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Intersectional Experiences of Stigma and Their Relation to Mental Health Outcomes Among Black and White Autistic Adults
No full textNon-autistic adults often harbor negative attitudes about autism and show a reluctance to interact
with autistic people. Despite common misconceptions, many autistic adults express a strong desire
for interaction, friendships, and romantic relationships but can struggle to achieve them. This gap
between autistic social desires and actual social experiences can increase the risk for poor mental
health outcomes for autistic adults. For those with multiple marginalized identities, the
compounding effects of stigma based on both race and disability may further worsen mental health
outcomes. Understanding the psychosocial factors that contribute to poor mental health in autistic
adults is of great significance, yet no study to date has investigated the direct connection between
social experiences, mental health status, and stigma for Black and White autistic adults. This
dissertation investigated this relationship using a mixed-method approach. In phase one, autistic
adults (N = 32) stratified by race (50% Black, 50% White) completed victimization and mental
health questionnaires, qualitative interviews about their social and mental health experiences, an
implicit association test to assess internalized stigma, and a videotaped semi-structured
conversation with the investigator. To assess the prevalence and severity of their victimization and
mental health challenges, a separate sample of Black and White non-autistic participants (N = 32,
50% Black, 50% White) also completed the questionnaire measures as a basis of comparison. In
the second phase of the study, videos of autistic participants from phase one were shown to non-
autistic raters, who provided their first impressions of each participant. Black autistic people were
rated as more likeable and trustworthy, and raters endorsed a greater interest in interacting with
these participants compared to White autistic people. Evidence of intersectional effects of race,
gender, and autism were also observed, with Black autistic men evaluated more favorably than
non-males, whereas White autistic men were evaluated less favorably than non-males. Peer
evaluations and internalized stigma significantly predicted mental health outcomes, with more
negative trait ratings, more positive social interest ratings, and greater internalized stigma
associated with more adverse mental health outcomes. Black and White autistic people did not
differ significantly in prevalence for most of the stigma and mental health measures, with autistic
participants overall reporting high levels of stigma, anxiety, and suicidality. However, the nature
of peer victimization, rather than the quantity of it, differed for Black and White autistic
participants. Qualitative data were analyzed using thematic analysis, with results revealing that
while many experiences of stigma are specific to one’s race or gender identity, Black and White
autistic adults experience several commonalities in their social experiences. In particular, autistic
adults across racial and gender identities reported that stigma and inaccessible social structures
often contributed to their social difficulties, with many choosing to withdraw from social
engagements to avoid potential stress, burnout, and meltdowns. These results suggest that holding
multiple marginalized identities can moderate experiences of stigma in autistic adults and
demonstrate the significant impacts that stigma can have on mental health in a diverse group of
autistic adults
Battery Thermal Management System for Electric Vehicles: Design, Optimization, and Control
No full textWe are witnessing a fast-growing demand in vehicle electrification nowadays due to the
widespread environmental consciousness, stringent emission regulations, and carbon neutrality implementation. As one of the most promising energy storage and electrification
solutions, lithium-ion battery has been widely employed for electric vehicles (EVs) due to
its excellent properties like high energy density, low maintenance, and long cycle life. However, there still exist multiple critical challenges in using lithium-ion battery at large scale as
the major power source, such as reliability issues, safety concerns, and especially the range
anxiety. Several promising solutions have been explored in the EV industry to mitigate the
drawback of range anxiety, such as larger capacity with high energy density and ultra-fast
charging. All these approaches challenge the temperature sensitive battery system as a side
effect by bringing in extra overburdened waste heat. Given these concerns, battery thermal management system (BTMS) plays an indispensable role in maintaining the maximum
temperature and temperature uniformity for EVs.
This dissertation proposes a novel J-type air-based cooling structure via re-designing conventional U- and Z- type structures. Aiming to further improve the thermal performance,
a surrogate-based optimization framework with two-stage cluster-based resampling is developed for BTMS structural optimization. Compared with the U- and Z- type, the novel
J-type structure is proved with significant advancements. Based on the optimized J-type
configuration, an operation mode switching module is designed to mitigate the temperature
unbalance by controlling the opening degree of two outlet valves. Tested by an integrated
driving cycle, results reveal that the J-type structure with its appropriate control strategy
is a promising solution for light-duty EVs using an air cooling technology.
Improving the energy efficiency is another potential approach to mitigate range anxiety.
