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Prediction and Control of In-Cylinder Processes in Heavy-Duty Engines Using Alternative Fuels
No full textThis Ph.D. thesis focuses on advancing diagnostic techniques and control-oriented models to enhance the efficiency and performance of internal combustion (IC) engines, particularly heavy-duty engines utilizing alternative fuels. The research endeavors to contribute to the field of model-based control of engines through the development and implementation of innovative methodologies. The primary emphasis is on the development of diagnostic methods, control-oriented models and advanced control strategies for compression ignition engines using alternative fuels. The first key topic explores the determination of the Most Representative Cycle for Combustion Phasing Estimation based on cylinder pressure measurements. The method developed extracts crucial information from experimental data obtained from four distinct engines: the heavy-duty single-cylinder GCI engine, the light-duty multi-cylinder diesel engine, a CFR engine, and a single-cylinder light-duty Spark Ignition (SI) engine. This work lays the foundation for precise combustion phasing estimation, a critical parameter for engine control. The second major contribution involves the development of control-oriented models for Variable Geometry Turbochargers (VGT) and inter-coolers. Two models are established: a data-driven turbocharger model and an empirical inter-cooler model. These models are meticulously calibrated and validated using experimental data from a multi-cylinder light-duty diesel engine, providing valuable insights into the behavior of these components under varying conditions. The outcomes contribute to facilitate predictive control of engine air systems. The third core aspect of the thesis revolves around Model Predictive Control of Combustion Phasing in heavy-duty compression-ignition engines utilizing alternative fuels. A combustion phasing and engine load model is derived from experimental data and incorporated into an MPC framework. The MPC strategy is subsequently tested in the heavy-duty GCI test cell and compared against a conventional Proportional-Integral-Derivative (PID) control strategy. The results showcase the effectiveness of the MPC approach in achieving precise control of combustion phasing, demonstrating its potential for optimizing engine performance.
In summary, this Ph.D. thesis contributes significantly to the field of engine controls by advancing diagnostic techniques, control-oriented models, and implementing a cutting-edge MPC-based control strategy for compression ignition engines using alternative fuels. The research findings not only enhance the understanding of in-cylinder processes but also pave the way for more efficient and sustainable heavy-duty engines using alternative fuels
The Voderettes: Gender, Labor, and Techno-Utopia at the 1939 New York World's Fair
No full textThis thesis explores the labor demands of the Voder, the electrical speech synthesis machine developed by Bell Labs to be a major component of AT&T's 1939 New York World's Fair exhibit. With the United States emerging from the Great Depression, and with political tensions escalating across the globe, the paper situates the Voder's labor demands within the historical context of the fair. Specifically, I explore the decision to have young women operate the Voder, the intricacies of the machine cloaked by the warm presence of its highly-skilled female operator. Using archival records from Bell Labs engineers, the paper exposes the previously unacknowledged engineering contributions of Voder operators in the years before the fair. These young women not only influenced major decisions about the Voder's mechanics but also gave early credence to the notion that developing a performance with the machine could make for a thrilling fair exhibit. Moreover, the paper argues that at the fair itself, AT&T and Bell Labs executives used the Voder operators to normalize a new vision of a technological utopia that relied heavily and conspicuously on the infrastructural labor of women. Given the Voder's legacy, as a tool that laid critical groundwork for voice encryption technology, the paper adds important context to the historical record, highlighting the young women at the heart of the machine
Three Essays on the Internet Economy
