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Digital Media Use by Nonprofit Organizations: Relationships, Causations, and Comparisons
No full textThis mixed-method dissertation investigates some of the least explored aspects of digital media
use by nonprofit organizations with three empirical and content-analysis chapters. Applying the
dialogic communication and resource dependency theories, Chapter 2 examines the social media
activity of faith-based organizations affiliated with six major religious groups in the U.S. The
findings indicate that the organizations’ size, age, and financial and human resources have
minimal impact on online communication activity. However, public grants and volunteers were
found to be significantly negatively correlated. In addition, mixed results for the relationship
between organizational factors and the nature of and one-way and two-way communication in
messaging were observed. Chapter 3, a survey-based study, offers an in-depth analysis of the
influence of transformational leadership attitudes towards online fundraising. Significant
relationships were found between transformational leaders’ intention of using social media and
online fundraising for organizational purposes and allocating resources for these purposes. The
fourth chapter compares the social media communication strategy of public and nonprofit
organizations. This was done through advancing Lovejoy and Saxton’s (2012) “Hierarchy of
Engagement” framework from a stakeholders theory’s perspective. An in-depth content analysis
of 700 tweets by public housing authorities (PHAs) and nonprofits working solely for the
housing cause indicated a similar trend of sharing “Information,” “Action,” and “Community”
messages. However, the second layer of analysis through subcategorization revealed significant
variance like messaging. In addition, both organizations' genre was found to be extensively using
third-party social media engagement tools and tweeting from cellphone devices. These studies
fill significant gaps in the literature and are also very timely and important as nonprofits are
likely to become increasingly reliant on digital media to fulfill their mission and service delivery.
In this respect, the response to complications arising from the COVID-19 pandemic has been a
preview of developments to come
Synthetic Design of Cerium-Based Intermetallics
No full textCe-based highly correlated systems are of interest due to Ce3+ (S=1/2) providing an ideal f-electron
system to study the interplay of localized magnetic moments and conduction electrons. The growth
of high-quality single crystals is of utmost importance to ensure the determination of intrinsic
anisotropic properties. This dissertation presents the single crystal growth and d etailed
characterization of Ce-containing intermetallics.
Motivated by the search for new spintronic devices based on topological materials, the first study
highlights the incorporation of Bi in the topological parent compound, CeSbTe. Sb net containing
CeSbTe has been studied to show the interplay of magnetism and topology. Inserting Bi, a larger
element, provides the opportunity to change the Fermi surface while preserving topologically
relevant features. We show the band structure engineering of potential topological materials
LnSb1-xBixTe (Ln = La, Ce, Pr; x ~ 0.2) and CeBiTe.
Continuing our search for novel quantum materials, our elucidation of crystal growth parameters
of Ce-based intermetallics, led to the identification of a new intermetallic homologous series
An+1MnX3n+1 (A = rare earth; M = transition metal; X = tetrels; n = 1 – 6) built up of structural
subunits such as AlB2, AuCu3, and BaNiSn3. The homologous series serves as a model system for
studying the coupling between localized f-electrons and conduction electrons. Additionally, the
stacking of heterostructural subunits is an exciting way to modify physical properties of related
phases, highlighting the importance of structural building blocks as a new avenue to study
magnetism and topology. Crystal growth, detailed single crystal structural modeling, and magnetic
and transport properties of Ce5Co4+xGe13-ySny (n = 4), Ce6Co5+xGe16-ySny (n = 5), and
Ce7Co6+xGe19-ySny (n = 6), are presented. The similarities between the synthetic profiles used to
grow n = 4 – 6 brought about new questions which led to our work investigating phase formation.
Finally, the process for designing in situ synchrotron experiments, including a new sample
environment and furnace apparatus for the use with flux grown intermetallics, is presented
Improving Auditory Responses After Hearing Loss
No full textNoise-induced hearing loss is a common and debilitating neurological injury. Exposure to loud
sounds can result in damage to cochlear structures and subsequently to losses in the transduction
of acoustic signals into neural activity. The loss of neural activity leads to auditory pathways
having less information available to them to process and evaluate any given acoustic scene. For
patients with noise-induced hearing loss, the degradation of acoustic processing leads to
difficulties in communication. Challenges in communication for patients with noise-induced
hearing loss arise from trouble understanding speech—especially in noisy environments.
