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Title: Using Niobium surface encapsulation and Rhenium to enhance the coherence of superconducting devices
Creator: Crisa, Francesco
Date: 2024
Description: In recent decades, the scientific community has grappled with escalating complexity, necessitating a more advanced tool capable of tackling...
Show moreIn 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$_2$, and Nb$_2$O$_5$. The primary challenge with niobium lies in the multitude of defects localized within the highly disordered Nb$_2$O$_5$ 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.
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Title: Utilizing Concurrent Data Accesses for Data-Driven and AI Applications
Creator: Lu, Xiaoyang
Date: 2024
Description: In the evolving landscape of data-driven and AI applications, the imperative for reducing data access delay has never been more critical,...
Show moreIn the evolving landscape of data-driven and AI applications, the imperative for reducing data access delay has never been more critical, especially as these applications increasingly underpin modern daily life. Traditionally, architectural optimizations in computing systems have concentrated on data locality, utilizing temporal and spatial locality to enhance data access performance by maximizing data and data block reuse. However, as poor locality is a common characteristic of data-driven and AI applications, utilizing data access concurrency emerges as a promising avenue to optimize the performance of evolving data-driven and AI application workloads.This dissertation advocates utilizing concurrent data accesses to enhance performance in data-driven and AI applications, addressing a significant research gap in the integration of data concurrency for performance improvement. It introduces a suite of innovative case studies, including a prefetching framework that dynamically adjusts aggressiveness based on data concurrency, a cache partitioning framework that balances application demands with concurrency, a concurrency-aware cache management framework to reduce costly cache misses, a holistic cache management framework that considers both data locality and concurrency to fine-tune decisions, and an accelerator design for sparse matrix multiplication that optimizes adaptive execution flow and incorporates concurrency-aware cache optimizations.Our comprehensive evaluations demonstrate that the implemented concurrency-aware frameworks significantly enhance the performance of data-driven and AI applications by leveraging data access concurrency.Specifically, our prefetch framework boosts performance by 17.3%, our cache partitioning framework surpasses locality-based approaches by 15.5%, and our cache management framework achieves a 10.3% performance increase over prior works. Furthermore, our holistic cache management framework enhances performance further, achieving a 13.7% speedup. Additionally, our sparse matrix multiplication accelerator outperforms existing accelerators by a factor of 2.1.As optimizing data locality in data-driven and AI applications becomes increasingly challenging, this dissertation demonstrates that utilizing concurrency can still yield significant performance enhancements, offering new insights and actionable examples for the field. This dissertation not only bridges the identified research gap but also establishes a foundation for further exploration of the full potential of concurrency in data-driven and AI applications and architectures, aiming at fulfilling the evolving performance demands of modern and future computing systems.
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Title: Defense-in-Depth for Cyber-Secure Network Architectures of Industrial Control Systems
Creator: Arnold, David James
Date: 2024
Description: Digitization and modernization efforts have yielded greater efficiency, safety, and cost-savings for Industrial Control Systems (ICS). To...
Show moreDigitization and modernization efforts have yielded greater efficiency, safety, and cost-savings for Industrial Control Systems (ICS). To achieve these gains, the Internet of Things (IoT) has become an integral component of network infrastructures. However, integrating embedded devices expands the network footprint and softens cyberattack resilience. Additionally, legacy devices and improper security configurations are weak points for ICS networks. As a result, ICSs are a valuable target for hackers searching for monetary gains or planning to cause destruction and chaos. Furthermore, recent attacks demonstrate a heightened understanding of ICS network configurations within hacking communities. A Defense-in-Depth strategy is the solution to these threats, applying multiple security layers to detect, interrupt, and prevent cyber threats before they cause damage. Our solution detects threats by deploying an Enhanced Data Historian for Detecting Cyberattacks. By introducing Machine Learning (ML), we enhance cyberattack detection by fusing network traffic and sensor data. Two computing models are examined: 1) a distributed computing model and 2) a localized computing model. The distributed computing model is powered by Apache Spark, introducing redundancy for detecting cyberattacks. In contrast, the localized computing model relies on a network traffic visualization methodology for efficiently detecting cyberattacks with a Convolutional Neural Network. These applications are effective in detecting cyberattacks with nearly 100% accuracy. Next, we prevent eavesdropping by applying Homomorphic Encryption for Secure Computing. HE cryptosystems are a unique family of public key algorithms that permit operations on encrypted data without revealing the underlying information. Through the Microsoft SEAL implementation of the CKKS algorithm, we explored the challenges of introducing Homomorphic Encryption to real-world applications. Despite these challenges, we implemented two ML models: 1) a Neural Network and 2) Principal Component Analysis. Finally, we hinder attackers by integrating a Cyberattack Lockdown Network with Secure Ultrasonic Communication. When a cyberattack is detected, communication for safety-critical elements is redirected through an ultrasonic communication channel, establishing physical network segmentation with compromised devices. We present proof-of-concept work in transmitting video via ultrasonic communication over an Aluminum Rectangular Bar. Within industrial environments, existing piping infrastructure presents an optimal solution for cost-effectively preventing eavesdropping. The effectiveness of these solutions is discussed within the scope of the nuclear industry.
