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Title: Deep Learning and Model Predictive Methods for the Control of Fuel-Flexible Compression Ignition Engines
Creator: Peng, Qian
Date: 2022
Description: Compression ignited diesel engines are widely used for transportation and power generation because of their high fuel efficiency. However,...
Show moreCompression ignited diesel engines are widely used for transportation and power generation because of their high fuel efficiency. However, diesel engines can cause concerning environmental pollution because of their high nitrogen oxide (NOx) and soot emissions. In addition to meeting the stringent emission regulations, the demand to reduce greenhouse gas emissions has become urgent due to the more frequent destructive catastrophes caused by global warming in recent decades. In an effort to reduce emissions and improve fuel economy, many techniques have been developed and investigated by researchers. Air handling systems like exhaust gas recirculation and variable geometry turbochargers are the most widely used techniques on the market for modern diesel engines. Meanwhile, the concept of low temperature combustion is widely investigated by researchers. Low temperature combustion can increase the portion of pre-mixed fuel-air combustion to reduce the peak in-cylinder temperature so that the formation of NOx can be suppressed. Furthermore, the combustion characteristics and performance of bio-derived fuel blends are also studied to reduce overall greenhouse gas emissions through the reduced usage of fossil fuels. All the above mentioned systems are complicated because they involve not only chemical reactions but also complex fluid motion and mixing processes. As such, the control of these systems is always challenging and limits their commercial application. Currentlymost control methods are feed-forward control based on load condition and engine speed due to the simplicity in real-time application. With the development of faster control unit and deep learning techniques, the application of more complex control algorithms is possible to further improve the emissions and fuel economy. This work focuses on improvements to the control of engine air handling systems and combustion processes that leverage alternative fuels.Complex air handling systems, featuring technologies such as exhaust gas recirculation (EGR) and variable geometry turbochargers (VGTs), are commonly used in modern diesel engines to meet stringent emissions and fuel economy requirements. The control of diesel air handling systems with EGR and VGTs is challenging because of their nonlinearity and coupled dynamics. In this thesis, artificial neural networks (ANNs) and recurrent neural networks (RNNs) are applied to control the low pressure (LP) EGR valve position and VGT vane position simultaneously on a light-duty multi-cylinder diesel engine. In addition, experimental examination of a low temperature combustion based on gasoline compression ignition as well as its control has also been studied in this work. This type of combustion has been explored on traditional diesel engines in order to meet increasingly stringent emission regulations without sacrificing efficiency. In this study, a six-cylinder heavy-duty diesel engine was operated in a mixing controlled gasoline compression ignition mode to investigatethe influence of fuels and injection strategies on the combustion characteristics, emissions, and thermal efficiencies. Fuels, including ethanol (E), isobutanol (IB), and diisobutylene (DIB), were blended with a gasoline fuel to form E10, E30, IB30, and DIB30 based on volumetric fraction. These four blends along with gasoline formed the five test fuels. With these fuels, three injections strategies were investigated, including late pilot injection, early pilot injection, and port fuel injection/direct injection. The impact of moderate exhaust gas recirculation on nitrogen oxides and soot emissions was examined to determine the most promising fuel/injection strategy for emissions reduction. In addition, first and second law analyses were performed to provide insights into the efficiency, loss, and exergy destruction of the various gasoline fuel blends at low and medium load conditions. Overall, the emission output, thermal efficiency, and combustion performances of the five fuels were found to be similar and their differences are modest under most test conditions.While experimental work showed that low temperature combustion with alternative fuels could be effective, control is still challenging due to not only the properties of different gasoline-type fuels but also the impacts of injection strategies on the in-cylinder reactivity. As such, a computationally efficient zero-dimension combustion model can significantly reduce the cost of control development. In this study, a previously developed zero-dimension combustion model for gasoline compression ignition was extended to multiple gasoline-type fuel blends and a port fuel injection/direct fuel injection strategy. Tests were conducted on a 12.4-liter heavy-duty engine with five fuel blends. A modification was made to the functional ignition delay model to cover the significantly different ignition delay behavior between conventional and oxygenated fuel blends. The parameters in the model were calibrated with only gasoline data at a load of 14 bar brake mean effective pressure. The results showed that this physics-based model can be applied to the other four fuel blends at three differentpilot injection strategies without recalibration. In order to also facilitate the control of emissions, machine learning models were investigated to capture NOx emissions. A kernel-based extreme learning machine (K-ELM) performed best and had a coefficient of correlation (R-squared) of 0.998. The combustion and NOx emission models are valid for not only conventional gasoline fuel but also oxygenated alternative fuel blends at three different pilot injection strategies. In order to track key combustion metrics while keeping noise and emissions within constraints, a model predictive control(MPC) was applied for a compression ignition engine operating with a range of potential fuels and fuel injection strategies. The MPC is validated under different scenarios, including a load step change, fuel type change, and injection strategy change, with proportional-integral (PI) control as the baseline. The simulation results show that MPC can optimize the overall performance through modifying the main injection timing, pilot fuel mass, and exhaust gas recirculation (EGR) fraction.