Ineffective communication can lead to lost productivity and social withdrawal, which profoundly
degrade the quality of life for people with noise-induced hearing loss. In addition to the loss of
acoustic input, noise-induced hearing loss also results in adaptive changes throughout the auditory
pathway. The nature and impact of these adaptations remains unclear and techniques which can
probe these adaptations by precisely modulating neural activity are needed. Vagus nerve
stimulation (VNS) paired with sound presentation has been shown to induce stimulus-specific
plasticity in the auditory cortex. To explore the possibility that VNS paired with sounds might
enhance neural responses to auditory cues following noise-induced hearing loss, a rodent model of noise-induced hearing loss was established. The model covered a wide range of behavioral
impairments and demonstrated behavioral deficits in speech sound detection and discrimination.
Importantly, rats with severe hearing loss demonstrated behavioral deficits most akin to clinical
complaints—difficulty discriminating speech in the presence of noise. The present rodent model
was then used to evaluate the ability of VNS paired with speech sound presentation to alter neural
responses to speech sounds following noise-induced hearing loss. For rats with moderate hearing
loss and which had intact neural responses to speech sounds, VNS-speech pairing did not
significantly alter the neural responses to speech sounds. However, for rats with severe noiseinduced hearing loss, VNS-speech pairing doubled the response strength to the consonant portion
of the speech sound, significantly improved neural discriminability, and did not alter the response
to the vowel. For rats with profound hearing loss, the response strength to the vowel portion of the
speech sounds was doubled and neural detection improved. Taken together, these findings suggest
that when neural responses are present but weakened, such as with the consonant portion in the
severe group or the vowel in profound group, VNS-speech pairing can strengthen those responses.
However, when responses are absent, such as with the lack of any evoked response to the
consonant in the profound group, VNS-speech pairing does not strengthen a response which is not
present. Where responses were robust, such as with the moderate group and the vowel response in
severe rats, VNS-speech paring does not interfere with those responses. This proof-of-concept
work demonstrates that the auditory pathways remain plastic following noise-induced hearing loss
and that they are manipulable using VNS-speech pairing. Future work is needed to establish if the
plastic changes induced by VNS-speech pairing result in improved behavioral outcomes
Switched-capacitor Featured Integrated Power Circuit Design for Next-generation Power Management
No full textThe rapid proliferation of Internet of Things (IoTs), automotive and consumer electronics
establishes strong demands on small system volume, low energy consumption and high level of
security. As a key part of these electronic systems, this drives the power electronics to be
unprecedentedly compact, efficient and reliable. Consequently, switched-capacitor (SC) power
circuits, as an unique family of power electronic circuits, have seen their promising roles in
improving power density, adaptability and design flexibility. However, severe design challenges
such as power passive implementations, substantial on-die power loss and critical chip activities
leakage must be addressed thoroughly. Accordingly, a set of SC featured integrated power circuits
are presented in this dissertation to address the above challenges, which have tremendous
significance for next-generation power management.
Firstly, a reconfigurable three-stage SC DC-DC converter is proposed to extend input range for
wireless sensor applications. A three-stage SC topology is constructed by using four fundamental
2:1 SC unit cells to realize eight step-down and step-up conversion ratios with series, parallel and
series-parallel configurations while retaining low complexity. A bootstrap rail sharing technique
is introduced to implement highly efficient and self-powered gate drivers for power switches. An
adaptive pulse emulated hysteretic control improves load transient response and adjusts quiescent
power adaptively with frequency-dependent biasing technique.
Secondly, a monolithic tri-state SC DC-DC converter is designed for high power density in IoT
devices. A tri-state SC topology is presented to reduce voltage stresses on power switches, enhance
integrated MOS capacitance density and lower switching noise. It enhances power delivery greatly
while achieving decent efficiency with on-die power loss reduction. Two-dimensional multiplechannel interleaving operation increases the equivalent switching frequency largely to further
reduce switching noise and improves light-load efficiency with active channel modulation.
Thirdly, a high step-down ratio hybrid SC DC-DC converter is developed in this dissertation. The
proposed converter adopts the front-end SC power circuits to withstand high input voltage stress
and lower switching node voltages in the following inductive topology. Thus, the on-duty time of
the converter is extended, and low voltage power devices are used for efficient and fast switching.
