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Title: The Feasibility of Honeycomb Structure to Enhance Daylighting and Energy Performance for High-Rise Buildings
Creator: Geng, Camelia Mina
Date: 2022
Description: The world population is increasing at a fast rate and the projection is that there will be more than 12 billion people by the year 2050. It is...
Show moreThe world population is increasing at a fast rate and the projection is that there will be more than 12 billion people by the year 2050. It is also expected that at least 70% of the population will reside and work in urban areas (mostly cities) in some sort of high-rise building. At the same time, the climate is rapidly changing to increase the effects of man-made global warming. Conceivably, energy conservation, daylighting performance, thermal comfort and environmentally friendly high-rise buildings are necessary to facilitate sustainable working and living environments. The roles of the architects and planners are paramount at this critical era of history of mankind; for one thing they are responsible for the planning and design of sustainable high-rise buildings.Recently, there has been significant research to connect a branch of Biophilia design, which is Biomorphic architecture. This has developed a wonderful design approach, termed the Biomorphic idea. This focuses on the enhancement of the physical and psychological connection with nature, to acquire more natural light and the outside connection targeting energy saving. More and more, high-rise buildings are being designed following Biomorphic approaches. As such, these buildings are defined as sustainable and primarily, because they are energy efficient and, and in many cases tend to minimize the use of fossil fuels while promoting the use of renewable and clean energy sources. As such, a honeycomb structure approach successfully applies to high-rise building design. The intend of this research document is to simulate Biomorphic honeycomb structure which is the hexagonal rotation ring structure including 32 stories in18 different hexagon high-rise building configurations, to develop true daylighting and energy. performance. This is achieved by the using Grasshopper-Climate Studio simulation tool and multiple fuzzy mathematics for decision making. This document will provide a comparison of daylighting including sDA, ASE, sDG and the illuminance results from these 3 series of the 18 models configuring different honeycomb structures of high-rise buildings. The results prove that the hexagon honeycomb structure for high-rise building is feasibility and targets green buildings standards such as LEED V4.1 The success of the method depends on developing multiple criteria of Poisson ratio and Gaussian curvature within the hexagon structure to create different honeycomb facades and rotation of the ring for office high-rise building which is also a qualitative nature of the Biomorphic design parameters.
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Title: Development of a Model To Investigate Inflammation Using Peripheral Blood Mononucleated Cells
Creator: Geevarghese Alex, Peter
Date: 2023
Description: Our modern culture in our society is facing one of the biggest risks in health which is high-calorie diet-related postprandial inflammation....
Show moreOur modern culture in our society is facing one of the biggest risks in health which is high-calorie diet-related postprandial inflammation. Chronic diseases may be caused if the energy-dense food is the choice meaning if it is uncontrolled, clinical studies have demonstrated this with the body's post-meal inflammatory response. We aimed to find the causes of postprandial inflammation in response to various dietary treatments and provide a model to demonstrate. We aimed to make use of in vivo and in vitro techniques and statistics to create a model. The created model would help us to design specific treatments to minimize inflammation with response to dietary. In addition to figuring out vital dietary additives, the model additionally facilitates the layout of individualized interventions to reduce inflammation, thereby improving long-time period health outcomes. We aim to understand the clinical observations of diet-induced postprandial inflammation on the molecular level. We desire to make contributions to reduce the impact of chronic inflammatory disorders that is associated with postprandial inflammation.
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Title: Development of a Model To Investigate Inflammation Using Peripheral Blood Mononucleated Cells
Creator: Geevarghese Alex, Peter
Date: 2023
Description: Our modern culture in our society is facing one of the biggest risks in health which is high-calorie diet-related postprandial inflammation....
Show moreOur modern culture in our society is facing one of the biggest risks in health which is high-calorie diet-related postprandial inflammation. Chronic diseases may be caused if the energy-dense food is the choice meaning if it is uncontrolled, clinical studies have demonstrated this with the body's post-meal inflammatory response. We aimed to find the causes of postprandial inflammation in response to various dietary treatments and provide a model to demonstrate. We aimed to make use of in vivo and in vitro techniques and statistics to create a model. The created model would help us to design specific treatments to minimize inflammation with response to dietary. In addition to figuring out vital dietary additives, the model additionally facilitates the layout of individualized interventions to reduce inflammation, thereby improving long-time period health outcomes. We aim to understand the clinical observations of diet-induced postprandial inflammation on the molecular level. We desire to make contributions to reduce the impact of chronic inflammatory disorders that is associated with postprandial inflammation.
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Title: A Reasoning System Architecture for Spectrum Decision-making
Creator: Das, Udayan D.
Date: 2021
Description: Spectrum is a public resource; yet understanding how spectrum is allocated and used is a daunting task. Usable spectrum is already fully...
