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Pages
- Title
- Armour Tech News, January 17, 1929
- Date
- 1929-01-17, 1929-01-17
- Collection
- Technology News Microfilm collection, 1928-1981
- Title
- Armour Tech News, September 27, 1928
- Date
- 1928-09-27, 1928-09-27
- Collection
- Technology News Microfilm collection, 1928-1981
- Title
- Armour Tech News, January 24, 1929
- Date
- 1929-01-24, 1929-01-24
- Collection
- Technology News Microfilm collection, 1928-1981
- Title
- Armour Tech News, October 25, 1928
- Date
- 1928-10-25, 1928-10-25
- Collection
- Technology News Microfilm collection, 1928-1981
- Title
- A Hybrid Data-Driven Simulation Framework For Integrated Energy-Air Quality (iE-AQ) Modeling at Multiple Urban Scales
- Creator
- Ashayeri, Mehdi
- Date
- 2020
- Description
-
To date, limited work has been done to collectively incorporate two key urban challenges: climate change and air pollution for the design of...
Show moreTo date, limited work has been done to collectively incorporate two key urban challenges: climate change and air pollution for the design of sustainable and healthy built environments. Main limitations to doing so include the existence of large spatiotemporal gaps in local outdoor air pollution data and a lack of a formal theoretical framework to effectively integrate localized urban air pollution data into sustainable built environment design strategies such as natural ventilation in buildings. This work hypothesizes that emerging advanced computational modeling approaches, including artificial intelligence (AI) and machine learning (ML) techniques, along with big open data set initiatives, can be used to fill some of those gaps. This can be achieved if urban air quality explanatory factors are properly identified and effectively connected to the current building performance simulation workflows.Therefore, the primary objective of this dissertation is to develop a hybrid AI-based data-driven simulation framework for integrated Energy-Air Quality (iE-AQ) modeling to quantify the combined energy reduction profiles and health risks implications of sustainable built environment design. This framework (1) incorporates dynamic human-centered factors, including mobility and building occupancy among others into the model, (2) interlinks land use regression (LUR), inverse distance weighting (IDW), and building energy simulation (BES) approaches via the R computational platform for developing the model, and (3) develops a web-based platform and interactive tool for visualizing and communicating the results. A series of novel machine learning approaches are tested within the workflow to improve efficiency and accuracy of the simulation model. A multi-scale model of urban air quality (using PM2.5 concentrations as the end point) and weather localization model with high spatiotemporal resolution was developed for Chicago, IL using low-cost sensor data. The integrated energy and air quality model was tested for the prototype office building at multiple urban scales in Chicago through applying air pollution-adjusted natural ventilation suitable hours.Results showed that the proposed ML approaches improved model accuracy above traditional simulation and statistical modeling approaches and that incorporating dynamic building-related factors such as human activity patterns can further improve urban air quality prediction models. The results of integrated energy and air quality (iE-AQ) analysis highlight that the energy saving potentials for natural ventilation considering local ambient air pollution and micro-climate data vary from 5.2% to 17% within Chicago. The proposed framework and tool have the potential to aid architects, engineers, planners and urban health policymakers in designing sustainable cities and empowering analytical solutions for reducing human health risk.
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- Title
- THE INTERACTION BETWEEN COINAGE OR ALKALI METALS AND POLYAROMATIC HYDROCARBONS
- Creator
- Liu, Shuyang
- Date
- 2020
- Description
-
Theoretical study on versatile chemistry of buckybowls and related polyaromatic hydrocarbons has been comprehensively accomplished and...
Show moreTheoretical study on versatile chemistry of buckybowls and related polyaromatic hydrocarbons has been comprehensively accomplished and documented. Polyaromatic hydrocarbons from simple double bond to fullerene C60, as one of major family in buckybowls has shown a wide potential in development of various specifically purposed materials. Complexes with coinage metals evidenced tunable donor ability of related polyaromatic systems’ π-surface. Moreover, functionalization with small ligands cations interact with these π-surface also show some patterns which have certain enlightenment to the experiment. By adding the methyl group on corannulene, to pursue the relationship between geometry and stabilization which provide an alternative strategy of developing. Further study of alkali metals interacts with annulene, continuously adding with crown ether to mimic experiment environment display an interesting pattern. In the end, extended topics of some applications with computational chemistry, such as the help of Raman spectrum of L-focus.
