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A Public Space Designed by Taiwanese Society: Case Study of Street Dancer Occupying Taipei Expo Park
No full textOccupying public space is a common action and dynamic among people. People can stay in a
park occupying a bench for an afternoon or sit in a train station hallway waiting for the train.
People could have many different purposes to occupy the public. Dancing in public spaces is an
everyday activity in street dance communities. In the case of Taipei Expo Park, there are spots
often occupied by street dancers for practicing or rehearsing their dance. While Taipei Expo Park
had met multiple times of transformations by the government for different uses and policies,
these public spaces are originally not designed for the activity that street dancers do. This
phenomenon shows there is a gap between the design of the space and its usage by people who
enter it. Under the paradigm of designing public spaces, "community-building" (社區營造
Shequ Ying Zao) is one of the most popular terms in Taiwanese discourse on the topic of urban
planning. This paper seeks to understand how street dancers complicate the idea of communitybuilding by reappropriating a space when they enter it, and what this means when an urban
design plan does not meet the people's needs, necessitating them to reshape the meaning of urban
design. I want to raise street dancers in Taiwan as an example that challenges the meaning of this
community-building interaction between the government and the people, and how this
phenomenon shows the position of the dancers in the social hierarchy, which embodies the
theory of Bourdieu’s cultural capital and symbolic violence
Computational Analysis of Porous Structures Considering Coupled Diffusion Law With Large Volume Expansion
No full textThe increasing interest of silicon anodes for lithium-ion batteries (LIBs) can be attributed to their
high charge capacity and recharge speed. Severe volume expansion from the lithiation process,
however, results in pulverization and mechanical failure of the silicon anode. To both improve
charge capacity and mechanical stability, designers have tested multiple geometries of
nanostructures varying from nanotubes to silicon hollow-spheres. Although failure analysis can be
performed by observing the surface morphology of these structures, the physical model of
lithiation and corresponding stress analysis is highly limited, not only because geometries of these
structures are quite complex to mode, but also because it requires modification of the diffusion
law due to large volume expansion which could alter the chemical potential. In this work, we will
formulate a modified coupled diffusion law to consider large volume expansion which the Si anode
experiences during lithiation and delithiation. We incorporate this generalized diffusion equation
into commercial package (ABAQUS) using user-subroutines, in order to develop a physical model
under various environments. Through a series of test cases, we can analyze the effects of large
deformation on the diffusion profile, as well as the effects of changing geometries on the stress
profile. Deformations and stress gradients are shown to be able to expedite the diffusion process,
while drastically decreasing the area along the diffusion path is shown to result in stress
concentrations. Based on these physical insights, we apply it to models with realistic porous
geometries to investigate general design guidelines: columnar void structures along parallel to the
diffusion path could alleviate stress concentration, compared to misaligned voids. Structures with
gradual area changes such as spherical voids could have a more relaxed stress field. This multi-
physical modeling technique could allow us to analyze structural stability and also serve as a
standard to develop nanostructures for optimal design
Halide Perovskite Light-emitting Devices: Ionic Doping and Nanostructuring in Single Layer LEC and Laser
No full textMetal halide perovskites, as a new type of hybrid semiconductors, have demonstrated promising
optoelectronic properties for state-of-the-art and emerging photonic technologies such as pure
color light-emitting diodes, cost-effective nano-lasers, and efficient photovoltaic devices. Owing
to highly tunable emission wavelengths, high absorption coefficient, high exciton binding energy,
narrow emission linewidth, and less expensive fabrication methods, perovskite materials are
excellent choices for the next generation of optoelectronic applications. In this dissertation, we
mainly focus on introducing and understanding the physics and processing of the perovskite lightemitting devices regarding their dynamic behavior associated with ionic doping and
nanopatterning effects in perovskite materials.
