One of the main objectives of evolutionary biology is to understand the reasons behind the maintenance of individual differences in a multitude of traits that influence fitness such as locomotor performance and behaviour. Because locomotor performance sets an "envelope" within which behaviour is expressed, it is likely that a multitude of co-adaptations exists between these two suites of traits. In recent years, a growing number of studies have identified associations of different strength and directions between performance and behaviour. Two main hypotheses have received support, on one hand locomotor performance could be "co-specialized" with behaviour in a manner that behaviour reduces predation risk, such that shyer, less active, less explorative animals should be the best sprinters and the most endurant. On the other hand, locomotor performance could "compensate" for behaviours that lead to increased predation risk, in a way that bolder, more active and explorative animals should be able to sprint faster and for longer. In my thesis I provide a review of published studies that successfully identify associations between locomotor performance and behaviour and classify each association as supporting the co-specialization or compensation hypothesis respectively. I further elaborate on the importance of using repeated measurements and (co)variance partitioning when studying correlations between labile traits. I also discuss one of the main challenges that comes with studying locomotor performance, namely the importance of the variation in motivation, both methodologically, by using different performance tests, but also physiologically, by using blood corticosterone measurements as indicators of such variation.

The objective of this dissertation is to provide key methodological advancements towards the use of transfer learning in Laser-Based Additive Manufacturing (LBAM), to assist practitioners in producing high-quality repeatable parts. Currently, in LBAM processes, there is an urgent need to improve the quality and repeatability of the manufacturing process. Fabricating parts using LBAM is often expensive, due to the high cost of materials, the skilled machine operators needed for operation, and the long build times needed to fabricate parts. Additionally, monitoring the LBAM process is expensive, due to the highly specialized infrared sensors needed to monitor the thermal evolution of the part. These factors lead to a key challenge of improving the quality of additively manufactured parts, because additional experiments and/or sensors is expensive. We propose to use transfer learning, which is a statistical technique for transferring knowledge from one domain to a similar, yet distinct, domain, to leverage previous non-identical experiments to assist practitioners in expediting part certification. By using transfer learning, previous experiments completed in similar, but non-identical, domains can be used to provide insight towards the fabrication of high-quality parts. In this dissertation, transfer learning is applied to four key domains within LBAM. First, transfer learning is used for sensor fusion, specifically to calibrate the infrared camera with true temperature measurements from the pyrometer. Second, a Bayesian transfer learning approach is developed to transfer knowledge across different material systems, by modelling material differences as a lurking variable. Third, a Bayesian transfer learning approach for predicting distortion is developed to transfer knowledge from a baseline machine system to a new machine system, by modelling machine differences as a lurking variable. Finally, compensation plans are developed from the transfer learning models to assist practitioners in improving the quality of parts using previous experiments. The work of this dissertation provides current practitioners with methods for sensor fusion, material/machine calibration, and efficient learning of compensation plans with few samples.

The effect of the electrical field enhancement at the junction discontinuities and its impact on the on-state resistance of power semiconductor devices was investigated. A systematic analysis of the mechanisms behind the techniques that can be used for the edge termination in power semiconductor devices was performed. The influence of the passivation layer properties, such as effective interface charge and dielectric permittivity, on the devices with different edge terminations was analyzed using numerical simulation. A compact analytical expression for the optimal JTE dose was proposed for the first time. This expression has been numerically evaluated for different targeted values of the blocking voltage and the maximum electric field, always resulting in the optimal field distribution that does not require further optimization with 2-D device simulator. A compact set of rules for the optimal design of super-junction power devices was developed. Compact analytical expressions for the optimal dopings and dimensions of the devices employed the field compensation technique are derived and validated with the results of numerical simulations on practical device structures. A comparative experimental study of several approaches used for the edge termination in SiC power diodes and transistors was performed. The investigated techniques included the mesa termination, high-k termination, JTE, and the combination of JTE and field plate edge termination. The mesa edge termination was found to be the most promising among the techniques investigated in this work. This stand-along technique satisfied all the imposed requirements for the ?ideal? edge termination: performance, reproducibility (scalability), and cost-efficiency. First of all, it resulted in the maximum one-dimensional electric field (E1DMAX) at the main device junction equal to 2.4 MV/cm or 93% of the theoretical value of critical electric field in 4H-SiC. Secondly, the measured E1DMAX was found to be independent of the voltage blocking layer parameters that demonstrate the scalability of this technique. Lastly, the implementation of this technique does not require expensive fabrication steps, and along with an efficient use of the die area results in the low cost and high yield.

The Power Electronic Building Block (PEBB) concept leads to multifunctional converter systems, which provide robustness and flexibility in heavily power electronics based power systems. Systems comprised of flexible modular converters may have multiple possible operation conditions with respect to individual converters that meet the overall system goals. In this thesis, an optimization method for such flexible online power electronic systems is developed to minimize power losses of the overall group of converters in the system. Here the objective is to allocate sharing such that compensation objectives are met while the power loss of the entire parallel group of compensators is minimized. Considering optimization of an online power electronic system, convergence time and running in the feasible region should be taken into account. This thesis is

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