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- Title
- Exploring differences in eating disorder symptomatology and treatment outcomes between sexual minority and heterosexual women in eating disorder treatment programs
- Creator
- Murray, Matthew Ford
- Date
- 2020
- Description
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Research on eating disorder (ED) symptomatology in sexual minority (SM) women is limited and has demonstrated inconsistent findings with...
Show moreResearch on eating disorder (ED) symptomatology in sexual minority (SM) women is limited and has demonstrated inconsistent findings with respect to how they differ from heterosexual women. Further, many studies combine SM women into one group, potentially masking important sub-group differences. Existing data appears to suggest that SM women may be at similar or increased risk for certain types of disordered eating behaviors and present with body image concerns that may differ from heteronormative female body ideals. However, it is unclear how weight and shape control behaviors differ across sexual orientations in women seeking treatment for EDs, and if there are differences in treatment outcomes. The present study used analyses of variance and covariance to test 1) group differences in frequency and severity of ED symptomatology and 2) differences in group by time interaction effects as an indicator of treatment outcomes in a sample of 3,120 adult women of diverse sexual orientations who presented for ED treatment between 2015 and 2018. Participants identified their sexuality as heterosexual, lesbian, bisexual, or other/unsure. Results indicated notable group differences in ED symptoms upon admission to treatment. Bisexual women, in particular, presented to treatment at younger ages, with higher BMIs, and more severe illnesses than heterosexual women. Further, results from the present study suggest that despite such differences, women across sexual orientation groups achieved similar treatment outcomes. These findings underscore the importance of subgroup analyses of ED symptoms in SM women and have both clinical and research implications related to ED psychopathology in this population.
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- Title
- Testing Models of Minority Stress and Cognitive Escape in a Large Sample of Lesbian/Gay Individuals
- Creator
- Manser, Kelly
- Date
- 2020
- Description
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Compared to heterosexual individuals, gay/lesbian individuals experience health and sociopolitical disparities. Health disparities include...
Show moreCompared to heterosexual individuals, gay/lesbian individuals experience health and sociopolitical disparities. Health disparities include higher prevalence of binge drinking, tobacco use, and cardiovascular disease (CVD) among gay/lesbian individuals. Sociopolitical disparities are rooted in structural stigma and include policies and norms that fail to protect, or actively discriminate against, gay/lesbian individuals. These health and sociopolitical disparities can be understood by converging two theories previously tested among gay/lesbian individuals – minority stress and cognitive escape. Minority stress theory asserts factors such as structural stigma may relate to proximal stress and negative health sequelae in targeted minority groups, while cognitive escape theory suggests escape-related behaviors like substance use may mediate links between systemic factors and individual health. This study used binary logistic regressions to test mediation models in which substance use mediated links between structural stigma and health within a large sample of gay/lesbian individuals. Structural stigma was operationalized as number of sexual orientation anti-discrimination laws, health was operationalized as presence versus absence of any CVD conditions, and substance use was operationalized as binge drinking and tobacco use. Models were tested in an aggregated sample, and also in sex/gender subsamples. In bivariate and component-path analyses, structural stigma predicted smoking frequency across samples. Stigma-binge drinking linkages were more salient among lesbian women compared to gay men. According to Sobel tests, smoking status mediated the stigma-CVD status relation for males-only and combined-sex samples. Study strengths, limitations, and implications are discussed.
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- Title
- FAST AUTOMATIC BAYESIAN CUBATURE USING MATCHING KERNELS AND DESIGNS
- Date
- 2019, 2019-12-20
- Publisher
- Chicago
- Description
-
Automatic cubatures approximate multidimensional integrals to user-specified...
Show moreAutomatic cubatures approximate multidimensional integrals to user-specified error tolerances. In many real-world integration problems, the analytical solution is either unavailable or difficult to compute. To overcome this, one can use numerical algorithms that approximately estimate the value of the integral. For high dimensional integrals, quasi-Monte Carlo (QMC) methods are very popular. QMC methods are equal-weight quadrature rules where the quadrature points are chosen deterministically, unlike Monte Carlo (MC) methods where the points are chosen randomly. The families of integration lattice nodes and digital nets are the most popular quadrature points used. These methods consider the integrand to be a deterministic function. An alternate approach, called Bayesian cubature, postulates the integrand to be an instance of a Gaussian stochastic process.
