Anthropogenic disturbance in a landscape can take various forms, including residential development, which has substantial impact on the world’s ecosystems. Exurban development, characterized by low density residential development outside urban areas, was and continues to be one of the fastest growing forms of residential development in North America. It has disproportionately large ecological impacts relative to its footprint, yet is mostly overlooked in scientific studies. Specifically, a lack of spatially explicit (disaggregate) data on exurban development at regional level has contributed to a very limited understanding of this interspersed low density development. The main goal of this dissertation is to provide an increased understanding of exurban development in terms of its location, locating factors, and conservation and ecological implications at regional level, especially to enable incorporation of exurban information in the decision making processes. For this I asked four specific questions in this dissertation: (i) Where exactly is exurban development? (ii) What are the most likely factors that influence exurban development location? (iii) How does current and future development conflict with conservation goals? And (iv) What is the extent of the exurban development’s ecological impacts? Using a heterogeneous landscape, the County of Peterborough (Ontario, Canada), as the case study this dissertation undertook a number of separate yet related analyses that collectively provided the improved understanding of exurban development. The investigation of traditionally used surrogates for development, like roads and census data, and a more direct remote sensing method, using moderate resolution SPOT/HRVIR imagery, provided insights and contributed to development of spatially explicit data on exurban development. The evaluation of several commonly hypothesized locating factors in relation to exurban development revealed some of the major influences on the location of this development, especially in the context of Ontario. This research contributed to our understanding of the future risks of land conversion and identification of potential conflict areas between development and conservation plans in the study area. Lastly, examining the ecological impact of exurban development including associated roads, in terms of functions such as barrier effects and landscape connectivity, highlighted the importance of these seldom included anthropogenic disturbances in land and conservation planning. The contributions of this research to the existing body of knowledge are threefold. First, this dissertation reveals the limitations associated with existing methods used to map exurban development and presents a relatively easy, effective, automated and operational method to delineate exurban built areas at regional level using GIS and remote sensing. Second, the analyses conducted in this dissertation repeatedly highlights the importance of incorporating fine level details on exurban development in land and conservation planning as well as ecological impact assessments and presents methods and tools that can systematically and scientifically integrate this information in decision making framework. Third, this study conducted one of a kind, comprehensive and spatially explicit study on exurban development in Canada, where there is near absence of such research. With the rarely available exurban built footprint data delineated for the study area, this study not only identified the potential locating factors, future conversion risk, and conflict areas between development and conservation plans, but also quantified ecological impact in terms of landscape function, namely barrier effects and landscape connectivity, using a relatively novel circuit theoretic approach that can directly inform land and conservation decision planning process.

Since the 1980s, satellite-borne synthetic aperture radar (SAR) has been investigated for early warning and monitoring of marine oil spills to permit effective satellite surveillance in the marine environment. Automated detection of oil spills from satellite SAR intensity imagery consists of three steps: 1) Detection of dark spots; 2) Extraction of features from the detected dark spots; and 3) Classification of the dark spots into oil spills and look-alikes. However, marine oil spill detection is a very difficult and challenging task. Open questions exist in each of the three stages. In this thesis, the focus is on the first stage—dark spot detection. An efficient and effective dark spot detection method is critical and fundamental for developing an automated oil spill detection system. A novel method for this task is presented. The key to the method is utilizing the spatial density feature to enhance the separability of dark spots and the background. After an adaptive intensity thresholding, a spatial density thresholding is further used to differentiate dark spots from the background. The proposed method was applied to a evaluation dataset with 60 RADARSAT-1 ScanSAR Narrow Beam intensity images containing oil spill anomalies. The experimental results obtained from the test dataset demonstrate that the proposed method for dark spot detection is fast, robust and effective. Recommendations are given for future research to be conducted to ensure that this procedure goes beyond the prototype stage and becomes a practical application.