The online flying capacitor voltage (VCF) rebalancing scheme adaptively adjusts the charge and
discharge times of flying capacitors to minimize power mismatch and improve device reliability
in steady state. The in-situ precharge rate regulation technique precisely controls two different
charge rates of flying capacitors at the start-up, avoiding power device breakdown.
Lastly, this dissertation presents an SC-assisted power cipher to improve hardware security against
power side-channel attacks. With a SC charge reshaper, the proposed power cipher adopts random
charge shaping technique to only encrypt input power profile by using noise injection, supply
masking and switching randomization. A parallel encryption interface is designed to manage the
interactions between power and security strictly without shoot-through current and regulate the
charge reshaper with random ON-time control for minimal power and performance overhead.
In this dissertation, the first reconfigurable three-stage SC DC-DC converter is implemented and
verified with fully transistor-level HSPICE simulations and the other three SC featured integrated
power circuits are fabricated on silicon and measured to successfully validate proposed converter
topologies and operation schemes. These experimental results provide strong evidences that the
SC power circuits can be integrated as an essential part of next-generation power management
strategically to achieve optimal performances among power density, efficiency and security design
matrix
Multilingual Extractive Question Answering With Conflibert for Political and Social Science Studies
No full textPolitical conflict and violence have emerged as prominent concerns for political scientists
in both academia and policy circles. The overwhelming influx of complex and dense news
makes it increasingly challenging to effectively monitor and analyze political events. To
address this challenge and contribute to the advancement of conflict research, we propose the
introduction of ConfliBERT English and ConfliBERT Spanish. These two domain-specific
pre-trained language models are specifically designed for the analysis of political conflict and
violence, and have undergone fine-tuning to excel in extractive question answering tasks,
which are not susceptible to hallucination. The pre-training of our ConfliBERT models
utilized our comprehensive conflict-specific corpus from diverse sources. In order to evaluate
the performance of ConfliBERT for extractive question-answering, We performed fine-tuning
on SQuAD v1.1 and NewsQA, two large question-answering datasets. Additionally, we
created ConfliQA English and Spanish, two crowd-sourced evaluation datasets for conflict-
domain extractive QA. Through extensive experimentation and evaluation on all versions
of ConfliBERT English and Spanish, we proved that ConfliBERT English outperforms in
analyzing political texts compared to BERT English baseline models, and provided detailed
insight into further developing ConfliBERT for low-resource languages
Effects of Dual Task on Balance Stability in Healthy Young Adults During Steady State and Gait Initiation
No full textThe goal of this thesis is to investigate dual task effects on anticipatory postural adjustment (APA)
outcomes to define and correlate to good balance and stability indicators during gait initiation.
Spatiotemporal parameters and joint kinematic outcomes during steady-state gait will be
investigated to validate the gait initiation findings and characterize healthy responses to dual task.
By identifying these characteristics, strategies to minimize falls in vulnerable populations can be
taken. In current literature, low gait speed during steady state is associated with fall risk
populations; however, low gait speed does not inherently cause the fall itself. Previous studies
have explored research into gait initiation being a causative factor, specifically anticipatory
postural adjustment outcomes. Research has suggested that gait initiation can provide indicators
of fall risk due to its important process associated with stability. With previous research focusing
on older adults and different conditions, healthy responses to APA remains unclear. In this thesis,
the APA outcomes of loading phase of the stepping limb, time to execute gait initiation, and center
of mass displacement were significant outcomes following dual task conditions. The study has
resulted in the interpretation that APA is normative process in gait initiation; however, there is an
optimal goldilocks range of APA. The results of these findings could pose to provide training
measures to improve balance that in line prevents falls
Deep Learning Strategies for Monaural Speech Enhancement in Reverberant Environments
No full textIn naturalistic settings, reverberation and background noise can introduce distortions into
speech captured by distant microphones, reducing overall quality and intelligibility. Distant speech processing is gaining recognition as a critical component in developing robust
human-machine solutions for interactions that do not require humans to wear wearable devices. Several back-end speech applications, including but not limited to speech recognition,
speaker identification, speaker verification, and speaker emotion/stress detection, benefit
from the addition of a front-end speech enhancement system. A variety of systems utilizing
single and multiple microphones have been developed to address this difficult task. However, as technology advances and devices become smaller, mobile, and smarter, there has
been a strong push to maintain the performance of these smart devices with fewer microphones. The focus of this dissertation is to develop systems for enhancing reverberant and
noisy speech signals with the goal of enhancing human communication and human-machine
interaction. We develop strategies for addressing dereverberation by designing real-valued,
complex-valued, generative, and adaptive neural networks to enhance the quality of speech
captured by a single microphone.