Show moreSpectrum is a public resource; yet understanding how spectrum is allocated and used is a daunting task. Usable spectrum is already fully allocated, but the demand for spectrum continues to grow and there are opportunities for utilizing spectrum in more efficient ways. Understanding how spectrum is allocated and its utilization in time and space is necessary to take advantage of these emerging opportunities. A combination of fragmented information from varied information sources, a complex regulatory environment, variability of regulations and physics by band, real-time spectrum usage dynamics, and a status quo with knowledge concentration among a few, makes understanding spectrum a considerable challenge for all stakeholders including researchers, students, policymakers, and new telecom operators. After considerable study of spectrum, its allocation, regulation, and usage, we have developed a system architecture that is a significant step towards easing the burden of understanding spectrum information. Our system architecture connects information from disparate sources and leads to a richer understanding of spectrum usage, how it is governed, and its potential for future use. Classes of information are modeled as knowledge graphs, and the interplay of knowledge graphs produces a richer set of insight and can lead to more informed decision-making. Further, we show mechanisms for connecting spectrum information with real-time observations to get a comprehensive view of spectrum usage dynamics. While focused on the United States, this work should be applicable to other spectrum contexts worldwide. This work, of considerable technical value, also has democratic value in making complex information accessible and allowing the public to determine whether spectrum, a natural resource, is being used for the public good.
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Title: MODELING AND CONTROL OF A GASOLINE-FUELED COMPRESSION IGNITION ENGINE
Creator: Pamminger, Michael
Date: 2021
Description: This work investigates a novel combustion concept, Gasoline Compression Ignition, that derives its superiority from the high compression ratio...
Show moreThis work investigates a novel combustion concept, Gasoline Compression Ignition, that derives its superiority from the high compression ratio of a compression ignition engine as well as the properties of gasoline fuel, such as longer ignition delay and higher volatility compared to diesel fuel. Gasoline Compression Ignition was experimentally tested on a 12.4L truck engine and the acquired data were leveraged to develop a physics-based 0-dimensional combustion model for an engine operating with a low-reactivity fuel. The proposed 0-dimensional combustion model was developed to account for the different stages in combustion caused by the fuel stratification of various injection events and fuel mass fractions. As the ignition delay model is an integral part of the entire combustion process and significantly affects the predictionaccuracy, special attention was paid to local phenomena influencing ignition delay. A 1-dimensional spray model by Musculus and Kattke was employed in conjunction with a Lagrangian tracking approach in order to estimate the local fuel-air ratio within the spray tip, as a proxy for reactivity. The local fuel-air ratio, in-cylinder temperature and pressure were used in an integral fashion to estimate the ignition delay. Heat release rates were modeled by using first-order non-linear differential equations. Model prediction errors in combustion phasing of less than 1 crank angle degree across most conditions were achieved. Modeling results of other combustion metrics such as combustion duration and indicated mean effective pressure are also suitably accurate. Also, the model has been shown to be capable of estimating the ringing intensity for most conditions. While the performance of the proposed model was very satisfactory, the high computational time made it unsuitable for simulations. The high computational cost was mostly caused by the 1-dimensional spray model which described the fuelstratifcation in the spray tip as a function of crank angle for multiple injection events. Insights obtained from the 1-dimensional spray model were leveraged and applied to a 0-dimensional model to reduce the computation time. With the reduced order model, the simulation time decreased by three orders of magnitude for an entire engine cycle over the combustion model with the 1-dimensional spray model. Capturing only the basic features of the spray propagation did not show a substantial increase in prediction error compared to the initially proposed model. In order for this model to reflect a virtual engine, the influence of changes in actuator settings on intake manifold dynamics was modeled with first-order transfer functions. The intake manifold dynamics in turn influence intake valve closure conditions and further the entire combustion process. The proposed model provides information about in-cylinder metrics such as combustion phasing and indicated mean effective pressure. By taking into account the losses due to gas-exchange and friction, the brake mean effective pressure was modeled. The model was also augmented to capture cycle-to-cycle variations, thereby ensuring a faithful representation of real engine behavior. The Gasoline Compression Ignition combustion model, the intake dynamics and gas-exchange and friction model as well as the cycle-to-cycle variations model were combined to create a full engine model. This Gasoline Compression Ignition engine model was used as the plant in a control system and implemented in Matlab/Simulink.The Gasoline Compression Ignition engine model was then leveraged to investigate control actions and engine behavior with and without limiting in-cylinder peak pressure as well as combustion noise. Controlling combustion noise is of particular interest for injection strategies where fuel introduction happens early in the cycle. State estimation was performed by means of a Kalman filter which feeds into a model predictive controller. The model predictive controller chooses control actions based on a predefined cost function under consideration of bounds reflecting physical constraints. The Gasoline Compression Ignition engine model was also utilized to establish a state-space model that serves the Kalman filter and model predictive controller for estimation and prediction. In addition, the proposed control architecture was investigated at two different levels of cycle-to-cycle variations. Disturbance rejection was implemented to reduce state fluctuations and control efforts when high cycle-to-cycle variations are present. The control algorithm is able to maintain the desired references for brake mean effective pressure and combustion phasing while controlling peak in-cylinder pressure and combustion noise.
