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Title: ESTIMATES OF FINE AND ULTRAFINE PARTICLE REMOVAL EFFICIENCY FOR RESIDENTIAL HVAC FILTERS USING IN-SITU SIZE-RESOLVED EFFICIENCY MEASUREMENTS
Creator: Zeng, Yicheng
Date: 2018
Description: Central heating, ventilating, and air-conditioning (HVAC) filters are commonly evaluated for their size-resolved particle removal efficiency ...
Show moreCentral heating, ventilating, and air-conditioning (HVAC) filters are commonly evaluated for their size-resolved particle removal efficiency (for particles 0.3 to 10 µm in diameter) by challenging them with a test aerosol in a laboratory setting. However, aerosol measurement and reporting classifications that are most commonly used in regulatory monitoring and building measurements include integral measures of mass-based concentrations (e.g., PM2.5, or the mass concentration of particles smaller than 2.5 µm) or total number concentrations (e.g., total UFPs, or ultrafine particles smaller than 100 nm). Because filter test standards have not traditionally considered these measures, building owners, occupants, and other key personnel cannot make informed decisions on HVAC filtration for these classifications. Moreover, because the removal efficiency for integral measures of total mass and number concentrations are also a function of the underlying particle size distributions that challenge the filter, one must consider the varied sources and size distributions of aerosols that filters encounter in real building applications. This work has two objectives: (1) to measure the in-situ size-resolved particle removal efficiency of a large number of commercially available residential HVAC filters, and (2) to use those size-resolved efficiency data to estimate integral measures of PM2.5 and total UFP removal efficiency for the same filters for typical residential indoor settings based on a literature survey of measured indoor particle size distributions. Particle concentration measurements were made upstream and downstream of a wide range of commercially available filters installed in a central air handling unit in an unoccupied residential apartment unit. A literature review was conducted to gather a variety of indoor particle size distributions (PSDs) from across the world and tri-modal lognormal distributions were fit to each of them. Finally, the particle removal efficiency for each filter for integral measures of indoor UFPs and PM2.5 were calculated for each indoor PSD. In-situ size-resolved measurements indicate that filters with similar rating values but from different manufacturers can have very different removal efficiencies for integral measures of PM2.5 and total UFPs, and that the assumption for indoor PSDs can greatly impact estimates of removal efficiency.
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Title: THE RELATION BETWEEN DEPRESSION AND TRAIT ANXIETY SYMPTOMS AND MATERNAL UTTERANCES DURING SONOGRAM PROCEDURES
Creator: Hamilton, Catharine Elizabeth
Date: 2018
Description: The present study examines the relation between depression and trait anxiety symptoms and women’s utterances during a routine ultrasound...
Show moreThe present study examines the relation between depression and trait anxiety symptoms and women’s utterances during a routine ultrasound procedure in the second trimester of pregnancy. Participants included a diverse group of 70 women seeking prenatal care at an academic medical center in the Midwestern United States. The Depression Anxiety Stress Scales (DASS-21) depression subscale and the State Trait Anxiety Inventory (STAI), trait form were used to assess symptoms of depression and trait anxiety, respectively. Audio and video of participants’ faces during the ultrasound examination were used to assess the content, sentiment, and number of utterances. Results of regression analyses indicated that higher levels of depression symptoms were significantly related to a lower proportion of fetus-related utterances to total utterances. Higher levels of depression symptoms and trait anxiety were significantly related to a lower proportion of positive fetus-related utterances to total fetus-related utterances, after controlling for gestational age. Higher levels of depression symptoms were significantly related to a higher proportion of negative-fetus-related utterances to total fetus-related utterances, after controlling for education. These findings suggest that pregnant women who are experiencing symptoms of depression and anxiety may exhibit certain types and patterns of utterances during routine prenatal sonogram procedures. Thus, observation of pregnant women’s naturalistic speech may provide helpful supplemental information to the traditional self-report measure in screening for symptoms of depression and anxiety.
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Title: Maternal-Fetal Attachment: Does it predict parenting outcomes?
Creator: Desai, Shivani S.
Date: 2018
Description: Maternal-fetal attachment (MFA) predicts critical aspects of the caregiver-child relationship, including parental sensitivity and engagement....
