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Title: VISUAL TRAINING AT ILLINOIS INSTITUTE OF TECHNOLOGY: AESTHETICS IN ARCHITECTURAL EDUCATION
Creator: Jones, Kristin
Date: 2016, 2016-05
Description: Over the course of 78 years, an aesthetic ideal which embodies architectural values has been collectively cultivated through Visual Training...
Show moreOver the course of 78 years, an aesthetic ideal which embodies architectural values has been collectively cultivated through Visual Training at the Illinois Institute of Technology (IIT) school of architecture in Chicago. This dissertation provides a comprehensive explanation of Visual Training as it has been conducted at IIT. It promotes a better understanding of the background of Visual Training, the pedagogical practice of the Visual Training exercises, and the ongoing value of the course. The dissertation traces the underpinnings of Visual Training through the writings of Walter Peterhans, founder and Professor of Visual Training at IIT from 1938-1960, Ludwig Mies van der Rohe, Professor and Director of Architecture at IIT from 1938-1958 and other key figures. The thesis shows how the Visual Training program at IIT was influenced by movements in philosophy and mathematics in early 20th century Germany and how existing pedagogy at the Bauhaus was adapted to serve its aim. The dissertation includes an explanation of each exercise in the Visual Training program. It makes explicit the point of each exercise, how each one is conducted and why. Original detailed descriptions, illustrated with examples, show what a student gains from each exercise and how the process of performing the exercise achieves that end. The dissertation compares the way in which the Visual Training program originally developed in relation to other courses in the architecture curriculum to disclose the meaning of architecture, and what has happened in Visual Training as the architecture program at IIT has continued to evolve. The thesis brings to light enduring epistemological tensions underlying architectural education.
Ph.D. in Architecture, May 2016
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Title: STRAWBERRY EXTRACT ATTENUATES ELEVATED FREE FATTY ACID AND/OR GLUCOSE MEDIATED IMPAIRED INSULIN SIGNALING IN VITRO IN HUMAN SKELETAL MUSCLE CELLS
Creator: Kangath, Archana
Date: 2013, 2013-07
Description: Elevated glucose and free fatty acids (FFA) are evident in individuals with insulin resistance (IR) and are associated with oxidative stress....
Show moreElevated glucose and free fatty acids (FFA) are evident in individuals with insulin resistance (IR) and are associated with oxidative stress. Anthocyanins are suggested to have favorable effects on human health due to their ability to modulate oxidative stress and cell signaling pathways in peripheral tissues. We hypothesized that strawberry anthocyanins (Str) will improve oxidative stress-mediated impaired insulin signaling in vitro in human skeletal muscle cells (HSMCs). Oxidative stress was generated using physiologically high glucose, FFA or a combination of both for 2–6 h with and without Str (0.1–1.0 mg/mL) in vitro in HSMCs. Cell viability was not affected by any of the above treatments/time. At the end of treatments, cells were treated with insulin (100 nM) for 20 min. Phosphorylation of Akt (p-Akt/Akt) was studied using AlphaScreen® Surefire® assay as a measure of insulin signaling. Our results indicate that FFA (2 mM) or combination of Glucose (15 mM) + FFA (2 mM) significantly decreased p-Akt/Akt compared to control cells treated with phosphate buffered saline (P<0.05). ST extract restored FFA and Glucose + FFA-induced reductions in p-Akt/Akt levels (P<0.05). These in vitro data suggest a role of strawberry in improving insulin sensitivity having potentially important implications for people with IR.
M.S. in Biological Sciences, July 2013
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Title: TOWARDS REDUCING BUILDING ENERGY CONSUMPTION: COMPARISON OF THE RESIDENTIAL PLANNING PATTERNS IN BEIJING, SHANGHAI AND GUANGZHOU
Creator: Wang, Nan
Date: 2013, 2013-07
Description: The objective of this research is to investigate the influence of urban planning patterns on building energy consumption in different climate...
Show moreThe objective of this research is to investigate the influence of urban planning patterns on building energy consumption in different climate zones. Urban planning patterns affect the thermal performance and energy efficiency of buildings. This research focuses on the planning aspects, explores the energy consumption of a group of buildings with different planning pattern by utilizing single building as a baseline, and identifies planning patterns that can help to reduce the total heating and cooling energy consumption of the buildings. The investigations are carried out in the three largest cities in China – Beijing, Shanghai, and Guangzhou, which represent different climate zones. The results of these three cities are compared to find out the influence of local climate on building energy consumption. Different planning patterns are investigated in this research through changing building parameters: aspect ratio, orientation and layout. It is found that these parameters have important influence on reducing building’s energy consumption. It is also found that in different climate conditions, the influence of planning patterns on building’s energy consumption is different, which suggests that the selection of planning pattern will be different for each location. This research makes a significant contribution to the reduction of building energy consumption from urban planning perspective. In addition, two case studies of carefully selected residential communities with fixed site and fixed density are evaluated, and some suggestions for improvement are proposed as a result of the study. Such suggestions can help to propose a prototype model guiding the future architectural and urban planning work.