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Title: VERSATILE AND DYNAMIC INCENTIVE-BASED WELLNESS PROGRAM
Creator: Janik, Raymond George
Date: 2020
Description: Rising healthcare spending is prompting companies to implement health promotion programs for their employees to reduce health cost. Several...
Show moreRising healthcare spending is prompting companies to implement health promotion programs for their employees to reduce health cost. Several studies have indicated that workplace health promotion programs do not always improve employee wellbeing or reduce company healthcare cost. Focus on short-term financial results rather than long-term employee health behavior and ineffective use of incentives have been blamed for this failure.The main goal of this research is to introduce a wellness program and incentive plan with focus on changing long-term employee health behavior so it would lead to sustainable improvement in productivity and reduction in healthcare cost. The proposed program includes multiple yearly wellness follow up events, along with wellness and fitness data collection questionnaires for timely feedback and diversified outcome-based incentives. Regression models are developed to provide estimates of biometric data that are critical to performance feedback and for estimating healthcare cost savings.The proposed wellness program is currently being tested at a 700-employee lighting company in southeast united states. The healthcare cost models estimate a return on investment of $1.8 for every dollar spent on the program.
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Title: Evaluation of Salmonella Proliferation on Alfalfa Sprouts during Storage at Different Temperatures
Creator: Lin, Chih Tso
Date: 2020
Description: Sprouts, a low-calorie vegetable rich in nutrition, have been a popular ingredient in many meals in the USA. They are grown either at...
Show moreSprouts, a low-calorie vegetable rich in nutrition, have been a popular ingredient in many meals in the USA. They are grown either at commercial sprout farms or at home and served raw or lightly cooked. However, sprouts are also known as a source of foodborne illness outbreaks. FDA Food Code identifies raw sprouts as a time/temperature control for safety food. However, little information is known about the growth profile of foodborne pathogens in sprouts stored at different temperatures. This study aimed at evaluating the proliferation of Salmonella in alfalfa sprouts during storage at 4, 10, and 25℃ under two different contamination routes: 1) sprouts that were inoculated with Salmonella after harvest and 2) sprouts that were grown from contaminated seeds. Alfalfa sprouts grown from uninoculated seeds and harvested after 5 days of sprouting were divided into 25-g portions. Each portion was inoculated with a cocktail of five Salmonella serovars at levels of 10^1, 10^3 or 10^5 CFU/g prior to storage at 4, 10, or 25℃. Alternatively, sprouts grown for five days from seeds spiked with 1% of seeds previously inoculated with the Salmonella cocktail were divided into 25-g portions and stored at 4, 10, or 25℃. At defined time points (Days 0, 2, 4, 7, 14, and 21), levels of Salmonella and background microflora in stored sprouts were determined by plate count. Alfalfa sprouts appeared fresh during the 21 days of storage at 4 or 10℃ but started to show signs of spoilage after 4 days of storage at 25℃. The total plate counts maintained at a level above 9 log CFU/g throughout 21 d of storage at 4 and 10℃ or during the first 7 d of storage at 25℃. Storing sprouts at 4 or 10℃ could inhibit the proliferation of Salmonella. After 21 d of storage, the Salmonella counts in inoculated sprouts decreased slightly, by 0.88 or 0.93 log units, respectively. For sprouts stored at 25℃, the Salmonella growth profile differed depending on the route of contamination and the level of Salmonella at the start of storage. In sprouts inoculated at levels of 1.41, 2.83, and 4.75 log CFU/g, the Salmonella counts increased to 6.62, 6.86, and 6.68 log units, respectively, during the first 4-7 days of storage. For alfalfa sprouts grown from contaminated seeds, the Salmonella counts remained at a level similar to that in the harvested sprouts (8.16 log CFU/g) during the first 7 d. Results from this study further the understanding of pathogen growth in sprouts and will aid in the development of guidelines for proper storage of sprouts.
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Title: The Relation Between Executive Functions and Academic Performance in Clinically-Referred Adolescents
Creator: Coultis, Nora Plumb
Date: 2021
Description: The literature on executive functioning (EF) and academic performance has focused on early academic performance in young children (Best et al....