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- Title
- China's Unwritten Code of Engineering Ethics. English Final Data Set
- Date
- 2020, 2020
- Publisher
- Springer
- Description
-
This dataset contains the final results of a survey completed by several hundred engineers in China about what they think about engineering...
Show moreThis dataset contains the final results of a survey completed by several hundred engineers in China about what they think about engineering ethics, their awareness of ethics in their work, and how Chinese engineers' view of engineering ethics is not very different from those of American Engineers.
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- Title
- SODIUM-BASED ENERGY METABOLISM AND DYNAMIC ENERGY DEPENDENCY OF CHLAMYDIA TRACHOMATIS
- Creator
- Liang, Pingdong
- Date
- 2019
- Description
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Chlamydia trachomatis is an obligate intracellular bacterium that is responsible for various human diseases including trachoma, genital tract...
Show moreChlamydia trachomatis is an obligate intracellular bacterium that is responsible for various human diseases including trachoma, genital tract infections, and lymphogranuloma venereum. Energy metabolism consists many essential pathways to generate energy for every organism. However, it remains much unknown in C. trachomatis. For decades, C. trachomatis has been considered as an “energy parasite”, which needs the energy supply from the host cells entirely. In contrast, genomic studies show that this bacterium is capable of encoding enzymes that involve energy metabolism. However, no experimental data were provided to support the genomic information due to the peculiar developmental cycle of C. trachomatis inside the host cells. In this project, the oxidative phosphorylation pathway of C. trachomatis is first identified with experimental results. This pathway starts with the sodium pumping NADH:Ubiquinone oxidoreductase enzyme complex (NQR) transferring the electrons along the respiratory chain and generating a sodium gradient across the membrane. C. trachomatis contains an A-type ATPase that can utilize this sodium gradient to generate ATP. In vitro experiments in mammalian cells with different respiratory inhibitors show that C. trachomatis is not an obligate energy parasite. Instead, it has a dynamic energy dependency on the host metabolism that the bacterium switches from entirely to partially relying on the host energy metabolism for its energy requirement. The sodium gradient established by NQR and/or other transporters is of great importance to chlamydial metabolism. Further, the respiratory inhibitors test on interferon-γ-induced persistence of C. trachomatis in mammalian cell cultures shows that an inhibited energy metabolism prevents and eliminates the persistent form. This study provides new insights about antibiotics development and therapeutic methods against C. trachomatis infections.
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- Title
- Foregrounding Temporality to Design with Emerging Futures
- Creator
- Heidaripour, Maryam
- Date
- 2020
- Description
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The rhetoric of today’s economy has framed entrepreneurship as a key contributor to inventing the future, which raises questions about who is...
Show moreThe rhetoric of today’s economy has framed entrepreneurship as a key contributor to inventing the future, which raises questions about who is counted as an insider, how the future is being designed, and for whom. The concentration of future-making has too long been in the hands of a few, given future’s tremendous impact on the many. This dissertation joins the growing body of scholarly explorations on channeling the design capacity to transition toward a future with a plural world system, where the economy offers a multiplicity of possibilities. Central to this exploration is to rethink how shaping futures might be done differently, with different people, and in different forms.By incorporating feminist temporality, I challenge the established mode of design investigation. My empirical chapters demonstrate the ways in which sharpening our temporal sensitivity could impact what we study, how we study it, and what we can find. In particular, I rearrange the power dynamics in design activities by opening up the position of knower to the emerging collectives. I then introduce the concept of designing a time-space yet to come that makes you wonder—an open invitation to rethink who we are and what we want to become.While it remains to be seen whether this contribution will have a meaningful impact on design knowledge, I argue that it makes a solid case for incorporating feminism in design. Feminist theory offers the theoretical underpinning for ontological reframing of design and helps us understand what other forms of design practice are emerging in this era of increasing complexity. I conclude with my take on an emerging design practice where the fundamental element of design is to enable other ways of knowing to inquire about what they truly want to become.
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- Title
- Nanopore Detection of Heavy Metal Ions
- Creator
- MohammadiRoozbahani, Golbarg
- Date
- 2019
- Description
-
Nanopore sensing is an emerging analytical technique for measuring single molecules. Under an applied potential bias, analyte molecules are...