This dissertation addresses robust front-end advancements with the following contributions: (i) An unsupervised speech activity detection (SAD) with dereverberation solution based on signal processing; and (ii) a supervised fully convolutional deep neural network capable of enhancing the magnitude spectrum of reverberant speech and reusing the
phase information to synthesize enhanced speech (e.g., SkipConvNet), (iii) a supervised deep
complex-valued network with self-attention adapted for the complex domain with the goal
of simultaneously enhancing the magnitude and phase of reverberant speech (e.g., FCSA),
(iv) a generative adversarial complex-valued deep neural network with the goal of regenerating the formant structure lost in speech due to reverberation (e.g., SkipConvGAN), and
finally (v) an inference-adaptive neural network that can adapt its processing to the level of
distortion present in a given speech utterance.
The proposed systems’ performance are evaluated using the REVERB challenge corpus
and also compare against various signal processing and deep learning approaches that have
been previously proposed to address the same issue of speech dereverberation. We evaluate
the proposed networks’ performance using speech quality metrics such as cepstral distance
(CD), signal-to-noise ratio (SNR), perceptual evaluation of speech quality (PESQ), and
signal-to-modulation energy ratio (SRMR). The experimental results demonstrate that the
proposed networks consistently outperform several previously proposed systems in terms of
overall speech quality.
These proposed solutions have made important strides to addressing the challenges of
distant based speech capture which include distortions due to reverberation and background
noise. Addressing distortions due to reverberation will offer opportunities to advance subsequent speech technologies in naturalistic environments
Studying the RNA Surveillance Activity of Mtr4 and the Tramp Complex Using Hydrogen Deuterium Exchange Mass Spectrometry
No full textMtr4 is an essential ribonucleic acid (RNA) helicase that plays a central role in RNA processing
and degradation as an activator of the nuclear exosome. Mtr4 can carry out its function alone or in
the Trf4/Air2/Mtr4 Polyadenylation complex (TRAMP). While recent structures highlight the
pathway of RNA through the helicase core, the limited resolution of these studies precludes a
detailed description of Mtr4-RNA interactions and how these change with different RNA
substrates. The molecular arrangement and mechanism of the TRAMP complex are also unknown.
To fill these gaps, we performed an extensive series of hydrogen-deuterium exchange (HDX),
binding and activity assays with Mtr4, the TRAMP complex, and different RNA substrates. Our
study reveals important RNA interactions that span multiple domains of the Mtr4 helicase,
including the Kyprides-Ouzounis-Woese (KOW) motif (or fist) of the Arch domain. Different
RNA substrates engage the KOW motif to different extents when they bind Mtr4, and this
engagement correlates with RNA binding and unwinding activity. Interactions between the KOW
motif and RNA also induce a large conformational change of the arm of the Arch domain. These
data support a substrate-specific role for the Arch domain in RNA recognition. We further uncover
molecular details about the arrangement and function of TRAMP. We show the path of RNA
binding in the Air2-Trf4 sub-complex and identify novel interfaces between Air2-Trf4 and Mtr4.
Air2 in fact binds to the Arch domain of Mtr4 and causes Mtr4 to adopt a conformation that
resembles an RNA-bound complex. Experiments with RNA and TRAMP also show that RNAbinding by Air2-Trf4 and Mtr4 is similar when the proteins are alone or in TRAMP. This,
combined with functional assays, clearly implies competition between the polyadenylation active
site of Trf4 and the helicase active site of Mtr4. Consistently, we show that Mtr4 will prevent
unregulated RNA polyadenylation by Trf4-Air2. Altogether our data shows that Mtr4 helicase
activity is regulated by the RNA substrate, as well as protein binding partners
Essays on Housing Market and Local Economy
No full textThis dissertation consists of two essays on housing market and local economy.
