (AHP), and we examined its practicality. Finally, using Focus Group Research, we analyzed the benefits and limitations of theintegrated model and improved solutions. Result: The results show that practitioners face many challenges in product requirement prioritization. The model we developed is suitable for a variety of scenarios. It helps practitionersmanage priorities and improve end-user satisfaction, which can solve these challenges to a certain extent. Conclusion: Our research collected many major challenges encountered by requirement analysts and product managers in the process of requirement prioritization. And developed a newrequirement prioritization model, got a better understanding of requirement prioritization whichcan inspire practitioners to build more better requirement prioritization models.

A main objective of Departments of Transportation is to improve the safety of the roadways over which they have jurisdiction. Safety projects, such as cable barriers and raised medians, are utilized to reduce both crash frequency and crash severity. The efficacy of these projects must be evaluated in order to use resources in the best way possible. Five models are proposed for the evaluation of traffic projects: (1) a Bayesian Poisson regression model; (2) a hierarchical Poisson regression model building on model (1) by adding hyperpriors; (3) a similar model correcting for overdispersion; (4) a dynamic linear model; and (5) a traditional before-after study model. Evaluation of these models is discussed using various metrics including DIC. Using the models selected for analysis, it was determined that cable barriers are quite effective at reducing severe crashes and cross-median crashes on Utah highways. Raised medians are also largely effective at reducing severe crashes. The results of before and after analyses are highly valuable to Departments of Transportation in identifying effective projects and in determining which roadway segments will benefit most from their implementation.

Wrist and forearm motion is governed both by its dynamics and the control strategies employed by the neuromuscular system to execute goal oriented movement. Two experiments were conducted to increase our understanding of wrist and forearm motion. The first experiment involved 10 healthy subjects executing planned movements to targets involving all three degrees of freedom (DOF) of the wrist and forearm, namely wrist flexion-extension (FE), wrist radial-ulnar deviation, and forearm pronation-supination (PS). A model of wrist and forearm dynamics was developed, and the recorded movements were fed into the model to analyze the movement torques. This resulted in the following key findings: 1) The main impedance torques affecting wrist and forearm movements are stiffness and gravity, with damping and inertial effects contributing roughly 10% of the total torque. 2) There is significant coupling between all degrees of freedom (DOF) of the wrist and forearm, with stiffness effects being the most coupled and inertial effects being the least coupled. 3) Neglecting these interaction torques results in significant error in the prediction of the torque required for wrist and forearm movements, suggesting that the neuromuscular system must account for coupling in movement planning. A second experiment was conducted in which 10 different healthy subjects pointed to targets arranged on a plane in front of the subjects. This pointing task required two DOF, but subjects were allowed to use all three DOF of the wrist and forearm. While subjects could have completed the task with FE and RUD alone, it was found that subjects recruited PS as well. Hypotheses regarding why subjects would recruit PS even though it was not necessary included the minimization of a number of cost functions (work, effort, potential energy, path length) as well as mechanical interaction between the DOF of the wrist and forearm. It was found that the pattern of PS recruitment predicted from the mechanical interaction hypothesis most closely resembled the observed pattern. According to this hypothesis, the neuromuscular system uses a simplified 2 DOF model of the joints most critical to the task (FE and RUD) to plan the task, while leaving the third DOF (PS) uncontrolled. The resulting interaction torques create the observed pattern of PS movement.

The analysis of visibility between two points on the earth's terrain is a common use of GIS software. Most commercial GIS software packages include the ability to generate a viewshed, or a map of terrain surrounding a particular location that would be visible to an observer. Viewsheds are often generated using "bare-earth" Digital Elevation Models (DEMs) derived from the process of photogrammetry. More detailed models, known as Digital Surface Models (DSMs), are often generated using Light Detection and Ranging (LIDAR) which uses an airborne laser to scan the terrain. In addition to having greater accuracy than photogrammetric DEMs, LIDAR DSMs include surface features such as buildings and trees. This project used a visibility algorithm to predict visibility between observer and target locations using both photogrammetric DEMs and LIDAR DSMs of varying resolution. A field survey of the locations was conducted to determine the accuracy of the visibility predictions and to gauge the extent to which the presence of surface features in the DSMs affected the accuracy. The use of different resolution terrain models allowed for the analysis of the relationship between accuracy and optimal grid size. Additionally, a series of visibility predictions were made using Monte Carlo methods to add random error to the terrain elevation to estimate the probability of a target's being visible. Finally, the LIDAR DSMs were used to determine the linear distance of terrain along the lines-of-sight between the observer and targets that were obscured by trees or bushes. A logistic regression was performed between that distance and the visibility of the target to determine the extent to which a greater amount of vegetation along the line-of-sight impacted the target's visibility. / Master of Science