Show moreNanopore sensing is an emerging analytical technique for measuring single molecules. Under an applied potential bias, analyte molecules are transported through the nanopore and cause ionic current modulations. Accordingly, the fingerprint of the analyte is reflected in the signature of the current blockage events. Due to its advantages such as lable-free and multi-analyte detection, nanopore sensing technology has been utilized as an attractive versatile tool to study a variety of topics, including biosensing of different species, such as DNA, RNA, proteins, peptides, anions, and metal ions.Metal ions play a crucial role in human health and environmental safety. Although metal ions are essential for numerous biological processes, the presence of the wrong metal, or even the essential metals in the wrong concentration or location, can lead to undesirable results and serious health concerns, including antibiotic resistance, metabolic disorders, mental retardation, and even cancer. Therefore, it is still of prime importance to develop highly sensitive and selective sensors for metal ions.In this dissertation, various nanopore sensing strategies to detect metal ions will first be discussed. These include: a) construction of metal ion binding sites in the nanopore inner surface; b) utilization of a biomolecule as a ligand probe; and c) employing enzymatic reactions. Then, three projects will be summarized. Among them, two projects are involved with detection of non-essential metal ions: uranyl and thorium ions, while the other is targeted at essential element, zinc ion. To be more specific, uranyl and thorium ions are detected by taking advantage of peptide molecules as ligand probes. In this case, the event signatures of peptide molecules in the nanopore are significantly different in the absence and presence of metal ions, which might be attributed to the conformational change of the biomolecules induced by the metal ion-biomolecule interaction. On the other hand, zinc ion is detected based on enzymatic reaction: without Zn2+, ADAM17 (a zinc dependent protease) is inactive and cannot cleave peptide substrate molecules; in contrast, with Zn2+ ion in the solution, the enzyme was activated, and its cleavage of the peptide substrate produced new types of blockage events with smaller residence time and amplitude values than those the peptide substrate.
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- Title
- Fast Automatic Bayesian Cubature Using Matching Kernels and Designs
- Creator
- Rathinavel, Jagadeeswaran
- Date
- 2019
- Description
-
Automatic cubatures approximate multidimensional integrals to user-specified error tolerances. In many real-world integration problems, the...
Show moreAutomatic cubatures approximate multidimensional integrals to user-specified error tolerances. In many real-world integration problems, the analytical solution is either unavailable or difficult to compute. To overcome this, one can use numerical algorithms that approximately estimate the value of the integral. For high dimensional integrals, quasi-Monte Carlo (QMC) methods are very popular. QMC methods are equal-weight quadrature rules where the quadrature points are chosen deterministically, unlike Monte Carlo (MC) methods where the points are chosen randomly.The families of integration lattice nodes and digital nets are the most popular quadrature points used. These methods consider the integrand to be a deterministic function. An alternative approach, called Bayesian cubature, postulates the integrand to be an instance of a Gaussian stochastic process. For high dimensional problems, it is difficult to adaptively change the sampling pattern. But one can automatically determine the sample size, $n$, given a fixed and reasonable sampling pattern. We take this approach using a Bayesian perspective. We assume a Gaussian process parameterized by a constant mean and a covariance function defined by a scale parameter and a function specifying how the integrand values at two different points in the domain are related. These parameters are estimated from integrand values or are given non-informative priors. This leads to a credible interval for the integral. The sample size, $n$, is chosen to make the credible interval for the Bayesian posterior error no greater than the desired error tolerance. However, the process just outlined typically requires vector-matrix operations with a computational cost of $O(n^3)$. Our innovation is to pair low discrepancy nodes with matching kernels, which lowers the computational cost to $O(n \log n)$. We begin the thesis by introducing the Bayesian approach to calculate the posterior cubature error and define our automatic Bayesian cubature. Although much of this material is known, it is used to develop the necessary foundations. Some of the major contributions of this thesis include the following: 1) The fast Bayesian transform is introduced. This generalizes the techniques that speedup Bayesian cubature when the kernel matches low discrepancy nodes. 2) The fast Bayesian transform approach is demonstrated using two methods: a) rank-1 lattice sequences and shift-invariant kernels, and b) Sobol' sequences and Walsh kernels. These two methods are implemented as fast automatic Bayesian cubature algorithms in the Guaranteed Automatic Integration Library (GAIL). 3) We develop additional numerical implementation techniques: a) rewriting the covariance kernel to avoid cancellation error, b) gradient descent for hyperparameter search, and c) non-integer kernel order selection.The thesis concludes by applying our fast automatic Bayesian cubature algorithms to three sample integration problems. We show that our algorithms are faster than the basic Bayesian cubature and that they provide answers within the error tolerance in most cases. The Bayesian cubatures that we develop are guaranteed for integrands belonging to a cone of functions that reside in the middle of the sample space. The concept of a cone of functions is also explained briefly.