We begin by investigating a novel and facile approach to overcome some important limitations of
Perovskite Light-Emitting Electrochemical Cells (PeLECs) such as intrinsic ion motion
degradation, low brightness, and short operational lifetime. In this method, we leverage the
advantages of new nanocomposite with an electrolyte polymer along with a lithium salt additive
(LiPF6) incorporated into the CsPbBr3 perovskite structure in order to passivate and suppress the
traps, defects, and pin-holes in perovskite thin films aiming to improve the morphology and
achieve high-performance single layer PeLEC for green emission. By implementing the material
characterization techniques, we scrutinize the optimization process for lithium salt additive and
demonstrate the advantages of LiPF6 additive including high photoluminescence quantum yield
(PLQY), and stable photoluminescence (PL) dynamics, electroluminescence (EL) stability, low
hysteresis, and high efficiency of devices. Inspired by the successes of ionic additives in these
types of PeLECs, we further investigate the operational stability of devices and reach 100 hours of
operational lifetime which is a 5.6-fold improvement over devices with no LiPF6 additive. We
further develop our research by utilizing a new synthesized ionic iridium complex to build a HostGuest system in PeLEC structure in order to effectively tune the color emission, improve the
morphology and consequently increase the efficiency of PeLECs for future display applications.
In the next part of this dissertation, we provide a unique method to construct a multilayer blue
Perovskite Light-Emitting Diode (PeLED) by utilizing the electron and hole transport layers as
well as Quasi-2D perovskite composition. We successfully show that implementing two long and
small ligands into the 3D perovskite precursor can beneficially form both small and large n phases
perovskite layers, for the selective energy transfer process, and eventually provide an extremely
efficient blue PeLED device. The maximum 10% EQE, maximum luminance 5500 cd m-2
, and 170 min half lifetime (T50) operational stability have been demonstrated. In the last section, we
present the novel nanoimprint lithography method in order to perform direct nanopatterning on
halide perovskite thin films to create laser cavities. With a meticulous approach that includes a
practical encapsulation method, we have exhibited the first demonstration of quasi-CW lasing from
directly patterned perovskites with a high-quality cavity design
The Influence of Age and Sex on: Neuroimmune Function, Cognition and Pain
No full textThe elderly population 65 years and older is rising, and so is the concern to develop targeted
geriatric care based on basic and translational research. However, while advances of medicine and
technology allow for long-lived lives, they are not necessarily healthier. Low grade chronic
inflammation is a predictable outcome of senescence, the process of aging and related
impairments, and can facilitate age-associated diseases. It not only accompanies all aged systems,
but it allows for a highly reactive immune response in the aged, which can influence other
biological functions such as neuroimmune crosstalk. Neurons are easily sensitized following
inflammation and can induce an adaptive response such as pain induced by peripheral injury as
well as cognitive impairment during infection. However, little is known about how the aged
inflammatory phenotype challenges such behaviors, especially in older females. Using a repertoire
of behavior, physiology, anatomy and biochemical approaches, this thesis proposes to understand
the interaction of age, sex, and neuroimmune function in the context of cognition, pain, and
inflammation. Further, it will attempt to identify age-dependent modulation of these behaviors by
cap-dependent translation. In the following chapters, I will demonstrate that development of acute
peripheral inflammation (edema and temperature) and inflammatory pain (evoked and
spontaneous) in the aged are highly dependent on cap-dependent translation while working
memory and cytokine production are mostly unaltered by eIF4E phosphorylation. I will emphasize
the lack of aging studies addressing sex as a biological variable and will demonstrate that sex can
determine the behavioral and inflammatory output in postoperative pain in aging. I further propose
future directions that should be addressed in hopes to identify therapeutic interventions for the
geriatric population, which focuses on neuron adaptations to heightened inflammation including
neurogenic inflammation and neuroplasticity
Deep Neural Network Based Representation Learning and Modeling for Robust Speaker Recognition
No full textAutomatic Speaker Verification (ASV) involves determining a person’s identity from audio
streams. ASV provides a natural and efficient way for biometric identity authentication.
Being able to perform text-independent speaker verification that does not require a fixed
input text phrase can significantly help verify or retrieve a target person. Speaker recognition
has many applications today including audio surveillance, computer access control, and
voice authentication. Smart home devices such as Google Home, Amazon Alexa, and Apple
Homepod can also benefit from ASV for personalized voice applications.
This dissertation address four related research problems. First, we investigate the impact
of non-linear distortion based on waveform peak clipping for automatic speech-based systems. We begin by defining various forms of clipping and then explore potential impact for
practical speech systems and speech corpora. Next, we present an overview of audio quality
assessment, illustrate the effect that clipping has on automated speaker recognition systems,
and provide findings of an investigation into the occurrence of clipping in a variety of data
sets used by the speech community. Second, we provide an unsupervised Adversarial Discriminative Domain Adaptation (ADDA) method for speaker verification when training and
testing data have mismatched conditions. ADDA needs just the source and unlabeled target
domain data in order to discover an asymmetric mapping that adapts the target domain
feature encoder to the source domain. The experimental findings demonstrate that trained
ADDA speaker embeddings can perform well on speaker classification for the target domain
data and are less sensitive to language shifts.