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Title: Improving Localization Safety for Landmark-Based LiDAR Localization System
Creator: Chen, Yihe
Date: 2024
Description: Autonomous ground robots have gained traction in various commercial applications, with established safety protocols covering subsystem...
Show moreAutonomous 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.
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Title: Empowering Visually Impaired Individuals With Holistic Assistance Using Real-Time Spatial Awareness System
Creator: Yu, Xinrui
Date: 2024
Description: The integration of artificial intelligence (AI) into daily life opens unprecedented avenues for enhancing the experiences of visually impaired...
Show moreThe 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.
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Title: Evaluating antimicrobial efficacy of GS-2 on reusable food packaging materials
Creator: Birje, Nupoor Prasad
Date: 2024
Description: Packaging plays an important role in maintaining the quality and safety of fresh produce throughout storage, transportation and end-use by...
Show morePackaging 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 <0.05 was considered significant. With 3% GS-2 concentration on plastic, there was > 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.
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Title: EVALUATION OF TIME-FREQUENCY DISTRIBUTIONS FOR ULTRASONIC IMAGING APPLICATIONS
Creator: Lu, Juan
Date: 2013-05-01, 2013-05
Description: This thesis presents the performance evaluation of generalized time-frequency distributions (GTFD) and model-based time-frequency (TF)...
Show moreThis thesis presents the performance evaluation of generalized time-frequency distributions (GTFD) and model-based time-frequency (TF) estimation of ultrasonic signals. Two new TF distributions which are related to generalized time-frequency distribution have been examined. These methods are singular value decomposition of Choi-Williams distribution (CWD-SVD), and 2D (time and frequency) Gaussian kernel applied to generalized time-frequency distribution. The application of Short-Time Fourier Transform (STFT) is studied for chirplets estimation. Then, the Wigner distribution (also called the Wigner-Ville distribution) of estimated Chirplets yield a precise TF representation. The performance of the STFT, the Morlet wavelet transform, the Wigner distribution (WD), the CWD and the CWD-SVD are compared. CWD-SVD is a very effective algorithm to keep the high clarity of the Wigner distribution and to suppress the undesirable cross-terms resulting from multi-component signals. The Gaussian echo model is used to obtain the analytical TF distribution. For CWD the proper range of exponential kernel parameter, , is attained. This range allows CWD to sustain a high concentrated auto-terms and significant suppression of cross-terms. For this range of the CWD-SVD extracts high clarity auto-terms and facilitate eliminating the residual cross-terms. To remove the cross-terms, singular value decomposition algorithm extracts basis functions corresponding to auto-terms. After discarding the basis functions and singular values of the cross-terms and noise, the basis functions and their singular values of auto-terms are used to reconstruct the TF distribution. The results of multi-component Gaussian echoes with significant time and frequency overlaps show that the CWD-SVD is able to eliminate residual cross-terms for xi which the CWD failed to eliminate. The numerical analysis of multi-component Gaussian echoes indicates that CWD-SVD has the ability to resist noise resulting in accurate estimates of center frequencies and arrival times. The generalized time-frequency distribution with 2D Gaussian kernel is able to separate two extremely close Gaussian echoes in the time-frequency domain. In this study, typical values of the 2D Gaussian kernel parameters for efficient cross-terms elimination are provided. The relationship between the kernel's parameters and Gaussian echoes' parameters is deduced. A practical method for TF analysis is to decompose the signal into sparse chirplets. Decomposition requires chirplet parameter estimation. In this study, the parameters of a signal which is composed of two overlapping chirplets are estimated using STFT. By this method the estimation results are found to be accurate confirming that the STFT is an effective method for decomposing and estimating chirplets in a multi-component signal.