In this dissertation, a model predictive control (MPC)-based energy management strategy
is developed to simultaneously control the BTMS, the air conditioning system, and the
regenerative power. A vehicle velocity forecasting framework is integrated with the MPC-based energy management to further improve the energy efficiency. Deep learning and image-based traffic light detection techniques have been leveraged for velocity forecasting. Results
show that the proposed energy management method has significantly improved the overall
EV energy efficiency
Low-Temperature and Photoactivated CVD on Organic Substrates
No full textChemical vapor deposition (CVD) is an attractive technique for depositing metallic thin films on
organic substrates. However, CVD often uses temperatures > 500 °C to initiate precursor
decomposition and generate highly reactive species. This can be problematic when using
attempting to deposit on organic thin films as they can degrade at temperatures < 200 °C. Here we
offer an alternative to thermal activation by using photolysis to generate reactive species at room
temperature. In this work we monitor decomposition pathways of photoactivated precursors by
employing TOF SIMS to identify molecular species remaining on the surface as well as test the
integrity of the surface post-deposition. XPS is used to identify organic surface- metal interactions,
and finally RGA is used to identify gas-phased decomposition products to further identify
photolytic pathways. In identifying the decomposition pathway, we aim to use this understanding
to further improve the deposition of metal on organic substrates
Residual Stress and Distortion in Machined Wrought and Additively Manufactured Metallic Components
No full textThe described research aims to elucidate the factors that influence post-machining residual
stress and part distortion in machining of wrought (rolled and heat-treated) aluminum al-
loys and additively manufactured stainless steel components. At present, even with carefully
designed machining process parameters, part distortions that arise in wrought aluminum
alloys due to the presence of inherent residual stress (IRS) makes it difficult to produce
high aspect-ratio thin-walled monolithic components requiring increasingly stringent toler-
ances. Lack of understanding on the effects of IRS during high-speed machining (HSM)
inhibits it’s adaption into the manufacturing work flow to determine appropriate process
parameters or tool path strategy necessary for competitive economic production that avoids
trial-and-error machining strategies with such parts. This work intends to shed light on the
influences of inherent material characteristics, such as IRS and inhomogeneous/anisotropic
material properties, on the machining response (due to coupled effects of IRS and MIRS)
in both wrought and additively manufactured metallic components. It is first hypothesized
that coupling between IRS and MIRS contributes to final part residual stress and distortion.
This coupling, contrary to assumptions prevalent in the existing literature, is believed to not
simply allow for superposition of the inherent and machining contributions, but rather call
for a nonlinear relationship that is dependent on various factors. In the presented work, the
effects of IRS and material inhomogeneity and anisotropy are analyzed through computa-
tional studies and experimental validations made possible through industrial collaborations.
The work described herein, first, studies the effect of IRS and MIRS on the final-state of
residual stress (FRS) and post-machining part distortion and suggests that there is indeed a
nonlinear coupling that exists between IRS and MIRS. Analyses have been performed using
2D orthogonal cutting and 3D end-milling computational models. The material model, val-
idated using the 2D model based on observations from published literature, is used in both
the 2D and 3D case studies. IRS in the wrought material has been modeled by employing
an iterative stress reconstruction algorithm (ISRA) to generate a compatible IRS field that
encompasses the entire wrought material based on limited experimental measurements. The
results from the 2D case studies support the existence of a nonlinear coupling between IRS
and MIRS that subsequently determines the FRS. The 3D case studies reveal distortion re-
sults that elucidate the coupling effect even further by showing that IRS not only influences
the distortion of the final part, but that the degree of influence is dependent on the coupling
of the IRS profile and the HSM tool path. The presence of said coupling can thus either
magnify or reduce the final part distortion. The above established model is further devel-
oped to simulate machining of a 2 mm thin-walled part of the same material. To compare
and validate the predictive capability of the computational model, corresponding machining
experiments are performed, followed by coordinate measuring machine (CMM) distortion
analysis on the thin wall. In order to characterize the material IRS and subsequently in-
corporate it appropriately in the computational model via ISRA, Neutron Diffraction (ND)
stress measurements are performed at various locations on a plate of the same material,
from which smaller blocks are extracted for machining. High-speed machining simulations
are performed and the results are compared with the CMM measurements. Results indi-
cate that the presence of IRS in the material significantly influences the distortion profile.