Show moreMaternal-fetal attachment (MFA) predicts critical aspects of the caregiver-child relationship, including parental sensitivity and engagement. However, little is known about the relation between MFA and specific parenting beliefs and attitudes that contribute to these positive parenting behaviors, such as parenting sense of competence and parenting stress. The aim of this longitudinal study was to examine if MFA predicts specific domains of parenting sense of competence and parenting stress when children are two years of age. Participants included 53 mainly Caucasian women with a mean age of 33.9 years. MFA was assessed during pregnancy (mean gestational age = 27.02 weeks) using the Maternal Fetal Attachment Questionnaire. Parenting sense of competence and stress were assessed when the children were 2 years of age with the Parenting Sense of Competence questionnaire and the Parenting Stress Index questionnaire. Results of regression analyses indicated that higher levels of MFA significantly predicted higher levels of parenting satisfaction, a domain of parenting sense of competence. They also indicated that higher levels of MFA predicted lower levels of two domains of parenting stress, including stress associated with attachment and role restriction. These findings suggest that prenatal attachment is important to assess during pregnancy, as it may predict future parenting beliefs and attitudes, including sense of competence and stress.
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Title: SPIN TRANSPORT AND SPIN-ORBIT TORQUES IN ANTIFERROMAGNETS
Creator: Saglam, Hilal
Date: 2019
Description: The electron has two fundamental degrees of freedom, i.e., charge and spin. Existing semiconductor electronics utilizes the charge degree of...
Show moreThe electron has two fundamental degrees of freedom, i.e., charge and spin. Existing semiconductor electronics utilizes the charge degree of freedom in its functionalities. Spintronics seeks, in addition, to exploit the spin degree of freedom, which can suggest promising pathways for low-power and faster operations. In conventional spintronics devices, ferromagnetic materials (FMs) have been employed as active components. However, it has recently been recognized that antiferromagnetic materials (AFMs) can also play an active role in spintronic devices. Antiferromagnets have several advantages over ferromagnets; for instance, they have net zero magnetization so that they are invisible to external magnetic fields. Also, they show resonances in the terahertz frequency range. Towards this end, this thesis focuses on spin transport and spin-orbit torques in various antiferromagnetic materials. With respect to the former, I demonstrated that spin currents can be transmitted efficiently through a metallic antiferromagnet FeMn. I detect two distinctly different spin transport regimes, which can be associated with electronic and magnonic spin currents. With respect to the latter, I investigated a possible correlation between two important spintronics concepts, i.e., spin-orbit torques and exchange bias since the ferromagnetic/antiferromagnetic interface is crucial for both phenomena. The measured spin Hall angles suggest that these two effects are independent of each other, although it is worthy to mention that there are still strong spin-orbit torques even when the antiferromagnet is directly exchange coupled to the ferromagnet. Furthermore, I discuss anomalous Hall effect (AHE) and anomalous Nernst effect (ANE) in another metallic antiferromagnet, FeRh, which undergoes a temperature driven antiferromagnetic-to-ferromagnetic phase transition. The temperature dependent results show a drastic suppression of both AHE and ANE signals in the antiferromagnetic phase. Interestingly, these non-vanishing signals are opposite in sign compared to their ferromagnetic counterparts, which can suggest changes of inherent symmetries in the electronic structure of FeRh across its magnetic phase transition.
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Title: DIAGNOSING AND TREATING ADHD: CLINICIAN CHARACTERISTICS, METHODS OF DIAGNOSIS, DIAGNOSTIC RATES, AND TREATMENT RECOMMENDATIONS
Creator: Haak, Christopher Luke
Date: 2019
Description: Attention-deficit/hyperactivity disorder (ADHD) is one of the top five most common referrals among all neuropsychologists (Sweet et al. 2015)...
Show moreAttention-deficit/hyperactivity disorder (ADHD) is one of the top five most common referrals among all neuropsychologists (Sweet et al. 2015) and continues to elicit public and professional concern about over-diagnosis in children (Sciutto & Eisenberg, 2007) and under-diagnosis in adults (Asheron et al., 2012; Kooji et al., 2010). In recent years, the prevalence of ADHD has increased (Polanczyk et al., 2007 & 2014, Thomas et al., 2015). It is unclear what is driving these changes though changes in criteria may be playing a role (van de Voort et al., 2014). Further, there has been little research on whether professional training, beliefs, and practice factors can influence the likelihood to diagnose ADHD. The purpose of this study was to examine the extent to which neuropsychologists’ professional characteristics, training, and beliefs about ADHD diagnosis and treatment influence their likelihood to diagnose ADHD. The study also evaluated whether there are differences in assessing and treating ADHD based upon the client population focus (child, lifespan, or adult) of neuropsychologists. Participants in this study were 106 neuropsychologists from across the United States and Canada who were recruited through neuropsychology listservs to participate in an online survey. Results indicated that population focus was associated with significant differences in approach to diagnosing and treating ADHD, with child- and lifespan-focused neuropsychologists reporting higher rates of ADHD diagnosis. Additionally, having a higher percent of clinical cases in which ADHD is a referral question and greater self-reported adherence to following full diagnostic criteria for making a diagnosis were associated with higher ADHD diagnostic rates, controlling for age, gender, ethnicity, and other professional characteristics. This study is among the first to examine specific clinician factors impacting diagnostic rates and its findings have several implications for practice and research.