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Title: Integrating Provenance Management and Query Optimization
Creator: Niu, Xing
Date: 2021
Description: Provenance, information about the origin of data and the queries and/or updates that produced it, is critical for debugging queries and...
Show moreProvenance, information about the origin of data and the queries and/or updates that produced it, is critical for debugging queries and transactions, auditing, establishing trust in data, and many other use cases.While how to model and capture the provenance of database queries has been studied extensively, optimization was recognized as an important problem in provenance management which includes storing, capturing, querying provenance and so on. However, previous work has almost exclusively focused on how to compress provenance to reduce storage cost, there is a lack of work focusing on optimizing provenance capture process. Many approaches for capturing database provenance are using SQL query language and representing provenance information as a standard relation. However, even sophisticated query optimizers often fail to produce efficient execution plans for such queries because of the query complexity and uncommon structures. To address this problem, we study algebraic equivalences and alternative ways of generating queries for provenance capture. Furthermore, we present an extensible heuristic and cost-based optimization framework utilizing these optimizations. While provenance has been well studied, no database optimizer is aware of using provenance information to optimize the query processing. Intuitively, provenance records exactly what data is relevant for a query. We can use this feature of provenance to figure out and filter out irrelevant input data of a query early on and such that the query processing will be speeded up. The reason is that instead of fully accessing the input dataset, we only run the query on the relevant input data. In this work, we develop provenance-based data skipping (PBDS), a novel approach that generates provenance sketches which are concise encodings of what data is relevant for a query. In addition, a provenance sketch captured for one query is used to speed up subsequent queries, possibly by utilizing physical design artifacts such as indexes and zone maps. The work we present in this thesis demonstrates a tight integration between provenance management and query optimization can lead a significant performance improvement of query processing as well as traditional database management task.
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Title: Extreme Fine-grained Parallelism On Modern Many-Core Architectures
Creator: Nookala, Poornima
Date: 2022
Description: Processors with 100s of threads of execution and GPUs with 1000s of cores are among the state-of-the-art in high-end computing systems. This...
Show moreProcessors with 100s of threads of execution and GPUs with 1000s of cores are among the state-of-the-art in high-end computing systems. This transition to many-core computing has required the community to develop new algorithms to overcome significant latency bottlenecks through massive concurrency. Implementing efficient parallel runtimes that can scale up to hundreds of threads with extremely fine-grained tasks (less than 100 microseconds) remains a challenge. We propose XQueue, a novel lockless concurrent queueing system that can scale up to hundreds of threads. We integrate XQueue into LLVM OpenMP and implement X-OpenMP, a library for lightweight tasking on modern many-core systems with hundreds of cores. We show that it is possible to implement a parallel execution model using lock-less techniques for enabling applications to strongly scale on many-core architectures. While the fork-join model is suitable for on-node parallelism, the use of joins and synchronization induces artificial dependencies which can lead to under utilization of resources. Data-flow based parallelism is crucial to overcome the limitations of fork-join parallelism by specifying dependencies at a finer granularity. It is also crucial for parallel runtime systems to support heterogeneous platforms to better utilize the hardware resources that are available in modern day supercomputers. The existing parallel programming environments that support distributed memory either discover the DAG entirely on all processes which limits the scalability or introduce explicit communications which increases the complexity of programming. We implement Template Task Graph (TTG), a novel programming model and its C++ implementation by marrying the ideas of control and data flowgraph programming. TTG can address the issues of performance portability without sacrificing scalability or programmability by providing higher-level abstractions than conventionally provided by task-centric programming systems, but without impeding the ability of these runtimes to manage task creation and execution as well as data and resource management efficiently. TTG implementation currently supports distributed memory execution over 2 different task runtimes PaRSEC and MADNESS.
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Title: Towards a Secure and Resilient Smart Grid Cyberinfrastructure Using Software-Defined Networking
Creator: Qu, Yanfeng
Date: 2022
Description: To enhance the cyber-resilience and security of the smart grid against malicious attacks and system errors, we present software-defined...
Show moreTo enhance the cyber-resilience and security of the smart grid against malicious attacks and system errors, we present software-defined networking (SDN)-based communication architecture design for smart grid operation. Our design utilizes SDN technology, which improves network manageability, and provides application-oriented visibility and direct programmability, to deploy the multiple SDN-aware applications to enhance grid security and resilience including optimization-based network management to recover Phasor Measurement Unit (PMU) network connectivity and restore power system observability; Flow-based anomaly detection and optimization-based network management to mitigate Manipulation of demand of IoT (MadIoT) attack. We also developed a prototype system in a cyber-physical testbed and conducted extensive evaluation experiments using the IEEE 30-bus system, IEEE 118-bus system, and IIT campus microgrid.
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Title: ROBUST AND EXPLAINABLE RESULTS UTILIZING NEW METHODS AND NON-LINEAR MODELS
Creator: Onallah, Amir
Date: 2022
Description: This research focuses on robustness and explainability of new methods, and nonlinear analysis compared to traditional methods and linear...