Show moreThe literature on executive functioning (EF) and academic performance has focused on early academic performance in young children (Best et al., 2011). Few studies have assessed the relation between EF abilities and academics in adolescents, which is particularly important because the demand on EF skills greatly increases in middle and high school (Best et al., 2011). Environmental factors, including completing multiple assignments, managing increased independent work, and changing classes, exacerbate the EF burden and reduce cognitive resources (Langberg et al., 2013; Samuels et al., 2016). Academic tasks also become more complex during middle and high school, for example, requiring solving algebraic problems, reading comprehension, and expository writing (Bull & Scerif, 2001; Sesma et al., 2009). Thus, complex academic tasks in adolescence likely require a higher demand on EF abilities compared to academic tasks in early childhood. The extant literature also has several limitations, such as focusing on only a couple of EF or academic domains and using parent- or teacher-report ratings rather than performance measures. Therefore, the aim of this study was to examine the relation between four domains of EF (i.e., working memory, inhibition, shifting, and planning) and three areas of academic performance (i.e., reading, writing, math) in a sample (N = 87) of clinically-referred middle and high school students. Contrary to expectation, results of hierarchical multiple regression analyses revealed that the measures of EF did not contribute significant additional variance to scores in reading and writing performance after controlling for IQ. It is notable that the EF variables did contribute a significant amount of additional variance to math scores after controlling for IQ and diagnosis. However, only working memory was significantly associated with math performance. This finding suggests that strategies designed to enhance working memory may be effective in improving math performance in students who are underperforming.
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Title: FACTORS INFLUENCING INDIVIDUALS’ PROVISION OF AUTONOMY SUPPORT TO THEIR PARTNERS WITH CHRONIC PAIN: A PATH ANALYSIS MODEL BASED ON SELF-DETERMINATION THEORY
Creator: Ivins-Lukse, Melissa N.
Date: 2021
Description: Receiving autonomy support from a relationship partner has been associated with increased physical activity among individuals with chronic...
Show moreReceiving autonomy support from a relationship partner has been associated with increased physical activity among individuals with chronic pain (ICP), but no studies have explored what factors may influence partners’ use of an autonomy supportive interpersonal style with an ICP. Self-determination theory (SDT) posits that contextual, perceptual, and individual factors influence how much individuals use an autonomy supportive interpersonal style through the mediators of basic psychological need satisfaction and autonomous motivation. The present study used path analysis to test a SDT model of the relationships between a contextual factor (autonomy support from health care provider), a perceptual factor (partner’s perception of ICP motivation for physical activity), an individual factor (partner catastrophizing about ICP’s pain), and the sequential mediators of relationship need satisfaction and autonomous motivation with respect to the dependent variable of partners’ use of an autonomy supportive interpersonal style. 176 partners of ICPs completed a cross-sectional survey including the Health Care Climate Questionnaire, partner-report revised Behavioral Regulation in Exercise Questionnaire, Pain Catastrophizing Scale – Significant Other version, Need Satisfaction Scale, Motivation to Help, and Interpersonal Behaviours Questionnaire-Self. The proposed model demonstrated poor fit to the data: χ2 (10) = 31.949, p < 0.001), RMSEA = 0.11 (90% CI = .07 to .16, p = 0.01), CFI = 0.81, and SRMR = .10. While the overall model was not supported, most individual pathways in the model were significant. Alternative analyses were conducted to identify a model with acceptable fit.
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Title: IDEOLOGICALLY MOTIVATED INTENTIONAL ADULTERATION: THEORY INTO INDUSTRIAL APPLICATION
Creator: DeVuyst, Adrian Jeffrey
Date: 2021
Description: Ideologically motivated intentional adulteration is an attempt to cause harm to consumers of food. Within the context of the United States of...
Show moreIdeologically motivated intentional adulteration is an attempt to cause harm to consumers of food. Within the context of the United States of America (US), the current methods of addressing this risk are evolving in the modern post-Food Safety Modernization Act (FSMA) era. Currently, the US has the Food and Drug Administration (FDA), which requires companies to have a food defense plan with a risk assessment, mitigation strategies, and recordkeeping. Additional options from Global Food Safety Initiatives (GFSI) benchmarked standards offer additional options for a company. However, even with these standards companies are still being impacted by intentional adulteration. Historical examples from the poisoning of bread in Hong Kong during British occupation and spreading of bacteria on salad bars by the followers of Rajneesh, to more modern examples of putting needles in strawberries and urinating on production equipment show a food defense system that is not always able to address intentional adulteration. The question of why companies are still having intentional adulteration comes up. The lack of food defense events and primary research on the topic creates a system where individual companies must gather data. Evaluations and surveys at a manufacturing site, N=11, indicates that there is high confidence among front line workers about their level of knowledge, but workers are unable to articulate the basic principles of food defense. Each individual company is required to create a personalized food defense system in the status quo, but the results of the survey given suggests that the data they could gather may be insufficient to create an effective food defense system.
