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Development of Graphene-like Carbons for Energy Storage and the Sequestration of Lanthanide and Actinide Elements
No full textPorous materials are solids which contains voids also known as pores. Porous materials are used
in a variety of applications that require a high surface area as well as controllable pore sizes and
pore architectures. Porous silica and carbons can be used in applications like as drug delivery,
gas separations, energy storage, and the sequestration of elements. Particularly, porous carbona
can be synthesized by chemical vapor deposition and can have exceptional porosity as well as
high electrical conductivity. The development of carbons that have both high porosity and high
electrical conductivity has surged since the discovery of graphene-like carbons. The ability to
form various type of porous carbons and understanding of the underlying growth mechanism is a
trending research topic. Furthermore, porous carbons can be used for extracting and selectively
separating rare earth elements, which are critical elements vital to the production of magnets,
batteries, metals, catalysts, glass, lighting, pigments, ceramics, aerospace products, and various
other textiles. Herein various high surface area and electrically conductive porous carbons are
synthesized and characterized; and a mechanism for the carbon synthesis is proposed. Then the
porous carbons are evaluated for the performance in liquid-solid extractions of lanthanide and
actinide elements and supercapacitors applications
Three Essays on Terrorism Impact on Development: a Case Study of Al-Shabaab Terrorist Spillover From Somalia to Kenya and Its Impact on Kenya’s Developmental Vision 2030
No full textIn this three essays dissertation, I examine the impact of Al-Shabaab terrorism on Kenya’s
overall development, dubbed Vision 2030. Launched in 2008, the Vision was anchored on
economic growth, social improvement, and political equality to “transform Kenya into a newly
industrializing, middle-income country by 2030,” however, with a rise in Al-Shabaab terrorism
in Kenya, Vision’s success is at risk. These essays include regression analysis results and
graphical representations to assess the terrorist group's impact on Kenya’s Vision 2030.
Chapter 1 provides a general introduction to the dissertation. Chapter 2 evaluates Al-Shabaab's
impact on Kenya's economy from 1995–2019. This chapter compares the group’s impact on
Kenya to Ethiopia, which experienced minimal terrorism. The graphical results show the
presence of Al-Shabaab terrorism in Kenya is associated with a reduction in international
tourism arrivals and Foreign Direct Investment (FDI) inflow compared to Ethiopia. Graphs show
a decline in tourist arrivals and the amount of FDI inflow to Kenya compared to Ethiopia,
particularly over the time terrorism incidence rose (2011 – 2016). For example, figures 16A and
16B show Kenya’s international tourism and FDI declined by 26% and 67.63%, respectively, on
average, compared to Ethiopia, which saw a 66.54% and 559.05% (figure 17A & 17B) increase
in international tourism arrivals and amount of FDI inflow respectively over the same timeline.
In Chapter 3, I examine the Al-Shabaab terrorist group’s impact on social development,
measured by education within Kenya. Education is proxies by average years of schooling and
assessed at a subnational (provincial) level. The regression results show the presence of Al-
Shabaab terrorism in Kenya is associated with lower mean years of education. The regression
outputs indicate that the single terrorism indicator (incidence) explained 21% of the variance and
that the model significantly predicts social development. Terrorism incidence contributes
considerably to the model (B = -0.1843694, p = 0.00000001049), while the number killed,
wounded, and damaged property is insignificant. Based on these results, a 1-unit increase in
terrorist incidence reduces school attendance by more than two months or 66 days on average.
In Chapter 4, I investigate the political impact of Al-Shabaab terrorism on Kenya compared to
Tanzania at the country level and the first administrative (province) level within Kenya. The
political impact of terrorism is operationalized through police and court level of corruption and
application of the rule of law, using the AFRO Barometer surveys from 2002–2018. The
graphical results show a positive association between Al-Shabaab terrorism presence, level of
corruption, and unequal application of the rule of law in Kenya compared to Tanzania, especially
during the Al-Shabaab terrorism peak. The increase in police and court corruption levels, and the
rise in unequal application of the rule of law, is worse in Kenya compared to Tanzania.
