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Spatial analysis is of primary importance in forestry. Many factors that affect tree development have spatial components and can be sampled across geographic space. Some examples of spatially structured factors that affect tree growth include soil composition, water availability, and growing space. Our goals for this dissertation were to test the efficacy of spatial analysis tools in a forestry setting and make recommendations for their use. Reliable spatial analysis tools will lead to more effective statistical testing and can lead to useful mapping of spatial patterns. The data for this project is from simulated even aged loblolly pine stands (Pinus taeda L.). These simulated stands are grown at regular spacing and we impose a range of parameters on the stands to simulate many possible scenarios. In chapter 3 of this dissertation we perform a sensitivity analysis to determine if our methods are suitable for further research and applications. In chapter 4 we perform our analysis on more realistic data generated by a spatially-explicit stand simulator, PTAEDA 4.1. In chapter 3 we performed a statistical simulation of plantation stands without effects of competition and mortality. We used redundancy analysis (RDA) to quantify spatial variability, partial redundancy analysis (pRDA) to test for spatial dependence, and spatially constrained cluster analysis to map soil productivity. Our results indicated that RDA and pRDA are reliable methods and future evaluation is appropriate. The results from the spatially constrained cluster analysis were less clear. The success or failure of the clustering algorithm could not be disentangled from the success or failure of the selection criterion used to predict the number of clusters. Further investigations should address this concern. In chapter 4 we used PTAEDA 4.1, a loblolly stand simulator, to simulate a range of site conditions and produce data that we could use for analysis. The results showed that RDA and pRDA were not reliable methods and ready for the field. Spatially constrained cluster analysis performed poorly when more realistic data was used and because of this further use was uncertain. It was clear from the results that levels of variation and spatial pattern complexity of microsites influenced the success rate of the methods. Both RDA and pRDA were less successful with higher levels of variation in the data and with increased spatial pattern complexity. In chapter 5 we related the coefficient of variation from our simulations in (chapters 3 and 4) to two sets of real plot data, including a clonal set and open pollinated set. We then implemented a spatial analysis of the real plot data. Our spatial analysis results of the two comparable data sets were unaffected by genetic variability indicating that the primary source of variability across plots appears to be soil and other factors, not genetic related. / Ph. D.

Determining the molecular mechanisms at the origin of protein function remains a challenge due to the complex non-covalent interactions that shape their structure. Since the non-covalent interactions arise from charge fluctuations, electric fields can be used as a tool to quantify the interactions between a target and its environment. The contribution of each component of the system is reflected in the direction and strength of the electric field exerted on the target, which can be calculated from molecular dynamics simulations. The interactions experienced by ligands in enzymatic active sites determine the catalytic activity of the enzyme. Ligands in synthetic enzymes lack interactions with the protein scaffold, which limit their efficiency. To substitute for the role of non-effective protein scaffold, we introduced a polar DNA fragment to the enzyme vicinity, inducing electrostatic interactions that will facilitate the reaction. We found that the introduction of a DNA fragment enhanced the original interactions between the residues in the active site and the ligand, without creating new interaction hot spots. Using electric fields, we calculated a reduction in activation energy of 2.0 kcal/mol when introducing the DNA fragment, indicating a promising avenue for catalytic improvement. Inspired by the success in using electric fields to understand enzyme catalysis in the context of electrostatic preorganization theory, we generalized these fundamental concepts to another type of proteins: voltage-gated ion channels. Our results indicate that electric fields also report on channel activity. We find an asymmetry in the number of active residues for channel function between the four domains and between the two gating motifs of the permeation pathway, with domain I being the major contributor in both cases. The importance of residues for channel activity is not a simple linear correlation of their distance with the functional motif, but a relationship dominated by non-covalent interactions. Finally, we investigate the effects of loop dynamics on enzyme product inhibition. We modify the chemical nature of the unstructured loops that obstruct the active site of DszB by glycosylating serine and threonine residues. We monitor the corresponding variations in loop dynamics and their effect on the interaction between the enzyme and the product. Overall, promising results were found using electric fields in the investigation of protein mechanisms that are mainly dominated by non-covalent interactions and provide insight into the role of the individual components in the system. / Doctor of Philosophy / Although weaker than covalent interactions, non-covalent interactions play a crucial role in molecular biological processes, especially in protein mechanisms. In order to modify the properties of proteins to our advantage, we need a metric with which we can map these interactions onto the protein structure. Different types of non-covalent interactions share one similarity: they originate from the change of electron distribution of interacting atoms, therefore can be captured by analyzing the protein- generated electric fields. Synthetic enzymes are designed to better adapt to varying environments and catalyze a broader reaction range. However, they are less effective than natural enzymes because the protein scaffold does not contribute to catalysis. Indeed, protein scaffolds in natural enzymes generate an electric field that lowers the reaction activation energy in the active site. Protein scaffolds in synthetic enzyme do not generate such electric fields. To address this issue, we modified the environment of synthetic enzyme KE15, introducing a polar DNA fragment to induce interactions in the active site. This modification strengthen the interactions between protein and ligand, leading to a decrease in the energy required for the reaction. While enzymes are famous for their generation of electric fields facilitating function, we demonstrated that this phenomenon also exist in voltage-gated ion channels Nav1.7. Residues were found to exert an electric field that can facilitate ion permeation. This is not simply because of their distance to the key regions, but a result of the non-covalent interactions regulating the mechanism, with different regions showing asymmetric importance in the process. Since the governing non-covalent interactions are relatively weak, proteins are flexible, especially protein loops. In enzyme DszB, this loop flexibility enables a conformational change when the ligand binds the active site. The change in loop conformation traps the product inside the active site, limiting enzymatic turnover. To prevent active site obstruction by these flexible loops, we attached glucose to a few loop residues to modify the hydrophobicity profile near the active site. The introduction of hydrophilic glucoses helps to pull the loops towards the solvent, rather than towards the active site, limiting product inhibition while preserving catalytic activity. Overall, our results show that electric field can be applied as a general method for protein studies, relating structure to function.

