Doctoral Dissertations
https://jewlscholar.mtsu.edu/handle/mtsu/3481
2020-02-18T12:35:10Z"Our Day Will Come': Contested Memory Sites, Community Engagement, and Public History Practice
https://jewlscholar.mtsu.edu/handle/mtsu/6153
"Our Day Will Come': Contested Memory Sites, Community Engagement, and Public History Practice
Fletcher, Michael Joseph
This dissertation takes a two-fold approach examining the subject of field surveys. First, we explore the ground-breaking Irish survey, Landscapes of Revolution. Second, using the Landscapes experience, we offer something of a “how to” guide to help small organizations with little to no budget conduct similar surveys.
The Landscapes of Revolution Archaeology Project is the first ever survey of the existing Irish War of Independence (1918-1921) landscape. Focusing on East County Cork, Landscapes of Revolution was conceived by Damian Shiels, an archaeologist based in Midleton, Co. Cork to draw attention to the precarious state of much of Ireland’s nineteenth and twentieth century, built heritage. The project was designed and executed with community engagement as a top priority. With both a limited budget, and personnel, the community members’ roles were to help identify and photograph potential structures and landscapes.
The field survey is one of the fundamental activities in many preservation projects. Nevertheless, because of their cost and manpower needs, some surveys might seem beyond the reach of smaller organizations such as local historical societies. The second part of this dissertation draws upon my experience with the Landscapes project. With virtually no budget or full-time staff, Damian Shiels and I designed a survey in which we successfully carried out all parts of the process, from research to field work, using community volunteers with a vested interest in their own history. In addition to outlining our process, from the methodology to public programs, the work includes pointers and recommendations designed to help these groups conduct their own surveys utilizing community support.
Machine Learning Predictions of Spectroscopic Properties and Carbonyl Reactivity From A Database of Charge Density Descriptors
https://jewlscholar.mtsu.edu/handle/mtsu/6150
Machine Learning Predictions of Spectroscopic Properties and Carbonyl Reactivity From A Database of Charge Density Descriptors
Donthula, Kiran Kumar
Carbonyl compounds are important to study because of their biological and industrial
significance. A database of critical point descriptors for valence-shell charge concentrations
and depletions of carbon atoms in a range of aldehydes, ketones, imides, and amides has
been created. For each critical point, the database contains data related to the probability
distribution of electrons (value of the total electron density, r, at bond critical points which
have been correlated with bond strength). This includes, data related to the curvature of r at
maxima and minima in carbon’s valence shell of charge concentration (VSCC) (Ñ2r(r) and
Hessian eigen values, which have been correlated with chemical reactivity). For both types
of critical points, radii from the enveloped carbon nucleus are included in the database.
Artificial neural networks (ANNs) are strong tools for predicting nonlinear functions,
and they are used in this study to both leverage charge density-based descriptors and learn
about their relative chemical significance. An ANN prediction scheme was developed for
the spectroscopic properties and interaction energies of carbonyl compounds, based on the
topological properties of electron density obtained from QTAIM (The input data necessary
for training and testing the proposed ANN scheme was data obtained from Quantum Theory
of Atoms In Molecules.). In 2009, Balabin and Lomakina [1] used three-layer feed-forward
artificial neural networks, with back propagation, to predict density functional theory (DFT)
energies that are comparable to those obtained with large basis set using lower-level energy
values as training data. These studies, and others, indicate that data-mining techniques, used
in conjunction with artificial neural networks, can be productively applied in the prediction
of properties that would otherwise be computationally expensive and time-consuming to
calculate.
For our study, we have selected 225 small systems of carbonyl group-containing
molecules as a training set, with each molecule containing 18 bond critical point descriptors
and 30 Laplacian critical point descriptors. These properties were used to train ANN for
predicting C=O stretching frequencies and 13C chemical shifts. Additional properties, such
as intermolecular interaction energies with nucleophiles are also estimated. Predictions are
made using the Laplacian critical point data, as well as the bond critical point data, both
separately and combined. The study was carried out using the leave-one-out cross
validation method. Expected Mean Absolute Percent Errors (MAPE) and Mean Absolute
Errors (MAE) are compared between these three data sets. The calculated MAPE for neural
network predictions of 13C shifts and C=O stretching frequencies are 1.38, 0.53. MAEs for
neural network predictions of covalent and van der Waals interaction energies are 3.44
kcal/mol and 4.78 kcal/mol. Here, all molecular wave functions have been generated using
Gaussian 09 [2], and electron density analysis is done using programs AIMAll [3] and
DenProp [4].