M.S. in Electrical Engineering, May 2013
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Title: MOTION ESTIMATION METHODS FOR RESPIRATORY GATED SPECT
Creator: Hurtado Jaramillo, Juan Sebastian
Date: 2014, 2014-12
Description: Single photon emission computed tomography (SPECT) is a type of nuclear imaging test that is used for detection of cardiac diseases....
Show moreSingle photon emission computed tomography (SPECT) is a type of nuclear imaging test that is used for detection of cardiac diseases. Unfortunately it suffers from several image degrading factors, including respiratory motion, that can affect the accuracy of diagnosis. Four-dimensional (4D) respiratory gated SPECT helps to correct this issue by performing the acquisition at several intervals and applying motion-compensated reconstruction methods. With this in mind, and to improve 4D reconstruction, three different motion estimation methods (i.e., optical flow equation, center of mass, and template matching) are applied in this project. Three different sets of dose levels are simulated using NCAT and one set of clinical data is used. The results for motion estimation on simulated data show that the template matching methods have better performance overall. Additionally, noise reduction by means of a spatial smoothing filter helps on the reduction of the average error. Rotational motion estimation using principal component analysis (PCA) was also studied to examine if there can be improvements over the translational motion methods. The initial outcome is that there is a small rotation that can be detected on the ideal reconstruction; the compensation of this rotation also helps to reduce the error obtained from translational motion, albeit by a small margin. Unfortunately, the same cannot be said when noisy reconstructions were used.
M.S. in Electrical Engineering, December 2014
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Title: ENSURING SECURITY AND PRIVACY IN BIG DATA SHARING, TRADING, AND COMPUTING
Creator: Jung, Taheo
Date: 2017, 2017-05
Description: We have witnessed huge values of the big data in the last decade, and it is evidential that the data bring large added values to the business...
Show moreWe have witnessed huge values of the big data in the last decade, and it is evidential that the data bring large added values to the business in various areas. Owing to such opportunities, the data collection and archival became one of the most successful business strategies in the industry, and more and more user-generated data are now being acquired, stored, provisioned, and consumed nowadays. Increased collection made human being more closely involved in the life cycle of the big data characterized by the acquisition, storage, provisioning, and consumption, and larger security and privacy challenges emerged. People’s awareness of such threats led to various efforts by the governments, industry, and academia, and our efforts described in this dissertation also belong to them. We have investigated the security and privacy challenges emerging in various parts of the life cycle big data experience nowadays, and I present our major discoveries in this dissertation which are composed of three major parts: (1) security and privacy in storage of big data; (2) theoretic foundations of privacy-preserving data computing; (3) security in big data trading. We addressed new or existing security/privacy threats existing in different parts of the big data life cycle by either leveraging existing works in intelligent ways or by proposing our novel technologies. The contributions of our discoveries cam be summarized as the protection of user privacy and data security while supporting the original functionalities at negligible extra computation/communication/storage overhead.
Ph.D. in Computer Science, May 2017
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Title: SLIP-LINK MODELING OF ENTANGLED POLYMERS: RHEOLOGICAL APPLICATIONS AND EXTRACTING FRICTION FROM ATOMISTIC SIMULATION
Creator: Katzarova, Maria
Date: 2016, 2016-05
Description: The Discrete Slip-link Model (DSM) is a robust mesoscopic theory that has great success predicting the rheology of flexible entangled polymer...