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Title: A Novel Remote Sensing System Using Reflected GNSS Signals
Creator: Parvizi, Roohollah
Date: 2020
Description: This dissertation presents a method to remotely sense freshwater surface ice and water using reflected signals from Global Navigation...
Show moreThis dissertation presents a method to remotely sense freshwater surface ice and water using reflected signals from Global Navigation Satellite Systems (GNSS). A portable ground-based sensor system is designed and built for collecting both scattered Global Positioning System (GPS) signals and independent validation data (lidar and camera) from the surface. GPS front-end signals are collected from both a direct receiving antenna facing upward and from a reflection-receiving antenna facing downward. Multiple data campaigns are conducted on the Lake Michigan waterfront in Chicago. A customized software receiver tests a new signal processing method to detect and acquire Global Navigation Satellite System (GNSS) signals reflected from the lake surface ice and collected by a downward-facing antenna. The method, modified differential coherent integration, multiplies time-shifted auto-correlation samples. The new method is evaluated against three conventional integration methods (coherent, incoherent, and differential integration) with signals from the direct antenna. With front-end samples from the reflection antenna, the new method is the only one of the four methods compared that acquires satellites in the reflected GPS signals, with three acquired using 10 ms of integration.The lidar surface scans are mapped with camera images and estimated reflection points to indicate the surface reflection type and to provide surface height relative to the sensors. For one satellite whose specular point is estimated to be on the ice surface, a Delay Doppler Map (DDM), signal-to-noise (SNR) ratio, and surface reflectivity (SR) are computed with the modified differential coherent integration method using the GPS. The DDM shows that, with modified differential integration, the satellite can be acquired in the reflected signal. For two satellites whose reflection points scan across ice and water over time the SNR and SR are computed over time. The SR is shown to be lower for liquid water than lake ice. This system concept may be used in the future for more complete mapping of phase changes in the cryosphere.
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Title: Economic and Computational Methods for the Control of Uncertain Systems
Creator: Zhang, Jin
Date: 2019
Description: The Economic Linear Optimal Control (ELOC) can improve the effective use of economic and dynamic information throughout the traditional...
Show moreThe Economic Linear Optimal Control (ELOC) can improve the effective use of economic and dynamic information throughout the traditional optimization and control hierarchy. This dissertation investigates the computational procedures used to obtain a global solution to the ELOC problem. The proposed method employs the Generalized Benders Decomposition (GBD) algorithm. Compared to the previous branch and bound approach, the application of GBD to the ELOC problem will greatly improve computational performance. A technological benefit of decomposing the problem into steady-state and dynamic parts is the ability to utilize nonlinear steady-state models, since the relaxed master problem is free of SDP type constraints and can be solved using any global nonlinear programming algorithm.In order to address the issue of model/plant mismatch, the dissertation will also investigate how to handle box-type uncertainties in ELOC. We consider two methods, a robust formulation for when the uncertainty is completely unknown and a Linear Parameter Varying formulation for when uncertainty can be measured in real time. In both cases, the infinite number of conditions that need to be satisfied are reduced to a finite set of constraints. The resulting problem formulations have a similar structure to the ELOC and can be solved globally by employing the generalized Benders decomposition.Despite a high-quality control law, the ultimate performance of closed-loop systems will be dictated by the quality and limitation of hardware element. Thus, hardware selection is also investigated in the dissertation. The cost-optimal hardware selection problem has been shown to be of the Mixed Integer Convex Programming (MICP) class. While such a formulation provides a route to global optimality, use of the branch and bound search procedure has limited application to fairly small systems. In this dissertation, we illustrate that a simple reformulation of the MICP and subsequent application of the GBD algorithm will result in massive reductions in computational effort.Finally, the problems of value-optimal sensor network design (SND) for steady-state and closed-loop systems are investigated. The value-optimal SND problem has been shown to be of the nonconvex mixed integer programming class. In the dissertation, it is demonstrated after transforming into an equivalent reformation, the application of GBD algorithm will significantly reduce the computational effort.
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Title: A Novel CNFET SRAM-Based Computing-In-Memory Design and Low Power Techniques for AI Accelerator
Creator: Kim, Young Bae
Date: 2023
Description: Power consumption and data processing speed of integrated circuits (ICs) is an increasing concern in many emerging Artificial Intelligence (AI...