Idealized material removal by element deletion simulation shows that IRS alone does not
induce significant distortion in these specific parts whereas the coupling between IRS and
MIRS is the prominent driver. The presence of IRS also tends to drive the distortions to-
wards experimentally measured result. It is also hypothesized that additively manufactured
parts that require post-process machining also experience similar coupling between IRS (in
the substrate and the build) and MIRS, which likewise is believed to significantly affect
post-machining part distortion. To test this hypothesis, first, a continuously coupled com-
putational fluid dynamics (CFD) and finite element analysis (FEA) framework is developed
in collaboration with other researchers. This model, involving directed energy deposition
(DED) builds, is employed to simulate manufacture of single-layer and double-layer, single-
bead DED builds. Corresponding experiments are performed to create samples, followed
by geometry scanning and ND stress measurements to compare with the simulation results.
The predicted final stress profiles agree well with experimentally measured profiles obtained
via ND stress measurements. Subsequently, the geometry and stress profile obtained from
the coupled CFD-FEA framework is employed to simulate an interlayer machining operation
in order to study the influence of IRS and inhomogeneous microstructure on post-machining
residual stress and distortion. A kinetic Monte-Carlo based microstructure prediction model
is calibrated and employed to obtain the microstructure in the build. The predicted mi-
crostructure is compared with EBSD measurements for validation. The microstructure is
then incorporated into the FEA model to create a representative volume element (RVE)
with varying strengths for grains of different sizes, based on a combined Johnson Cook - Hall
Petch material model. It is found that IRS and inhomogeneous microstructure influences
the part distortion after machining. IRS shows a greater influence on distortion compared
to microstructure. Finally, the IRS is found to influence the bulk behavior of the material
more significantly, whereas, the microstructure influences the local behavior of the material.
With this new understanding of the influence of various factors on post-machining stress and
part distortion, machining operations can be improved further to reduce waste and improve
part compliance to tight tolerances, including in new hybrid manufacturing markets
Lifeworlds of Organizations and Entrepreneurship: Perspectives on Institutional Dynamics and "Becoming"
No full textThe starting point for my reflections is the insight that both institutions and human beings are
always situated in a lifeworld. Lifeworld means everything that exists around us insofar as it is
directly, immediately experienced in everyday life. A lifeworld could imprint on individuals’
mindsets, mark institutional and cultural legacies, and evolve along with the interactions between
actors and institutions. My dissertation work has approached the overarching questions of how
organizational strategies come to be deeply rooted in culture and institutions as well as how
institutional actors (i.e., entrepreneurs in my study) refashion institutions. In the opening chapter,
I apply an imprinting lens to examine the effect of macro-institutional events on CEO decisionmaking. Specifically, I focus on a lifeworld marked by famine (i.e., China’s Great Famine of 1959-
61) and examine how such resource-scarcity experience influences CEOs’ resource allocation in
firm innovation. In the second chapter, I take a historical embeddedness view of institutions to
explain the contemporary widespread firm corruption in various regions of Africa by tracing the
issue back to the injustice legacies left by the lifeworld of the slave trade (c.1400s-1900s). In the
third chapter, I take a processual view of institutions to explore the situated entrepreneurial
dynamics in refreshing regional institutional outlooks. Two representative case studies (i.e., New
England Concord Circle during the19th century and Texas oil and gas industry in the 20th century)
demonstrate a hermeneutical cycle between actors and structures, and show how this cycle’s
catalyst lies in the technology-culture nexus. My fourth and final chapter evolves from the third,
focusing on the individuals/collectives’ subjective experience of time within a given lifeworld. I
explore the interactions between entrepreneurs’ temporal orientation and the institutions’ temporal
ambience and identify a four-part taxonomy of institutional work based on the basic possibilities
of imaginative novelty (The Wizard of Oz), imaginative replication (Castle in the Air), nostalgic
replication (Golden Age), or nostalgic novelty (Seeking Roots). Correspondingly, I offer
representative cases that demonstrate the movements of regional institutional trajectory over time
on a temporal compass of entrepreneurs and their institutions. Overall, in this dissertation work, I
apply interdisciplinary approachesfrom sociology, history, and philosophy to bear on social issues,
including resource-scarcity experience/firm innovation, injustice legacy/firm corruption,
generational units/institutional reconstruction, and temporality/institutional image. I hope my
dissertation inspires dialogues with other scholars who may ponder, challenge, and resonate with
my work
Inversion Asymmetry, Flavortronics, and Nonlinear Optics in Two-dimensional Materials
No full textInterests in two-dimensional (2D) materials have grown tremendously after the successful
isolation of a single layer graphene. The properties of 2D materials are often very different
from their 3D counterparts. They offer great flexibilities in tuning their electronic and optical
properties through numerous ways. For example, electronic properties not only greatly vary
with the number of layers in the materials, they can also depend strongly on the relative twists
among different layers. Besides scientific advances and discoveries, these findings have led to
enormous efforts being put in band gap engineering and the more recent moir ́e engineering
to ensure that they fulfill their unprecedented potential in technological applications. In this
dissertation, we study this emerging and exciting platform.