Show moreThis research focuses on robustness and explainability of new methods, and nonlinear analysis compared to traditional methods and linear analysis. Further, it demonstrates that making assumptions, reducing the data, or simplifying the problem results in negative effect on the outcomes. This study utilizes the U.S. Patent Inventor database and the Medical Innovation dataset. Initially, we employ time-series models to enhance the quality of the results for event history analysis (EHA), add insights, and infer meanings, explanations, and conclusions. Then, we introduce newer algorithms of machine learning and machine learning with a time-to-event element to offer more robust methods than previous papers and reach optimal solutions by removing assumptions or simplifications of the problem, combine all data that encompasses the maximum knowledge, and provide nonlinear analysis.
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Title: Automated Successive Baseline Schedule Model
Creator: Patel, Mihir Prakashbhai
Date: 2021
Description: The construction project involves many stakeholders and diverse phases. Usually, a construction schedule is initially set up as a simple ideal...
Show moreThe construction project involves many stakeholders and diverse phases. Usually, a construction schedule is initially set up as a simple ideal case scenario, but then, during construction, the project faces modifications such as delay, acceleration, and change in logic caused by the project’s complexity and inherent risk. To recover the damage(s) caused by these modifications, the parties responsible for them should be identified accurately. Researchers and practitioners developed and used various delay analysis models to quantify delays, but the selection of the model depends on the time of analysis, available information, and expertise of the analyst. So, the results can be biased. The general problem is that most delay analysis models consider only delays in quantifying impacts rather than every type of modification that impacted the project, including CPM logic changes and adding/removing activities during construction. This study proposes a new successive baseline model to enable the precise analysis of the impacts of all sorts of modifications that occur during construction. This model can achieve unbiased and accurate results. The analysis process can also be computerized into a web application to improve efficiency and productivity. The fundamental concepts of the various modifications that can occur in the work schedule during construction and the analysis of the modifications’ impacts are presented in this study. Issues related to concurrency, float ownership, type of modification, selection of delay analysis model, and challenges with automation are also highlighted to broaden the understanding disagreements of the parties to a construction contract. A case example is presented to prove the accuracy and usefulness of the proposed model and web application.
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Title: New Insights to Thermoelectrics from Fundamental Transport Properties to Potential Materials and Device Design
Creator: Pan, Zhenyu
Date: 2022
Description: Thermoelectric (TE) materials have been widely studied as their ability to make direct energy conversion between heat and electricity. However...
Show moreThermoelectric (TE) materials have been widely studied as their ability to make direct energy conversion between heat and electricity. However, the conversion efficiency is still low compared with conventional devices no matter in power generation or electrical cooling. Therefore, most efforts have been made to improve the zT of TE materials, which is the commonly accepted metric for determining the performance of TE materials. But the progress is slow as the key parameters governing the zT is interrelated to each other which makes improving one often at the cost of the others leading to a narrow use of TE applications. Thus this thesis does not confine itself only in searching for high zT TE materials but also exploring useful things which are buried or ignored in previous thermoelectric researches from fundamental transport properties to TE device design. Firstly, we reevaluated the photo-Seebeck effect, which has been known for decades, and demonstrated that it is a powerful tool for semiconductor study as it allows the determination of mobilities, photo-carrier densities, even weighed mobilities (hence effective masses) of both electrons and holes and impact of defects all from a single sample. We then investigated a newly discovered low dimensional material, 2D tellurene, which has the potential to decouple the interrelated parameters to achieve a high zT. Lastly, we reconsidered the question that whether zT is the only merit index determining TE device performance. We hope this thesis can shed some light on thermoelectrics both from fundamental transport properties to device design.
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Title: How Does Self-Stigma Influence Functionality in People with Serious Mental Illness? A Multiple Mediation Model of "Why-Try" Effect, Coping Resources, and Personal Recovery
Creator: Qin, Sang
Date: 2022
Description: People with serious mental illness (SMI) face self-stigma effects that often undermine their functionality. Functionality herein refers to a...
Show morePeople with serious mental illness (SMI) face self-stigma effects that often undermine their functionality. Functionality herein refers to a person's execution of tasks (i.e., activities) and engagement in life situations (i.e., participation). This study used a path model to examine three mediating factors between self-stigma and functionality: The "why-try" effect, coping resources, and personal recovery. Specifically, the “why-try” effect was viewed as an extension of self-stigma harm that occurred when people suffered from a loss of self-esteem and self-efficacy. Coping resources were conceptualized as individuals’ strengths and the support they had to overcome negative stigma outcomes, particularly stigma stress. Endorsement of personal recovery, namely pursuing self-defined life goals despite illness—had a buffering effect reducing self-stigma. These three mediators were examined simultaneously using an archival dataset. Due to poor internal consistency, coping resources were eventually removed from the model; the subsequent, revised model achieved a good model fit. Results showed that people with SMI experiencing self-stigma were found to have an enhanced "why-try" effect as well as reduced personal recovery, leading to a decline in functionality. Implications of the results and future research directions are discussed.