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- Title
- Self-Stigma & Vicarious Stigma Experienced by Parents of Children with Mental Health Challenges
- Creator
- Serchuk, Marisa Dyan
- Date
- 2019
- Description
-
Research has been limited regarding the stigma experienced by parents of children with mental health challenges. It is commonly understood...
Show moreResearch has been limited regarding the stigma experienced by parents of children with mental health challenges. It is commonly understood that stigma effects people with lived-experience (e.g., a child with mental health challenges), however, stigma has been noted to have a wide scope, which extends to family members as well. Parents of children with mental health challenges have been found to endorse aspects of self-stigma, specifically regarding public stereotypes of blame and feelings of incompetence. Vicarious stigma is a fairly new area of research, which describes the sad and/or angry response a parent may experience when witnessing their child being stigmatized. The purpose of this study is to examine emotional and behavioral outcomes related to specific types of stigma experienced by parents of children with mental health challenges. Archival data from a larger study of adult participants (N=50), who identified as having a child (age 3-10 years old) with mental health challenges, completed measures examining self-stigma, vicarious stigma, stress, depression, quality of life, disclosure, secrecy coping, and help-seeking. A novel measurement for vicarious stigma was introduced and examined in this study. Results found higher levels of self-stigma and dimensions of vicarious stigma were associated with higher levels of depression as well as diminished quality of life. Higher levels of self-stigma were also associated with lower perceived benefits of disclosing and greater levels of secrecy coping. These findings highlight the importance of further examining the role of stigma for parents of children with mental health challenges.
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- Title
- A SYSTEMATIC APPROACH TO UNDERSTANDING ALIGNMENT BETWEEN THE EXISTING AND SELF-ADOPTED ENVIRONMENTAL EDUCATION STANDARDS: UNITED STATES SIXTH TO TWELFTH GRADE ENVIRONMENTAL SCIENCE STANDARDS
- Creator
- Connell, Margaretann Grace
- Date
- 2019
- Description
-
The purpose of this thesis was to conduct a systematic approach to determine the alignment between the existing and self-adopted science 6th...
Show moreThe purpose of this thesis was to conduct a systematic approach to determine the alignment between the existing and self-adopted science 6th-12th grade EE science standards for 10 U.S. National States (6th-8th [AZ; ID; MA; WY]) and (9th-12th [NE; NYS; OH; PA; SC; TX]). The criteria for States’ selection were based on States with SASS (non-NGSS adoption) and 2) demographics - random selection from the 10 U.S. EPA Regions. The Existing Environmental Education Standards (EEES) (GCDEE, Hungerford et al., 1980; NAAEE Guidelines, Simmons, 2010a; Tbilisi, UNESCO, 1978) were aligned with the 10 States. The investigation was conducted by a DCA (Mayring, 2002). Data were analyzed using MAXQDA 2018.1(VERBI, 2017), judged by a Content Match (La Marca et al., 2000), and measured by the adapted criteria for Categorical Concurrence and Range of Knowledge Correspondence (Webb, 1999). Instruments to score the output were: 1). CEEI – Tbilisi/GCDEE (K-12), and EEI – NAAEE Guidelines (6-8; 9-12). Results for the Content Match of the EEES revealed that 50% of the States were Partly Aligned and other 50% were Not Aligned with the NAAEE Guidelines Code Coverage. Additionally, the Content Match with Tbilisi/GCDEE revealed that 20% of the States (OH, PA) were Fully Aligned and the other 80% Partly Aligned . The States’ science standards ability to reach appropriate levels of alignment was due to the scientific specificity of those States with implicit EE standards. Moreover, it was difficult to come to a common ground to expect complete alignment based on the socioecological approaches and interdisciplinary nature (Kyburz-Graber, 2013; Simmons, 2010a) of the EEES. Therefore, it is now left up to the policymakers at the State levels to work with stakeholders and come to a consensus in support of EE standards that are relevant, fair, and balanced with multidisciplinary, socioecological approaches to promote of an environmentally literate citizenry.