No full textIn an era of digital platforms, the integrity and visibility of consumer reviews, the dynamics of digital advertising markets, and the role of software development kits (SDKs) emerge as pivotal elements shaping user experiences and platform economics. My research spans three distinct but interconnected domains: the impact of safety reviews on Airbnb, the effects of privacy protections on digital advertising markets, and the significance of SDK releases in the evolution of Apple's iOS app market. We find that critical reviews concerning the safety of an Airbnb listing's vicinity influence guest bookings negatively and, therefore, could boost platform revenues if such reviews were obscured, highlighting a misalignment between consumer interests and platform revenue objectives. This effect is more pronounced in low-income and minority neighborhoods, suggesting a nuanced impact on different community segments. In the digital advertising sector, we identify that data frictions disproportionately harm small publishers, especially when associated with smaller ad intermediaries, underscoring the vulnerability of niche players to market and regulatory changes. Lastly, our analysis of the iOS app market reveals the instrumental role of SDK releases in fostering the app ecosystem's growth, independent of the expanding iPhone user base. Together, these findings underscore the complex interplay between consumer feedback, technological advancements, and market dynamics in digital environments, urging a balanced approach that safeguards consumer interests while fostering innovation and equitable market practices
Targeting Respiratory Pathways to Combat Persistence: Investigating the Role of Respiratory Complexes in Klebsiella Aerogenes Persister Cell Formation
No full textKlebsiella aerogenes is a multidrug-resistant nosocomial pathogen that poses significant challenges due to its ability to form persister cells that evade antibiotic treatment and contribute to chronic infections. This study explored the relationship between respiratory complexes and persister cell formation, focusing on NDH-2, NQR, and cytochrome oxidases. The research used a combination of wild-type and mutant strains, along with respiratory inhibitors and antibiotics, to identify key metabolic pathways influencing persister survival.The results revealed that NDH-2 plays a pivotal role in persister cell formation, with NDH-2 mutants demonstrating a significant reduction in persister populations over time. Comparative analysis of respiratory mutants highlighted differential reliance on oxidative phosphorylation, with NDH-2 being essential for maintaining energy metabolism during stress. In contrast, mutants lacking NQR or nuo complexes exhibited slower colony count declines, suggesting a shift to low-energy states that mimic persister characteristics.
These findings emphasize the critical role of the respiratory chain, particularly NDH-2, in bacterial persistence and highlight its potential as a therapeutic target. By disrupting energy metabolism, it may be possible to eradicate persister cells and reduce the recurrence of infections caused by K. aerogenes, providing a promising avenue for combating multidrug-resistant pathogens
Selective Interface Reaction Studies during Atomic Layer Deposition Process Based on Surface Properties of Metal Oxide Substrate and Chemical Properties of Precursors
No full textSite-selective atomic layer deposition (SS-ALD) on metal oxide nanoparticles will require subtle surface chemical control through site-specific surface functionalization (e.g. selective hydration) or highly discriminating reactivity. Two main factors, including the properties of substrate and proton-affinity of the precursors, which would affect the selectivity of ALD process will be discussed and the mechanism will be revealed in this thesis.The adsorption step on substrate surface of the metal precursors will affect the first half reaction of ALD process. Our hypothesis here is that surface hydroxyl group on different oriented metal oxide surface have different desorption temperature points. In this case, we can control the adsorption step by simply adjusting substrate temperature to targeting temperature point where it allows us to achieve selectively removal of the targeting hydroxyl groups while leaving the hydroxyl remain on desired site.
Selectively dihydroxylation on In2O3 (111) and (211) single crystal surface is studied, and further SS-ALD is achieved on high-oriented In2O3 (111) thin-film by simply thermal treatment, which indicated that discriminant dihydroxylation can be a promising pathway to SS-ALD. Thermal dehydration of facet-specific anatase TiO2 truncated-octahedron, disks, and rods samples is investigated by Fourier-transform infrared spectroscopy (FTIR). Potential facet-specific reaction window was revealed. However, the various oxidation state on transition metal created more complicated surface atomic arrangement, which make the study of thermal dihydroxylation on nanoparticles even more complicated. Thus, we turn our focus back on the In2O3 material. Well-constructed and facet specific In2O3 nanoparticle cube, octahedron and trapezohedron with only one of (100), (111), and (211) surface exposed, respectively, were synthesized and investigated by DRIFT. Further ALD treated In2O3 nanoparticles were studied.