Length-based stock assessment models estimating mortality rates are attractive choices for assessing fisheries with data deficiencies. The U.S. Caribbean is exploring using these models and trying to optimize their commercial sampling program for such a model. A simulation model was constructed to compare two length-based mortality estimators, the Beverton-Holt and Gedamke-Hoenig models. The simulations also tested aspects of the Gedamke-Hoenig model previously not thoroughly addressed, such as the effects of varied life history parameters, violating the assumptions of constant growth and recruitment, sample sizes (n), and sampling program length (Ys) on total mortality rate estimates (Z). Given the scenarios investigated, the Beverton-Holt model produced consistently biased, but more stable results when n was low, variation was high for both growth and recruitment, and sampling began after the change in Z took place. The Gedamke-Hoenig model was generally less biased and detected changes in Z, but produced variable results of the current Z, especially with low sample sizes and high variability. In those situations, both models can be carefully interpreted together for management advice. In the Gedamke-Hoenig model results, a clear pattern emerged in the mean accuracy and precision of the model where after an asymptote was reached, increasing n did not improve the means. The variance of the model improved with both increasing n and increasing Ys. Outliers were predictable and could be accounted for on a case-by-case basis. The model developed here can be a tool for guiding future stock assessment model choice and sample design in the U.S. Caribbean and other regions. / Master of Science

Yes / Molecular dynamics (MD) simulations contain considerable information with regard to the motions and fluctuations of a protein, the magnitude of which can be used to estimate conformational entropy. Here we survey conformational entropy across protein fold space using the Dynameomics database, which represents the largest existing dataset of protein MD simulations for representatives of essentially all known protein folds. We provide an overview of MD-derived entropies accounting for all possible degrees of dihedral freedom on an unprecedented scale. Although different side chains might be expected to impose varying restrictions on the conformational space that the backbone can sample, we found that the backbone entropy and side chain size are not strictly coupled. An outcome of these analyses is the Dynameomics Entropy Dictionary, the contents of which have been compared with entropies derived by other theoretical approaches and experiment. As might be expected, the conformational entropies scale linearly with the number of residues, demonstrating that conformational entropy is an extensive property of proteins. The calculated conformational entropies of folding agree well with previous estimates. Detailed analysis of specific cases identify deviations in conformational entropy from the average values that highlight how conformational entropy varies with sequence, secondary structure, and tertiary fold. Notably, alpha-helices have lower entropy on average than do beta-sheets, and both are lower than coil regions. / National Institutes of Health, US Department of Energy Office of Biological Research, National Energy Research Scientific Computing Center, Swedish Research Council, Knut and Alic Wallenberg Foundation

This dissertation addresses model form uncertainty quantification, non-deterministic simulations, and sensitivity analysis of the results of these simulations, with a focus on application to analysis of unmanned aircraft systems. The model form uncertainty quantification utilizes equation error to estimate the error between an identified model and flight test results. The errors are then related to aircraft states, and prediction intervals are calculated. This method for model form uncertainty quantification results in uncertainty bounds that vary with the aircraft state, narrower where consistent information has been collected and wider where data are not available. Non-deterministic simulations can then be performed to provide uncertainty quantified estimates of the system performance. The model form uncertainties could be time varying, so multiple sampling methods were considered. The two methods utilized were a fixed uncertainty level and a rate bounded variation in the uncertainty level. For analysis using fixed uncertainty level, the corner points of the model form uncertainty were sampled, providing reduced computational time. The second model better represents the uncertainty but requires significantly more simulations to sample the uncertainty. The uncertainty quantified performance estimates are compared to estimates based on flight tests to check the accuracy of the results. Sensitivity analysis is performed on the uncertainty quantified performance estimates to provide information on which of the model form uncertainties contribute most to the uncertainty in the performance estimates. The proposed method uses the results from the fixed uncertainty level analysis that utilizes the corner points of the model form uncertainties. The sensitivity of each parameter is estimated based on corner values of all the other uncertain parameters. This results in a range of possible sensitivities for each parameter dependent on the true value of the other parameters. / Ph. D. / This dissertation examines a process that can be utilized to quantify the uncertainty associated with an identified model, the performance of the system accounting for the uncertainty, and the sensitivity of the performance estimates to the various uncertainties. This uncertainty is present in the identified model because of modeling errors and will tend to increase as the states move away from locations where data has been collected. The method used in this paper to quantify the uncertainty attempts to represent this in a qualitatively correct sense. The uncertainties provide information that is used to predict the performance of the aircraft. A number of simulations are performed, with different values for the uncertain terms chosen for each simulation. This provides a family of possible results to be produced. The uncertainties can be sampled in various manners, and in this study were sampled at fixed levels and at time varying levels. The sampling of fixed uncertainty level required fewer samples, improving computational requirements. Sampling with time varying uncertainty better captures the nature of the uncertainty but requires significantly more simulations. The results provide a range of the expected performance based on the uncertainty. Sensitivity analysis is performed to determine which of the input uncertainties produce the greatest uncertainty in the performance estimates. To account for the uncertainty in the true parameter values, the sensitivity is predicted for a number of possible values of the uncertain parameters. This results in a range of possible sensitivities for each parameter dependent on the true value of the other parameters. The range of sensitivities can be utilized to determine the future testing to be performed.