Show moreThe Discrete Slip-link Model (DSM) is a robust mesoscopic theory that has great success predicting the rheology of flexible entangled polymer liquids and gels. In the most coarse-grained version of the DSM, we exploit the university observed in the shape of the relaxation modulus of linear monodisperse melts. For this type of polymer we present analytic expressions for the relaxation modulus. The high-frequency dynamics which are typically coarse-grained out from the DSM are added back into these expressions by using a Rouse chain with fixed ends. We find consistency in the friction used for both fast and slow modes. Using these analytic expressions, the polymer density, the molecular weight of a Kuhn step, Mk, and the low-frequency cross-over between the storage and loss moduli, G' and G", it is now straightforward to estimate model parameter values and obtain predictions over the experimentally accessible frequency range. Moreover it has previously been shown that the two static parameters can be obtained from primitive path analysis of molecular dynamics simulations. In this work, two ways are shown for obtaining the friction parameter (i) from atomistic simulations of short chains using the free-volume theory, and (ii) from atomistic simulations of entangled chains by scaling the chain center-of-mass mean-square displacement from the slip-link model to that of the atomistic simulation. Futhermore three standing challenges for molecular theories of polymers (i) predictions for uniaxial extension of star-branched polymer melts (ii) predictions for blends of star-branched and linear chains and (iii) predictions for normal stress differences in start-up of shear and followoing cessation are addressed here using the DSM. Additionally the DSM is used to predict the mechanical properties of a cross-linked polydimethylsiloxane (PDMS) network swollen with non-reactive entangled PDMS solvent. These successful predictions strongly suggest that the observed rheological modification in the swollen blend arises from the constraint dynamics between the network chains and the dangling ends.
Ph.D. in Chemical Engineering, May 2016
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Title: CLOSED-LOOP CONTROL GUST SUPPRESSION AT OF LIFT FOR LONGITUDINAL LOW REYNOLDS NUN4BERS
Creator: Kerstens, Wesley
Date: 2011-07-26, 2011-07
Description: Experiments are conducted to investigate the ability of variable pressure, pulsed-blowing actuators and zero-net-mass-flux actuators to...
Show moreExperiments are conducted to investigate the ability of variable pressure, pulsed-blowing actuators and zero-net-mass-flux actuators to maintain a constant lift force on a low aspect ratio, semi-circular wing in a longitudinally gusting flow. Dynamic models of the lift response to actuation and the lift response to longitudinal gusting are obtained through modern black-box system identification methods. Robust, H∞, closed-loop controllers are synthesized using a mixed sensitivity, loopshaping approach. An additional feed-forward disturbance compensator is designed based on a model of the unsteady aerodynamics. The controllers show suppression of lift fluctuations at low gust frequencies, f < 1Hz (reduced frequency, k < 0.09). At higher frequencies, the control performance degrades due to limitations related to the time for a disturbance, created by the actuators, to convect over the wing and establish the flow field that leads to enhanced lift on the wing. Similar limitations are observed for both wings even though the bandwidth of the zero-net-mass-flux actuators is an order of magnitude higher than that of the pulsed-blowing actuators.
M.S. in Mechanical and Aerospace Engineering, July 2011
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Title: THE EFFECT OF NANOPARTICLE SELF-STRUCTURING ON WETTING AND SPREADING OF NANOFLUIDS ON SOLID SURFACES
Creator: Kondiparty, Kirtiprakash
Date: 2011-11, 2011-12
Description: Nanofluids are suspensions of nanometer-sized particles in liquids. The nanoparticles self-structure at the three-phase contact region...
Show moreNanofluids are suspensions of nanometer-sized particles in liquids. The nanoparticles self-structure at the three-phase contact region resulting in the structural disjoining pressure gradient which causes enhanced the spreading of nanofluids compared to simple fluids without nanoparticles. In this thesis, we attempt to understand the effect of the structural disjoining pressure on the spreading dynamics of nanofluids on solid surfaces. We observed nanoparticle self-structuring phenomena during film thinning on a smooth hydrophilic glass surface using a silica-nanoparticle aqueous suspension and reflected light interferometry. Our experiments revealed that film formed from small drop is thicker and contains more particle layers than a film formed from large drop. The data for the film-meniscus contact angle verses film thickness were obtained and used to calculate the structural energy isotherm of an asymmetric film. We studied the effect of structural disjoining pressure on the wedge meniscus profile formed by an oil drop on solid surface surrounded by nanofluid using Laplace Equation augmented with the structural disjoining pressure. Our analyses indicate that a suitable combination of the nanoparticle concentration, nanoparticle size, contact angle, and capillary pressure can result not only in the displacement of the three-phase contact line, but also in the spontaneous spreading of the nanofluid as a film on solid surface. We validated our theoretical predictions using experiments where we observed spreading of nanofluid on glass surface displacing a sessile drop of canola oil. The dynamic spreading of the nanofluid on a solid surface between a sessile oil drop on solid surface was experimentally measured using reflected light microscopy. We xiv obtained the rate of nanofluid spreading by plotting the position of the inner contact line with time. The nanofluid film was found to spread at a constant velocity. We modeled the spreading dynamics of the nanofluid film using the lubrication approximation of the Navier-Stokes Equation, taking into consideration the structural disjoining pressure in the over-all pressure balance. The model was evaluated by estimating the rate of nanofluid spreading for the 10v% nanofluid. The rate of spreading thus predicted by the dynamics model for 10v% nanofluid was in good agreement with the experimental observations.
