Nuclear Power Plants (NPP) undergo fault and sensitivity analysis with scenario modelling to predict catastrophic events, specifically releases of Cesium 137 (Cs-137). The purpose of this thesis is to find which of 108 input-features from Modular Accident Analysis Program (MAAP)simulation code are important, when there is large release of Cs-137 emissions. The features are tested all together and in their groupings. To find important features, the Machine learning (ML) model Random Forest (RF) has a built-in attribute which identifies important features. The results of RF model classification are corroborated with Support Vector Machines (SVM), K-Nearest Neighbor (KNN) and use k-folds cross validation to improve and validate the results, resulting in a near 90% accuracy for the three ML models. RF is successful at identifying important features related to Cs-137 emissions, by using the classification model to first identify top features, to further train the models at identifying important input-features. The discovered input-features are important both within their individual groups, but also when including all features simultaneously. The large number of features included did not disrupt RF much, but the skewed dataset with few classified extreme events caused the accuracy to be lower at near 90%.

The dear enemy effect is a widespread behavioral phenomenon that promotes selective pacifism in order to minimize costs of territorial defense. The dear enemy hypothesis predicts heightened aggression towards unfamiliar conspecifics compared to familiar neighbors where territorial boundaries are established. The purpose of this study was to test the dear enemy hypothesis in Neolamprologus multifasciatus, a social cichlid fish endemic to the shell beds of Lake Tanganyika. They establish breeding groups that control distinct territories, each governed by a dominant male. Territories can be very densely spaced, and conspecific border intrusions generally trigger aggressive responses from the focal group. The dominant males are responsible for the majority of territory defense. In this field experiment, 27 breeding groups were each given two treatments, each consisting of artificial introduction of an out-of-group dominant male. One male originated from an immediately neighboring group, the other from a distant group.  A downward-facing camera recorded group members’ aggressivity exhibited towards the experimentally presented male. The behavior was manually scored according to a behavior ethogram and analyzed with either linear mixed effects models or generalized linear mixed effects models. No clear dear enemy effect was detected within Neolamprologus multifasciatus. Several reasons are proposed for this result. Firstly, a contingent “ceiling effect” in terms of aggressive behavior imaginably deterred efforts of dear enemy detection. Conversely, perhaps negligible necessity of stranger-neighbor recognition within Neolamprologus multifasciatus has resulted in discriminatory behavior being absent. In conclusion, no dear enemy effect was detected and future research will address possible alternative explanations.

Due to the serious impact wind damage has on homes in the Gulf Coast region, policy makers, community developers, and homeowners are seeking ways to lessen impacts. One potential tool to increase properties’ resiliency in the event of a periodic and catastrophic event is wind mitigation, the process of adding features to a building, i.e. a house, to increase the strength of the structure amid a storm such as a hurricane. In this research, I evaluate the tiers of FORTIFIED homes as the mitigation strategies. I use Zillow ZTRAX and Institute of Business and Home Safety data to estimate how each level of FORTIFIED home designation affects property value. The results show FORTIFIED Gold designation on a new home has a 0%-8.4% increase on property value. I place my finding into a BCA of FORTIFIED designation to evaluate how this one benefit fits into the greater picture.

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