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Title: Decreasing Body Dissatisfaction in Male College Athletes: A Pilot Study of the Male Athlete Body Project
Creator: Perelman, Hayley
Date: 2020
Description: Body dissatisfaction is associated with marked distress and often precipitates disordered eating symptomology. Body dissatisfaction in male...
Show moreBody dissatisfaction is associated with marked distress and often precipitates disordered eating symptomology. Body dissatisfaction in male athletes is an important area to explore, as research in this field often focuses on eating disorders in female athletes. The current body of literature regarding male college athletes suggests that they experience pressures associated with both societal muscular ideals and sport performance. While there is a clear association between drive for muscularity and body dissatisfaction in college male athletes, no study to date has evaluated the efficacy of a body dissatisfaction intervention for this population. Therefore, the present study sought to investigate the efficacy and feasibility of a pilot intervention program that targeted body dissatisfaction in male college athletes. Participants were randomized into an adapted version of the Female Athlete Body Project (i.e., the Male Athlete Body Project) or an assessment-only control condition. A total of 79 male college athletes (39 in treatment condition) completed this study for a retention rate of 84.9%. Participants in the experimental group attended three 80-minute group sessions once a week for three weeks. All participants completed measures of body dissatisfaction, internalization of the body ideal, drive for muscularity, negative affect, and sport confidence at three time points: baseline, post-treatment (three weeks after baseline for the control condition), and one-month follow-up. Hierarchical Linear Modeling was used to assess differences between conditions across time. Participation in the MABP improved men’s satisfaction with specific body parts, drive for muscularity, and body-ideal internalization at post-treatment. Men in the MABP also reported improvements in appearance evaluation and overweight preoccupation at post-treatment and one-month follow-up, and in negative affect at one-month follow-up only. Improvements in drive for muscularity were retained at one-month follow-up. This study provides preliminary evidence for the feasibility and efficacy of the Male Athlete Body Project.
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Title: INTELLIGENT SOLID STATE CIRCUIT BREAKERS USING WIDE BANDGAP SEMICONDUCTORS
Creator: Zhou, Yuanfeng
Date: 2021
Description: Electricity, in its predominant form of alternating current (AC), is at the heart of modern civilization. However, direct current (DC)...
Show moreElectricity, in its predominant form of alternating current (AC), is at the heart of modern civilization. However, direct current (DC) electricity is re-emerging, offering higher transmission efficiency, better system stability, better match with modern electrical loads, and easier integration of renewable and storage resources than AC. DC power is gaining tractions in HVDC or MVDC grids, DC data centers, photovoltaic farms, EV charging infrastructures, shipboard, and aircraft power systems. However, DC fault protection remains a major challenge. Interruption of DC currents is extremely difficult due to the lack of current zero crossings which are naturally available in AC power systems. Conventional mechanical breakers only offer a very limited DC current interruption capability even after significant power derating. Hybrid circuit breakers (HCBs) offers a relatively low conduction loss but a response time too slow to protect many low-impedance DC grids. Solid state circuit breakers (SSCBs) can quickly interrupt a DC fault current within tens of microseconds but suffer from high conduction losses. Furthermore, it is generally difficult for an SSCB to distinguish between a short circuit fault and a normal inrush current condition during the start-up of a capacitive load.The purpose of this thesis is to develop a tri-mode, intelligent solid-state circuit breaker technology using wide bandgap semiconductors (especially Gallium Nitride transistors), referred to as iBreaker. The iBreaker design methodology includes the use of mΩ-resistance GaN and SiC devices, new circuit topology and control techniques beyond the commonly used ON/OFF switch configuration, and integration of intelligent functions without increasing component count. The iBreaker adopts a distinct pulse width modulation (PWM) current limiting (PWM-CL) state in addition to the conventional ON and OFF states to facilitate soft startup, fault authentication, and fault location functions. Key design elements, such as use of wide bandgap (particularly GaN) switches, tri-mode operation, combined digital and analog control, the bidirectional buck topology, variable PWM frequency control and universal hardware/software architecture, are discussed in detail. Multiple iBreaker prototypes, rated at 380 V/20 A and 1000 V/10 A, respectively, are built and tested to validate the proposed SSCB design concept. 99.95% transmission efficiency, passive cooling, and μs-scale response time are demonstrated experimentally.
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Title: SAFETY AND MOBILITY IMPACTS ASSESSMENT OF THE CHICAGO BIKE LANE PROGRAM
Creator: Zhao, Yu
Date: 2021
Description: In recent years, bike as a travel mode is getting increasingly popular among large cities in the U.S. These cities also found promoting bike...
