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Using Niobium surface encapsulation and Rhenium to enhance the coherence of superconducting devices
No full textIn recent decades, the scientific community has grappled with escalating complexity, necessitating a more advanced tool capable of tackling increasingly intricate simulations beyond the capabilities of classical computers. This tool, known as a quantum computer, features processors composed of individual units termed qubits. While various methods exist for constructing qubits, superconducting circuits have emerged as a leading approach, owing to their parallels with semiconductor technology.In recent years, significant strides have been made in optimizing the geometry and design of qubits. However, the current bottleneck in the performance of superconducting qubits lies in the presence of defects and impurities within the materials used. Niobium, owing to its desirable properties, such as high critical temperature and low kinetic inductance, stands out as the most prevalent superconducting material. Nonetheless, it is encumbered by a relatively thick oxide layer (approximately 5 nm) exhibiting three distinct oxidation states: NbO, NbO, and NbO. The primary challenge with niobium lies in the multitude of defects localized within the highly disordered NbO layer and at the interfaces between the different oxides. In this study, I present an encapsulation strategy aimed at restraining surface oxide growth by depositing a thin layer (5 to 10 nm) of another material in vacuum atop the Nb thin film. This approach exploits the superconducting proximity effect, and it was successfully employed in the development of Josephson junction devices on Nb during the 1980s.In the past two years, tantalum and titanium nitride have emerged as promising alternative materials, with breakthrough qubit publications showcasing coherence times five to ten times superior to those achieved in Nb. The focus will be on the fabrication and RF testing of Nb-based qubits with Ta and Au capping layers. With Ta capping, we have achieved the best T1 (not average) decay time of nearly 600 us, which is more than a factor of 10 improvements over the bare Nb. This establishes the unique capping layer approach as a significant new direction for the development of superconducting qubits.Concurrently with the exploration of materials for encapsulation strategies, identifying materials conducive to enhancing the performance of superconducting qubits is imperative. Ideal candidates should exhibit a thin, low-loss surface oxide and establish a clean interface with the substrate, thereby minimizing defects and potential sources of losses. Rhenium, characterized by an extremely thin surface oxide (less than 1 nm) and nearly perfect crystal structure alignment with commonly used substrates such as sapphire, emerges as a promising material platform poised to elevate the performance of superconducting qubits
Resolvent Analysis of Turbulent Flow over Compliant Surfaces: Optimization Methods and Stability Considerations.
No full textThis thesis delves into the manipulation of turbulence properties through innovative compliant surface designs. Turbulence, known for its unpredictable fluid movements, presents substantial challenges across engineering disciplines, particularly in optimizing system efficiency and minimizing energy losses. This research explores the potential of compliant surfaces to control and mitigate the adverse effects of turbulent flow, thereby enhancing the performance and reliability of engineering systems.Employing the resolvent analysis method, this work investigates the interaction between turbulent flows and surfaces capable of dynamic adaptation. The study evaluates the impact of these surfaces on turbulence suppression through the application of both space-dependent and independent compliance models, where the compliance model is characterised by an admittance, which represents the relationship between the instantaneous surface pressure and surface velocity. This approach allows for a nuanced understanding of how different surface properties can influence the behavior of turbulent flows.A significant contribution of this thesis is the comprehensive stability analysis conducted to assess the implications of compliant surfaces on the linear stability of the dynamical system. By examining the eigenvalues of the mean-linearized system, the research identifies the conditions under which compliant surfaces may induce or mitigate instabilities within turbulent flows. This analysis