Moreover, epitaxial growth of In2O3 on YSZ (100) and (111) substrates were obtained by thermal ALD method. Post-annealing treatments applied on these epitaxial growth In2O3 samples. The relationship between the grow orientation of single crystal surface and electrical performance are investigated and discussed.
Finally, some preliminary studies of the relationship between the ligand-dependent precursor and ALD performance were conducted by using the in-situ FTIR method. Surface reaction mechanisms during the ALD process with porous AAOAl film as substrate were studied. Selectivity exhibited from DMAI precursor was investigated and discussed. ALD performance and related surface reaction of Ga precursor such as Ga2(NMe2)6, Ga(amd)(NMe2)2, and Ga(amd)3 were studied by in-situ FTIR as well.
Overall, thermal evolution of surface hydroxyl group on faceted TiO2, In2O3 nanoparticles were probed by temperature variable FTIR. Selective proton-dependent chemical reaction can be achieved on different oriented metal oxide surface. Orientation dependent electrical properties was studied on high-oriented In2O3 (111) and In2O3 (100) surface. An alternative way of in-situ FTIR measurement to study the surface reaction during ALD process was designed and investigated. Despite the remaining work, the concept and methodology of site-selective atomic layer deposition (SS-ALD) and associated probing techniques have the potential to captivate researchers in the fields of surface chemistry, semiconductors, and electrical devices
A dual-aperture fluorescence ratio method to quantify depth of fluorescence for intraoperative margin assessment in oral squamous cell carcinoma solid tumor resections
No full textOral squamous cell carcinoma (OSCC) is a significant contributor to the global cancer burden, with surgical resection being the primary course for treatment. However, achieving clear surgical margins remains a challenge owing to the complex anatomy of the head and neck, and it is estimated that as many as 30% of patients are sent home after surgery with residual cancer. Current surgical margin assessment techniques, such as post-operative histopathology and intraoperative frozen section analysis, either take too long to complete in an intraoperative time frame or have limitations in accuracy and efficiency. Fluorescence-guided surgery (FGS) has emerged as a promising approach to enhance intraoperative surgical margin assessment, with a few clinical research groups in the US and the Netherlands demonstrating the potential of epidermal growth factor receptor (EGFR)-targeted FGS in OSCC to identify at least a fraction of insufficient margins, intraoperatively. However, existing methods have demonstrated limited accuracy in detecting so-called “close” margins (cancer is found between 1-5 mm from the surgical margin), which are associated with an increased risk of local recurrence compared to “clear margins (>5 mm of healthy tissue at the surgical margin).This thesis evaluated the potential for a dual-aperture fluorescence ratio (dAFR) imaging approach to be used to improve the accuracy of close margin detection and localization in OSCC surgical margins. The dAFR technique involves dividing high numerical-aperture (NA) fluorescence images by low NA images taken from the same point-of-view, creating a ratio image that has been hypothesized to be correlated with depth of the fluorescence inclusion, in a manner that is insensitive to variability in tissue optical properties, in sub-diffuse photon propagation regimes.
The validity of the hypothesis that dAFR signals can be correlated directly to depth of fluorescence in OSCC surgical margins was first explored in a set of Monte Carlo photon propagation simulations. The Monte Carlo simulation framework was also used to (1) guide the selection of the optimal range of numerical apertures (NAs) to use for the wide and narrow apertures for dAFR, (2) for evaluating the accuracy and precision of dAFR depth estimation across varying tissue optical properties, and (3) for evaluating dAFR depth estimation accuracy when tissue optical properties were heterogeneous, as would be expected in clinical OSCC margin imaging applications.