Show moreIn recent years, bike as a travel mode is getting increasingly popular among large cities in the U.S. These cities also found promoting bike mode can potentially mitigate traffic congestion issues, reduce carbon emission and improve the quality of life for residents. Therefore, many cities-initiated bike-related programs promote the bike mode from all aspects, such as establishing a shared bike system and developing bike-related facilities. Specifically, bike lane installation is widely seen in large cities as a pivot component of bike promotion programs. Due to the installation of bike lanes on the existing network, vehicles’ safety and mobility performance may be affected due to the variation of facilities. This study attempts to propose a methodology to quantify the safety and mobility impacts on vehicles brought by bike lane installation. The proposed method accounts for safety impact by using predicted crashes in conjunction with field observed crash data for empirical Bayes (EB) before-after comparison group analysis. The mobility impact is captured by comparing the segment average travel time before and after the bike lane installation. Further, vehicle volume information is involved in the consumer surplus computation to quantify the variation in vehicle safety, and mobility performance resulting from the bike lane installation. A case study is conducted using a real data set from the city of Chicago bike lane program. The results reveal that the safety and mobility impacts vary mainly depending on the type of bike lane installed and location.
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Title: The Development of a Measure of Public Stigma Towards Adults With Autism
Creator: Beedle, Robert Brian
Date: 2022
Description: Adults with autism (AwA) report experiences of stigma and discrimination. Yet, quantitative research suggests that public attitudes are...
Show moreAdults with autism (AwA) report experiences of stigma and discrimination. Yet, quantitative research suggests that public attitudes are relatively benign. This research discrepancy is compounded by the present lack of a stakeholder-informed, theoretically-guided measure of the stigma towards AwA. The objective of the present study was to develop a measure of stigma towards AwA following best practices survey methodology. First, existing related measures were reviewed for possible candidate items, yielding 36 draft questions related to the stigma of AwA. Next, seven stakeholders in the AwA community were recruited to provide feedback on their experiences of stigma and discrimination, as well as feedback on the draft items. Following stakeholder feedback, draft items were edited, added, or removed based on feedback from the participants with AwA and their lived experiences, resulting in a revised measure of 51 candidate items. Finally, these 51 items underwent a quantitative phase with participants recruited through MTurk (N = 357). Exploratory factor analyses were conducted in order to generate a data driven factor structure that reflected stigma theory. The end result was a 20-item, four factor solution measuring numerous components of stigma within factors including cognitive components of stigma, blame, positive and negative affect, and comfort with close contact. The resulting measurement tool was titled the Public Stigma towards Adults with Autism Scale (PSAWA) and demonstrated strong psychometric properties. The tool has utility for further studying stigma towards AwA and assessing stigma interventions.
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Title: Reconditioning Dharavi: A Toolkit of Strategies for Incremental Development
Creator: Bhogle, Saylee Deepak
Date: 2022
Description: The 2003 Global Report on Human Settlements (Un-Habitat, 2003) defines a "slum" as a densely populated metropolitan area that is distinguished...
Show moreThe 2003 Global Report on Human Settlements (Un-Habitat, 2003) defines a "slum" as a densely populated metropolitan area that is distinguished by a variety of low-income settlements, subpar housing, and squalor. Dharavi, on the other hand, is far more than a "slum." In the heart of Mumbai, Dharavi is an economically prosperous and socially active informal town. Mumbai is a thriving metropolis with many different realities and patterns, even though it appears to be a slum filled with squatters. However, the region has recently become a hub for informal settlements and urban problems associated with poor hygiene in developing countries. People’s misconceptions about Dharavi stem from a failure to recognize its social capital and economic power: the area encompasses a variety of economic networks, production types, income levels, land tenure arrangements, and religious activities and festivities. Dharavi is made up of 85 separate groups with a strong feeling of belonging and high expectations for stability and improved economic position and living standards. It is also clear that these folks are capable of building and enhancing their shelter if they have the resources to do so. To develop all these qualities, Dharavi's Social Capital must be recognized and promoted as an asset to the city of Mumbai. A community such as Dharavi requires ‘urban acupuncture’; where mediation of the littlest kind will have the greatest effect. Dharavi, like any other "Informal" city, requires rigorous examination to be fully comprehended. It is a unique location where a large flood of migrants has managed to build jobs and their city. My underlying attitude to this location is a conflicting desire to save and replace it. The desire to save is linked to the aesthetic of informality as well as the intense sociality, diversity, and production of the streets and lanes - a fascinating and diversified urban ensemble. The desire to eliminate stems from hopeless states of sterilization, ventilation, light, open space, and congested areas. As a result, a reliable strategy for combining the two methodologies and locating a functioning arrangement should be developed. The government has been trying to redevelop this area for the past 50 years but hasn’t been successful in doing so. In contrast to the existing redevelopment plan, which promotes uniform top-down development, my concept anticipates techniques for progressive self-development, including "bottom-up" finance models and architectural approaches. After identifying various patterns and carefully examining behavior patterns, production systems, and existing community facilities, a toolkit of methods can be built that can be used in various places and "outboxes." The simple homogeneity of solutions for Dharavi's changing conditions has been avoided. Dharavi's current identity and "mixed-use" paradigm have been respected, with Home recognized as an instrument of production. The proposed design has been tested for various environmental factors using different tools for natural lighting and ventilation. The outdoor areas are also analyzed for thermal comfort since a lot of social activities take place in these areas. Communal areas have been designed to accommodate micro infrastructure systems while also increasing productivity. As a result, a system of self-development triggers has been created that can improve present conditions while also supporting the community's need for stability. Simultaneously, by focusing on property ownership as an economic driver, the proposed approach can provide a type of "social mobility" for Dharavi's residents.