Show morePower consumption and data processing speed of integrated circuits (ICs) is an increasing concern in many emerging Artificial Intelligence (AI) applications, such as autonomous vehicles and Internet of Things (IoT). In addition, according to the 2020 International Technology Road map for Semiconductors (ITRS), the high power consumption trend of AI chips far exceeds the power requirements. As a result, power optimization techniques are highly regarded in nowadays AI chip designs. There are various low-power methodologies from the system level to the layout level, and we are focusing on transistor level and register transfer level (RTL) through this thesis. In this thesis, we propose a novel ultra-low power voltage-based computing-in- memory (CIM) design with a new SRAM bit cell structure for AI Accelerator. The basic working principle of CIM (Computing-in-memory) is to use the existing internal embedded memory array (e.g. SRAM) instead of external memory, and it reduces unnecessary access to external memory by calculating with internal embedded mem- ory. Since the proposed our SRAM bit cell uses a single bitline for CIM calculation with decoupled read and write operations, it supports much higher energy eciency. In addition, to separate read and write operations, the stack structure of the read unit minimizes leakage power consumption. Moreover, the proposed bit cell structure provides better read and write stability due to the isolated read path, write path and greater pull-up ratio. Compared to the state-of-the-art SRAM-CIM, our proposed SRAM-CIM does not require extra transistors for CIM vector-matrix multiplication. We implemented a 16k (128⇥128) bit cell array for the computation of 128x neurons, and used 64x binary inputs (0 or 1) and 64⇥128 binary weights (-1 or +1) values for the binary neural networks (BNNs). Each row of the bit cell array corresponding to a single neuron consists of a total of 128 cells, 64x cells for dot-product and 64x replicas cells for ADC reference. And 64x replicas cells consist of 32x cells for ADC reference and 32x cells for o↵set calibration. We used a row-by-row ADC for the quantized outputs of each neuron, which supports 1-7 bits of output for each neuron. The ADC uses the sweeping method using 32x duplicate bit cells, and the sweep cycle is set to 2N1 +1, where N is the number of output bits. The simulation is performed at room temperature (27C) using 32nm CNFET and 20nm FinFET technology via Synopsys Hspice, and all transistors in bitcells use the minimum size considering the area, power, and speed. The proposed SRAM-CIM has reduced power consumption for vector-matrix multiplication by 99.96% compared to the existing state-of-the-art SRAM-CIM. Moreover, because of the separated reading unit from an internal node of latch, there is no feedback from the read access circuit, which makes it read static noise margin (SNM) free. Furthermore, for the low power AI accelerator design, we propose a new AI accelerator design method that applies low power techniques such as bus specific clock gating (BSCG) and local explicit clock gating (LECG) at the register-transfer- level (RT-level). And evaluates them on the Xilinx ZCU-102 FPGA SoC hardware platform and 45nm technology for ASIC, respectively. It measures dynamic power using a commercial EDA tool, and chooses only a subset of FFs to be gated selectively based on their switching activities. We achieve up to a 53.21% power reduction in the ASIC implementation and saved 32.72% of the dynamic power dissipation in the FPGA implementation. This shows that our RTL low power schemes have a powerful possibility of dynamic power reduction when applied to the FPGA design flow and ASIC design flow for the implementation of the AI system.
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Title: An adaptive personalized multivariable, multimodule artificial pancreas system based on a plasma insulin cognizant model predictive control
Creator: Hajizadeh, Iman
Date: 2019
Description: An adaptive and personalized multivariable artificial pancreas system is proposed for effective glycemic control and disturbance rejection...
Show moreAn adaptive and personalized multivariable artificial pancreas system is proposed for effective glycemic control and disturbance rejection without manual user announcements for meals and exercise. Adaptive models identified through system identification techniques are integrated with a physiological compartment model to characterize the time-varying glucose-insulin dynamics. The real-time estimation of plasma insulin concentration to quantify the insulin in the bloodstream in patients with type 1 diabetes mellitus is presented. The identified time-varying models are employed for the design of an adaptive model predictive control formulation that is cognizant of the plasma insulin concentration. A feature extraction method based on glucose measurements is used to detect rapid deviations from the desired set-point caused by significant disturbances and subsequently modify the constraints of the optimization problem for negotiating between the aggressiveness and robustness of the controller to suggest the required amount of insulin. A predictive hypoglycemia module with carbohydrate suggestion is also designed to prevent any potential hypoglycemia events. A controller performance assessment algorithm is developed to analyze the closed-loop behavior and modify the parameters of the artificial pancreas control system. To this end, various performance indices are defined to quantitatively evaluate the controller efficacy in real-time. The controller assessment and modification module also incorporates on-line learning from historical data to anticipate impending disturbances and proactively counteract their effects.
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Title: AGENT-BASED MODELING OF IMMUNE RESPONSE IN THE DEVELOPMENT OF TYPE 1 DIABETES
Creator: Xu, Qian
Date: 2020
Description: Diabetes is a chronic disease that affects a large number of people around the world and cause many co-morbidities ranging from cardiovascular...