In the first chapter, we propose a new method for modeling competition in electricity spot markets, namely, by approximating the supply functions of the competitors with cubic splines. We argue that this method is preferable to approximation by linear or piecewise-affine functions, which have been the main approaches to date. We apply our method to the firms competing in the Texas market. We also show that, more often than not, we will observe that the marginal revenue functions of the firms will have increasing segments which may lead to multiple profit-maximizing optima for a firm. In the second chapter, we model the effects of forward contracting on power prices in wholesale electricity markets. In contrast to most of the previous literature, we explicitly model power retailers, and introduce risk aversion. As expected, increasing the number of players have pro-competitive effects on the spot price of electricity. We also find that as the generators bid more competitively, spot and forward prices converge. Our model also captures the effects of level and variability of power demand on the players' contracting decisions. In the final chapter, we depart from equilibrium approach and utilizing agent-based modeling, analyze the effects of increased power demand price sensitivity on the level and volatility of power prices. We find that as the price sensitivity increases at the demand side, power price as well as its volatility decrease significantly. We also argue that the celebrated Herfindahl-Hirschman Index to measure market concentration is not a suitable metric for power markets.

A literature survey on modelling of the resistance spot welding process has been carried out and some of the more interesting models on this subject have been reviewed in this work. The underlying physics has been studied and a brief explanation of Heat transfer, electrokinetics and metallurgy in a resistance spot welding context have been presented.\nl\hsLastly a state of the art model and a simplified model, with implementation in the FEM software LS-DYNA in mind, have been presented.

Since the 1980s, satellite-borne synthetic aperture radar (SAR) has been investigated for early warning and monitoring of marine oil spills to permit effective satellite surveillance in the marine environment. Automated detection of oil spills from satellite SAR intensity imagery consists of three steps: 1) Detection of dark spots; 2) Extraction of features from the detected dark spots; and 3) Classification of the dark spots into oil spills and look-alikes. However, marine oil spill detection is a very difficult and challenging task. Open questions exist in each of the three stages. In this thesis, the focus is on the first stage—dark spot detection. An efficient and effective dark spot detection method is critical and fundamental for developing an automated oil spill detection system. A novel method for this task is presented. The key to the method is utilizing the spatial density feature to enhance the separability of dark spots and the background. After an adaptive intensity thresholding, a spatial density thresholding is further used to differentiate dark spots from the background. The proposed method was applied to a evaluation dataset with 60 RADARSAT-1 ScanSAR Narrow Beam intensity images containing oil spill anomalies. The experimental results obtained from the test dataset demonstrate that the proposed method for dark spot detection is fast, robust and effective. Recommendations are given for future research to be conducted to ensure that this procedure goes beyond the prototype stage and becomes a practical application.