is pivotal in developing compliant surface designs that not only reduce turbulence-induced energy losses but also ensure the stability of the flow, a critical consideration for practical engineering applications.The findings of this thesis offer valuable insights into the role of surface compliance in turbulence control, paving the way for further research and the development of advanced engineering solutions. Through a detailed investigation of the interactions between compliant surfaces and turbulent flows, this work contributes to the broader field of fluid dynamics and underscores the potential of innovative surface designs in achieving more efficient and sustainable engineering systems
Two Essays on Mergers and Acquisitions
No full textThis dissertation is composed of two self-contained chapters that both relate to mergers and acquisitions (M&A). In the first essay, we examine the Delaware (DE) reincorporation effect on firms’ post-IPO behaviors on mergers and acquisitions. We find that firms’ DE reincorporation decisions enhance the likelihood of engaging in M&A as targets. However, as a tradeoff, DE reincorporated firms get lower takeover valuations compared to stay-at-home-state firms, and the acquisition of reincorporated firms is less likely to be successful. Our second essay aims to explore the role of the options market in price discovery for M&A. We find that the predictive power of the changes in implied volatility of the target firm stock for the takeover outcome is statistically and economically significant. The risk arbitrage portfolios incorporating filters derived from the options on stocks of the target firms generate annualized risk-adjusted abnormal returns between 2.6% and 5%, depending on the portfolio weighting method, the threshold of filters for the implied volatility change, and the asset pricing models applied for abnormal returns. The results are robust to different empirical setups and are not explained by traditional factors
Retrospective Quantitative T1 Imaging to Examine Characteristics of Multiple Sclerosis Lesions
No full textQuantitative MRI plays an essential role in assessing tissue abnormality and diseaseprogression in multiple sclerosis (MS). Specifically, T1 relaxometry is gaining popularity
as elevated T1 values have been shown to correlate with increased inflammation,
demyelination, and gliosis. The predominant issue is that relaxometry requires parametric
mapping through advanced imaging techniques not commonly included in standard clinical
protocols. This leaves an information gap in large clinical datasets from which quantitative
mapping could have been performed.
We introduce T1-REQUIRE, a retrospective T1 mapping method that approximates
T1 values from a single T1-weighted MR image. This method has already been shown to
be accurate within 10% of a clinically available reference standard in healthy controls but
will be further validated in MS cohorts. We also further aim to determine T1-REQUIRE’s
statistical significance as a unique biomarker for the assessment of MS lesions as they
relate to clinical disability and disease burden.
A 14-subject comparison between T1-REQUIRE maps derived from 3D T1
weighted turbo field echoes (3D T1w TFE) and an inversion-recovery fast field echo (IRFFE) revealed a whole-brain voxel-wise Pearson’s correlation of r = 0.89 (p < 0.001) and
mean bias of 3.99%. In MS white matter lesions, r = 0.81, R2 = 0.65 (p < 0.001, N = 159),
bias = 10.07%, and in normal appearing white matter (NAWM), r = 0.82, R
2 = 0.67 (p <
0.001), bias = 9.48%.
Mean lesional T1-REQUIRE and MTR correlated significantly (r = -0.68, p <
0.001, N = 587) similar to previously published literature. Median lesional MTR correlated
significantly with EDSS (rho = -0.34, p = 0.037), and lesional T1-REQUIRE exhibited
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significant correlations with global brain tissue atrophy as measured by brain parenchymal
fraction (BPF) (r = -0.41, p = 0.010, N = 38). Multivariate linear regressions between T1-
REQUIRE NAWM provided meaningful statistical relationships with EDSS (β = 0.03, p
= 0.027, N = 38), as well as did mean MTR values in the Thalamus (β = -0.27, p = 0.037,
N = 38).
A new spoiled gradient echo variation of T1-REQUIRE was assessed as a proof of concept
in a small 5-subject MS cohort compared with IR-FFE T1 maps, with a whole brain voxel-wise
correlation of r = 0.88, R2 = 0.77 (p < 0.001), and Bias = 0.19%. Lesional T1 comparisons
reached a correlation of r = 0.75, R2 = 0.56 (p < 0.001, N = 42), and Bias = 10.81%.