Ph.D. in Chemical Engineering, December 2011
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Title: AN OPTIMIZATION OF MANUFACTURING PROCESS SELECTION THROUGH COSTING AND SYSTEMATIC ANALYSIS
Creator: Klima, Kevin
Date: 2017, 2017-05
Description: In order for any company to remain competitive, there is a constant push to cut costs while keeping customers satisfied by providing quality,...
Show moreIn order for any company to remain competitive, there is a constant push to cut costs while keeping customers satisfied by providing quality, robust products. While current methods for predicting the most cost effective manufacturing process have proven to offer a significant amount of utility for design engineers, the issue remains that for a cost estimation to be truly accurate, the component has to be completely or nearly completely designed, which could require a significant amount of upfront development time. The goal was to develop a design tool to predict the most cost effective process to manufacture a new component based solely on already available historical data and basic knowledge of the design requirements of the new part. This study focused on steel components that could be manufactured either as a fabrication or as a casting. Two real-world applications were studied from two separate industries, with each application being designed with each process. Common cost estimation techniques were used to develop models for predicting the cost for each component to offer insight for how the cost would be expected to vary with quantity. As a means of ensuring robustness and that each competing model was structurally equivalent, each model had to pass critical exceptional and fatigue load cases in FEA while also meeting predefined success criteria. Using the results from the structural analysis and cost estimation, a design tool was developed as a means of objectively predicting how a component with similar application requirements would most cost effectively be manufactured based on the desired quantity of parts that needed to be produced. By using historical information of similar components that have the possibility of being manufactured in more than one way, more effective and systematic decision making for how a new component should be manufactured was shown to be possible. A third, independent case study was also selected as a real world example from industry as a means of validating and assessing the sensitivity of the weighting used in the development of the design tool. This was used to further refine the tool for the use in analyzing future components.
M.S. in Manufacturing Engineering, May 2017
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Title: OPTIMIZATION AND EXPERIMENTAL VALIDATION OF ELECTROSTATIC ADHESIVES
Creator: Shah, Jainam
Date: 2013-04-25, 2013-05
Description: Electrostatic adhesion provides an attachment mechanism for robotic grippers that is both controllable and e ective over a wide range of...
Show moreElectrostatic adhesion provides an attachment mechanism for robotic grippers that is both controllable and e ective over a wide range of surfaces including conduc- tive, semi-conductive and insulating materials. The adhesives function by utilizing a set of high voltage electrodes that generate an electric eld. This electric eld polarizes the substrate material, thus generating an adhesion force. Optimizing the geometry of these conductive electrodes provides enhanced adhesion forces that in- creases attachment robustness. Previous researchers have attempted to increase the adhesion level of an electrostatic adhesive but no e ort has been made to optimize the geometry and con guration of the electrodes. This thesis presents a method to increase the adhesion level of electrostatic adhesives by optimizing the electrode geo- metric parameters: width of the electrodes, thickness of the electrodes, gap between the electrodes and number of electrodes. With the optimized electrode geometry, an improvement of up to 500 percent in shear stress is achieved compared to previously published values.
M.S. in Mechanical Engineering, May 2013
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Title: ESSAYS IN ENVIRONMENTAL FINANCE
Creator: Li, Jing
Date: 2013, 2013-07
Description: The Clean Development Mechanism (CDM) is a mechanism de ned in the Ky- oto protocol that incentivizes parties to the protocol to fund...