The main purpose of this thesis is to improve the fundamental knowledge of non-isothermal tempering of martensite phase and its effects on the reduction in hardness (softening) with respect the base metal occurring at the heat affected zone (HAZ) of resistance spot welded dual-phase (DP) steels. This thesis also aims at understanding the influence of HAZ-softening on the joint performance of various DP steel grades. The tempering of martensite occurring at the sub-critical HAZ (SC-HAZ) of resistance spot welded DP600, DP780 and DP980 steels has been systematically evaluated by microhardness testing through Vickers indentation and the degree of tempering has been correlated to the HAZ-softening. From the joint performance analysis of similar and dissimilar steel grade combinations assessed through standardized testing methods, three important issues have been targeted: a) the joint strength (maximum load to failure), b) the location of failure (failure mode), and c) the physical characteristic of the weld that determines certain type of failure (weld nugget size). In addition, a partial tensile test has been conducted in order to evaluate the initiation of failure in dissimilar steel grade combinations. It has been shown that HAZ-softening lowered the weld size at which transition from interfacial to pullout failure mode takes place along with increased load-bearing capacity and higher energy absorption. Thus, it is concluded from mechanical testing that HAZ-softening benefits the lap-shear tensile joint performance of resistance spot welded DP steels by facilitating pullout failures through failure initiation at the SC-HAZ (tempered region). Instrumented nanoindentation testing was employed to further investigate HAZ-softening along the SC-HAZ by evaluating individual phases of ferrite matrix and tempered martensite islands. Although the ferrite matrix presented a slight reduction in hardness at nanoscale, higher reduction in hardness (softening) resulted for tempered martensite; thus confirming that tempered martensite is the major contributor to softening at micro-scale. A comparison between nanohardness and microhardness testing made at different distances from the line of lower critical temperature of transformation (Ac1) allowed revealing the actual extension of the SC-HAZ. In this regard, good correlation was obtained between nanohardness results along the SC-HAZ and the microstructural changes analyzed by electron microscopy (i.e., the tempering of martensite occurring at various distances far from Ac1 was correlated to low temperature tempering of dual phase steels). An in-depth analysis of the tempering of martensite phase at high temperature in DP steel subjected non-isothermal conditions i.e., rapid heating, extremely short time at peak temperature and rapid cooling (resistance spot welding), has been carried out mainly through analytical transmission electron microscopy (TEM). In addition, an isothermal tempering condition (i.e., slow heating and long time at peak temperature) in DP steel has been evaluated for complementing the analysis. Both non-isothermal and isothermal conditions have been correlated to the softening behaviour. TEM analysis of the base metal in the DP steel indicated that the morphology of the martensite phase is dependent on its carbon content, and its tempering characteristics are similar to that of equal carbon containing martensitic steel. The isothermally tempered structure is characterized by coarsening and spheroidization of cementite (θ) and complete recovery of the martensite laths; whereas precipitation of fine quasi-spherical intralath θ-carbides, coarser plate-like interlath θ-carbides, decomposition of retained austenite into elongated θ-carbides, and partial recovery of the lath structure were observed after non-isothermal tempering of DP steel. This difference in tempering behaviour is attributed to synergistic effect of delay in cementite precipitation due to higher heating rate, and insufficient time for diffusion of carbon that delays the third stage of tempering process (cementite coarsening and recrystalization) during non-isothermal. The finer size and the plate-like morphology of the precipitated carbides along with the partial recovery of the lath structure observed after non-isothermal tempering strongly influenced the softening behaviour of DP steel. The chemical analysis of θ-carbides through extraction replicas for three different DP steels revealed that the chemistry of the carbides is inherited from the parent DP steel during non-isothermal tempering at high temperature confirming that non-isothermal tempering DP steel is predominantly controlled by carbon diffusion.

The synthetic aperture radar (SAR) onboard Earth observing satellites has been acknowledged as an integral tool for many applications in monitoring the marine environment. Some of these applications include regional sea-ice monitoring and detection of illegal or accidental oil discharges from ships. Nonetheless, a practicality of the usage of SAR images is greatly hindered by the presence of speckle noises. Such noise must be eliminated or reduced to be utilized in real-world applications to ensure the safety of the marine environment. Thus this thesis presents a novel two-phase total variation optimization segmentation approach to tackle such a challenging task. In the total variation optimization phase, the Rudin-Osher-Fatemi total variation model was modified and implemented iteratively to estimate the piecewise smooth state by minimizing the total variation constraints. In the finite mixture model classification phase, an expectation-maximization method was performed to estimate the final class likelihoods using a Gaussian mixture model. Then a maximum likelihood classification technique was utilized to obtain the final segmented result. For its evaluation, a synthetic image was used to test its effectiveness. Then it was further applied to two distinct real SAR images, X-band COSMO-SkyMed imagery containing verified oil-spills and C-band RADARSAT-2 imagery mainly containing two different sea-ice types to confirm its robustness. Furthermore, other well-established methods were compared with the proposed method to ensure its performance. With the advantage of a short processing time, the visual inspection and quantitative analysis including kappa coefficients and F1 scores of segmentation results confirm the superiority of the proposed method over other existing methods.