Show moreDiabetes is a chronic disease that affects a large number of people around the world and cause many co-morbidities ranging from cardiovascular diseases, neuropathy, retinopathy and blindness and kidney failure. The economic burden induced by diabetes is not only caused by the wage loss and medical burden, but also with the cost of treatment of diabetes and co-morbidities caused by diabetes. Clinical research for treatment and cure of diabetes is costly. Computer modeling and simulation studies provide an economical alternative to conduct preliminary evaluation of new hypotheses and alternatives in new therapies. The most promising results obtained from simulations can then be investigates experimentally, improving the efficiency of experiments and clinical studies. This work focuses on the development of an agent-based model to describe the destruction of islets and β cells and the development of Type 1 diabetes. The whole process of inflammation related to diabetes takes place in pancreatic lymph node, circulation, and pancreatic tissue with islets. The infiltration to islets and insulin-producing β cell damage happens in the pancreatic tissue with islets; the lymphocytes activation and antigen presentation majorly happened in the pancreatic lymph node. Therefore, the model described activities taking place in the islets in the pancreatic tissue section and pancreatic lymph nodes, the interactions among T cells, α/β cells, antigen presentation cells and immunosuppression cells. Cell behavior was obtained from the literature that published experiment results and used to develop the rules followed by the agents representing various types of cells and their interactions. The agent-based model provides a framework to describe relationship between lymphocytes and β cell through the trends of cell variations in the inflammation and demonstrates the effects of these cells in the disease development. Two different systems, a mouse model and a human model have been developed. The simulation results with the mouse model indicate that the different types of regulatory cells play different roles in suppressing inflammation. Among them, the regulatory T cells play the most important role in suppressing inflammation, but the B regulatory cell conversion is the key to induce the cascade of regulatory cell generation in inflammatory environment when there are no regulatory cytokines in the environment. The simulation results with the human model are mostly similar with mouse model, however, their effect of potential therapies such as addition of Tregs did not do as well as that in mouse model. The treatment method might be adjusted by combining other cytokines or immunosuppression cells in human assays.
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Title: Developing Adaptive and Predictive Modules for the Second Generation of Multivariable Insulin Delivery System for People with Type-1 Diabetes
Creator: Askari, Mohammad Reza
Date: 2023
Description: In this research, we are developing the second generation of multivariable automated insulin delivery system (mvAID) for people with Type 1...
Show moreIn this research, we are developing the second generation of multivariable automated insulin delivery system (mvAID) for people with Type 1 diabetes (T1D). AID system is improved by integrating missing data from sensors into the system, reconciling outliers in the data, and eliminating the effects of artifacts in signals from wearable devices. Behavioral patterns of individuals with T1D are captured by data-driven models. The model predictive control algorithm of the mvAID uses these patterns for making decisions and predicting glucose concentrations in the future more accurately. A pipeline algorithm is developed for removing noise and motion artifacts from wristband signals. Then, energy expenditure, physical activity, and acute psychological stress (APS) are estimated from wearable device signals to detect and quantify disturbances affecting the concentration of blood glucose concentration. Additionally, different modules were designed for predicting risky glycemic episodes and are used to build the second generation of the mvAID system. The techniques developed are tested with historical data sets from various clinical experiments and free-living data, and with simulations made by using our multivariable glucose, insulin and physiological variables simulator (mGIPsim).
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Title: Language, Perception, and Causal Inference in Online Communication
Creator: Wang, Zhao
Date: 2021
Description: With the proliferation of social media platforms, online communication is becoming increasingly popular. The nature of a wide audience and...
Show moreWith the proliferation of social media platforms, online communication is becoming increasingly popular. The nature of a wide audience and rapid spread of information make these platforms attractive to public entities, organizations, and individuals. Marketers use these platforms to advertise their products and collect customer feedbacks (e.g. Amazon, Airbnb, Yelp, IMDB). Politicians use these platforms to directly speak with the public and canvass for votes (e.g., Twitter, Youtube, Snapchat). Individuals use these platforms to connect with friends and share daily life (e.g., Twitter, Facebook, Instagram, Weibo). The various platforms allow users to build public image and increase reputation through a fast and cheap way. However, due to the lack of regulations and low effort of online communication, some users try to manage their public impression using vague and tricky expressions during communication, making it hard for the audience to identify the authenticity of the public messages. Studies across many disciplines have shown that words and language play an important role in effective communication but the nature and extent of this role remain murky. Prior works have investigated wording effect on audience perception, but we still need automatic methods to estimate the causal effect of lexical choice on human perception in large scale. Getting insights into the treatment effect of subtle linguistic signals is crucial for intelligent language understanding and text analysis.The causal estimation of wording effect on perception also provides us an alternative way to understand the causal relationship between word features and perception labels. Comparing with correlational associations between features and labels, which is typically learned by statistical machine learning models, we find inconsistencies between the causal and correlational associations. These inconsistencies suggest possible spurious correlations in text classification and it's significant to address this issue by applying causal inference knowledge to guide statistical classifiers.In this thesis, our first goal is to investigate wording effect in online communication and study causal inference in text. We start from a deceptive marketing task to quantify entities' word commitment from online public messaging and identify potentially inauthentic entities. We then propose several frameworks to estimate the causal effects of word choice on audience perception by adapting Individual Treatment Effect estimation from causal inference literature to our problem of Lexical Substitution Effect estimation. The findings from these projects motivate us to explore our second goal of applying causal inference knowledge to improve statistical model robustness. Specifically, we study the causal and correlational associations in text and discover possible spurious correlations in text classifiers. Then, by extending the causal discovery, we propose two frameworks to improve text classifier robustness and fairness either by directly removing bias correlations or by training a robust model with automatically generated counterfactual samples.