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Title: RESIDENTIAL LOAD DATA COMPRESSION AND LOAD DISAGGREGATION
Creator: Xu, Runnan
Date: 2021
Description: Non-Intrusive Load Monitoring (NILM) for residential applications aims to dis-aggregate the total electricity consumption of a household into...
Show moreNon-Intrusive Load Monitoring (NILM) for residential applications aims to dis-aggregate the total electricity consumption of a household into the single appliance information. For the customer side, users can change their consumption habit and save more electricity. For the utility, generation scheduling will be more accurate, efficient, and secure. Furthermore, energy management system, demand response and fault diagnosis will benefit from the real time information provided by the NILM. This dissertation first proposes a data compressed method suitable for the NILM data. Then a real time disaggregation based on the Kalman filter is proposed to obtain the appliance state information. A model-free lossless data compression method for time series in smart grids (SGs), namely, Lossless Coding considering Precision (LCP) method is proposed. The LCP method encodes the current datapoint only using the immediate previous datapoint by differential coding, XOR coding, and variable length coding and transmits the encoded data once generated. It does not use the dynamics (e.g., many previous datapoints) or prior knowledge (e.g., mathematical models) of the time series. It considers the patterns, potential applications, and associated precision to preprocess the time series and especially suits high-resolution time series with long steady periods. The LCP method features low-latency and generalizability which enables real-time data communication for different time-critical tasks. Sub-metered load profiles in REDD dataset, high-resolution LIFTED dataset, AMPds dataset and PMU dataset are used to evaluate the performance of the LCP method. The results show that the LCP method demonstrates high compression ratio, low latency, and low complexity compared to state-of-the-art Resumable Data Com-pression (RDC) method, DEFLATE based on LZ77 & Huffman coding, and Lempel-Ziv-Markov Chain Algorithm (LZMA). An online method based on the transient features of individual appliances and system steady-state characteristics is proposed to estimate the appliances’ working states. It determines the number of states for each appliance via Density-based Spatial Clustering of Applications with Noise (DBSCAN) method and models the transition relationship among different states. The states of working appliances are identified from aggregated power signals by implementing the Kalman filtering method into the Factorial Hidden Markov Model (FHMM) and by the verification of system states which are the combination of working states of individual appliances. The proposed method is event based and the use of transient features extracted from event detection could achieve fast state inference and is suitable for online load disaggregation. The proposed method is tested on high-resolution dataset such as LIFTED and outperforms other related methods, including Segment-wise Integer Quadratic Constraint Programming (SIQCP), Combinatorial Optimization (CO), and the exact FHMM (FHMM_EXACT), in terms of accuracy, f1 score, and computational time.
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Title: IMPACT OF DATA SHAPE, FIDELITY, AND INTER-OBSERVER REPRODUCIBILITY ON CARDIAC MAGNETIC RESONANCE IMAGE PIPELINES
Creator: Obioma, Blessing Ngozi
Date: 2020
Description: Artificial Intelligence (AI) holds a great promise in the healthcare. It provides a variety of advantages with its application in clinical...
Show moreArtificial Intelligence (AI) holds a great promise in the healthcare. It provides a variety of advantages with its application in clinical diagnosis, disease prediction, and treatment, with such interests intensifying in the medical image field. AI can automate various cumbersome data processing techniques in medical imaging such as segmentation of left ventricular chambers and image-based classification of diseases. However, full clinical implementation and adaptation of emerging AI-based tools face challenges due to the inherently opaque nature of such AI algorithms based on Deep Neural Networks (DNN), for which computer-trained bias is not only difficult to detect by physician users but is also difficult to safely design in software development. In this work, we examine AI application in Cardiac Magnetic Resonance (CMR) using an automated image classification task, and thereby propose an AI quality control framework design that differentially evaluates the black-box DNN via carefully prepared input data with shape and fidelity variations to probe system responses to these variations. Two variants of the Visual Geometric Graphics with 19 neural layers (VGG19) was used for classification, with a total of 60,000 CMR images. Findings from this work provides insights on the importance of quality training data preparation and demonstrates the importance of data shape variability. It also provides gateway for computation performance optimization in training and validation time.
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Title: Highrise
Creator: Ma, Shasha
Date: 5/4/2011, 2011-05
Description: Highrise structure study
Sponsorship: Land, Peter

Title: Computationally Efficient Predictive Control Strategies for Autonomous Vehicles
Creator: Bhattacharyya, Viranjan
Date: 2021
Description: This thesis aims at developing computationally efficient (hence real-time applicable) control strategies for autonomous vehicles in the...