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Title: Population dynamics and pathogens of the western bean cutworm (Striacosta albicosta)
Creator: Bunn, Dakota C.
Date: 2022
Description: Understanding an herbivorous pest’s population dynamics is necessary to ensure proper integrated pest management strategies are being...
Show moreUnderstanding an herbivorous pest’s population dynamics is necessary to ensure proper integrated pest management strategies are being developed and used. The western bean cutworm is a pest of both corn and dry beans that is understudied and difficult to manage due to its nocturnal lifestyle, adaptation to current management techniques and a general lack of understanding regarding its population structure. Our studies focused on the effects of host plant and pathogens on western bean cutworm population structure and found that mainly adults which developed on corn are contributing to the next generation of western bean cutworm in Michigan, making corn and dry beans unsuitable as co-refuges in insecticide resistance management strategies.We also found a 100% prevalence of the Nosema sp. in the adult population of western bean cutworm in Michigan. This prevalence, when paired with the consistent crop damage caused annually by the western bean cutworm, which confirms an abundance of cutworms are present, suggests the infection is slow acting and non-lethal to its host. Following sequencing, assembly, and annotation of the Nosema sp. genome, we were unable to provide a reason for the Nosema sp.’s low virulence, however, we were able to confirm the presence of all 6 polar tube proteins. Upon further examination of the Nosema sp. genome we were able to determine that it is a true Nosema with genome size of ~9.57 Mbp (~20% of which are transposable elements), which is within the typical range for this genus.
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Title: Carrier phase multipath characterization and frequency-domain bounding
Creator: Benz, Chloe
Date: 2022
Description: Safely relying on Global Navigation Satellite Systems (GNSS) measurements for position estimation using multi-sensor navigation algorithms,...
Show moreSafely relying on Global Navigation Satellite Systems (GNSS) measurements for position estimation using multi-sensor navigation algorithms, especially in critical phases of flight – such as takeoff or landing – requires precise knowledge of the errors affecting position estimates and their extrema values at any time. This work investigates a method for characterization and power-spectral density (PSD) bounding of GNSS carrier phase multipath error intended for use in sensor fusion for aircraft navigation. In this dissertation, two methods of GNSS carrier phase multipath characterization are explored: single frequency dual antenna (DA) and single antenna dual frequency (DF). However, since not all aircraft are equipped with multiple GNSS antennas, because the DA method entails a meticulous tracking of the lever arm between the two antennas, and as multipath seen by two antennas in a short baseline configuration may cancel out, the DF method is preferred and is the main emphasis of this work. By subtracting carrier phase measurements collected by a receiver overtwo distinct frequencies, a composite measurement containing ionospheric delay and carrier phase multipath is obtained. The ionospheric delay has slower dynamics than multipath, so it is removed using a high pass filter. The filter cutoff frequency is carefully picked based on a study of ionospheric delay dynamics. The DF method is validated on a rooftop GPS carrier phase dataset, and finally, directions and considerations for its ultimate intended use on airborne collected GNSS carrier phase data are provided.
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Title: KERNEL FREE BOUNDARY INTEGRAL METHOD AND ITS APPLICATIONS
Creator: Cao, Yue
Date: 2022
Description: We developed a kernel-free boundary integral method (KFBIM) for solving variable coefficients partial differential equations (PDEs) in a...
Show moreWe developed a kernel-free boundary integral method (KFBIM) for solving variable coefficients partial differential equations (PDEs) in a doubly-connected domain. We focus our study on boundary value problems (BVP) and interface problems. A unique feature of the KFBIM is that the method does not require an analytical form of the Green’s function for designing quadratures, but rather computes boundary or volume integrals by solving an equivalent interface problem on Cartesian mesh. We decompose the problem defined in a doubly-connected domain into two separate interface problems. Then we evaluate integrals using a Krylov subspace iterative method in a finite difference framework. The method has second-order accuracy in space, and its complexity is linearly proportional to the number of mesh points. Numerical examples demonstrate that the method is robust for variable coefficients PDEs, even for cases when diffusion coefficients ratio is large and when two interfaces are close. We also develop two methods to compute moving interface problems whose coefficients in governing equations are spatial functions. Variable coefficients could be a non-homogeneous viscosity in Hele-Shaw problem or an uptake rate in tumor growth problems. We apply the KFBIM to compute velocity of the interface which allows more flexible boundary condition in a restricted domain instead of free space domain. A semi-implicit and an implicit methods were developed to evolve the interface. Both methods have few restrictions on the time step regardless of numerical stiffness. Theyalso could be extended to multi-phase problem, e.g., annulus domain. The methods have second-order accuracy in both space and time. Machine learning techniques have achieved magnificent success in the past decade. We couple the KFBIM with supervised learning algorithms to improve efficiency. In the KFBIM, we apply a finite difference scheme to find dipole density of the boundary integral iteratively, which is quite costly. We train a linear model to replace the finite difference solver in GMRES iterations. The cost, measured in CPU time, is significantly reduced. We also developed an efficient data generator for training and derived an empirical rule for data set size. In the future work, the model could be expanded to moving interface problems. The linear model will be replaced by neural network models, e.g., physics-informed neural networks (PINNs).