Show moreThe Clean Development Mechanism (CDM) is a mechanism de ned in the Ky- oto protocol that incentivizes parties to the protocol to fund sustainable development projects in countries that are not party to the protocol. In the rst chapter of this paper, I introduce the CDM and how the nancing mechanism works. In the second chapter, I analyze a target contract nancing structure for di erent CDM projects in order to see under what conditions the nancing structure is e cient and to explore the contract's allocation of pro t among the rms. In the two broad categories of CDM projects I consider, I nd the optimal investment decision for the investor and for the overall system. I also analyze how the residual value of technology would a ect the nancing, target contract's e ciency, and allocation of pro t. In the third chapter, I conduct empirical analysis on the actual CDM outputs, Certi ed Emission Reduction units (CERs), for a sample of wind CDM projects in China. I nd that CDM projects greatly under perform relative to the promises they make. Based on this under-performing records, in the fourth chapter, I analyze the economic bene ts investors could gain if they were able to directly fund a portfolio of CDM projects and obtain returns from the anticipated CER issuances and underlying energy generated from the portfolio of CDM projects. I consider a variety of funding constraints that the CDM fund/portfolio manager (CDM-PM) may face and determine their economic performance against actual CDM project data for wind CDM projects in China.
PH.D in Management Science, July 2013
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Title: Rapid Rail Transit Oriented City for One Million in Calfornia
Creator: Moore, Jeremy Edward
Date: 2011-11-22, 2011-12
Description: The city of Aubretia will be a city for approximately one million people situated at the northern divergence point of the proposed California...
Show moreThe city of Aubretia will be a city for approximately one million people situated at the northern divergence point of the proposed California High-Speed Rail Authority system and the current San Joaquin Amtrak passenger rail service, located near Madera city. Having the largest population out of all 50 states and three cities —Los Angeles, San Diego and San Jose—in the top ten list of most populous US cities, California certainly has the demand to support a high-speed rail system. The 2010 United States Census has also demonstrated a continuing trend of residents relocating from the Midwest and East Coast to the Sun belt states. All inhabitants of Aubretia will have access to a heavy two-rail system providing transportation to any other point in the city in 45 minutes or less. Development will be restricted to an area approximately one half mile in radius or a ten minute walking distance centered on each transit stop. Along with standard rapid transit stops in each development area there is also an interface with a central transportation hub connecting the Aubretia Metro to Amtrak and California High-Speed Rail as well as ground transportation options like regional bus and for-hire vehicles. The planned population level for Aubretia is based on a city size that can be served with a two-track automated metro system. The system will be capable of accommodating a majority of rush hour commuters at three persons per square meter density with a minimum headway of 90 seconds.
M.S. in Architecture, December 2011
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Title: GOTTA EAT TO LIVE, GOTTA STEAL TO EAT: THE INVESTIGATION OF SERIOUS DISRUPTIVE BEHAVIOR, TEMPERAMENT, AND EXECUTIVE DYSFUNCTION AMONG HOMELESS YOUTH
Creator: Kaszynski, Katie
Date: 2014, 2014-07
Description: Background: Homeless youth are at risk for many adverse outcomes, including poor physical health, traumatic experiences, victimization, poor...
Show moreBackground: Homeless youth are at risk for many adverse outcomes, including poor physical health, traumatic experiences, victimization, poor academic achievement, cognitive deficits, psychopathology, and substance use. Research demonstrates that these individuals engage in substantial disruptive behavior (e.g., stealing, dealing drugs, breaking and entering, engaging in prostitution), which further increases their risk of negative outcomes. Individual factors, including innate temperament and executive functioning skills have been shown to relate to one another and be independently related to behavior problems, as evidenced by research investigating housed youth. Homeless youth are shown to exhibit poor effortful control, high distress, executive dysfunction, and substance abuse; factors of which have not been fully examined in relationship to persistent behavior problems as reflected in antisocial personality disorder (ASPD). Study Aim: The current study evaluated the association between temperament, executive functioning, and substance use disorders in their relation to the likelihood of meeting criteria for ASPD among homeless youth (ages 18-22). It was hypothesized that these variables would significantly relate to meeting criteria for ASPD in this population. Procedure: 87 homeless individuals (mean age = 19.27) who were residing at a homeless shelter at the time of the study (in Chicago or Los Angeles) participated over the course of two testing sessions. Each individual completed measures of ASPD and substance use disorders (MINI), temperament (ATQ), and executive functioning (D-KEFS), among other measures that are part of a larger studying conducted at University of Chicago Medical Center (UCMC). Results: Results suggested that temperament (specifically effortful control) executive dysfunction (specifically cognitive shifting), and substance use disorder (specifically substance abuse) were significantly related to the likelihood of a homeless individual meeting criteria for ASPD. Youth who showed poorer effortful control, better ability to shift attention between sets of information, and substance abuse were at a greater likelihood of meeting criteria for ASPD. Conclusions: These findings indicate that aspects of temperament, specific executive skills, and substance abuse are important variables in determining the likelihood of ASPD among a population of homeless individuals. Clinical implications, limitations, and suggestions for interventions are discussed.
