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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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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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