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Title: Testing a pilot intervention aiming to increase transgender allyship among future healthcare providers
Creator: Yoder, Wren
Date: 2021
Description: Transgender individuals often experience poor health outcomes related to a lack of provider knowledge and comfort around transgender issues. ...
Show moreTransgender individuals often experience poor health outcomes related to a lack of provider knowledge and comfort around transgender issues. Ally identity development and cultural humility theories have been used to develop interventions shown to improve attitudes, knowledge, and skills related to being an ally to the transgender community. Additionally, healthcare providers have reported a desire for online tools related to transgender healthcare, and online interventions can be more cost effective than traditional in-person trainings. The current study developed an hour-long online intervention composed of six activities aiming to increase attitudes, knowledge, skills, and identification as an ally to the transgender community. Tests were conducted to assess whether these domains increased significantly from baseline to post in the intervention condition compared to the control condition and whether the increase was maintained at 2-week follow up. The sample included cisgender (i.e., male or female) students studying a subject related to healthcare recruited online through Prolific (N = 78). Results indicated that knowledge and skills increased significantly from baseline to post in the intervention condition compared to the control condition, and increases were maintained at 2-week follow up. However, this was not the case for attitudes and identity. These findings largely replicate existing research on knowledge about transgender individuals and provide new insights into skills, attitudes, and identity related to transgender allyship. Findings can inform future research on transgender allyship intervention design and allyship theory as well as support improvements in clinical practice and policy related to transgender healthcare services.
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Title: Investigating The Impact of Tall Building Ordinances (TBOs) on the Evolution of Ultra-Tall Buildings Typology: Case Studies in Chicago and Dubai
Creator: Alkoud, Amjad
Date: 2023
Description: Zoning ordinances are instruments that tangibly and intangibly shape cities; control urban morphology, demography, and visual identity; and...
Show moreZoning ordinances are instruments that tangibly and intangibly shape cities; control urban morphology, demography, and visual identity; and determine the inhabitants' life quality, well-being, and comfort. Tall building ordinances (TBOs), in turn, control the vertical growth of cities and the development of tall buildings as distinctive actors in the built environment today. With the recent proliferation of developing Ultra-tall buildings in cities around the world, ordinances should offer flexibility, adaptability, and responsiveness to the dynamic nature of emerging needs and technological potentials.This dissertation investigates the emergence of Ultra-tall buildings as a new typology in major metropolises and the interaction between the building ordinances and the construction of Ultra-tall. The work presented in this dissertation implements two primary research methods: cross-sectional surveys and longitudinal studies, documenting supertall buildings completed in two major cities, Chicago and Dubai. The discussions and findings are supported by structured interviews with architects and engineers actively involved in designing and constructing Ultra-tall buildings. The cross-sectional survey comprises all supertall buildings (i.e., buildings above 1000 feet in height) completed as of 2022 in Chicago, the cradle of the "modern" high-rise with 318 towers of 100-plus meters and eight supertall towers of 300-plus meters; and Dubai, the new experimental land of supertall construction with 298 towers of 100-plus meters and 28 towers of 300-plus meters height. The longitudinal case studies provide additional information and knowledge about selected examples in Chicago and Dubai, derived from personal structured interviews conducted in both cities. Several additional survey cases from China, NYC, and London were investigated for their importance and uniqueness in supporting the research discussions and findings. This research aims to bridge the gap between the building ordinance literature and Ultra-tall building design practices on the one hand. On the other hand, it sheds light on the necessity to realize Ultra-tall buildings as a distinct typology entitled to its particular set of ordinances.The research findings are intended to help architects, engineers, policymakers, and planning authorities ensure a sustainable socioeconomic future and mitigate the negative impact of Ultra-tall constructions in major cities. This goal is assumed to be achieved by developing a set of recommendations, strategies, and universal criteria to implement a more flexible and responsive approach toward emerging human needs and technologies.