A prototype dAFR-capable imaging system was designed based on the findings of the simulation work and was constructed considering factors such as accessibility, rapid imaging capability, safety, field-of-view, and image quality. The performance of the constructed system was then first evaluated through phantom experiments, where resinous materials with optical properties matching those of biological tissue and fluorescence at various depths were used to experimentally demonstrate the depth sensitivity of dAFR compared to single-aperture fluorescence (sAF) imaging.
The dAFR system was then deployed to a clinic at the University Medical Center Groningen (UMCG) in The Netherlands to be tested out in a clinical pilot study. There, the surgical margins of 3 OSCC patients were imaged with our dAFR approach and the more conventional sAF approach and the correlations between dAFR and sAF measurement to histopathology measured margin thickness was evaluated at 12 different margin locations from 3 patients. In this pilot group of patients, dAFR provided significantly higher accuracy in detecting close margins compared to sAF (p < 0.02), with an area under the receiver operating characteristic curve (AUC of the ROC) of 1.0 for close margins.
These results embody the first clinical demonstration of close margin detection in an intraoperative timeframe (< 2 min of imaging). Though the sample size was small (n = 3), these preliminary results have been used to leverage funding of the ongoing development of a second system prototype and the commencement of an 80-patient clinical study over the next 5 years. Other future research directions include the optimization of imaging hardware, correction for surface topography, expansion of clinical studies to other cancer types, integration with other imaging modalities, development of user-friendly interfaces, automated margin detection and localization of insufficient margins, and enhanced co-registration of margin localization in excised margins with in vivo anatomical structures to guide potential extended resection
Machine Learning-Assisted Age of Information Optimization in Practical CSMA-Based Wireless Networks
No full textThe demand for timely information updates is growing with the proliferation of real-time applications such as remote monitoring, autonomous vehicles, and Internet of Things (IoT) networks. The age of Information (AoI), a critical performance metric, quantifies the freshness of received information. This dissertation addresses the challenge of minimizing AoI in practical wireless networks employing carrier sense multiple access (CSMA). We propose a comprehensive framework combining analytical modeling and deep learning techniques to optimize AoI under diverse network conditions.First, we introduce a deep learning-facilitated framework that enables a tagged node to adaptively adjust its update rate based on local observations and background traffic, minimizing its AoI. Extensive simulations in IEEE 802.11 networks validate this framework’s effectiveness. We then extend the framework to IEEE 802.15.4 networks, incorporating key protocol characteristics such as clear channel assessments and retransmissions. Simulation results demonstrate its generalization capability and significant AoI improvement.Next, we develop a novel stochastic hybrid systems (SHS)-based analytical model to evaluate AoI in finite-buffer CSMA networks. By incorporating collision probabilities and leveraging deep learning to handle heterogeneous background traffic, this model achieves accurate AoI analysis. Numerical results from ns-3 simulations confirm its robustness and scalability across various scenarios. Furthermore, we explore the interplay between AoI and other metrics, introducing ``throughput weighted age of information (TwAoI)" to evaluate the joint performance of information freshness and channel utilization, and investigating the trade-off between information freshness and sampling cost.This research bridges theoretical insights and practical implementation, providing a suite of tools for AoI optimization in CSMA networks. The findings serve as a guideline for system designers and network administrators aiming to support real-time applications in dynamic and distributed wireless environments
Ultrasound Image Guided Robot Arm for Targeted Delivery of Therapeutic Drugs and MicroRNA for Cancer Therapy