PH.D in Architecture, July 2013
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Title: FATIGUE AND BARKHAUSEN EFFECT
Creator: Tong, Wei
Date: 2013, 2013-05
Description: Piezomagnetism designates a change in the magnetization of materials induced by mechanical actions such as tension or compression. The type of...
Show morePiezomagnetism designates a change in the magnetization of materials induced by mechanical actions such as tension or compression. The type of Barkhausen effect that occurs in this work consists of sudden, discontinuous jumps in a material’s magnetization that appear in response to smooth (continuous) stress variations. A series of strain controlled fatigue tests with an alternating sinusoidal waveform were carried out to study the relationship between the endurance limit and the Barkhausen effect. Results of fatigue tests on steel specimens exhibiting Barkhausen pulses at various stages are reported and a threshold-crossing analysis is applied to the test results. These studies show that when the fatigue limit is approached, the Barkhausen pulses become, in general, more intense in amplitude and quantity than at other stress levels. A hypothetical mechanism is proposed that relates the intensity of the Barkhausen response to the inception of micro-cracking and rearrangements of the mechanical lattice at the microscopic level.
PH.D in Civil Engineering, May 2013
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Title: ANALYZING REPRODUCING KERNEL APPROXIMATION METHODS VIA A GREEN FUNCTION APPROACH
Creator: Ye, Qi
Date: 2012-04-22, 2012-05
Description: In this thesis, we use Green functions (kernels) to set up reproducing kernels such that their related reproducing kernel Hilbert spaces ...
Show moreIn this thesis, we use Green functions (kernels) to set up reproducing kernels such that their related reproducing kernel Hilbert spaces (native spaces) are isometrically embedded into or even are isometrically equivalent to generalized Sobolev spaces. These generalized Sobolev spaces are set up with the help of a vector distributional operator P consisting of finitely or countably many elements, and possibly a vector boundary operator B. The above Green functions can be computed by the distributional operator L := P TP with possible boundary conditions given by B. In order to support this claim we ensure that the distributional adjoint operator P of P is well-defined in the distributional sense. The types of distributional operators we consider include not only di erential operators but also more general distributional operators such as pseudo-di erential operators. The generalized Sobolev spaces can cover even classical Sobolev spaces and Beppo-Levi spaces. The well-known examples covered by our theories include thin-plate splines, Mat´ern functions, Gaussian kernels, min kernels and others. As an application for high-dimensional approximations, we can use the Green functions to construct a multivariate minimum-norm interpolant s f;X to interpolate the data values sampled from an unknown generalized Sobolev function f at data sites X Rd. Moreover, we also use Green functions to set up reproducing kernel Banach spaces, which can be equivalent to classical Sobolev spaces. This is a new tool for support vector machines. Finally, we show that stochastic Gaussian fields can be well-defined on the generalized Sobolev spaces. According to these Gaussian-field constructions, we find that kernel-based collocation methods can be used to approximate the numerical solutions of high-dimensional stochastic partial differential equations.
Ph.D. in Applied Mathematics
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Title: LONGITUDINAL MEASUREMENT EQUIVALENCE IN TRAINING EVALUATION: TESTING FOR ALPHA, BETA, AND GAMMA CHANGE IN SELF-EFFICACY
Creator: Wright, Sean Christopher
Date: 2013, 2013-12
Description: When evaluating a training program, it is common to make longitudinal comparisons between pre-training and post-training measures of one or...
Show moreWhen evaluating a training program, it is common to make longitudinal comparisons between pre-training and post-training measures of one or more learning outcomes. This study was conducted to demonstrate the need to investigate the type of change observed in a self-efficacy measure collected pre- and post-training using a military sample. Using a variation of CFA methods developed by Schmitt (1982), tests were conducted to determine whether pre-post differences in the measure were due to a true (i.e., alpha) change in the construct, or if trainees had recalibrated (i.e., beta change) or reconceptualized (i.e., gamma change) their use of the self-efficacy measures over time. In addition, the potential relationship between task difficulty and the type of observed change was investigated. Analyses found that gamma change did occur with regard to the covariance between the latent factors of self-efficacy, and partial beta change occurred with regard to the latent factor variance. Task difficulty subgroup analyses reflected a very similar result to the primary analysis suggesting that task difficulty has little or no effect on the type of change observed. Implications for training evaluation research are discussed, as well as study limitations and future research directions.
M.S. in Psychology, December 2013
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Title: INFLUENCE OF THE FREQUENCY OF LOADING ON THE PIEZO-BARKHAUSEN RESPONSE OF STEEL FOR FATIGUE LIFE ASSESSMENT
Creator: Coulibaly, Jibril
Date: 2014, 2014-07
Description: Steel can fail prematurely under repeated cyclic loading below its proportional limit (or yielding point). Provided that a certain threshold...