For the stretch-test we chose the E. coli. enzyme D-fructose-6-phosphate aldolase (FSA)
[5], which catalyzes a nucleophilic addition reaction of a carbon nucleophile (ketone) to a
carbon electrophile (aldehyde). The covalent interaction energy between a nucleophile and
an electrophile within the binding pocket of an enzyme (FSA) is predicted by our ANN
with an absolute error of 3.2 kcal/mol.
DISCIPLINARITY AND POLITICS: JAMES BERLIN AND THE POLITICAL TURN IN COMPOSITION
https://jewlscholar.mtsu.edu/handle/mtsu/6149
DISCIPLINARITY AND POLITICS: JAMES BERLIN AND THE POLITICAL TURN IN COMPOSITION
Mitchell, Joseph L
ABSTRACT
The recent publication of Composition, Rhetoric, and Disciplinarity (2018)—a collection of essays addressing what the editors refer to as the “disciplinarity problem”—signals the return of a perennial concern in the field of rhetoric-composition studies. Contributors to this collection refer to a multitude of “turns” representing divergent interests that pull against desires to establish and maintain a more unified disciplinarity. These tensions seem most intense for “turns” focused on political agendas, such as “the public,” “the global,” and “the queer.” Interestingly, the idea of “turns” has also recently received field-wide attention as indicated not only in Composition, Rhetoric, and Disciplinarity, but also in a special issue of College English dedicated to “Reimagining the Social Turn” (2014). Contributors to that issue, as well as later respondents, have argued over the political origins and nature of the so-called “social turn,” again highlighting the tension between desires for a well-defined discipline and a kind of disciplinary fluidity (if not anti-disciplinarity) constantly reshaped by shifting political concerns.
This dissertation enters these recent discussions on “disciplinarity” and “the social turn,” making the case that rhetoric-composition studies still struggles with disciplinarity because, in part, the field has overlooked the presence of a political turn in the midst of what is now called “the social turn.” More specifically, the dissertation argues that the adoption of the phrase social turn may obfuscate an explicitly focused “political turn” that dominated the field during the period of 1987-1993, a turn largely enabled by James Berlin’s efforts to politicize composition classes and, thereby, the discipline of rhetoric and composition studies. Thus, by elucidating this “political turn,” the dissertation complicates recent histories of composition studies; moreover, it suggests that current discussions of the field’s disciplinarity have been hampered by this incomplete understanding of its recent history.
Resonant Sets in Benzenoid Systems
https://jewlscholar.mtsu.edu/handle/mtsu/6148
Resonant Sets in Benzenoid Systems
Chen, Xi
A benzenoid system $H$ is a finite 2-connected plane bipartite graph in which every interior face is bounded by a regular hexagon. A benzenoid system is called as cata-condensed if it is outer planar. A perfect matching is a set of independent edges which cover every vertex exactly once. A set of disjoint hexagons $S$ of a benzenoid system $H$ is a resonant set if the subgraph obtained from $H$ by deleting all vertices of hexagons in $S$ has a perfect matching. The resonant set is forcing if the subgraph has a unique perfect matching. In chapter 2, we define a forcing resonance polynomial of $H$ as $f(x)=\sum_{i=1}^{cl(H)} a_i x^i$ where $a_i$ is the number of distinct forcing resonant set of size $i$ and $cl(H)$ is the Clar number of $H$. We put all coefficients of this polynomial in a vector called as coefficient vector. We design a recursive algorithm to compute the forcing resonance polynomial of cata-condensed benzenoid systems with $n$ hexagons. The forcing resonance polynomial of $H$ can be used to enumerate the number of forcing resonant sets and its coefficient vector can be applied to predict the stability of benzenoid system more accurately than Clar number and Kekul\'e count, which are all traditional stability indicators of molecules. The coefficient vector is also better than HOMO-LUMO gap in terms of describing the structural characteristics of molecules. In chapter 3, we also design an algorithm to reconstruct the cata-condensed benzenoid systems in a specific case.
Forcing set is concept originated from the research on the application of Kekul\'e structure in the resonance theory in chemistry. This concept has been generalized to any graph $G$. For example, let $G$ be a graph with $m$ edges and $n$ vertices. A face of $G$ is forcing face if the subgraph of G obtained by deleting this face and all edges incident to this face has a unique perfect matching. In chapter 4, we give a forcing face detection algorithm based on a well-known unique perfect matching algorithm in $O(m^2\text{log}n^4)$ time. We also give an algorithm to construct graphs with unique perfect matching through odd bridges, inspired by reversely thinking this unique perfect matching algorithm. We present a forcing face construction algorithm based on the proposed unique perfect matching construction algorithm.