The significance of these findings means that there is the potential to provide
supplementary quantitative information in clinical datasets where quantitative protocols
were not implemented. Large MS data repositories previously only containing structural
T1 weighted images now may be used in big data relaxometric studies with the potential
to lead to new findings in newly uncovered datasets. Furthermore, T1-REQUIRE has the
potential for immediate use in clinics where standard T1 mapping sequences aren’t able to
be readily implemented
Evaluating antimicrobial efficacy of GS-2 on reusable food packaging materials
No full textPackaging plays an important role in maintaining the quality and safety of fresh produce throughout storage, transportation and end-use by consumers. Single-use packaging poses several environmental impacts; therefore use of reusable packaging is being encouraged in the fresh produce supply chain. However, the utilization of harmful chemicals and inadequate sanitation standards limit the reuse of packaging materials. To overcome these limitations, this study focuses on testing a non-toxic, water-soluble antimicrobial; GS-2 coating to facilitate the reuse of food packaging and reduce the risk of microbial contamination. In this study, the antimicrobial activity of GS-2 was evaluated against foodborne pathogens; Escherichia coli, Listeria monocytogenes and Salmonella enterica on plastic and cardboard coupons at 1 h and 15 min treatment times and 0.3%, 1% and 3% concentration. These coupons were also stored at 4℃ and 90% R.H. and 18℃ and 45% R.H. inoculated on different days up to 42 d with E. coli or L. monocytogenes to study retention of activity of GS-2. Additionally, the efficacy of GS-2 to reduce transfer of bacteria from cardboard and plastic to tomato was investigated. The initial level of inoculum was 9 log CFU/surface for all experiments. Cardboard and plastic without GS-2 were used to compare the reduction of bacteria on the treated surfaces. The differences in the population of bacteria were evaluated using Student’s T-Test and ANOVA; p 4.50 log CFU/surface reduction of all three bacteria in 1 h. There was a lower reduction of the population on cardboard as compared to plastic for all bacteria, the reduction obtained was 1.83, 2.65 and 3.42 log CFU/surface for E. coli, L. monocytogenes and S. enterica, respectively, in 1 h. There was no significant difference between 15 min and 1 h treatments for cardboard. Further, the highest reduction of bacteria was obtained with 3% GS-2 on plastic. For cardboard, no significant difference in population reduction was obtained for E. coli or S. enterica, with 1% or 3% GS-2. However, for L. monocytogenes there was a higher reduction with 3%. GS-2 remained active on the surface of plastic and cardboard for a period of six weeks. For cardboard, there was a lower reduction of bacteria and there was no trend in the population reduction from 0 to 42 d, with the populations remaining within a range of 4-5 log CFU/surface. There was a significant transfer of E. coli or L. monocytogenes from plastic surfaces without GS-2 to tomato at 5-6 log CFU/tomato. However, the transfer of bacteria from the GS-2-coated plastic to the tomato was below the limit of enumeration. For cardboard, the population was below the limit of enumeration, irrespective of the GS-2 coating. Based on the results, GS-2 is a promising antimicrobial that reduces the microbial load on packaging surfaces and prevents cross-contamination of fresh produce. The retention of GS-2 activity makes it suitable for reusable packaging applications
Improving Localization Safety for Landmark-Based LiDAR Localization System
No full textAutonomous ground robots have gained traction in various commercial applications, with established safety protocols covering subsystem reliability, control algorithm stability, path planning, and localization. This thesis specifically delves into the localizer, a critical component responsible for determining the vehicle’s state (e.g., position and orientation), assessing compliance with localization safety requirements, and proposing methods for enhancing localization safety.Within the robotics domain, diverse localizers are utilized, such as scan-matching techniques like normal distribution transformations (NDT), the iterative closest point (ICP) algorithm,probabilistic maps method, and semantic map-based localization.Notably, NDT stands out as a widely adopted standalone laser localization method, prevalent in autonomous driving software such as Autoware and Apollo platforms.In addition to the mentioned localizers, common state estimators include variants of Kalman Filter, particle filter-based, and factor graph-based estimators. The evaluation of localization performance typically involves quantifying the estimated state variance for these state estimators.While various localizer options exist, this study focuses on those utilizing extended Kalman filters and factor graph methods. Unlike methods like NDT and ICP algorithms, extended Kalman filters and factor graph based approaches guarantee bounding of estimated state uncertainty and have been extensively researched for integrity monitoring.Common variance analysis, employed for sensor readings and state estimators, has limitations, primarily focusing on non-faulted scenarios under nominal conditions. This approach proves impractical for real-world scenarios and falls short for safety-critical applications like autonomous vehicles (AVs).To overcome these limitations, this thesis utilizes a dedicated safety metric: integrity risk. Integrity risk assesses the reliability of a robot’s sensory