Show moreSteel can fail prematurely under repeated cyclic loading below its proportional limit (or yielding point). Provided that a certain threshold in the loading magnitude, known as the endurance limit, is not exceeded, the material can sustain an infinite number of cycles (N 107). Recent interests in the piezo-magnetic behavior of ferromagnetic steels have been successfully providing reliable and physical indicators of fatigue properties of the material. Cyclic mechanical strain applied to ferromagnetic steels produce abrupt variations in the magnetization of the material referred to as piezo-Barkhausen pulses. Among the many variables influencing fatigue characteristics, the influence of the frequency of loading upon the piezo-Barkhausen response of steel is investigated herein. While the amplitude of the pulses varies, the cumulative piezo-Barkhausen indicator of damage is independent of the frequency and provides a specific measure of damage for a given level of applied strain and may be a way to evaluate service life. The distribution of the dominant frequency of the observed pulses also permits one to separate the stable states of loading from those leading to fatigue failure. Further observations on the piezo-Barkhausen signals particularities have also been made to provide a greater insight on the micro mechanisms of fatigue and damage accumulation in steel.
M.S. in Civil Engineering, July 2014
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Title: DISJUNCTNESS PROPERTIES RESULTING FROM CONCATENATION OF GROUP TESTING MATRICES
Creator: Clardy, Melinda Bulin
Date: 2015, 2015-05
Description: This thesis discusses matrix properties as they relate to the idea of non-adaptive group testing. This is accomplished by first considering...
Show moreThis thesis discusses matrix properties as they relate to the idea of non-adaptive group testing. This is accomplished by first considering the history of group testing and then exploring existing results. The next chapter of this thesis discusses taking a given binary matrix and using this as an inner code with some symbol matrix as an outer code to create a new binary matrix. The process is called a concatenation construction and we will cover a few types including the orthogonal array construction, a 𝜆-separating hash family construction, code concatenation, and DNA Sudoku. We conclude by elaborating on primary results coming from orthogonal array construction and 𝜆-separating hash family constructions. These give results pertaining specifically to Steiner systems and cover-free families.
M.S. in Applied Mathematics, May 2015
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Title: IMPACT OF THERMAL PROCESSING ON THE STRUCTURAL AND IMMUNOLOGICAL PROPERTIES OF MAJOR EGG AND MILK ALLERGENS
Creator: Chandra, Srinivasa Rao
Date: 2011-05-03, 2011-05
Description: The underlying mechanism of food allergy is not well understood. Research has increasingly focused on the characterization of food allergens....
Show moreThe underlying mechanism of food allergy is not well understood. Research has increasingly focused on the characterization of food allergens. Since most foods are cooked prior to consumption, information relating to the impact of thermal processing on the properties of allergenic proteins is critical for allergen risk assessment. This study examined the impact of thermal processing on the structure and the antigenic potential of the major egg and milk allergens, ovomucoid (OVO) and -lactoglobulin (BLG) both A and B variants respectively. OVO and BLG were subjected to thermal processing under moist and dry heat conditions for 10 min. No significant changes in the solubility of both proteins were observed after boiling, autoclaving or dry heating up to 204C. At 232C, a significant protein loss was observed. Inhibition ELISA was used to determine the effect of heat treatment on the capacity of these proteins to bind rabbit derived IgG antibodies. While boiling and autoclaving caused a decrease in IgG binding of OVO, an increase in IgG binding of BLG was observed under the same experimental conditions. A similar pattern that a decrease in antigen binding potentials by OVO and an increase in antigen binding potentials by BLG A and B variants was noticed during dry heat treatment at temperatures 232C and above. Structural analyses were performed using circular dichroism spectroscopy (CD) and differential scanning calorimetry (DSC). Both proteins showed variations in the secondary structure when subjected to heating in water and PBS. In the presence of water, variable temperature scan with CD resulted in transition temperatures of OVO and BLG variants in the range of 70-75oC and 80-85oC respectively. There is no significant change in the secondary structure of BLG variants prepared in PBS. DSC study showed the transition temperatures of 84oC, 129oC for OVO and at 80oC, 195oC for BLG (A & B) variants under moist and dry heat conditions respectively. Overall, both proteins were highly resistant to thermal denaturation and retained their antigenic potential at typical cooking temperatures.
M.S. in Food Safety and Technology, May 2011
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Title: FACTORS AFFECTING THE SANITIZER EFFICACY IN PREVENTING SALMONELLA TRANSFER DURING POSTHARVESTWASHING OF CUT LETTUCE
Creator: Zang, Mingxia
Date: 2014, 2014-07
Description: This study investigated the spread of Salmonella during postharvest washing of contaminated lettuce and determined the factors that affect the...