Ph.D. in Psychology, July 2014
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Title: NON-INTRUSIVE LOAD MONITORING AND DEMAND RESPONSE FOR RESIDENTIAL ENERGY MANAGEMENT
Creator: Iwayemi, Abiodun
Date: 2016, 2016-05
Description: Compared to cellphone bills which itemize billing into local, international, text messaging, and data, todays electricity bills are opaque....
Show moreCompared to cellphone bills which itemize billing into local, international, text messaging, and data, todays electricity bills are opaque. Residential electricity customers receive a monthly bill detailing their aggregate energy usage, without any insight into which appliances are responsible for what proportions of their bill. We therefore created a Non-intrusive load monitoring framework that uses only data available from smart meters and the price signals from the Electric utility, and combine it with Optimal Stopping Rule-based schedulers to create a framework to equip residents with the information they need to be more energy efficient while balancing their costs and comfort. Non-intrusive load monitoring provides homeowners with detailed feedback on their electricity usage, but an open area is automated appliance labeling and the creation of generalizable appliance models that can be trained in one home, and deployed in another. Manually labeling such events to use them for disaggregating residential appliances is a costly and tedious task, and we developed two approaches for semisupervised learning of appliance signatures. The first approach uses 1-Nearest neighbor semi-supervised learning, and we developed a stopping criterion which reduces the likelihood of mislabeling appliance instances. This approach was extended to a cluster-then-label semi-supervised learning approach which can use only 3 labeled samples of each appliance to label and classify similar appliances within the home. Our approach enables the comparison of unequal length time series, and incorporates additional features extracted from the appliance time series. Finally, we develop a hybrid framework that combines detailed appliance models learned via Non-intrusive load monitoring with optimal stopping rule schedulers. We evaluated the performance of these models in terms of cost and delay, and explored the effect that errors in the real-time price and appliance models have on appliance running costs to demonstrate how our approach outperforms scheduling using only day head prices.
Ph.D. in Electrical Engineering, May 2016
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Title: AIRFOIL LONGITUDINAL GUST RESPONSE IN ATTACHED, SEPARATING, AND DETACHED SURGING FLOW
Creator: Weirich, Jeremy Michael
Date: 2013, 2013-12
Description: Longitudinally gusting ow over a nominally two dimensional airfoil is exam- ined over a range of incidence angles and reduced frequencies. The...
Show moreLongitudinally gusting ow over a nominally two dimensional airfoil is exam- ined over a range of incidence angles and reduced frequencies. The response of the airfoil to these gusting ows is compared with classical theory, and is found to follow theory moderately well at low incidence angles across all reduced frequencies. At high incidence angles, the predictive power of the classical model is found to decrease signi cantly. The aerodynamic forces are also decomposed into circulatory and non- circulatory e ects and their relative strength is examined. The circulatory e ects are found to be minimal and constant at low incidence angles, while high incidence angles show strong variation, indication that the wake structure experiences signi - cant changes. The noncirculatory e ects are found to depend linearly on the reduced frequency and a ect the ow more strongly at lower incidence angles. An argument is made for the utility of dividing analysis of the ow into attached ow, separating ow, and fully detached ow regimes. The division is clear when examining how the normalized force coe cients change with reduced frequency, and provides a useful tool for predicting the transition of the ow between regimes.
M.S. in Aerospace Engineering, December 2013
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Title: Stigma, depression, and help-seeking: Experiences of parents/caregivers of children with mental health challenges
Creator: Serchuk, Marisa D.
Date: 2023
Description: The impacts of stigma on people with lived-experience are widely recognized, however, stigma has been noted to extend to family members. The...
Show moreThe impacts of stigma on people with lived-experience are widely recognized, however, stigma has been noted to extend to family members. The current investigation examines how specific types of stigma experienced by parents/caregivers (N=250) of children with mental health challenges are related to symptoms of depression and attitudes towards help-seeking. Results found that higher levels of public stigma, self-stigma, and vicarious stigma were associated with higher levels of depression and were differentially associated with attitudes towards help-seeking. Findings from this investigation add to the small body of literature examining stigma experienced by parents/caregivers of children with mental health challenges.
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