readings and localization algorithm performance under both faulted and non-faulted conditions. With a proven track record in aviation, integrity risk has recently been applied to robotics applications, particularly for evaluating the safety of lidar localization.Despite the significance of improving localization integrity risk through laser landmark manipulation, this remains an under explored territory. Existing research on robot integrity risk primarily focuses on the vehicles themselves. To comprehensively understand the integrity risk of a lidar-based localization system, as addressed in this thesis, an exploration of lidar measurement faults’ modes is essential, a topic covered in this thesis.The primary contributions of this thesis include: A realistic error estimation method for state estimators in autonomous vehicles navigating using pole-shape lidar landmark maps, along with a compensatory method; A method for quantifying the risk associated with unmapped associations in urban environments, enhancing the realism of values provided by the integrity risk estimator; a novel approach to improve the localization integrity of autonomous vehicles equipped with lidar feature extractors in urban environments through minimal environmental modifications, mitigating the impact of unmapped association faults. Simulation results and experimental results are presented and discussed to illustrate the impact of each method, providing further insights into their contributions to localization safety
Using Niobium surface encapsulation and Rhenium to enhance the coherence of superconducting devices
No full textIn recent decades, the scientific community has grappled with escalating complexity, necessitating a more advanced tool capable of tackling increasingly intricate simulations beyond the capabilities of classical computers. This tool, known as a quantum computer, features processors composed of individual units termed qubits. While various methods exist for constructing qubits, superconducting circuits have emerged as a leading approach, owing to their parallels with semiconductor technology.In recent years, significant strides have been made in optimizing the geometry and design of qubits. However, the current bottleneck in the performance of superconducting qubits lies in the presence of defects and impurities within the materials used. Niobium, owing to its desirable properties, such as high critical temperature and low kinetic inductance, stands out as the most prevalent superconducting material. Nonetheless, it is encumbered by a relatively thick oxide layer (approximately 5 nm) exhibiting three distinct oxidation states: NbO, NbO, and NbO. The primary challenge with niobium lies in the multitude of defects localized within the highly disordered NbO layer and at the interfaces between the different oxides. In this study, I present an encapsulation strategy aimed at restraining surface oxide growth by depositing a thin layer (5 to 10 nm) of another material in vacuum atop the Nb thin film. This approach exploits the superconducting proximity effect, and it was successfully employed in the development of Josephson junction devices on Nb during the 1980s.In the past two years, tantalum and titanium nitride have emerged as promising alternative materials, with breakthrough qubit publications showcasing coherence times five to ten times superior to those achieved in Nb. The focus will be on the fabrication and RF testing of Nb-based qubits with Ta and Au capping layers. With Ta capping, we have achieved the best T1 (not average) decay time of nearly 600 us, which is more than a factor of 10 improvements over the bare Nb. This establishes the unique capping layer approach as a significant new direction for the development of superconducting qubits.Concurrently with the exploration of materials for encapsulation strategies, identifying materials conducive to enhancing the performance of superconducting qubits is imperative. Ideal candidates should exhibit a thin, low-loss surface oxide and establish a clean interface with the substrate, thereby minimizing defects and potential sources of losses. Rhenium, characterized by an extremely thin surface oxide (less than 1 nm) and nearly perfect crystal structure alignment with commonly used substrates such as sapphire, emerges as a promising material platform poised to elevate the performance of superconducting qubits
Evaluation of the efficacy of power ultrasound technology coupled with organic acids to reduce listeria monocytogenes on peaches and apples
No full textFresh fruits and vegetables are prone to microbial contamination throughout different phases of human handling, processing, transportation, and distribution.
Emerging technologies, such as power ultrasound, have received attention due to their
capacity to reduce or eliminate foodborne bacterial pathogens on these commodities.
Power ultrasound, when combined with certain antimicrobials, has demonstrated its
effectiveness as a valuable tool for washing fresh produce. The objective of this study
was to examine the effectiveness of power ultrasound combined with organic acids on the
reduction of Listeria monocytogenes on fruits. In this study, peaches and apples were
subjected to surface inoculation with a four-strain cocktail of L. monocytogenes and dried
for 1 h. Stomacher bags, containing 225 mL of citric, lactic, or malic acids at
concentrations of 1%, 2%, or 5%, were employed for treating inoculated peaches and
apples. The acid treatment was used alone, or in combination with power ultrasound for
2, 5, or 10 min. Water was used for controls. Before treatment, the initial population of L.