Show moreThis study investigated the spread of Salmonella during postharvest washing of contaminated lettuce and determined the factors that affect the efficacy of chlorine in preventing Salmonella cross-contamination. A bench-scale washing system was assembled. It consisted of a 15 gal tank equipped with a submersible pump and various probes to measure wash water properties including pl-l, temperature, chlorine level. oxidation reduction potential (ORP), turbidity, and total organic carbon (TOC) level. Eight grams of cut romaine lettuce inoculated with approximately 7 log CFU/g of Salmonella typhimurium expressing green fluorescent protein were added to 40 L of sterile tap water or spent wash water collected from a commercial produce processing facility together with 800 g of uninoculated lettuce and washed for 2 min. Washing trials were performed at 3°C under different levels of chlorine treatment (0. 5, 10, 20 and 30 ppm of sodium hypochlorite). Small-scale (100 mL) experiments were performed to determine the effects of organic matter (lettuce homogenate) and solids (play sand) on the efficacy of chlorine in wash water. Without chlorine treatment, the spread of Salmonella occurred during lettuce washing in both sterile tap water and in spent industry water. With 5 ppm ofchlorine. although no Salmonella was detected in the wash water, the pathogen was found in the uninoculated lettuce after culture enrichments. When the chlorine level increased to either 10 or 20 ppm of chlorine, no Salmonella was detected in the wash water or the uninoculated lettuce. Similar experiments were performed using the spent industry water. Salmonella transfer occurred at chlorine of 10 or 20 ppm. Transfer was prevented when the chlorine level was raised to 30 ppm. IX Data from the small-scale experiments indicated that the increase in organic contents resulted in a lower level of free chlorine and a greater survival of Salmonella. At 5 ppm of chlorine, the level of Salmonella increased from not detectable « 1 log CFU/ml) to completely unaffected (4.3 ± 0.4 log CFU/ml) when the TOC level increased from 10.6 ± 10.4 to 164.0 ± 21.2 mg/L. Also, the increase in solid contents led to a greater survival of Salmonella even though the free chlorine level remained unchanged. At 0.5 ppm of chlorine, the level ofSalmonella increased from not detectable « 1 log CFU/ml) to completely unaffected (4.2 ± 0.1 log CFU/ml) when the turbidity level increased from 0.7 ± 0.5 to 378.2 ± 53.5 NTU. In summary, the use of sufficient chlorine could prevent Salmonella cross-contamination but the effective chlorine level was affected by the organic load and solid content present in the wash water. Since the chlorine efficacy was the key to preventing cross-contamination and organic load was an important factor affecting the chlorine efficacy, measurements of the organic load was needed to determine the level ofchlorination needed in the wash water. However, current methods for measuring TOC took a few hours to perform. Turbidity measurements which could be done in a minute had been frequently used in the produce industry to determ ine wash water quality. In this study. it was found that a linear correlation could be established between turbidity and TOC suggesting that turbidity could be a good predictor of the organic load in produce wash water.
M.S. in Food Safety and Technology, July 2014
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Title: IN VITRO ASSAYS FOR ASSESSING THE IMPACT OF PROCESSING ON THE ALLERGENIC POTENTIAL OF MILK ALLERGENS
Creator: Zhao, Yuhui
Date: 2014, 2014-07
Description: A better understanding of how processing may affect the allergenicity of proteins will allow a more accurate allergen risk assessment and...
Show moreA better understanding of how processing may affect the allergenicity of proteins will allow a more accurate allergen risk assessment and achieve a greater success in food allergy management. To determine whether food processing can affect the allergenic potential of proteins, immunochemical methods are commonly used, however, these methods are not able to determine changes in the proteins’ ability to elicit allergic reactions. Although food challenge studies are the gold standard for determining the allergenicity of proteins, such studies are seldom performed due to ethical reasons and the difficulty in recruiting a sufficient number of study participants. In recent years, a number of mediator release assays have been developed to measure the allergenic potential of food proteins in vitro. How these cell-based assays can be used to assess the impact of thermal or other processing conditions on the allergenic potential of proteins remains to be determined. This study evaluated the use of sensitized rat basophilic leukemia (RBL) cells for assessing the impact of boiling on the allergenic potential of milk allergens by measuring changes in the proteins’ ability to induce cell degranulation and the release of β-hexosaminidase. RBL-2H3 cells sensitized with rat IgEs that were specifically raised against NIST non-fat milk standard reference material, β-lactoglobulin (BLG), α-lactalbumin (ALA) and α-casein (αCAS) were challenged with various concentrations of each allergen in its native and processed (boiled in PBS for 10 min) forms. Cell degranulation caused by the allergen was monitored by measuring the release of β-hexosaminidase. All allergens (NIST milk, BLG, ALA, and αCAS) were able to induce the release of β-hexosaminidase, up to 43.1%. A decrease in % release of β-hexosaminidase was observed when cells were challenged with a decreasing concenration xi of the ALA and milk allergens. Boiling of nonfat milk, ALA, BLG and αCAS caused an increase in the % release of β-hexosaminidase from sensitized cells across antigen concentrations from 10-6 mg/mL to 1 mg/mL. In conclusion, this study demonstrated that the in vitro mediator release assay method may serve as a useful model to evaluate the impact of thermal processing on the allergenic potential of milk allergens.
M.S. in Food Safety and Technology, July 2014
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Title: CONSIDERING THE FORM OF TALL BUILDINGS TO ACHIEVE MINIMUM STRUCTURAL WEIGHT REGARDING ALONG WIND EFFECT
Creator: Alaghmandan, Matin
Date: 2014, 2014-07
Description: One of the most influential parameters in structural design of tall buildings, in addition to gravity loads, is the lateral load resulting...