Show moreThis thesis aims at developing computationally efficient (hence real-time applicable) control strategies for autonomous vehicles in the presence of uncertainty, while incorporating high fidelity vehicle dynamics. The motivation for the control strategies is to ensure safety and improve energy efficiency of the vehicles. In this research, an effort has been made to develop control strategies to strike a balance between these competing factors. The specific contributions are: development of a new hierarchical control framework that can guarantee avoidance of red-light idling in the presence of uncertainty in preceding vehicle information/prediction in connected environment (hence improves system mobility); exploitation of a data-driven modeling approach for identifying a linear predictor for the nonlinear vehicle dynamics, which facilitates formulation of a convex equivalent problem of the original non-convex problem (hence facilitates computational tractability); introduction of a novel vehicle dynamics-aware fast game-theoretic planner for behavior and motion planning of vehicles in uncertain and unconnected environments. This thesis explores both the possible directions of future autonomous vehicles: connected and unconnected autonomous vehicles. In particular, the first problem relates to longitudinal fuel efficient driving (eco-driving) in a connected urban environment, where the connected and automated vehicles (CAVs) aim at the improvement of fuel efficiency and reduction of red-light idling (stop and go motion). The CAVs also focus on ensuring collision avoidance with the preceding vehicles despite the prediction uncertainty in future trajectory of preceding vehicles. This problem assumes vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, and is a longitudinal control problem. The next problem considers the uncertainty in prediction of future states of neighbouring vehicles in an unconnected environment and involves both lateral and longitudinal control. Following previous research, the interactive nature of driving is modeled using game-theory and a computationally efficient game-theoretic planner is introduced. Simulation results show the efficacy of the proposed methods in terms of computational tractability and fuel-efficiency.
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Title: Investigation in the Uncertainty of Chassis Dynamometer Testing for the Energy Characterization of Conventional, Electric and Automated Vehicles
Creator: Di Russo, Miriam
Date: 2023
Description: For conventional and electric vehicles tested in a standard chassis dynamometer environment precise regulations on the evaluation of their...
Show moreFor conventional and electric vehicles tested in a standard chassis dynamometer environment precise regulations on the evaluation of their energy performance exist. However, the regulations do not include requirements on the confidence value to associate with the results. As vehicles become more and more efficient to meet the stricter regulations mandates on emissions, fuel and energy consumption, traditional testing methods may become insufficient to validate these improvements, and may need revision. Without information about the accuracy associated with the results of those procedures however, adjustments and improvements are not possible, since no frame of reference exists. For connected and automated vehicles, there are no standard testing procedures, and researchers are still in the process of determining if current evaluation methods can be extended to test intelligent technologies and which metrics best represent their performance. For these vehicles is even more important to determine the uncertainty associated with these experimental methods and how they propagate to the final results. The work presented in this dissertation focuses on the development of a systematic framework for the evaluation of the uncertainty associated with the energy performance of conventional, electric and automated vehicles. The framework is based on a known statistical method, to determine the uncertainty associated with the different stages and processes involved in the experimental testing, and to evaluate how the accuracy of each parameter involved impacts the final results. The results demonstrate that the framework can be successfully applied to existing testing methods and provides a trustworthy value of accuracy to associate with the energy performance results, and can be easily extended to connected-automated vehicle testing to evaluate how novel experimental methods impact the accuracy and the confidence of the outputs. The framework can be easily be implemented into an existing laboratory environment to incorporate the uncertainty evaluation among the current results analyzed at the end of each test, and provide a reference for researchers to evaluate the actual benefits of new algorithms and optimization methods and understand margins for improvements, and by regulators to assess which parameters to enforce to ensure compliance and ensure projected benefits.
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Title: A Kernel-Free Boundary Integral Method for Two-Dimensional Magnetostatics Analysis
Creator: Jin, Zichao
Date: 2023
Description: Performing magnetostatic analysis accurately and efficiently is crucial for the multi-objective optimization of electromagnetic device designs...