In this report six different models for predicting the electrical spot price on the Nordic power exchange, Nord Pool, are developed and compared. They are evaluated against the already existing model as well as the naive test, which is a forecast where the last week’s observations are used as a prognosis for the coming week. The models developed are constructed so that the models for different time resolutions are combined to create a full model. Harmonic regression with a linear trend are used to identify a yearly trend while SARIMAX/SARIMA time series models are used on a daily and hourly basis to reveal dependencies in the data.   The model with the best prediction performance is shown to be a SARIMAX model with temperature as exogenous variable on a daily resolution, together with a SARIMA model on an hourly resolution. With an average MAPE of 12.69% and a MAPE2 of 6.90% it has the smallest prediction error out of all of the competing models when doing one week forecasts on the whole year 2009.

In this thesis, one methodology for natural gas storage valuation is developed and two methodologies are improved. Then all of the three methodologies are applied to a storage contract. The first methodology is called "intrinsic rolling with spot and forward", which takes both the spot and forward prices into account in the valuation. This method is based on the trading strategy by which a trader locks the spot and forward positions by solving an optimization problem based on the market information on the first day. In the following days, the trader can obtain added value by adjusting the positions based on new market information. The storage value is the sum of the first day's value and the added values in the following days. The problem can be expressed by a Bellman equation and solved recursively. A crucial issue in the implementation is how to compute the expected value in the next period conditioned on the information in current period. One way to compute the expected value is Monte Carlo simulation with ordinary least square regression. However, if all of the state variables, spot, and forward prices are incorporated in the regression there are too many terms, and the regression becomes uncontrollable. To solve this issue, three risk factors are chosen by performing principle component analysis. Dimension of the regression is greatly reduced by only incorporating the three risk factors. Both the second methodology and the third methodology only consider the spot price in the valuation. The second methodology uses Monte Carlo simulation with ordinary least square regression, which is based on the work of Boogert and Jong (2006). The third methodology uses stochastic dual dynamic programming, which is based on the work of Bringedal (2003). However, both methodologies are improved to incorporate bid and ask prices. Price models are crucial for the valuation. Forward prices of each month are assumed to follow geometric Brownian motions. Future spot price is also assumed to follow a geometric Brownian motion but for a specific month its expectation is set to the corresponding forward price on the valuation date. Since the simulation of spot and forward prices is separated from the storage optimization, alternative spot and forward models can be used when necessary. The results show that the value of the storage contract estimated by the first methodology is close to the market value and the value estimated by the Financial Engineering Associates (FEA) provided function. A much higher value is obtained when only spot price is considered, since the high volatility of the spot curve makes frequent position change profitable. However in the reality traders adjust their positions less frequently.

The effects of AC and DC on cooling rate, solute distribution and nugget shape after solidification, which are responsible for microstructure of the fusion zone, during resistance spot welding are realistically and extensively investigated. The finite difference method is used to predict transport variables in workpieces and electrodes during heating, melting, cooling and freezing periods. The model accounts for electromagnetic force, heat generations at the electrode-workpiece interface and faying surface between workpieces, and dynamic electrical resistance including bulk resistance and contact resistances at the faying surface and electrode-wokpiece interfaces, which are function of hardness, temperature, electrode force, and surface condition. The computed results show that in contrast to DC, using AC readily produces the nugget in an ellipse shape. Deficit and excess of solute content occur in a thin layer around the boundary and interior of the nugget, respectively. The effects of dynamic electrical resistance subject to AC (Alternative current) on transport variables, cooling rate, solute distribution and nugget shape after solidification during resistance spot welding are realistically and extensively investigated. The model accounts for electromagnetic force, heat generation and contact resistances at the faying surface and electrode-workpiece interfaces and bulk resistance in workpieces. Contact resistance are comprised of constriction and film resistances, which are functions of hardness, temperature, electrode force and surface condition. The computed results show that the weld nugget readily occurs by increasing constriction resistance and Curie temperature. High Curie temperature enhances convection and solute mixing, and readily melts through the workpiece surface near the electrode edge. Aside from finding the significant effect of Curie temperature on resistance spot welding, this study indicates that any mean (For example, adjusting solute content) to reduce Curie temperature can be a new way to control weld quality.