Show moreOne of the most influential parameters in structural design of tall buildings, in addition to gravity loads, is the lateral load resulting from wind and to some extend earthquakes. Tall buildings have to be designed for a larger base shear, resulting from wind forces rather than from seismic forces; however, ductile detailing is used when needed to account for seismic demands. The wind effect occurs primarily in two main modes of action: across wind and along wind. Wind also produces three different types of effects on tall buildings: static, dynamic, and aerodynamic. Structurally, static effect is measured independent of time, while dynamic analysis takes into account the system responses to the changes through the period of time. When the building is flexible, it interacts with the wind load and affects its response, which is called aerodynamic effect. To reduce the static and dynamic impact, there are two main solutions: Architectural and Structural. Architects can mitigate wind effect on tall buildings by designing the form aerodynamically or at least by utilizing aerodynamic modifications, which are categorized in macro and micro modifications such as tapering and setback. Structural engineers can reduce wind effect by choosing and designing efficient structural systems regarding the form of the buildings such as tube systems and diagrid system. This thesis, by utilizing the architectural and structural strategies to reduce wind effect, introduces a new design method in the realm of tall buildings. An innovative design method of tall buildings calls for an integrated approach with considerations for both architectural and structural design requirements and concepts. This design method which considers integrated architectural and structural concepts to reduce the along wind effect, for this particular research, is an effort to achieve the minimum weight of the structure. This method creates an innovative computational workbench to design efficient tall buildings, regarding the along wind effect. This can be achieved by connecting an architectural parametric design procedure in AutoLisp (AutoCAD) to generate the models with different forms of modifications, a Computational Fluid Dynamics program (ANSYS) to do the CFD simulations, a structural analysis program (SAP2000) and the ParaGen visualization interface to consider and compare the results to evaluate the models regarding the criteria such as weight of the structure (ParaGen has also a Genetic Algorithm based optimization procedure that is not used in this research). In this research, the most important architectural and structural parameters of seventy-three of the tallest buildings are also studied. Based on the information gathered, the future trend in the geometry and form and also the structural design used for tall buildings can be predicted. It is necessary to mention, based on the past and current trends in architectural features and structural design of tall buildings, and also the future prediction, the required architectural and structural parameters for an innovative proposed design method can be helped. However, it is not clear whether this trend is in a right way or not, it has to be improved based on the architectural and structural efficient parameters. The research shows that a careful study of core design concepts in architectural features and structural design of tall buildings, along with an integrated approach considering various design requirements, can be an effective method in design of new generation of tall buildings.
Ph.D. in Architecture, July 2014
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Title: Factors Affecting Sanitizer Efficacy on Preventing Cross-Contamination of E. Coli 0157:H7 During Postharvest Washing of Romaine Lettuce
Creator: Li, Yichen
Date: 2011-12-05, 2011-12
Description: Outbreaks of E. coli O157:H7 infections have been a continuing food safety challenge for the produce industry. Previous studies showed that...
Show moreOutbreaks of E. coli O157:H7 infections have been a continuing food safety challenge for the produce industry. Previous studies showed that contamination that originated in the farm can spread during postharvest processing. The objective of this study was to examine the spread of E. coli O157:H7 during postharvest washing of contaminated lettuce and to determine factors affecting the efficacy of sanitizer use in preventing cross-contamination. A bench-scale washing system was established to simulate industry operations. This system was equipped with a submersible pump and instruments to measure wash water properties including pH, temperature, chlorine level, oxidation reduction potential (ORP), turbidity and total organic carbon (TOC). Fresh-cut romaine lettuce (8 or 20 g) inoculated with approximately 8 log CFU/ml of E. coli O157:H7 with green fluorescence protein (GFP) were added into 40 L of tap water or industry water together with uninoculated lettuce (800 or 2000 g). The wash procedure lasted for 2 minutes. Washing operations were performed at two temperatures (3°C and 20°C) combined with different levels of chlorine treatments (0, 5, 20 and 30 ppm). Smaller-scale (50 – 100 mL) washing experiments were performed separately to determine the effects of organic contents and solid contents on the efficacy of sanitizer. Without chlorine treatment, the spread of E. coli O157:H7 occurred in both tap water and industry water at both 20°C and 3°C. With 20 ppm chlorine, no E. coli O157:H7 was detected in either wash water or uninoculated lettuce after washing in tap water. In industry water, chlorine level at 30 ppm or above could prevent crosscontamination of E. coli O157:H7. Neither the lettuce load nor the wash water temperature was proved to affect the efficacy of sanitizer. At 5 ppm of chlorine, increases in organic carbon (0% to 20%) led to the drop of free chlorine which resulted in a decrease in the microbial reduction from 2.51 to 0.01 log CFU/g. Increases in solid contents (0 g/L to 20 g/L) also caused a decrease in the microbial reduction from 2.52 to 1.17 log CFU/g but it did not change the free chlorine concentration. The utility of ORP as a measure of the antimicrobial efficiency of wash water was evaluated. ORP readings increased with increasing chlorine levels but reached a plateau and failed to correlate with the concentration of chlorine at chlorine levels > 20 ppm.