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Title: Efficient management of uncertain data
Creator: Feng, Su
Date: 2023
Description: Uncertainty arises naturally in many application domains. It can be caused by an uncertain data source (sensor errors, noise, etc.). Data...
Show moreUncertainty arises naturally in many application domains. It can be caused by an uncertain data source (sensor errors, noise, etc.). Data preprocessing techniques (data curation, data integration, etc.) can also results in uncertainty to the data. Analyzing uncertain data without accounting for its uncertainty can create hard to trace errors, with severe real world implications. Certain answers are a principled method for coping with the uncertainty that arises in many practical data management tasks. Unfortunately, this method is expensive and may exclude useful (if uncertain) answers. Other techniques from incomplete database record and propagate more detailed uncertainty information. However, most of these approaches are either too expensive to be practical, or only focus on a narrow class of queries and only work for a specific representation. In this thesis, we investigate models and query semantics for uncertain data management and present a framework that is general and practically efficient, backed up by fundamental theoretical foundations and with formally proven correctness guarantees. We first propose Uncertainty Annotated Databases (UA-DB), which combine an under- and over-approximation of certain answers to combine the reliability of certain answers with the performance of a classical database system. We then introduce attribute-annotated uncertain databases (AU-DB), which extend the UA-DB model with attribute-level annotations that record bounds on the values of an attribute across all possible worlds. AU-DB extends UA-DBs to encode a compact over-approximation of possible answers which is necessary to support non-monotone queries including aggregation and set difference. With a further extension to AU-DB that supports ranking and windowed aggregation queries using native implementation on modern DBMS, our approaches scale to complex queries and large datasets, and produces accurate results. Furthermore, they significantly outperforms alternative methods for uncertain data management.
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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: Enhancing Explanation Generation in the CaJaDE system using Interactive User Feedback
Creator: Lee, Juseung
Date: 2022
Description: In today’s data-driven world, it is becoming increasingly difficult to interpret and understand query results after going through several...
Show moreIn today’s data-driven world, it is becoming increasingly difficult to interpret and understand query results after going through several manipulation steps, especially on a large database. There is a need for automated techniques that explain query results in a meaningful way. A recent study, CaJaDE(Context-Aware Join-Augmented Deep Explanations), presents a novel approach to generating explanations of query results including crucial contextual information. However, it becomes difficult to interpret explanations since the search space increases exponentially.In this thesis, we propose a new approach that introduces a user interaction model for a purpose of enhancing the generation of explanations in the CaJaDE system. We implemented a user interaction model that consists of three modules: User Selection, Recommendation Score, and User Rating. With these modules, our approach guides a user while exploring relevant join graphs, and lets them be involved in the decision-making process while generating join graphs. We demonstrate through performance experiments and user study that our approach is an effective method for users to understand explanations.
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Title: H1 LUBRICANT TRANSFER FROM A HYDRAULIC PISTON FILLER INTO A SEMI-SOLID FOOD SYSTEM
Creator: Chao, Pin-Chun
Date: 2020
Description: The machinery used to prepare, and process food products need grease and oil for the lubrication of machine parts. H1 (food-grade) lubricants...
Show moreThe machinery used to prepare, and process food products need grease and oil for the lubrication of machine parts. H1 (food-grade) lubricants commonly used in the food industry are regulated as indirect additives by the FDA because they may become components of food through transfer due to incidental contact between lubricants and foods. The maximum level of H1 lubricants currently permitted in foods is 10 ppm, which was derived from FDA data gathered over 50 years ago. Although modern equipment has been designed to minimize the transfer of lubricants during processing and packaging, incidental food contact can still occur resulting from leaks in lubrication systems or over-lubrication. However, there is a lack of data for the FDA to evaluate and determine whether safety issues in the aspect of chemical contamination should be addressed concerning the use of food-grade lubricants in the production of foods. This research was conducted to determine the transfer of an H1 lubricant (Petrol-Gel) into a semi-solid model food from a hydraulic piston filler during conventional operating conditions at 25°C and 50°C. Xanthan gum solutions with concentrations of 2.3% at 25°C and 1.9% at 50°C were used to simulate the viscosity of ketchup at 50°C (970 cP). Petrol-Gel H1 lubricant with a viscosity grade of 70 cSt at 40°C was selected and the aluminum (Al) in the lubricant was targeted as a tracer metal. Analytical methods to quantify Al in both Petrol-Gel and xanthan gum solutions were successfully developed and validated by using inductively coupled plasma – mass spectrometry (ICP-MS) combined with microwave-assisted acid digestion technique. The concentration of Al in the Petrol-Gel was determined to be 3103 ± 26 μg/g. A total of 1.35 g of Petrol-Gel was applied to four ring gaskets in the filler, and 50 g samples of xanthan gum solution were collected into a 100-mL polypropylene tube (DigiTube) with low leachable metals during 500 filling cycles (the full capacity of the piston filler hopper).Results showed that the concentrations of Petrol-Gel transferred into 2.3% xanthan gum solution at 25°C ranged from 1.6 to 63.5 μg/g. A total of 64.47 mg of the applied Petrol-Gel (1.35 g) was transferred into 25 liters of the solution. The average concentration of Petrol-Gel in 2.3% xanthan gum solution was calculated to be 2.84 μg/g, which was lower than the current regulatory limit of 10 ppm. In general, the transfer of Petrol-Gel during the first 100 filling cycles was higher at 50°C than at 25°C. The concentration of Petrol-Gel transferred into 1.9% xanthan gum solution at 50°C for the first 100 filling cycles ranged from 1.6 to 35.06 μg/g and was 6.37 μg/g on average. This research will help FDA to calculate more realistic limits of the H1 lubricants permissible in foods at modern food processing conditions as well as estimate consumer dietary exposure to these indirect food additives.