The first essay, included in Chapter 1, is “Real Estate Investors and Property Taxation”.
In this paper, we study the inequality in property taxation in the U.S. single-family home
market based on a property’s assessed value. Comparing the assessment ratio of properties
owned by investors with those of owner-occupiers, we find a 3.0%–4.7% assessment discount
nationwide for properties owned by large investors (i.e., those owning more than 100 prop-
erties) relative to owner-occupied homes in the same area. This difference translates into an
estimated total annual property tax savings of 104 million for large investors across the
country. Further evidence based on micro-level appeals data in Cook County, Illinois, and
Florida suggests that the institutional assessment discount results from a higher likelihood
of appeal and more favorable outcomes upon a successful appeal for large investors. States
with a fairer property taxation administration, a higher market share by large investors, and
a higher property tax burden show a greater assessment discount for large investors.
The second essay, included in Chapter 2, is “Housing Stability and New Business Creation”.
It is a joint work with Steven Xiao. We examine whether stronger legal renter rights en-
courage business creation by enhancing housing stability. In California, passages of city or-
dinances that protect renters from arbitrary evictions increase the number of new businesses
by 9.9% and the proportion of female (8.0%) and racial-minority business owners (12.1%).
These firms can survive in the long run and perform no worse than others. Household-level
analysis shows that renters are less likely to move, more likely to become self-employed
and generate more business income after law passages. Our evidence suggests that enhancing housing stability can benefit the local economy by promoting self-employment and job creation
Magnetic Storm Effects on the Occurrence and Characteristics of Plasma Bubbles
No full textDuring geomagnetic storms actions of prompt penetration electric fields (PPEF) during the main
phase disturbs the equatorial ionosphere. In addition, disturbance dynamo electric fields (DDEF)
can follow during the recovery phase to further modify the plasma dynamics of the low-latitude
ionosphere. The eastward PPEF and westward DDEF cause sudden or ongoing upward and
downward plasma drifts that can cause changes in seasonal-longitudinal occurrence patterns of
plasma bubbles. Ionospheric irregularities like plasma bubbles occur all around the year within
equatorial latitudes, but their occurrence varies seasonally and with changes in solar activity. The
short-term changes in the plasma vertical drift during storms cause enhancement or suppression
in the occurrence and intensity of plasma bubbles. Our study investigates the changes in the
plasma bubble occurrence pattern and characteristics during different phases of storms. The
Communications/Navigation Outages Forecast System (C/NOFS) satellite mission was designed
to investigate the ionospheric conditions that lead to the formation of plasma irregularities. We
have studied the effects of magnetic storms on the formation and evolution of plasma bubbles
during the satellite’s lifetime (2008-2014). During this period encompassing solar minimum and
maximum conditions, many magnetic storms of varying intensity developed. Each storm was
isolated and divided into initial, main, and recovery phases based on the SYM-H index data
observed from geomagnetic observatories. Interplanetary Magnetic Field (IMF) data measured
by the Advanced Composition Explorer (ACE) satellite was used to observe fluctuations in
magnetic fields during storms. Measurements of plasma density from the Plasma Langmuir
Probe (PLP) were used to identify plasma bubble occurrences, and determine their local times,
depths, widths, etc. A bubble detection algorithm was developed to detect bubbles from the
plasma density data. Measurements of plasma vertical velocities from the Ion Velocity Meter
(IVM) were used to determine evening PRE peak velocities and bubble internal vertical
velocities. Analysis of 109 storms of varying intensities with available bubble and PRE data
between May 2008 and August 2014 has revealed that the most intense plasma bubbles occur
during a storm’s main phase when Bz turns southwards as PRE velocities tend to increase during
those times. New bubbles develop with large PRE values and the bubble lifetime extends into the
recovery phase. Comparisons of bubble depths and internal vertical velocities between the
storm’s main phases and quiet periods before have shown significant improvement during
storms. The augmentation of the plasma bubbles’ depth and internal velocity become more
prominent when the bubble intensities were low during the quiet period before the storms.
Furthermore, bubble intensities decrease by the end of the recovery phase along with the decline
in the PRE velocities. The growth and decline of the bubble occurrence and characteristics
signify the important roles of PPEF and DDEF during storms on the low-latitude ionosphere