In the third topic, a generalized global context modeling framework is proposed for speaker
recognition. We first present a data-driven attention based global time-frequency context
model, which can better capture long-range time-frequency dependencies and channel variances. It aims to obtain a better combination of the Non-local block and Squeeze&Excitation
block to adaptively recalibrate the learned feature map and provides time-frequency attention to specific regions. Further, we propose a data-independent Discrete Cosine Transform
(DCT) based global context model. A multi-DCT attention mechanism is presented to improve the modeling power with different DCT bases. We also use global context information
to enhance important channels and recalibrate salient time-frequency locations by computing
the similarity between global context and local features. We show that the proposed global
context modeling method can be easily incorporated into a CNN model with little additional computational costs and effectively improves the speaker verification performance by
a large margin. Lastly, in topic four, we investigate the effects of reverberation and noise for
self-supervised speaker verification. In order to normalize extrinsic variations of two random
segments taken from one spoken utterance, a number of alternate training data augmentation methodologies are investigated. We systematically simulate alternate levels and types of
reverberation and noise on the test data for performance comparison. The experiments show
a clear correlation between microphone distance, reverberation time, signal-to-noise ratio,
and the verification result. Taken collectively, the investigative studies have contributed to
a more comprehensive understanding of speaker recognition, as well as advancing algorithm
robustness for real-world speaker systems
A Novel 3-D Flux Structure for Switched Reluctance Machines
No full textSwitched Reluctance Machines (SRMs) receive considerable attention from industry and
academia over the past few decades. SRMs are generally inexpensive, reliable, and mechanically
robust when compared to other types of electrical machines. Design of magnetic configurationfor
SRMs is a prevalent topic in the literatures. The focus of these design practices can be
categorized into boosting average torque, torque pulsation mitigation, loss reduction (copper
loss, windage loss, core loss), vibration suppression, and acoustic noise suppression to name a
few. The present dissertation proposes a novel 3-D flux structure containing both radial and axial
flux structures. The step-by-step design procedure and the background reasoning are discussed in
detail. The illustrations and reasoning in this study highly relies on 3D finite element analysis
(FEA) and magnetostatic measurements. Through multiple revisions and optimizations, a
prototype of the proposed machine is chosen and fabricated to verify the claims. Besides
switched reluctance machine, the proposed 3D concept can be used in other types of electric
machinery
Saving Lives With Analytics: Incorporating the Human-technology Frontier in the Design of Alert Systems for Early Detection of Sepsis
No full textBy addressing key issues on both sides of the human-technology frontier, this dissertation
focuses on improving care quality for a prevalent, deadly, and costly condition: sepsis. Sepsis
is the body’s overwhelming response to infection, affecting nearly two million people annually
and accounting for half of all hospital deaths in the United States. Detecting sepsis early
and providing timely treatment can significantly impact patient outcomes. To facilitate early
detection, healthcare providers are increasingly leveraging automated sepsis alert tools. In
close collaboration with a large hospital group in the Midwest U.S., we sought to address the
shortcomings of an alert system commonly employed in hospitals while also enhancing its
capabilities to improve sepsis care processes.
In the first part, focusing on the human side of alert systems, we empirically study how
clinical teams provide care in compliance with evidence-based standards using an alert system.
Specifically, we consider clinical teams consisting of two roles, nurse and physician, and
investigate the impact of nurses’ timely completion of alert-related tasks (i.e., acknowledging
the alert and notifying physicians within a designated time frame) on physicians’ compliance
with sepsis care standards. We find that nurses’ timely response to alerts has a positive
spillover effect on physicians’ compliance, and this effect becomes stronger as workload
increases and weaker as the number of false alerts increases.