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Title: PROGRAM SURVIVABILITY THROUGH K-VARIANT ARCHITECTURE
Creator: BEKIROGLU, BERK
Date: 2021
Description: Numerous software systems, particularly mission and safety-critical systems, require a high level of security during their execution....
Show moreNumerous software systems, particularly mission and safety-critical systems, require a high level of security during their execution. Enhancing software security through architecture is a highly effective method of defending against cyberattacks. The N-version is a software architecture that was developed to increase the security of software systems. In the N-version architecture, functionally equivalent versions of a program run concurrently to complete a mission or task. Each version is developed independently by a different team using only the software specifications in common. As a result, each version is expected to contain unique vulnerabilities. Due to the high cost of developing and maintaining an N-version system, this architecture is typically used only in high-budget projects requiring a high-security level. The K-variant, an alternative architecture for enhancing system security, is explained and analyzed in this thesis. In contrast to the N-version architecture, each variant is automatically generated using source-to-source program transformation techniques. Automation significantly reduces the cost of developing variants in the K-variant architecture. The K-variant architecture can help protect systems from memory exploitation attacks. Various attack strategies can be used against K-variant systems in order to increase the likelihood of a successful attack. Various attack strategies are proposed and investigated in this thesis. Furthermore, experimental studies are being conducted to investigate various defense mechanisms against proposed attack strategies. The effectiveness of each defense mechanism against various attack strategies is evaluated by using a metric of the probability of an unsuccessful attack. Additionally, various source code program transformation techniques for generating new variants in the K-variant architecture have been proposed and investigated experimentally. This thesis also describes a machine learning technique for estimating the survivability of K-variant systems under various attack types and defense strategies. To make the design of K-variant systems easier, a neural network model is proposed. With the developed tool that utilizes the neural network model, fast and accurate predictions about the survivability of K-variant systems can be obtained.
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Title: ESTIMATES OF AIR EXCHANGE RATES THROUGH THE USE OF TOTAL VOLATILE ORGANIC COMPOUND DECAY MEASUREMENTS
Creator: Bradley, Christopher
Date: 2021
Description: Indoor air exchange rates are commonly used to assess the overall fitness of a building and assess its performance. More recently, air...
Show moreIndoor air exchange rates are commonly used to assess the overall fitness of a building and assess its performance. More recently, air exchange has become a concern due to the COVD-19 pandemic, requiring replacement air to ensure safety; especially so considering that humans spend much of their time indoors. Building science has focused on air exchange to quantify needs for thermal loads, balancing the overall tightness of a building with the amount of energy consumed. Moreover, guidelines have been created by several different organizations to maintain adequate ventilation to remove indoor air pollution, replacing it with clean outdoor air. Research focuses on how to maintain a comfortable and safe quality of indoor air while balancing the needs of the energy crisis.When installed with proper HVAC systems, air exchange rates can be set to a recommended value based upon the conditions of the environment. Buildings without mechanical ventilation face another issue, mainly that they only rely on natural ventilation and the infiltration rate. Temperature differences between the indoor and outdoor environment and the condition of wind speed and direction create pressure differences across the building envelope, influencing the infiltration rate, which can change the amount of air exchange in buildings with natural or mechanical ventilation. Currently, air exchange rates are commonly measured using tracer gases. More frequently used gases have included perfluorocarbon, sulfur hexafluoride, and carbon dioxide, though none of these have proven to be ideal tracers. Alongside this, cost and burden on the participants of these studies often limit the amount of measurements made. Numerous studies have been conducted on how to model the air exchange rate by the changes in concentrations, but accuracy depends on the amount of information available. Other attempts have been made to characterize buildings by their infiltration rate to make estimations, but other questions have arisen about the accuracy of these methods. Due to their ubiquity in indoor environments, volatile organic compounds have been suggested as a plausible tracer gas for measuring air exchange rates. The plausibility of this method raises questions, such as their behavior within the indoor environment, their ability to be measured and the cost to measure concentrations, and the analytical requirements to characterize the rates of removal as air exchange rates. However, due to the rapid increase of available technology in low cost, lightweight, high-resolution sensors, this novel method of using VOCs, especially indicators of total VOCs (TVOCs), may prove fruitful in measuring air exchange within specific microenvironments. Analysis of time-series TVOC concentration measurements taken from a study conducted in multiple residences was conducted to investigate the feasibility of using these measurements, and especially naturally occurring elevation and decay periods, as a proxy for calculating air exchange rates. Though the removal rates of these compounds fell within the range of typical air exchange rates for residential spaces, the results of this analysis suggest the method has potential but with limitations, including the unknown behavior of the individual compounds comprising TVOC measurements within the space, proximity and mixing effects, and potentially invalid comparisons to air exchange rates given from a LBLX model rather than simultaneous tracer gas tests. Future work should explore simultaneous use of TVOC measurements alongside conventional tracer gas testing to further explore the potential utility of such methods.