monocytogenes on apples was lower compared to the initial population on peaches, with
apples showing a 1.94 log CFU/fruit reduction. Water controls demonstrated no
significant log reduction in both apples and peaches. The greatest L. monocytogenes
reduction on apples occurred when treated with 1% citric acid for 2 min with power
ultrasound where L. monocytogenes was significantly reduced from 6.98±0.88 log
CFU/fruit to 5.56±0.91 log CFU/fruit. The greatest L. monocytogenes reduction on
peaches occurred when treated with 5% citric acid for 5 min with power ultrasound
where L. monocytogenes was significantly reduced from 7.44±0.45 log CFU/fruit to
6.68±0.40 log CFU/fruit.
Overall, the combined effect of acid and power ultrasound was more pronounced
in apples than in peaches. The survival of L. monocytogenes on apples and peaches
appeared to be highly dependent on the specific treatment and hurdle technology applied.
The combination of ultrasound hurdle technology with acid washing has proven effective
in reducing L. monocytogenes on both peaches and apples, with a more significant impact
observed on apples. While acid washing is a more economical option compared to
ultrasound technology, the efficiency of microorganism reduction is considerably
enhanced when power ultrasound is combined with organic acids. Looking ahead, the
development of cost-effective power ultrasound methods could facilitate widespread
adoption of ultrasound hurdle technology in the produce industry
Integrating Deep Learning And Innovative Feature Selection For Improved Short-Term Price Prediction In Futures Markets
No full textThis study presents a novel approach for predicting short-term price movements in futures markets using advanced deep-learning models, namely LSTM, CNN_LSTM, and GRU_LSTM. By incorporating cophenetic correlation in feature preparation, the study addresses the challenges posed by sudden fluctuations and price spikes while maintaining diversification and utilizing a limited number of variables derived from daily public data. However, the effectiveness of adding features relies on appropriate feature selection, even when employing powerful deep-learning models. To overcome this limitation, an innovative feature selection method is proposed, which combines cophenetic correlation-based hierarchical linkage clustering with the XGBoost importance listing function. This method efficiently identifies and integrates the most relevant features, significantly improving price prediction accuracy. The empirical findings contribute valuable insights into price prediction accuracy and the potential integration of algorithmic and intuitive approaches in futures markets. Moreover, the developed feature preparation method enhances the performance of all deep learning models, including LSTM, CNN_LSTM, and GRU_LSTM. This study contributes to the advancement of price prediction techniques by demonstrating the potential of integrating deep learning models with innovative feature selection methods. Traders and investors can leverage this approach to enhance their decision-making processes and optimize trading strategies in dynamic and complex futures markets
Empowering Visually Impaired Individuals With Holistic Assistance Using Real-Time Spatial Awareness System
No full textThe integration of artificial intelligence (AI) into daily life opens unprecedented avenues for enhancing the experiences of visually impaired individuals, offering them greater autonomy and quality of life. This thesis introduces a Visually Impaired Spatial Awareness (VISA) system designed to assist visually impaired individuals holistically through a structured approach. At the foundational level, the VISA system incorporates several key technologies to interpret the surroundings and assist in basic navigation tasks. It utilizes Augmented Reality (AR) markers to facilitate recognition of places and aid in navigation, employs neural network models for advanced object detection and tracking, and leverages depth information for accurate object localization. Progressing to the intermediate level, the VISA system integrates the data obtained from object detection and depth sensing to assist in more complex navigational tasks such as obstacle avoidance and pathfinding toward a desired destination. At the advanced level, the VISA system synthesizes the capabilities developed at the foundational and intermediate levels to enhance the spatial awareness of visually impaired users, allowing them to undertake complex tasks, such as navigating complex environments and locating specific items. The VISA system also emphasizes efficient human-machine interaction, incorporating text-to-speech and speech-to-text technologies to facilitate natural and intuitive communication between the user and the system. The VISA system's performance was evaluated in different environments simulating real-world scenarios. The experimental results show that the user can interact with our system intuitively with minimal effort, and affirm that the VISA system can effectively assist the visually impaired user in locating and reaching for objects, navigating indoors, identifying merchandise, and recognizing both handwritten and printed texts