In the second part, we design an alert system for early detection of sepsis. Our design accounts
for both the technology and human sides of alert systems, which together determine the
efficacy of alerts. On the technology side, we personalize alerts based on a patient’s individual
characteristics, thus improving the accuracy of alerts. On the human side, we acknowledge
the role of users—caregivers—and account for their compliance behavior in following care
standards embedded in alerts’ workflow. Taking into account these two aspects (i.e., accurate
prediction on the technology side and timely action on the human side), we develop an
optimization framework in which predictive models are integrated with a prescriptive model
to determine when to give a warning to caregivers about the possible presence of sepsis. Using
clinical data from our partner hospital, we validate the performance of our personalized and
compliance-aware design. Our design detects more sepsis cases and alerts earlier than the
hospital’s alert system, resulting in higher quality-adjusted life days for patients.
In the third part, focusing on the technology side of alert systems, we use data readily available
in the electronic health records to develop an intelligible machine learning algorithm that
predicts sepsis. In particular, we use the explainable boosting machine classifier to predict
sepsis based on patients’ individual characteristics and physiological measurements. We then
show that our algorithm provides accuracy comparable to state-of-the-art (unintelligible)
machine learning approaches.
Alert systems can help healthcare providers deliver high-quality sepsis care only if they
provide accurate and timely alerts, smoothly integrate with clinical workflows, and ensure
caregivers’ compliance with embedded guidelines. By considering these key determinants of
an effective alert system, this dissertation takes a step toward improving sepsis-care quality
fNIRS and Pupillometry Correlates of Attentional States
No full textThis dissertation aims to examine the localization and function of BA 10 specifically looking into
its role in maintaining and switching between attentional states using functional near-infrared
spectroscopy (fNIRS) and pupillometry. Previous findings from fMRI report a medial-lateral
BA10 dissociation during stimulus-oriented (SO) and stimulus-independent (SI) tasks.
Pupillometry has been previously used to investigate different attention states, dissociating
focused, attention (on-task) trials from mind-wandered, distracted (off-task) states. However, so
far there is no evidence that fNIRS hemodynamic responses (HR) and pupillary responses (PR)
could be used to better characterize and identify attention states. Replacing fMRI with fNIRS, I
divided SO and SI tasks into on-task and off-task groups and showed that fNIRS exhibit similar
medial-lateral BA10 dissociations for SO tasks. I also showed that both HR and PR can be used to
distinguish on-task SO from off-task SO groups. Finally, I explored whether fNIRS and
pupillometry signals share similar biomarkers which may enhance the identification of these
attention states using these measures simultaneously. This study was able to successfully
characterize attention states and dissociate them on BA10 but did not confirm that simultaneous
signal acquisition of these two methods would enhance the identification of attentional states
Development of Softening Polymer-based Spinal Cord Stimulation Leads for Chronic Applications
No full textSpinal cord stimulation (SCS) leads are a type of neural interface that can be implanted in the
epidural space and deliver electrical pulses to change or block signals traveling to the brain. SCS
leads are commonly used to alleviate chronic back pain resulting from failed back surgery
syndrome and other injury and disease-related conditions. Recently, researchers have tried to
develop treatments to restore movement in patients with a spinal cord injury. However, clinical
cervical SCS has been limited by the size of the SCS leads, the small epidural space around the
cervical spinal cord, and the large degree of movement of the neck. Furthermore, commercially
available SCS leads are not compatible for their use in small animal models where most of the
preclinical research is done. Therefore, in this dissertation work, we (1) have developed a thinfilm softening SCS lead that can be implanted in the cervical spinal cord of a rat model and evoke
muscle responses after SCS. (2) We have also developed ester-free nondegradable softening
polymer-based SCS leads, and (3) have shown their in vitro long-term electrochemical stability to
assess their potential use in chronic studies
Development of a Gaseous Ammonia Sensor
No full textAmmonia is a commonly found volatile organic compound which can be present in both liquid
and gaseous forms. For many years, researchers have been interested in understanding the human
body’s generation and excretion of ammonia as a byproduct of natural bodily functions. One
method of excretion comes through outgassing from one’s skin. Many have hypothesized that
this concentration may fluctuate as a result of changes in a subject’s health. This research
provides the groundwork for two non-invasive sensor approaches for the detection of ammonia
in a range relevant to the typical emanated gas concentration range. The developed sensors use
electrochemical methods and two thin film approaches as interfaces for ammonia chemisorption.
Ultimately, this work may provide an avenue towards the development a sensor capable of
detecting outgassed ammonia from the skin of human subjects