Millions of years of evolution have led to a wealth of highly adapted functional surfaces in nature. Among the most fascinating are superhydrophobic surfaces which are highly water-repellent and shed drops very easily owing to their chemical hydrophobicity combined with micropatterning. Superhydrophobic materials have attracted a lot of attention due to their practical applications as ultra-low friction surfaces for ships and pipes, water harvesters, de-humidifiers and cooling systems. At small length scales, where surface tension dominates over gravity, these surfaces show a wealth of phenomena interesting to physicists, such as directional flow, rolling, and drop bouncing. This thesis focuses on two examples of dynamic drop interactions with micropatterned surfaces and studies them by means of a lattice Boltzmann simulation approach. Inspired by recent experiments, we investigate the phenomenon of the self-propelled bouncing of coalescing droplets. On highly hydrophobic patterned surfaces drop coalescence can lead to an out-of-plane jump of the composite drop. We discuss the importance of energy dissipation to the jumping process and identify an anisotropy of the jumping ability with respect to surface features. We show that Gibbs' pinning is the source of this anisotropy and explain how it leads to the inhibition of coalescence-induced jumping. The second example we study is the novel phenomenon of pancake bouncing. Conventionally, a drop falling onto a superhydrophobic surface spreads due to its inertia, retracts due to its surface tension, and bounces off the surface. Here we explain a different pathway to bouncing that has been observed in recent experiments: A drop may spread upon impact, but leave the surface whilst still in an elongated shape. This new behaviour, which occurs transiently for certain impact and surface parameters, is due to reversible liquid imbibition into the superhydrophobic substrate. We develop a theoretical model and test it on data from experiments and simulations. The theoretical model is used to explain pancake bouncing in detail.

The evaluation of the noise produced by flows has reached a high level of importance in the past years. The physics surrounding flow-induced noise is quite complex and sensitive to various flow conditions like temperature, shape. Empirical models were built in the past for some special geometries but they cannot be used in a general case for a shape optimization for instance. Experimental aeroacoustic facilities represent the main tool for acoustic analyses of flow fields, but are quite expensive because extreme care must be exercised not to introduce acoustic perturbations in the flow (silent facilities). These tools allow a good analysis of the physical phenomena responsible for noise generation in the flow by a comparison of the noise sources and the flow characteristics (pressure, turbulence,...). Nevertheless, the identification and location of noise sources to compare with flow structures requires quite complex methods. The numerical approach complements the experimental one in the sense that the flow characteristics are deeply analyzed where experiments suggest noise production. For the numerical approach, the turbulence modeling is quite important. In the past, some models were appreciated for their good prediction of some aerodynamic parameters as lift and drag for instance. The challenge is now to tune these models for a correct prediction of the noise sources. In the low subsonic range, the flow field is completely decoupled from acoustics, and noise sources can be computed from a purely hydrodynamic simulation before this information is transferred to an acoustical solver which will compute the acoustic field at the listener position. This post processing of the aerodynamic results is not obvious since it can introduce non-physical noise into the solution. This project considers the aspect of noise generation in turbulent jets and especially the noise generated by vortex pairing, as it occurs for instance in jet flows. The axisymmetric version of the flow solver SFELES has been part of this PhD research, and numerical results obtained on the jet are similar to the experimental values. Analyses performed on the numerical results are interesting to go to complete turbulence modeling for aeroacoustics since vortex pairing is one of the basic acoustical processes in vortex dynamics. Currently, a standard static Smagorinski model is used for turbulence modeling. However, this model has well known limitations, and its influence on the noise sources extracted from the flow field is not very clear. For this reason, it is planned to adopt a dynamic procedure in which the subgrid scale model automatically adapts to the flow. We planned also to perform simulations with the variational multiscale approach to better simulate the different interactions between large and unresolved scales. The commercial software ACTRAN distributed by Free Field Technologies is used for the computation of sound propagation inside the acoustic domain.

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