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Title: INTELLIGENT STREET LIGHTING AND REMOTE POWER UNITS AS CASE STUDIES FOR CITIES TO DECARBONIZE
Creator: Burgess, Patrick G.
Date: 2022
Description: There is a scientific consensus that atmospheric warming caused by the release of emissions will reach critical levels in our lifetime if...
Show moreThere is a scientific consensus that atmospheric warming caused by the release of emissions will reach critical levels in our lifetime if significant efforts are not made to decarbonize our buildings and power grid. The City of Los Angeles is a prime example of the challenges of decarbonizing, balancing global, federal, and state policies and issues and addressing environmental justice. The first research case studies of the details and challenges of decarbonization efforts include the implementation of the first networked light-emitting diode (LED) streetlights in the city of Chicago on IIT’s campus to improve the reliability and economics of its main campus, 2.5 mi south of downtown Chicago. Research shows that these networked LED streetlights greatly reduce a city's rising energy costs, but the CSMART project team has set out to prove the benefits of integrating an intelligent communications and control system with an existing smart grid infrastructure, such as an existing network and supervisory control and data acquisition (SCADA) systems. In addition to assessing the economic and environmental drivers for the intelligent streetlight solution, the project team is dedicated to assessing the potential cybersecurity vulnerabilities of such a system and working to mitigate or eliminate them. The second research case study covers off-grid remote power units providing continuous illumination for safer streets and safer driving that is unaffected by power outages. Thanks to individual lighting control potentially allowing for dimming, blinking, and even color changing, streetlights powered by RPUs can be used as emergency signaling devices, directing traffic during a city evacuation or other emergency. The RPU control and monitoring can be accessed through the cloud, thereby avoiding reliance on local servers.
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Title: Assessing the Impact of Understanding Nature of Scientific Knowledge and Understanding Nature of Scientific Inquiry on Learning about Evolution in High School Students
Creator: Jimenez Pavez, Juan Paulo
Date: 2022
Description: Nature of Scientific Knowledge (NOSK) and Nature of Scientific Inquiry (NOSI) are important components of scientific literacy and important...
Show moreNature of Scientific Knowledge (NOSK) and Nature of Scientific Inquiry (NOSI) are important components of scientific literacy and important educational objectives in science education. Recent literature theorizes that understanding both NOSK and NOSI increases students' understanding of science content knowledge. However, this assumption has yet to be tested empirically. Much research has been done on developing informed views of NOSK and NOSI for students in kindergarten through twelfth grade, but research on the effect of understanding NOSK and NOSI on facilitating science learning in high school appears limited.The main purpose of this study was to empirically test the assumption that understanding NOSK and NOSI improves science student content learning, in particular learning about evolution. This study also aimed to determine which NOSK and NOSI aspects are most useful in such an endeavor. Using a quasi-experimental, nonequivalent control group design, a sample of 453 9th grade high school students from 12 classes in a large Chilean city were randomly assigned to intervention and control groups via classroom clusters (Intervention groups = 6, Control groups = 6). Students in the intervention groups were given a special online explicit and reflective five-week NOSK/NOSI Unit, followed by an online five-week Evolution Content Unit, as a treatment. Those in the control groups received only the online five-week Evolution Content Unit. To measure understanding of NOSK, understanding of NOSI, and understanding about evolution, students answered three valid and reliable instruments: The Views of Nature of Science (VNOS D+), the Views about Scientific Inquiry (VASI), and a multiple-choice Evolution Content Test. The students' answers to the VNOS D+ and VASI questionnaires were scored as naive, mixed, or informed according to the level of understanding for each aspect, and the answers to the evolution content test were scored as correct or incorrect. The results of this study showed that the NOSK/NOSI Unit was effective in improving understanding of NOSK and NOSI aspects in the intervention groups. The results also showed that the Evolution Content Unit was effective in improving understanding about evolution in both groups. However, students in the intervention groups outperformed their peers in the control groups by scoring higher on the Evolution Content Test. Further analysis revealed that students with informed views of NOSK and NOSI achieved better scores on the Evolution Content Test than students with naive views, supporting the argument that understanding NOSK and NOSI facilitates learning about evolution. In addition, all aspects except for the difference between Theories and Laws (NOSK) had a significant positive impact on learning about evolution. Taken together, the findings of this dissertation support the assumption that understanding NOSK and NOSI improves learning about evolution. Furthermore, most NOSK and NOSI aspects seem to foster understanding about evolution. These are new insights, especially about the importance of understanding NOSI for learning about evolution. Some limitations for this study include the remote context in which the study took place and the potential bias in the qualitative analysis of the VNOS D+ and VASI questionnaires.
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