Our era of electronics is made possible through advances in semiconductor technology based
on the precise manipulation of electronic charge degree of freedom. However, there are
additional degrees of freedom, such as spin, layer and valley, that electrons in materials may
possess. Methods to fabricate workable devices based on the manipulation of these degrees
of freedom to process and store information have been extensively studied in the literature.
Here, we take a step further. We consider another degree of freedom, SU(3) flavor, that
exists in the so-called Q-valleys of n-type few-layer transition metal dichalcogenides. In the
quantum Hall regime, Landau levels form triplets that are each three-fold degenerate. When
each Landau level triplet is one-third filled or empty, we predict that a pure flavor nematic
phase and a flavorless charge-density-wave phase will occur respectively below and above
a critical magnetic field. Electrons carry flavor-dependent electric dipole moments even at
zero magnetic field, giving rise to a nematic ferroelectric state. We further show that the
flavor degree of freedom can be manipulated by an electric field, leading to a new concept:
flavortronics.
The local density of states of electrons in materials will be modified when they are scattered
off impurities. This results in quasiparticle interference (QPI) that can be probed by scanning
tunneling spectroscopy. We then study QPI of Q-valley electrons scattering off localized non-
magnetic and magnetic impurities. More importantly, we propose that QPI provides a way
to observe the above predicted nematic ferroelectric state.
Finally, we study a moir ́e metamaterial, namely twisted double bilayer graphene (TDBG).
The electronic and optical properties in twisted multilayer systems are very different from
the single layer counterpart. The highly tunable quantum geometric properties of TDBG
give rise to tunable photoresponses that are closely related to the polarization states, power
and wavelength of the incident light. This close relationship enables us to generate a set of
photovoltage maps that can be used to train a convolutional neural network to decode the
properties of an unknown incoming light from its unique photovoltage map. This enables an
unprecedented intelligent light sensing in an extremely compact, on-chip manner
Task Learning as a Mechanism of Transfer in Cognitive Intervention: Neuro-cognitive Predictors and Outcomes of Early, Middle and Late Stages of Task Learning
No full textInvestigation into methods of addressing cognitive loss exhibited later in life is of paramount
importance to the field of cognitive aging. The passive protective factors of cognitive reserve and
continued education, as well as the active factor of cognitive intervention, have all been found to
ameliorate expected declines in cognition in adults aged sixty-five and up, and all three of these
factors heavily involve the learning process. This dissertation presents three studies derived from
a longitudinal cognitive intervention, each designed to illuminate factors which influence the
learning process and in turn how that process bolsters cognition. The cognitive intervention in
question was a working-memory-based video-game-like training task, designed to be both
engaging and adaptive to the abilities of individual participants. The first study identified a
measure of verbal episodic memory as well as the volume of a brain region involved in language,
verbal memory and cognitive control (the left inferior frontal gyrus) as predictors of individual
learning rates on the training task. These two neuro-cognitive measures were more predictive of
task learning when considered in conjunction than when considered separately, indicating a complimentary effect. The second study compared daily performance on the training task with
several daily factors known to influence cognition, including perceived wellbeing, stress,
business, and sleep. Auto-regressive analyses conducted in Study 2 were able to identify
meaningful predictors of performance-over-time on the training task in fifty percent of cases.