Furthermore, within Kenya, the level of official government corruption at terrorism onset is
worse in the Northeastern and Coast provinces compared to provinces without Al-Shabaab
Conscripts or Volunteers? Assessing the Impact of Organizational Behaviors and Attitudes on Korean Military Sector Performance
No full textMilitary organizations across the world are faced with challenges in recruiting and retaining
high-quality personnel because of increasing inter-sectoral competition, changes in social values,
and low unemployment. Although intrinsic motivation, organizational commitment, job
satisfaction, and job stress are essential for employee retention and performance, there has been
little research into these factors within the military sector. The three separate studies that
compose this dissertation were undertaken to investigate the impact of soldiers' behaviors and
attitudes on individual's performance and to examine differences in the organizational attitudes
and individual performances of conscripts versus volunteer soldiers. The first study examines the
relationship between intrinsic motivation and individual performance and explores whether this
relationship is mediated by job stress. The second paper explores differences in affective
organizational commitment, job satisfaction, and individual performance between conscripts
versus volunteer soldiers. This study also investigates the impact of job satisfaction on affective
organizational commitment and individual performance in the military sector. The third study
investigates whether there are differences in intrinsic motivation, organizational commitment,
and job satisfaction between conscripts and volunteer soldiers. This study investigates the impact
of intrinsic motivation on organizational commitment and job satisfaction in the military sector.
The results of the three studies note three significant findings: (1) intrinsic motivation has a
negative association with job stress, and the relationship between intrinsic motivation and
military performance is mediated by job stress; (2) volunteer soldiers have higher job satisfaction
and total fitness levels than conscripts, and job satisfaction has a statistically significant positive
effect on affective organizational commitment and total fitness levels in military organizations;
and (3) volunteer soldiers have higher intrinsic motivation, organizational commitment, and job
satisfaction than conscripts. Intrinsic motivation has a statistically significant positive effect on
military organizations' organizational commitment and job satisfaction. Implications, limitations,
and suggestions for future research are also discussed in the study
Energy Trading in Local Electricity Markets With Distributed Energy Resources
No full textThe energy system has been under dramatic transformation in recent years because of the advent of smart grid technologies and the increasing penetration of distributed energy resources
(DERs), such as distributed photovoltaic (PV), wind turbines, energy storage systems, electric vehicles (EV), smart appliances, and others. While the increasing penetration of DERs
helps the grid decarbonization, it imposes challenges on power system operation and economics. Some regions such as California that possesses the largest PV installation in the
U.S., will have to deal with the fast solar capacity expansion. When the sun shines, the
systems must make frequent regulations to offset the imbalance between demand and renewable energy production. After sunset, utility companies must rapidly increase other forms of
generators to compensate for the loss of solar power. Besides, DERs have also disturbed the
traditional electricity market by introducing a larger proportion of flexibility to the demand
side. Therefore, it calls for a reconsideration of the economic model for future electricity
markets.
The concept of local electricity market (LEM) has emerged as a promising trading framework
for future smart grids. Different from the wholesale electricity market, LEM enables participants to share their resources such as excess renewable generation, unused energy storage
capacity, extra rooftop space, flexible appliances, etc., to other entities who are in shortage.
LEM can be leveraged not only to efficiently manage the local supply and demand, but also
to decrease the local community’s reliance on the main grid.
This dissertation proposes to mitigate the over-generation issues with ever-increasing DERs
in foreseeable future by designing and evaluating different LEM architectures considering the
characteristics of distributed solar, residential load, multiple market entities/stakeholders,
and their interactions. More specifically, in this dissertation LEMs are categorized into two
main groups: peer-to-peer (P2P) models and subscription models, to investigate the interactions between different participating entities with various supply/demand and optimization
goals. Besides, different pricing strategies are explored, to incentivize local customers to
actively manage their energy resources. For P2P markets, we explore potential cooperative
and non-cooperative energy sharing and trading strategies among prosumers and consumers.
For subscription markets, unique and discriminated dynamic pricing strategies that take in
account of customers’ different consumption flexibility with centralized ES are developed. In
addition, different data availability and privacy concerns in the LEMs are also investigated.
Experimental results indicate that the proposed LEM schemes are beneficial and efficient,
which are practical and supportive for the future grid decentralization and decarbonization
with the capacity expansion of DERs
Software Defined Orchestration of Network and Compute Resources
No full textEdge computing is an attractive architecture to efficiently provide compute resources to
many applications that demand specific QoS requirements. The edge compute resources are
in close geographical proximity to where the applications’ data originate from and/or are
being supplied to, thus avoiding unnecessary back and forth data transmission with a data
center far away.
This dissertation works towards a federated edge computing system in which compute re-
sources at multiple edge sites are dynamically aggregated together to form distributed super-
cloudlets and best respond to varying application-driven loads.