M.S. in Food Safety and Technology, December 2011
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Title: IN VITRO CELLULAR ASSAY FOR ASSESSING THE IMPACT OF PROCESSING ON THE ALLERGENIC POTENTIAL OF EGG ALLERGENS
Creator: Wei, Kunni
Date: 2013-04-24, 2013-05
Description: An understanding of the impact of processing on food allergenicity is critical for allergen risk assessment. In vitro cell-based assays have...
Show moreAn understanding of the impact of processing on food allergenicity is critical for allergen risk assessment. In vitro cell-based assays have been developed to assess the allergenicity of food proteins by measuring the capacity of these proteins to induce degranulation in effector cells. This study evaluated the use of sensitized rat basophilic leukemia (RBL) cells for assessing the allergenic potential of ovalbumin (OVA), ovomucoid (OVM) and NIST whole egg standard reference material (NIST egg) by measuring the proteins’ ability to induce the release of β-hexosaminidase and histamine. RBL-2H3 cells at a concentration of ~105 cells/mL were seeded in six-well plates and incubated for 24 hours. After washing with PBS and resuspension in Eagle’s minimal essential medium, cells were sensitized by incubation for 24 h with various dilutions of anti-OVA, anti-OVM or anti-egg antisera derived from Brown Norway or Wistar rats. The sensitized cells were then challenged with different concentrations of unheated or boiled (for 10 min) OVA, OVM or NIST egg. After 45 min, cell degranulation was determined by measuring the increase in β-hexosaminidase and histamine in culture supernatant. The release of β-hexosaminidase was observed after the cells were challenged with OVA, OVM or NIST egg. Assay optimization studies using sensitized cells challenged with OVA indicated that the greatest degree of release occurred when RBL-2H3 cells were sensitized with 1:10 dilution of anti-sera and challenged with 0.01mg/mL of OVA. Histamine release was not detected under any serum or OVA concentration. Boiling caused a decrease in the recovery of OVA and NIST egg (70.34±3.61% and 73.69±7.66% respectively), but did not affect the recovery of OVM (96.28±2.95%). Under the optimal assay conditions, boiled OVA and boiled NIST egg x triggered the release of more β-hexosaminidase than the unheated forms of these proteins in most cases. However, boiling did not significantly affect the ability of OVM to induce the release of β-hexosaminidase. Due to the large variation in % release of β-hexosaminidase obtained from different trials, the RBL mediator release assay may not be an accurate method to evaluate the impact of thermal processing on the allergenic potential of egg allergens.
M.S. in Food Safety and Technology, May 2013
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Title: A Novel Explainability Approach For Spectrum Measurement Insight
Creator: Nagpure, Vaishali
Date: 2023
Description: Spectrum is an extremely valuable natural resource in high demand. Although the spectrum has been fully allocated, there is no comprehensive...
Show moreSpectrum is an extremely valuable natural resource in high demand. Although the spectrum has been fully allocated, there is no comprehensive method for understanding about how it’s being used. Spectrum measurements are highly complex spatiotemporal data sets that play a key role in understanding spectrum use and require very specialized domain information for understanding. To leverage existing and future spectrum measurements to the fullest extent, it is necessary to have a systematic way to connect them to the contextual information that helps provide meaning to the data. To analyze and interpret the measurements, a variety of contextual information is needed. This research develops a novel approach for spectrum measurement understanding that unifies five years of wideband spectrum measurement summary data together with relevant contextual information from a variety of sources in a spectrum knowledge graph. Both quantitative and qualitative information is modeled and implemented on a Neo4j graph database platform. This modeling formalizes the relationships that help spectrum stakeholders “connect the dots” and provide deeper understanding of RF spectrum utilization. The knowledge graph can be queried to extract a wide variety of insights thus making spectrum knowledge more widely accessible to a variety of stakeholders.
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Title: IMPROVING DEEP LEARNING BASED SEMANTIC SEGMENTATION USING CONTEXT INFORMATION
Creator: Xia, Zhengyu
Date: 2021
Description: Semantic segmentation is an important but challenging task in computer vision because it aims to assign each pixel a category label accurately...
Show moreSemantic segmentation is an important but challenging task in computer vision because it aims to assign each pixel a category label accurately. Nowadays, applications such as autonomous driving, path navigation, image search engine, or augmented reality require accurate semantic analysis and efficient segmentation mechanisms. In this thesis, we propose multiple models to improve the performance of semantic segmentation. In the first part, we focus on the single-task network, which aims to improve the performance of semantic segmentation. Our research includes exploiting context information using mixed spatial pyramid pooling to extract dense context-embedded features in FCN-based semantic segmentation. We also propose a GAF module to generate a global context-based attention map to guide the shallow-layer feature maps for better pixel localization. In the second part, we focus on a multi-task network that incorporates semantic segmentation to improve other computer vision tasks such as object detection. Specifically, a multi-task network, along with a learning strategy is designed to let semantic segmentation and object detection assist each other since they are highly correlated. Also, we include weakly-supervised multi-label semantic segmentation learning to deal with the shortage of high-quality training examples and to improve the performance of cross-domain object detection. In the third part, we focus on improving the performance of video panoptic segmentation, which is a unified network that incorporates semantic segmentation and instance segmentation using video streams. We design a new ConvLSTM pyramid to transmit spatio-temporal contextual information in our video panoptic segmentation network. Specifically, we propose a modified ConvLSTM to generate temporal contextual information. Also, we design an MSTPP module to obtain mixed spatio-temporal context-embedded feature maps. Experimental results on different datasets show that our proposed method achieves better performance compared with the state-of-the-art methods.