Show morePerforming magnetostatic analysis accurately and efficiently is crucial for the multi-objective optimization of electromagnetic device designs. Therefore, an accurate and computationally efficient method is essential. Kernel Free Boundary Integral Method is a numerical method that can accurately and efficiently solve partial differential equations. Unlike traditional boundary integral or boundary element methods, KFBIM does not require an analytical form of Green’s function for evaluating integrals via numerical quadrature. Instead, KFBIM computes integrals by solving an equivalent interface problem on a Cartesian mesh. Compared with traditional finite difference methods for solving the governing PDEs directly, KFBIM produces a well-conditioned linear system. Therefore, the numerical solution of KFBIM is not sensitive to computer round-off errors, and the KFBIM requires only a fixed number of iterations when an iterative method (e.g., GMRES) is applied to solve the linear system.In this research, the KFBIM is introduced for solving magnetic computations in a toroidal core geometry in 2D. This study is very relevant in designing and optimizing toroidal inductors or transformers used in electrical systems, where lighter weight, higher inductance, higher efficiency, and lower leakage flux are required. The results are then compared with a commercial finite element solver (ANSYS), which shows excellent agreement. It should be noted that, compared with FEM, the KFBIM does not require a body-fitted mesh and can achieve high accuracy with a coarse mesh. In particular, the magnetic potential and tangential field intensity calculations on the boundaries are more stable and exhibit almost no oscillations.Furthermore, although KFBIM is accurate and computationally efficient, sharp corners can be a significant problem for KFBIM. Therefore, an inverse discrete Fourier transform (DFT) based geometry reconstruction is explored to overcome this challenge for smoothening sharp corners. A toroidal core with an airgap (C-core) is modeled to show the effectiveness of the proposed approach in addressing the sharp corner problem. A numerical example demonstrates that the method works for the variable coefficient PDE. In addition, magnetostatic analysis for homogeneous and nonhomogeneous material is presented for the reconstructed geometry, and results carried out from KFBIM are compared with the results of FEM analysis for the original geometry to show the differences and the potential of the proposed method.
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Title: Independence and Graphical Models for Fitting Real Data
Creator: Cho, Jason Y.
Date: 2023
Description: Given some real life dataset where the attributes of the dataset take on categorical values, with corresponding r(1) × r(2) × … × r(m)...
Show moreGiven some real life dataset where the attributes of the dataset take on categorical values, with corresponding r(1) × r(2) × … × r(m) contingency table with nonzero rows or nonzero columns, we will be testing the goodness-of-fit of various independence models to the dataset using a variation of Metropolis-Hastings that uses Markov bases as a tool to get a Monte Carlo estimate of the p-value. This variation of Metropolis-Hastings can be found in Algorithm 3.1.1. Next we will consider the problem: ``out of all possible undirected graphical models each associated to some graph with m vertices that we test to fit on our dataset, which one best fits the dataset?" Here, the m attributes are labeled as vertices for the graph. We would have to conduct 2^(mC2) goodness-of-fit tests since there are 2^(mC2) possible undirected graphs on m vertices. Instead, we consider a backwards selection method likelihood-ratio test algorithm. We first start with the complete graph G = K(m), and call the corresponding undirected graphical model ℳ(G) as the parent model. Then for each edge e in E(G), we repeatedly apply the likelihood-ratio test to test the relative fit of the model ℳ(G-e), the child model, vs. ℳ(G), the parent model, where ℳ(G-e) ⊆ℳ(G). More details on this iterative process can be found in Algorithm 4.1.3. For our dataset, we will be using the alcohol dataset found in https://www.kaggle.com/datasets/sooyoungher/smoking-drinking-dataset, where the four attributes of the dataset we will use are ``Gender" (male, female), ``Age", ``Total cholesterol (mg/dL)", and ``Drinks alcohol or not?". After testing the goodness-of-fit of three independence models corresponding to the independence statements ``Gender vs Drink or not?", ``Age vs Drink or not?", and "Total cholesterol vs Drink or not?", we found that the data came from a distribution from the two independence models corresponding to``Age vs Drink or not?" and "Total cholesterol vs Drink or not?" And after applying the backwards selection likelihood-ratio method on the alcohol dataset, we found that the data came from a distribution from the undirected graphical model associated to the complete graph minus the edge {``Total cholesterol”, ``Drink or not?”}.
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Title: Investigating anti-biofilm and anti-persister activities of natural compounds and antimicrobial proteins
Creator: Jin, Xing
Date: 2020
Description: Bacterial biofilm formation is frequently involved in the development of chronic infectious diseases. Inhibiting biofilms is challenging due...