In order to provide such collaborative system, we build a compute domain that relies on
a multi-layer transport networks consist of a Wavelength Division Multiplexing (WDM)
optical domain, an Ethernet packet switching domain in a single turnkey solution. The
software defined networking (SDN) PROnet Orchestrator is designed and implemented to
concurrently manage the resources offered by the optical network equipment, compute nodes,
and associated Ethernet switches and achieve the key functionalities of the proposed super-
cloudlet architecture
The Invisible Foreigner: European Women as the Eroticized Other in Later Iranian Art
No full textThis dissertation examines Persian murals and architectural decoupage decorations featuring
nude and semi-nude representations of European women. I began my research of these littlenoticed images by traveling to Iran and documenting these unconventional works of art and the
contexts within which they were found. These untouched historical works of art exist in Safavid
(1502–1736) and later Qajar-era (1785–1925) private houses, mostly in the city of Isfahan, and
consist of wall paintings as well as European prints affixed to the walls and ceilings of these
houses. My research demonstrates that nude and erotic depictions of women were not only
present in Iran, but that Persian murals and architectural decoupage decorations featuring nude
and semi-nude representations of European women were increasingly viewed by larger
audiences and survive to the present day. These historical works of art seem remarkable when we
recall that much of this period was also a peak of Safavid Shia conservatism and orthodox
juridical influence. The erotic representation of European women in Persian houses points to and
is the by-product of a more general theme in my research. Namely, how the European in general,
and the European woman in particular, was perceived as the Other in the Persian Muslim mind.
The implications of this phenomenon are that the exoticization and eroticization of the European
female in Iran occurred long before modern Western colonial endeavors began in the Middle
East. It becomes clear that the eroticized portrayals of European women on the walls of Persian
houses are not examples of imperialism and colonial domination as such a power dynamic did
not yet exist in seventeenth-century Iran. Nor should they be reduced to the mere geopolitical or
historical analysis; on the contrary, these portrayals of the female body, both in the West and
Islamic East, address a trans-geographical issue which is the principal focus of this research.
How the female body is perceived, treated, and represented across time and space irrespective of
any East-West dichotomous construct
Machine Learning-based Solutions for Comprehending and Mitigating Imperfections of Semiconductor Manufacturing and Testing
No full textIn recent years, significant technological advancements have been made in the semiconductor industry; However, with these advancements, comes a lot of manufacturing and testing
challenges that have a direct impact on the cost and yield of the overall outcome. While
advanced technology nodes enable production of more powerful devices that have a smaller
form factor, operation of such devices is more susceptible to process variations. To address
the impacts of process variations without impacting the performance of devices, manufacturers employ post-silicon calibration techniques. One major pitfall of post-silicon calibration
is the need to perform numerous test measurements and adjustments that significantly contribute towards the overall test time, thereby hindering the profit margins of new products.
Along with the minimal cost expectations, there are higher quality expectations in terms of
extremely low number of defects. This results in implementing exhaustive and contemporary
test solutions that result in a non-negligible amount of good devices being discarded.
In this work, several machine learning-based solutions are proposed to address the increasing
test costs and to recover some of the yield loss. An adaptive test cost reduction technique is
proposed to identify the optimal operating voltage for a High-Volume Manufacturing (HVM)
device, by taking advantage of the correlation that exists between different test measurements
and operating voltages. Another test cost reduction technique was proposed to enable testing
a device across multiple temperature corners, where the current testing process is extremely
time consuming and expensive. Towards addressing the problem of impairments that affect
the performance of Radio Frequency (RF) transmitters due to process variations, a machine-
learning based solution was proposed to classify and decompose the RF impairments. The
model leverages unique signatures left by the impairments on the transmitted signal constellation points. Towards recovering yield loss caused by using conservative test programs that
help achieve higher quality expectations, a machine learning based solution was proposed,
which exploits the statistical correlation between two key groups of tests currently performed
for these devices. Finally, to avoid die damage that occurs due to misalignment of the blades
used in the wafer dicing equipment, a machine learning-based solution was proposed that
takes advantage of the acoustic emissions recorded by the dicing equipment. All the proposed solutions have been evaluated using dataset provided by our industry liaisons and the results are shown in this work
Extraction of Seismic Properties and Models From, and Full Waveform Inversion of, Dispersed Seismic Waves
No full textSurface waves, which propagate along boundaries between two different media, play an
important role in resolving geological structures of different scales targeted from global
seismology, exploration seismology, geotechnical engineering, to nondestructive testing. Over
the past half century or so, different methods have been explored to process and invert surface
waves for underground model properties, especially the shear wave velocity. However, there
are still many problems waiting to be solved. Conventional dispersion curve inversion (DCI)
is limited to 1-D model assumption and has increased uncertainty when the structure is
complicated. It also requires picking of dispersion curves from field data, which is often a
labor intensive process. Although, methods in the framework of full waveform inversion of
surface waves yield models with good resolution, both laterally and vertically when carefully
implemented and applied, they are computationally intensive and can easily suffer from the
cycle skipping problem. Wave-equation based dispersion curve inversion method combine
some of the advantages of those in conventional dispersion curve inversion and full waveform
inversion, but also requires picking of dispersion curves from both field and synthetic data.