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Title: EVENT-BASED NONINTRUSIVE LOAD MONITORING
Creator: Yan, Lei
Date: 2021
Description: Non-Intrusive Load Monitoring (NILM) is an important application to monitor household appliance activities and provide related information to...
Show moreNon-Intrusive Load Monitoring (NILM) is an important application to monitor household appliance activities and provide related information to house owner or/and utility company via a single sensor installed at the electrical entry of the house. With this information, utilities can perform many tasks such as energy conservation, planning gen-eration more wisely, and demand response (DR) study. For house owners, they can un-derstand their bill more clearly and make better budget plan. For researchers, NILM sys-tem is a good foundation for energy management in buildings and can provide valuable power information for smart homes design. This dissertation aims to develop and demon-strate a complete and accurate event-based NILM system, which includes (1) an edge-cloud framework for event-based NILM, (2) an adaptive event detection method, (3) a two-stage event-based load disaggregation method; and (4) a high-resolution (50Hz) NILM dataset. Event detection is the first step in event-based NILM and it can provide deter-ministic transient information to identify appliances. However, existing methods with fixed parameters suffer from unpredictable and complicated changes in smart meter data such as long transition, high fluctuation and near-simultaneous events in both power and time domains. This dissertation presents an adaptive method to detect events based on home appliance load data with high sampling rate (>1Hz) by flexibly tuning the parame-ters according to the data being processed. The proposed method runs fast over the data stream and captures the transient process by multi-timescales searching as well. The mi-cro-timescale and macro-timescale window could deal with near-simultaneous events and long-transition events, respectively. Transient load signatures are extracted from detected events and stored in a sequential tree struct that can be used for NILM and load recon-struction, etc. Case studies on a 20Hz dataset, the LIFTED dataset of 50Hz, and the BLUED dataset of 60Hz demonstrate that the proposed method is able to work on data of different sampling rates and outperforms other methods in event detection. The ex-tracted load signatures could also improve the efficiency of NILM and help develop oth-er applications. This dissertation presents an online transient-based electrical appliance state track-ing method for NILM. The proposed Factorial Particle based Hidden Markov Model (FPHMM) method takes advantage of transient features in high-resolution data to infer states in the transient process and conducts steady state verification to rectify falsely identified appliances. The FPHMM method can overcome the common feature similarity problem in NILM by combining particle filter method and Markov Chain Monte Carlo sampling method, and by mining the intra-relationship of states inside a single appliance and the inter-relationship of states among multiple appliances. The FPHMM method is tested on the LIFTED dataset with appliance-level details and high sampling rates. Test-ing results demonstrate that the FPHMM method is effective in resolving the feature similarity issue. A modified mean shift algorithm with different levels of bandwidth is proposed as well to cluster the extracted features from event detection. Based on the clustered fea-tures, another solution is proposed to decode the states of appliance in two stages. The first stage uses Bayesian Inference Factorial HMM (BI-FHMM) solver to accelerate com-putational speed and improve accuracy by integrating the load signatures and statistical inference. The second stage then verifies and rectifies the results obtained from the first stage. Test results demonstrate that the proposed approach achieves good performance and can be applied to existing smart meters.
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Title: THE EFFECTS OF MODIFIED SURFACES ON INSULIN CRYSTALLIZATION
Creator: Hammadi, Okba Tahar
Date: 2021
Description: Engineered nucleation features (ENFs) were designed with the hope to improve the efficiency of protein crystallization and increase...
Show moreEngineered nucleation features (ENFs) were designed with the hope to improve the efficiency of protein crystallization and increase reproducibility both in quality and quantity. These ENFs were tested with human insulin as the protein of choice since it has flexible parameters, only one cofactor, and a large amount of commercially available crystal ready protein. Insulin crystallization on the ENFs will produce more crystals while also having a reduced crystallization on-set time compared to the control glass surface. The ENFs were compared to control surfaces under similar conditions and observed over time to record both onset-times and end times. The ENFs performed markedly better in on-set times, having an overall 87%-time reduction when compared to the control drops. The drops placed on the ENF produced more than 2.5x the number of crystals in the control drops.
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Title: TASK-BASED LOAD FORECASTING AND ROBUST RESOURCE SCHEDULING IN SMART GRID
Creator: Han, Jiayu
Date: 2021
Description: In microgrids, the uncertainty of load and renewables and lack of generation capacity will lead to a wide variety of operation problems in...