Show moreBacterial biofilm formation is frequently involved in the development of chronic infectious diseases. Inhibiting biofilms is challenging due to their tolerance against conventional antibiotics which are not effective to penetrating biofilm matrix to kill the cells residing in biofilms. Metabolically dormant cells known as persisters are also not eradicated by antibiotic treatment. Therefore, novel antimicrobial drugs that can kill non-growing persisters or inhibit biofilms are needed urgently. Here, we investigate the anti-biofilm and anti-persister activities of new drug candidates including plant extracts, fatty acids and colicins. We firstly screened 50 different plant extracts on enterohemorrhagic E. coli and Listeria monocytogenes, and identified Cancavalia ensiformis-derived lectin Concanavalin A (ConA) inhibits biofilm formation of enterohemorrhagic E. coli and Listeria monocytogenes by binding to carbohydrates on bacterial cell surface. Biofilm results support that ConA lectin can be applied for developing anti-adherent and anti-biofilm agents to control biofilms. Also, fatty acids may be promising candidates as anti-persister or anti-biofilm agents, because some fatty acids exhibit antimicrobial effects. We screened a fatty acid library consisting of 65 different fatty acid molecules for altered persister formation. We found that undecanoic acid, lauric acid, and N-tridecanoic acid inhibited E. coli persister cell formation including enterohemorrhagic E. coli EDL933. These fatty acids were all medium chain saturated forms. Furthermore, the fatty acids repressed EHEC biofilm formation (for example, by 8-fold for lauric acid) without having antimicrobial activity. This study demonstrates that medium chain saturated fatty acids can serve as anti-persister and anti-biofilm agents that may be applied to treat bacterial infections. Colicins, a type of antimicrobial bacteriocins, are considered as a viable alternative of conventional antibiotics due to their unique cell killing mechanisms that can damage cells by pore-forming on the cell membrane, nuclease activity, and cell wall synthesis inhibition. In this study, we utilized cell-free protein synthesis to produce colicins with different modes of action. We optimized the production yield and activity of colicins in cell-free system. Also, we tested effect of cell-free produced colicins on persister cell formation and biofilm formation. We illustrated that colicins kill persister cells and biofilm cells. Moreover, colicins produced from the engineered probiotic E. coli cells, which can be used as a living medicine, specifically and significantly eradicate target biofilms without affecting other bacterial population. Colicins have great potential to be an antibiotic alternative, and engineered probiotic E. coli is a potential candidate for engineered bacterial therapeutics.
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Title: Parking Demand Forecasting Using Asymmetric Discrete Choice Models with Applications
Creator: Zhang, Ji
Date: 2023
Description: Using discrete choice models to forecast travelers parking location choice has been a branch of parking demand research for many years. The...
Show moreUsing discrete choice models to forecast travelers parking location choice has been a branch of parking demand research for many years. The most used discrete choice models have fairly simple mathematical expressions, such as the probit and logit models. The application of simple models helps release the computational burdens brought by parameter estimation tasks in practice, but the cost is the unwanted properties of classic models such as the “symmetry property” that we argue is often undesirable in many fields. To some extent, the symmetry property of related models limits the shape of curves that makes the model fitting less flexible technically. This study addresses the following question: “Can discrete choice models with asymmetry property outperform classic models with symmetry property in forecasting travelers’ parking location choices?” The contributions of this study include: (1) providing a new perspective of using asymmetric discrete choice models to explain and forecast individual’s parking location choice; and (2) completing the travel demand forecasting process from choices of the destination zone centroid to the parking location, enabling parking choice forecasting. This provides a generalized framework to calibrate and validate asymmetric discrete choice models with the field observed parking facility-specific arrival profile data integrated into a large-scale, high-fidelity regional travel demand model. Further, an experimental study is conducted to compare the performance of the proposed asymmetric discrete choice models in the parking demand forecasting framework. The results suggest that asymmetric discrete choice models for individual’s parking choice modeling outperform the symmetric discrete choice models such as the logit models owing largely to their flexibility of parameter fitting and training using the available dataset.
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Title: Estimation of Platinum Oxide Degradation in Proton Exchange Membrane Fuel Cells
Creator: Ahmed, Niyaz Afnan
Date: 2024
Description: The performance and durability of Proton Exchange Membrane Fuel Cells (PEMFCs) can be significantly hampered due to the degradation of the...
Show moreThe performance and durability of Proton Exchange Membrane Fuel Cells (PEMFCs) can be significantly hampered due to the degradation of the platinum catalyst. The production of platinum oxide is a major cause of the degradation of the fuel cell system, negatively affecting its performance and durability. In order to predict and prevent this degradation, this research examines a novel method to estimate degradation due to platinum oxide formation and predict the level of platinum oxide coverage over time. Mechanisms of platinum oxide formation are outlined and two methods are compared for platinum oxide estimation. Linear regression and two Artificial Neural Network (ANN) models, including a Recurrent Neural Network (RNN) and Feed-forward Back Propagation Neural Network (FFBPNN), are compared for estimation. The estimation model takes into account the influence of cell temperature and relative humidity.Evaluation of relative errors (RE) and root mean square error (RMSE) illustrates the superior performance of RNN in contrast to GT-Suite and FFBPNN. However, both RNN and GT-Suite showcase an average error rate below 5% while the FFBPNN had a higher error rate of approximately 7%. The RMSE of RNN shows mostly less compared to FFBPNN and GT-Suite, however, at 50% training data, GT-Suite shows lowest RMSE. These findings indicate that GT-Suite can be a valuable tool for estimating platinum oxide in fuel cells with a relatively low RE, but the RNN model may be more suitable for real-time estimation of platinum oxide degradation in PEM fuel cells, due to its accurate predictions and shorter computational time. This comprehensive approach provides crucial insights for optimizing fuel cell efficiency and implementing effective maintenance strategies.
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