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Municipal electorates across America are vocal, unrepresentative networks. With lower turnout rates than state and national elections, the local electoral process disproportionately elects white, older, home-owning officials. Voting and elected bodies align demographically, thus leading to a policy that disproportionately reflects the interests of white, older, home-owning voters (Levine Einstein, Ornstein, & Palmer, 2019). This cycle is problematic because it halts the passage of policy that reflects the interest of historically underrepresented voters: young people and people of color. I argue that, for local races, campaign methods disproportionately mobilize the social networks that white, older, home-owning voters belong to. Members of these groups disproportionately occupy low-density housing-building types that can be accessed and mobilized by all campaigns. I suggest a relationship exists between housing density and turnout, with voters residing in low-density housing participating at higher rates in local elections. Therefore, local races have smaller budgets and fewer reserves to invest in mobilizing voters residing in high-density housing. To assess this relationship, I compare housing density - whether a voter lives in low density or high-density housing - and individual voting records from 2017 to 2021 across four municipalities in Massachusetts: Cambridge, Boston, Somerville, and Worcester. I expect to find that compared to voters living in low-density housing, those residing in high-density housing - disproportionately young voters and voters of color - are turning out at lower rates in local elections than in the 2018 Midterm and 2020 Presidential Elections. To change this cycle, scholars must pay more attention to the role housing density plays in inhibiting local mobilization efforts, and campaigns must collaborate to mobilize voting members of all social networks, especially those residing in high-density housing.

The relationship between volume production and stand density, often termed the 'growth-density relationship', has been studied since the beginnings of forestry and yet no conclusive evidence about a general pattern has been established. Throughout the literature claims and counterclaims concerning the growth-density relationship can be found. Different conclusions have been attributed to the diverse range of definitions of volume and stand density among problems with study design and other pitfalls. Using data from two thinning studies representing non-intensively and intensively managed plantations, one spacing trial, and one thinning experiment a comprehensive analysis was performed to examine the growth-density relationship in loblolly pine. Volume production was defined as either gross or net periodic annual increment of total, pulpwood, or sawtimber volume. These definitions of volume production were then related to seven measures of stand density including the number of stems per hectare, basal area per hectare, two measures relative spacing and three measures of stand density index. A generalized exponential and power type function was used to test the hypothesis that volume production follows either an increasing or unimodal pattern with stand density. These patterns were tested using all combinations of the six definitions of volume production and the seven measures of stand density. Significance of the parameters indicated that different patterns existed depending on the type of management (intensive vs. non-intensive), if thinning is performed, and depending on the definitions of growth and density. The growth-density pattern was generally the same between gross and net production although different patterns emerged when comparing total, pulpwood, and sawtimber volumes. The definitions of stand density which used diameter as a measure of average tree size were more highly correlated with volume production and produced similar patterns while the number of stems per hectare was the least correlated. Further analysis was performed to evaluate Langsaeter's hypothesis which states that volume production is constant and optimal across a wide range of stocking. A mixed-model approach was used to test the equality in mean volume production across a range of planting densities and thinning intensities. To account for the effects of age, the equality in mean volume production was tested separately across a range of ages from 8 to 25 years within the spacing trial data and across a range of one to six years since thinning within the thinning experiment. A multiple comparison test indicate that pattern of volume production and stocking is highly related to the two stages of self-thinning. In young stands, within the distance-independent mortality stage, volume production increases with increasing planting density and therefor increasing stocking. During the distance-dependent mortality stage the assumption of constant and optimal volume production across a wide range of stocking is generally correct. However when mortality began to reduce canopy closure to the point that the residual stand could not recover gaps in the canopy a decline in volume production occurred resulting in a decreasing relationship with increasing stocking. Finally, a system of equations were constructed to describe volume production at the individual tree and stand levels. From this model it was determined that stand level volume production follow an increasing pattern with stand density. / Ph. D.

Enzyme-catalyzed biofuel cells would be a great alternative to current battery technology, as they are clean, safe, and capable of using diverse and abundant renewable biomass with high energy densities, at mild reaction conditions. However, currently, three largest technical challenges for emerging enzymatic fuel cell technologies are incomplete oxidation of most fuels, limited power output, and short lifetime of the cell. Synthetic pathway biotransformation is a technology of assembling a number of enzymes coenzymes for producing low-value biocommodities. In this work, it was applied to generate bioelectricity for the first time. Non-natural enzymatic pathways were developed to utilize maltodextrin and glucose in enzymatic fuel cells. Three immobilization approaches were compared for preparing enzyme electrodes. Thermostable enzymes from thermophiles were cloned and expressed for improving the lifetime and stability of the cell. To further increase the power output, non-immobilized enzyme system was demonstrated to have higher power densities compared to those using immobilized enzyme system, due to better mass transfer and retained native enzyme activities. With the progress on pathway development and power density/stability improvement in enzymatic fuel cells, a high energy density sugar-powered enzymatic fuel cell was demonstrated. The enzymatic pathway consisting of 13 thermostable enzymes enabled the complete oxidation of glucose units in maltodextrin to generate 24 electrons, suggesting a high energy density of such enzymatic fuel cell (300 Wh/kg), which was several folds higher than that of a lithium-ion battery. Maximum power density was 0.74 mW/cm2 at 50 deg C and 20 mM fuel concentration, which was sufficient to power a digital clock or a LED light. These results suggest that enzymatic fuel cells via synthetic pathway biotransformation could achieve high energy density, high power density and increased lifetime. Future efforts should be focused on further increasing power density and enzyme stability in order to make enzymatic fuel cells commercially applicable. / Ph. D.