Show moreIn microgrids, the uncertainty of load and renewables and lack of generation capacity will lead to a wide variety of operation problems in both grid-connected mode and islanded mode. This motivates the design of the state-of-art microgrid master controller for microgrid energy management, load forecasting, and demand response. Uncertainty in renewables and load is a great challenge for microgrid operation, especially in islanded mode as the microgrid may be small in size and has limited flexible resources. A multi-timescale, two-stage robust dispatch model is proposed to optimize the microgrid operation. The proposed one uses only one model to combine the hourly and sub-hourly dispatch together, which means the day-ahead hourly dispatch results must also satisfy the sub-hourly conditions. At the same time, the feasibility of the day-ahead dispatch result is verified in the worst-case condition considering the high-level uncertainty in renewable energy output and load consumptions. In addition, battery energy storage system (BESS) and solar PV units are integrated as a combined solar-storage system in the proposed model and the output power of the combined solar-storage system remains unchanged on an hourly basis. Furthermore, both BESS and thermal units provide regulating reserve to manage solar and load uncertainty. The model has been tested in a controller hardware in loop (CHIL) environment for the Bronzeville Community Microgrid system in Chicago. The simulation results show that the proposed model works effectively in managing the uncertainty in solar PV and load and can provide a flexible dispatch in both grid-connected and islanded modes.When the generation capacity of an islanded microgrid is less than the load demand, load curtailment is inevitable. This dissertation proposes a multi-objective optimization model to minimize the load curtailments. Specifically, the proposed model minimizes the generation cost and total load curtailments and also minimizes the maximum load curtailment. Furthermore, the impact of the penalty coefficients of total load curtailment and maximum load curtailment is analyzed, which provides a strategy to choose the value of the two penalty coefficients according to different practical purposes. The proposed model can be used in both microgrid generation scheduling and microgrid planning problems. It was tested in the Bronzeville Community Microgrid system and the results showed that the proposed model can reduce the total load curtailment and maximum load curtailment.Load forecasting is one of the most important and studied topics in modern power systems. However, traditional load forecasting is an open-loop process as it does not consider the end use of the forecasted load. This dissertation proposes a closed-loop task-based day-ahead load forecasting model labeled as LfEdNet that combines two individual layers in one model, including a load forecasting layer based on deep neural network (Lf layer) and a day-ahead stochastic economic dispatch (SED) layer (Ed layer). The training of LfEdNet aims to minimize the cost of the day-ahead SED in the Ed layer by updating the parameters of the Lf layer. Sequential quadratic programming (SQP) is used to solve the day-ahead SED in the Ed layer. The test results demonstrate that the forecasted results produced by LfEdNet can lead to lower cost of day-ahead SED at the expense of slight reduction in forecasting accuracy.
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Title: Advances in Machine Learning: Theory and Applications in Time Series Prediction
Creator: London, Justin J.
Date: 2021
Description: A new time series modeling framework for forecasting, prediction and regime switching for recurrent neural networks (RNNs) using machine...
Show moreA new time series modeling framework for forecasting, prediction and regime switching for recurrent neural networks (RNNs) using machine learning is introduced. In this framework, we replace the perceptron with an econometric modeling unit. This cell/unit is a functionally dedicated to processing the prediction component from the econometric model. These supervised learning methods overcome the parameter estimation and convergence problems of traditional econometric autoregression (AR) models that use MLE and expectation-maximization (EM) methods which are computationally expensive, assume linearity, Gaussian distributed errors, and suffer from the curse of dimensionality. Consequently, due to these estimation problems and lower number of lags that can be estimated, AR models are limited in their ability to capture long memory or dependencies. On the other hand, plain RNNs suffer from the vanishing and gradient problem that also limits their ability to have long-memory. We introduce a new class of RNN models, the $\alpha$-RNN and dynamic $\alpha_{t}$-RNNs that does not suffer from these problems by utilizing an exponential smoothing parameter. We also introduce MS-RNNs, MS-LSTMs, and MS-GRUs., novel models that overcome the limitations of MS-ARs but enable regime (Markov) switching and detection of structural breaks in the data. These models have long memory, can handle non-linear dynamics, do not require data stationarity or assume error distributions. Thus, they make no assumptions about the data generating process and have the ability to better capture temporal dependencies leading to better forecasting and prediction accuracy over traditional econometric models and plain RNNs. Yet, the partial autocorrelation function and econometric tools, such as the the ADF, Ljung-Box, and AIC test statistics, can be used to determine optimal sequence lag lengths to input into these RNN models and to diagnose serial correlation. The new framework has capacity to characterize the non-linear partial autocorrelation of time series and directly capture dynamic effects such as trends and seasonality. The optimal sequence lag order can greatly influence prediction performance on test data. This structure provides more interpretability to ML models since traditional econometric models are embedded into RNNs. The ability to embed econometric models into RNNs will allow firms to improve prediction accuracy compared to traditional econometric or traditional ML models by creating a hybrid utilizing a well understood traditional econometric model and a ML. In theory the traditional econometric model should focus on the portion of the estimation error that is best managed by a traditional model and the ML should focus the non-linear portion of the model. This combined structure is a step towards explainable AI and lays the framework for econometric AI.
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