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Title: Examination of Power Ultrasound and Organic Acid-based Hurdle Technology in the Reduction of Salmonella Enterica on Peaches and Apples
Creator: Mathias, Hina Valida
Date: 2023
Description: Fresh produce includes fruit matrices like whole peaches and apples that are minimally processed and are a popular choice among different...
Show moreFresh produce includes fruit matrices like whole peaches and apples that are minimally processed and are a popular choice among different types of demographics because of their nutrition content and health benefits. However, there have been increasing pathogen outbreaks in these matrices over the past few decades, which are majorly rooted in cross contamination either due to poor handling pre and post processing or the insufficient reduction of the pathogen at processing by the applied hurdle technology. While chemical sanitizers are a popular option in the food industry, the awareness and demand for green consumerism and sustainability have created a need for research to determine the efficacies of organic acids and non-thermal technologies like power ultrasound in the reduction of different pathogens on different food matrices. This study focusses on the S. enterica reduction capabilities of three organic acids – citric, malic, and lactic alone and in combination with 40 kHz power ultrasound at 1, 2 and 5% for treatment times of 2, 5 and 10 min on whole yellow peaches and gala apples. Peaches and apples were spot inoculated with a four-strain cocktail of S. enterica, resulting in 9 log CFU/fruit. Post air drying for 1 h, the fruits were treated with water, 1, 2, or 5% citric, lactic, or malic acid for 2, 5 or 10 min with and without power ultrasound treatment at 40 kHz. The population of S. enterica on the fruits was enumerated before and after treatment. Three independent trials with triplicate samples were performed for each condition. Population differences were evaluated via Student's t-test and ANOVA; p<0.05 was considered significant. The initial level of inoculum ranged from 8.67 ± 0.41 to 8.20 ± 0.26 log CFU/peach and 7.28 ± 0.60 to 8.17 ± 0.37 log CFU/apple in peaches and apples, respectively. Water treatments showed pathogen reduction as high as 1.22 log CFU/peach and 1.02 log CFU/apple. Citric acid treatments on peaches showed significant pathogen reduction at higher time increments at 5% with a reduction of S. enterica as high as 2.24 log CFU/peach after 10 min. Malic acid showed the highest recorded log reduction in peaches at 5% and 10 min being 4.20 log CFU/peach (n=1/9, samples above the enumeration limit) and apples at 5% and 5 min being 3.71 log CFU/apple (n=4/9, samples above the enumeration limit) both in combination with an ultrasound. Lactic acid, unlike the other two organic acids, showed a pathogen reduction of over 3 log CFU/fruit at 2% after 10 min, with the highest pathogen reductions of 3.76 log CFU/peach and >3.62 log CFU/apple at 5% and10 min. There was no particular trend with significant enhancement of pathogen reduction either with time increment or the addition of ultrasound and varied with the varying acids, treatment conditions and fruit matrices.
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Title: Effect of Pre-Processing Data on Fairness and Fairness Debugging using GOPHER
Creator: Sarkar, Mousam
Date: 2023
Description: At present, Artificial intelligence has been contributing to the decision-making process heavily. Bias in machine learning models has existed...
Show moreAt present, Artificial intelligence has been contributing to the decision-making process heavily. Bias in machine learning models has existed throughout and present studies’ direct usage of eXplainable Artificial Intelligence (XAI) approaches to identify and study bias. To solve the problem of locating bias and then mitigating it has been achieved by Gopher [1]. It generates interpretable top-k explanations for the unfairness of the model and it also identifies subsets of training data that are the root cause of this unfair behavior. We utilize this system to study the effect of pre-processing on bias through provenance. The concept of data lineage through tagging of data points during and after the pre-processing stage is implemented. Our methodology and results provide a useful point of reference for studying the relation of pre-processing data with the unfairness of the machine learning model.
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