This pattern of influences varied greatly between participants, indicating a highly individualized
influence of these variables. The third study observed that individual differences in learning of
the training task were related to training-related gains in a measure of nonverbal reasoning, with
participants who learned the training task faster showing relatively greater transfer (i.e. gains) to
that measure of reasoning. Collectively, the three studies presented in this dissertation offer a
novel insight into training-related cognitive benefits via the identification of a discreet “path of
transfer” resultant from this training. Specifically, these studies identify a pattern of influence by
which verbal episodic memory (and its related brain region) is determinant of learning of a
working-memory task (the training task), and learning of that task itself is determinant of
training-related gains to nonverbal reasoning. This pattern of findings serves as a testable
hypothesis for future studies of working-memory based cognitive training in older adults, and
this “path of transfer” approach may serve as a useful tool in examining the concept of transfer
more generally
Melody Recognition, Repeated Exposure, and Timbre Change
No full textPrevious research showed that people are less likely to recognize a recently heard melody if it
has changed timbre. This dissertation investigates how changing timbre influences melody
recognition. Participants in Experiment 1 (N = 33) sorted a series of timbres into groups. The
data were analyzed with DiSTATIS and five timbres were derived as perceptually different and
were used in Experiment 2. In Experiment 2, participants (N = 145) heard a series of melodies
repeatedly over five sessions. Participants were assigned to one of three conditions: one in which
the timbre of the melodies never changed, one in which timbres changed one time (either at
Session 3 or Session 5), or timbre changed every session. Participants rated melodies on a 4-
point confidence scale of recognition. Results suggest that regardless of the rate of timbre
change, recognition improves over multiple exposures. Exploratory analyses suggest that high
levels of music training may significantly improve performance and that different kinds of
melody recognition are influenced differently by timbre change
From Worlds: Creative Practice as Carrier Bag
No full textThe following thesis investigates a body of sculptures called “chimeras” for the myriad tensions
between materiality, form, and content they embody. Using synthetic materials shared with
post-minimalists such as Eva Hesse and Lynda Benglis, as well as Dallas-based artist Dan Lam, the
chimeras cultivate an intimate dialogue between each other and challenge the definition of
beauty.
Following the tentacular thinking binding Donna Haraway’s Cthulucene and Ursula K LeGuin’s
Carrier Bag Theory of Fiction, the chimeras build a world inspired by speculative fiction, pop
culture, and the natural world
New Models for Flutter and Edgewise Instability Analysis of Vertical and Horizontal Axis Wind Turbines for Land-based and Floating Offshore Conditions
No full textWind energy is a vital part of renewable energy sector that is increasingly becoming popular to
reduce the adverse effect of traditional power production methods in increasing the global
temperature. As the demand for wind energy increases, the sizes of the blades of wind turbines are
also increasing with the availability of novel materials and manufacturing techniques. On the other
hand, these very large wind turbines might be susceptible to design challenges and instability
problems because of their sheer size which typically are not concerns for relatively smaller
turbines. This has motivated the development of models to predict the unstable behavior of very
large vertical axis wind turbines (VAWTs) and horizontal axis wind turbines (HAWTs). This work
presents modeling method of rotor-platform system for offshore floating vertical axis wind
turbines. Effect of structural design parameters on flutter instability of 2-bladed and 3-bladed
VAWTs are studied. An analysis is presented on the effect of floating platform on flutter behavior
of rigid body and flexible modes of vibration of the coupled system. A fundamental understanding
of how the floating system impacts the resonance and flutter properties of VAWT is sought and
presented.
Further study has been performed on the impact of aerodynamic modeling assumptions that are
conventionally implemented to predict flutter of wind turbines. The shortcomings of simplifying
assumptions of standard aerodynamic theory have been demonstrated, and new aerodynamic
model is developed to address those shortcomings. Then, this new model is applied to both
horizontal axis wind turbines as well as vertical axis wind turbines. Comparative analysis is done
of the effect of standard and new aerodynamic model in terms their predictive capability of flutter
for both land-based and floating vertical axis wind turbines. Large number of horizontal axis wind
turbines with varying sizes and geometry are studied for flutter and edgewise instability with the
newly developed aerodynamic model. Similarly, vertical axis wind turbines are examined with the
newly developed aerodynamic model.
This study also aims at validating numerical models with experimental results. To achieve that
goal, a subscale floating VAWT system is manufactured, and experimental test is performed on it
to extract modal dynamic properties. The measured structural properties are used to calibrate the
rotor model, and free decay test results are used to generate a floating platform model. Finally, the
rotor and platform model are coupled and modal analysis (frequency analysis) is performed and
the model is further refined by comparing the test results and model predictions.
Key findings of this dissertation confirm that moving a VAWT from land-based to floating
configuration has the potential to alleviate both resonance and flutter concerns. Developed new
aerodynamic model shows higher flutter prediction of tower, propeller and edgewise modes of
land-based and floating VAWT compared to the prediction by standard aerodynamic model. For
large HAWT blades, the new aerodynamic model has more impact on 3-bladed case than on 2-
bladed case in terms of flutter and edgewise instability RPM prediction. Validation study on modal
dynamics of floating VAWT confirm reasonably accurate modeling of coupled rotor-platform
floating model