This dissertation focuses to partially solve some of the above issues and leads to more work
that can be done in the future.
To automate the picking of dispersion curves from surface waves, which is required for many
approaches for shallow-subsurface characterization using surface waves, my first project
presents a convolutional-neural-network (CNN) based machine learning approach to automatically pick the curves for the fundamental and higher modes along the two azimuths of any
2D seismic profile. Various attributes such as amplitudes, coherency, and local phase velocity
as well as frequency and wavenumber of dispersion curves are derived; different sub-sets of
these are tested in the CNN training process to assess the best combinations. We use a U-net
architecture that is modified to convert the conventional 2D image segmentation problem in
the (f,k) domain into direct multi-mode curve fitting and a subsequent picking process.
To make the automatic picking algorithm more practical, we (1) introduce a second loss
function that combines conventional wavenumber residuals and curve slope residuals; (2)
use the transfer learning strategy, in which the network is pre-trained with synthetic data
and then with a relatively small portion of the field data, to improve the efficiency of the
algorithm; (3) evaluate two categories of uncertainty, the epistemic uncertainty from the
method itself and input data, and uncertainty from non-deterministic factors such as random
initialization of model weights and random shuffle of samples in training in the CNN, and in
GPU parallelism. The epistemic uncertainty is an important indicator of the picking quality
and can be used as a weighting of data in subsequent inversion; (4) perform post-processing
to determine the effective dispersive frequency range of the picked curves by using different
criteria, such as long/short moving average ratios (MAR) of squared picked wavenumbers,
posterior uncertainty etc. The effectiveness of the automatic picking process is demonstrated
in this study through applications to a field OBN dataset where different modes of Scholte
waves were recorded.
To reduce cycle skipping in FWI and to increase resolution of the estimated model, my
second project develops and illustrates concurrent elastic full-waveform inversion (FWI)
of P and S body waves and Rayleigh waves using interleaved envelope- and waveform-
based misfit functions, in a gradually-increasing frequency, multi-scale, inversion strategy, to
estimated both lateral and horizontal variations of models, which breaks the 1D assumption
of conventional DCI. Computing correlation coefficients between the observed and predicted
data, and between the inverted and correct models, provides quantitative measures of the
composite contributions, of the starting model, the chosen data flow, and the depth extent of
the solution space, to the fits of the corresponding solutions. Treating the whole wavefield
as a single data set means that it is not necessary to separate, or even to identify, different
types of body and surface waves
Unsupervised Driving Anomaly Detection in Naturalistic Driving Scenarios
No full textNew developments in advanced driver assistance systems (ADAS) can help drivers deal with
risky driving maneuvers, preventing potential hazard scenarios. A key challenge in these
systems is to determine when to intervene. While there are situations where the needs
for intervention or feedback is clear (e.g., lane departure), it is often difficult to determine
scenarios that deviate from normal driving conditions. These scenarios can appear due to
errors by the drivers, presence of pedestrian or bicycles, or maneuvers from other vehicles.
We formulate this problem as a driving anomaly detection, where the goal is to automatically
identify cases that require intervention. We aim to create unsupervised multimodal solutions
that do not depend on predefined rules, or hyperplanes learned from labeled data describing
few target events. This model should recognize anomalous driving scenarios even if similar
scenarios are never observed in the training data. Toward this goal, this dissertation focuses
on three main transformative goals: (a) to build robust unsupervised methods for driving
anomaly detection, (b) to make the approach scalable so multiple modalities can be easily
added, and (c) to make the approach interpretable so it is intuitive to understand why a
given segment is detected as anomalous.
Our first aim is to build robust unsupervised methods for driving anomaly detection. We
address this goal by proposing a novel conditional generative adversarial networks (GAN)
where the models are constrained by the signals previously observed. The difference of the
scores in the discriminator between the predicted and actual signals is used as a metric for
detecting driving anomalies. Our model consider (1) physiological signals from the driver, (2)
vehicle information obtained from the controller area network (CAN) bus sensor. The original model was implemented with fully connected layers and hand crafted features from the
physiological and CAN-Bus signals. This model was improved with two important changes.