Quantum chemistry methodologies can be used to address a wide variety of chemical problems. Key to the success of quantum chemistry methodologies, however, is the selection of suitable methodologies for specific problems of interest, which often requires significant assessment. To gauge a number of methodologies, the utility of density functionals (BLYP, B97D, TPSS, M06L, PBE0, B3LYP, M06, and TPSSh) in predicting reaction energetics was examined for model studies of C-O bond activation of methoxyethane and methanol. These species provide excellent representative examples of lignin degradation via C-O bond cleavage. PBE0, which performed better than other considered DFT functionals, was used to investigate late 3d (Fe, Co, and Ni), 4d (Ru, Rh, and Pd), and 5d (Re, Os, and Ir) transition metal atom mediated Cβ -O bond activation of the β–O–4 linkage of lignin. Additionally, the impact of the choice of DFT functionals, basis sets, implicit solvation models, and layered quantum chemical methods (i.e., ONIOM, Our Own N-layered Integrated molecular Orbital and molecular Mechanics) was investigated for the prediction of pKa for a set of Ni-group metal hydrides (M = Ni, Pd, and Pt) in acetonitrile. These investigations have provided insight about the utility of a number of theoretical methods in the computation of thermodynamic properties of transition metal hydrides in solution. As single reference wavefunction methods commonly perform poorly in describing molecular systems that involve bond-breaking and forming or electronic near-degeneracies and are typically best described with computationally costly multireference wavefunction-based methods, it is imperative to a priori analyze the multireference character for molecular systems so that the proper methodology choice is applied. In this work, diagnostic criteria for assessing the multireference character of 4d transition metal-containing molecules was investigated. Four diagnostics were considered in this work, including the weight of the leading configuration of the CASSCF wavefunction, C02; T1, the Frobenius norm of the coupled cluster amplitude vector related to single excitations and D1, the matrix norm of the coupled cluster amplitude vector arising from coupled cluster calculations; and the percent total atomization energy, %TAE. This work demonstrated the need to have different diagnostic criteria for 4d molecules than for main group molecules.

No / This paper outlines the current challenges of driver behaviour modelling for real-world applications and presents the novel method to identify the pattern of usage to predict upcoming journeys in probability sense. The primary aim is to establish similarity between observed behaviour of drivers resulting in the ability to cluster them and deploy control strategies based on contextual intelligence and datadriven approach. The proposed approach uses the probability density function (PDF) driven by kernel density estimation (KDE) as a probabilistic approach to predict the type of the upcoming journey, expressed as duration and distance. Using the proposed method, the mathematical formulation and programming algorithm procedure have been indicated in detail, while the case study examples with the data visualisation are given for algorithm validation in simulation. / aiR-FORCE project, funded as Proof of Concept by the Institute of Digital Engineering

With the advent of the internet and advances in computing power, the collection of very large high-dimensional datasets has become feasible { understanding and modelling high-dimensional data has thus become a crucial activity, especially in the field of pattern recognition. Since non-parametric density estimators are data-driven and do not require or impose a pre-defined probability density function on data, they are very powerful tools for probabilistic data modelling and analysis. Conventional non-parametric density estimation methods, however, originated from the field of statistics and were not originally intended to perform density estimation in high-dimensional features spaces { as is often encountered in real-world pattern recognition tasks. Therefore we address the fundamental problem of non-parametric density estimation in high-dimensional feature spaces in this study. Recent advances in maximum-likelihood (ML) kernel density estimation have shown that kernel density estimators hold much promise for estimating nonparametric probability density functions in high-dimensional feature spaces. We therefore derive two new iterative kernel bandwidth estimators from the maximum-likelihood (ML) leave one-out objective function and also introduce a new non-iterative kernel bandwidth estimator (based on the theoretical bounds of the ML bandwidths) for the purpose of bandwidth initialisation. We name the iterative kernel bandwidth estimators the minimum leave-one-out entropy (MLE) and global MLE estimators, and name the non-iterative kernel bandwidth estimator the MLE rule-of-thumb estimator. We compare the performance of the MLE rule-of-thumb estimator and conventional kernel density estimators on artificial data with data properties that are varied in a controlled fashion and on a number of representative real-world pattern recognition tasks, to gain a better understanding of the behaviour of these estimators in high-dimensional spaces and to determine whether these estimators are suitable for initialising the bandwidths of iterative ML bandwidth estimators in high dimensions. We find that there are several regularities in the relative performance of conventional kernel density estimators across different tasks and dimensionalities and that the Silverman rule-of-thumb bandwidth estimator performs reliably across most tasks and dimensionalities of the pattern recognition datasets considered, even in high-dimensional feature spaces. Based on this empirical evidence and the intuitive theoretical motivation that the Silverman estimator optimises the asymptotic mean integrated squared error (assuming a Gaussian reference distribution), we select this estimator to initialise the bandwidths of the iterative ML kernel bandwidth estimators compared in our simulation studies. We then perform a comparative simulation study of the newly introduced iterative MLE estimators and other state-of-the-art iterative ML estimators on a number of artificial and real-world high-dimensional pattern recognition tasks. We illustrate with artificial data (guided by theoretical motivations) under what conditions certain estimators should be preferred and we empirically confirm on real-world data that no estimator performs optimally on all tasks and that the optimal estimator depends on the properties of the underlying density function being estimated. We also observe an interesting case of the bias-variance trade-off where ML estimators with fewer parameters than the MLE estimator perform exceptionally well on a wide variety of tasks; however, for the cases where these estimators do not perform well, the MLE estimator generally performs well. The newly introduced MLE kernel bandwidth estimators prove to be a useful contribution to the field of pattern recognition, since they perform optimally on a number of real-world pattern recognition tasks investigated and provide researchers and practitioners with two alternative estimators to employ for the task of kernel density estimation. / PhD (Information Technology), North-West University, Vaal Triangle Campus, 2014