No full textMolecular imaging has revolutionized medical diagnostics by providing detailed insights into biological processes at the molecular level within the living subject. Ultrasound Molecular Imaging (USMI) has emerged as a promising diagnostic imaging modality by utilizing targeted contrast agents to unveil crucial molecular information, including vascular biomarkers associated with cancer and other diseases. Despite its potential, the transition of Ultrasound Contrast Agents (UCA) from preclinical evaluation to FDA-approved clinical use faces challenges due to the short in vivo half-life of Micro-Bubbles (MBs), necessitating repeated administrations for comprehensive assessments. Moreover, conventional ultrasound imaging methods suffer from limited scanning areas and single-target focus, leading to low throughput in preclinical evaluations.This thesis addresses these challenges by proposing a robot-assisted whole-body scanning pipeline for preclinical evaluations in Ultrasound Molecular Imaging. By integrating a robotic arm into the imaging setup, this approach enhances scanning flexibility and precision, enabling scans across the entire body of a mouse. This extension of the imaging time window allows for comprehensive assessments without the need for repeated contrast agent administrations. Additionally, the ability to simultaneously scan multiple targets within the same session significantly increases the throughput of preclinical assessments, thereby improving the efficiency and reliability of Ultrasound Molecular Imaging in clinical translation
Quantification of Imaging Markers at Different MRI Contrast Weightings, Vasculature, and Across Field Strengths
No full textQuantitative MRI measures physical characteristics of tissue, which creates a set scale with units that allows longitudinal monitoring and cross-patient and cross-center studies. It enables earlier detection of disease, complements biopsy, and provides a clear numeric scale for differentiation of disease states. However, quantitative MRI acquisitions and post-processing are not trivial, which makes it hard to implement the clinical setting. This along with the variability in clinically used acquisitions and post-processing techniques leads to difficulty in establishing reliable, consistent, and accurate quantitative information. There is a critical need for rigorous validation of quantitative imaging biomarkers, both for current and novel quantitative imaging techniques. This dissertation seeks to both validate current quantitative MR imaging techniques and develop new ones in the heart and brain by: 1) examining the data variability and the loss in tag fidelity that occurs when quantitative cardiac tagging is incorrectly run post-Gadolinium injection; 2) quantifying the negative impact of unexpected relaxometric behavior observed in low field MR imaging for low inversion times during T1 mapping; 3) validating retrospectively calculated T1 as a biomarker for Multiple Sclerosis progression; 4) and prototyping an oxygen extraction fraction (OEF) mapping technique for the purpose of stroke prediction and establishment of a numeric scale for tissue health for stroke patients.Assessment of pre-Gadolinium and post-Gadolinium cardiac tag quality showed that post-Gadolinium tags are less saturated (p = 0.012) and have a wider range of saturation, contrast, and sharpness. This results in a loss of information in the late cardiac cycle and impeding quantification of myocardial function.Investigation of 64mT T1 mapping revealed unique relaxometric behavior in that at low inversion times (<250 ms), the signal response curve displayed an increase in signal intensity or a plateau in signal intensity dependent on T1 relaxation time. Inclusion of this increase or plateau in signal intensity negatively impacted T1 fitting algorithms, leading to their failure or incorrectly calculated T1 values. The maximum peak signal intensity before the null point was found to be 210 ms, which impacts current low field T1 mapping protocols which use an initial inversion time of 80-110 ms.Validation of retrospectively calculated T1 as a biomarker in Multiple Sclerosis revealed that T1 of normal appearing brain tissue correlates with measures of Multiple Sclerosis progression (EDSS, BPF, and disease duration) with normal appearing white matter T1 correlating with BPF (r = -0.49, p = 0.0018); putamen T1 correlating with EDSS (r = 0.48, p = 2.40e-03), with BPF (r = 0.69, p = 2.04e-06), and disease duration (r = -0.37; p = 0.02); and globus pallidus T1 correlating with disease duration (r = -0.42; p = 0.0093). Lesion T1 is reflective of MS severity whereas MTR is not.Finally, development of an oxygen extraction fraction (OEF) mapping technique showed that application of independent component analysis (ICA) to cardiac gated spiral-trajectory phase images yielded components that feature stenosis features observed in magnitude images. These ICA components form the basis of OEF mapping from phase images. This dissertation presents four studies that seek to improve either current quantitative MR imaging protocols in the heart, or to develop and validate new quantitative MR imaging techniques in the brain for the purpose of monitoring disease progression or predicting disease
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