First, we explore an end-to-end solution extracting feature representations directly from the
data, using convolutional neural network (CNNs). This model also leverages temporal information using long short-term memory (LSTM). Second, we improve the anomaly score using
a triplet-loss function to further contrast the predicted and actual signals. The triplet-loss
function creates an unsupervised framework that rewards predictions closer to the actual
signals, and penalizes predictions deviating from the expected signals. This approach maximizes the discriminative power of the feature embeddings to detect anomalies, leading to
measurable improvements over the results observed by our previous approach implemented
with fully connected layers.
The second aim is to make the driving anomaly detection approach scalable so multiple
modalities can be easily added. This is important as individual modalities have limitations.
For example, by considering only the vehicle CAN-Bus data and driver’s physiological data,
our proposed approach can only detect abnormal driving scenarios when the driver reacts
to the driving environment. If a driver fails to notice an abnormal driving scenario, these
signals will not change and our driving anomaly scores will fail to capture the event. A
model should be scalable, so we can incorporate other modalities that, for example, describe the environmental information. Our proposed approach trains a conditional GAN to
extract latent features from each modality, which are independently pre-trained. An attention mechanism model combines the latent representations from the modalities. The entire
framework is trained with the triplet loss function to generate effective representations to
discriminate normal and abnormal driving segments. This approach is implemented with
five different modalities (vehicle’s CAN-Bus signals, driver’s physiological signals, distance
to nearby pedestrians, distance to nearby vehicles and distance to nearby bicycles), achieving
improved performance over alternative approaches.
The third aim is to make the approach interpretable so it is possible to understand why
a given segment is detected as anomalous. We address this goal with two alternative approaches. The first approach is an example-based query algorithm that combines the aforementioned attention-based conditional GAN model with the multi-label k-nearest neighbors
(ML-KNN) algorithm. Our approach relies on few manually labeled driving segments that
are efficiently used as anchors to retrieve the causes of driving anomalies in a given driving
segment. These anchors are projected into the embedding created by unsupervised driving anomaly detection systems, providing an ideal space to compare an anomalous driving
segment detected by the system with the anchors. The second alternative framework is an
unsupervised approach based on the contrastive multiview coding (CMC) framework to capture the correlations in representations extracted from different modalities. The approach
learns a more discriminative representation space for unsupervised anomaly driving detection. We use CMC to train our model to extract view-invariant factors by maximizing the
mutual information between multiple representations from a given view, and increasing the
distance of views from unrelated segments. The approach is efficient, scalable and interpretable, where the distances in the contrastive embedding for each view can be used to
understand potential causes of the detected anomalies.
The proposed solutions are evaluated and trained with 130 hours of naturalistic data manually annotated with driving events. The results demonstrate the benefits of the proposed
solutions. Collectively, these advances represent transformative contributions to build scalable, interpretable, and discriminative algorithms to identify anomaly driving events
Some Methods and Applications of Large-scale Genomic Data Analysis
No full textModern genomic and epigenomic studies have found numerous genomic regions that interact
with biochemical factors and regulate gene activities. A lot of studies focus on transcriptional
regulation of disease related genes that such genomic regions mediate. Identification of driver
elements associated with protein-noncoding regions for a disease is a challenging and unsolved
problem.
Chapter 2 develops a novel statistical test based on single sequence modeling, named DNAprotein binding changer test. The test predicts insertions and deletions of bases in the
genome (InDels) that change protein binding to DNA. It is the first computational and
statistical approach that directly evaluates InDel influence on protein binding. The binding
changer test statistic we propose is based on binding p-values to the reference and mutation
sequences. We employ importance sampling algorithm such that the binding changer pvalue is computed with sequence pairs generated from an importance distribution. We
derive the importance distribution along with the optimal tilting parameter that determines
the importance distribution to maximize the algorithm efficiency. The binding changer test
is a general approach for any protein-binding motifs and InDel mutations found in any
disease types. The simulation studies demonstrate that the test is very successful in Type
I error control, statistical power increase, and binding changer InDel prediction. From the
application to leukemia data, we obtain potential InDels responsible for leukemia through
creating or eliminating transcription factor MYC binding.
Chapter 3 introduces an integrative analysis to improve the prediction of cancer driver SNVs
(Single Nucleotide Variants) that change transcriptional regulation and influence cancer
genes by leveraging cancer-specific data collected from experiments. It utilizes an existing
noncoding mutation scoring scheme which enables to retain SNVs with high priority. Highly
expressed and non-housekeeping TF (transcription factor) genes are selected with mRNA
expression data. The SNVs that may cause the TF binding change to the DNA sequence are
further predicted with atSNP binding change detection methods. Its application to leukemia
SNV data finds potential leukemia associated genes along with driver SNVs and TFs in the
cis-regulatory structure. Further, we confirm that the integrative approach improves the
power of detecting regulatory mutations