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- Title
- KINETIC MODEL FRAMEWORKS OF ANIMAL CELL CULTURES FOR CONTROL AND OPTIMIZATION
- Creator
- Yilmaz, Denizhan
- Date
- 2019
- Description
-
This dissertation proposes four different kinetic model frameworks that havebeen developed for optimization and control of monoclonal antibody...
Show moreThis dissertation proposes four different kinetic model frameworks that havebeen developed for optimization and control of monoclonal antibody producing mammalian cell cultures to improve biopharmaceutical production by decreasing the costof trial and error experimentation. The developed models mainly describe the transient metabolic behavior of mammalian cell culture under different culture conditionsand predicts cell growth and death, cell metabolism, and monoclonal antibody synthesis, and production. These models are developed via ordinary differential equationsbased on the assumption of well-mixing reactor. All developed models were calibrated, and their predictive capabilities were tested with experimental reports published in the literature. Good agreement was obtained between model predictions and experimental data. The presented results illustrate that the developed models successfully describe and predict the transient behavior of mammalian cell cultures and can be a useful tool for biopharmaceutical production.
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- Title
- STRATEGIES TO MAXIMIZE DOSE REDUCTION IN SPECT MYOCARDIAL PERFUSION IMAGING
- Creator
- Juan Ramon, Albert
- Date
- 2019
- Description
-
Radiation exposure in medical imaging has become a topic of major concern, gaining intense attention within the clinical and research...
Show moreRadiation exposure in medical imaging has become a topic of major concern, gaining intense attention within the clinical and research communities. In 2009, the National Council on Radiation Protection and Measurements (NCRP) announced radiation exposure of patients via medical imaging increased more than sixfold between the 1980s and 2006, with cardiac nuclear medicine, specifically myocardial perfusion imaging (MPI) with single-photon emission computed tomography (SPECT) being the second biggest culprit. The goal of this work is to evaluate several strategies to enable radiation dose to be minimized while maintaining current levels of diagnostic accuracy in the clinic. We achieve dose reduction through optimization of advanced image reconstruction strategies, to obtain higher-quality images at a given dose (noise) level, through a machine learning approach to predict the optimal dose for each patient, and through advanced deep learning (DL) algorithms to improve the quality of reconstructed images. Our ultimate objective is to provide the nuclear cardiology field with a new set of algorithms and guidelines for selecting administered activity levels and image reconstruction procedures in the clinic. The project is based on a clinical study in which imaging and various other data are being collected for a set of patients. The project has the following components. First, we investigate a global dose-reduction approach (i.e., reducing dose by a uniform proportion across all patients) via optimization of image reconstruction strategies. Specifically, we maximize perfusion-defect detection (diagnostic accuracy) over a range of simulated dose levels using clinical data into which we have introduced simulated defects. We measure diagnostic performance using clinically validated model observers from the Quantitative Perfusion SPECT (QPS) software package. We investigate the diagnostic accuracy over a range of dose levels ranging from those currently used in the clinic down to one-eighth of this level. We consider the following image-reconstruction: filtered-backprojection (FBP) with no correction for physics effects, and ordered-subsets expectation-maximization (OS-EM) with several combinations of attenuation correction (AC), scatter correction (SC), and resolution correction (RC).Second, we propose a patient-specific ("personalized") dose reduction approach based on machine learning that aims to predict the minimum radiation dose needed to obtain consistent perfusion-defect detection accuracy for each individual patient. This prediction is based on patient attributes, especially body measurements, and various clinical variables. We compare the diagnostic accuracy produced by predicted personalized doses to that produced by standard clinical dose levels to validate the predictive models.Third, we verify that the dose minimization results obtained in the context of perfusion-defect detection also maintain diagnostic accuracy in evaluating cardiac function, as characterized by myocardial motion.Finally, we propose a deep learning (DL) method to denoise SPECT-MPI reconstructed images. The method is a 3D convolutional neural network trained to predict standard-dose images from low-dose images. We quantify the extent to which dose reduction can be achieved using the proposed DL structure when dose is reduced uniformly across patients or by means of our patient-specific approach.
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- Title
- SI NANOSTRUCTURED COMPOSITE AS HIGH PERFORMANCE ANODE MATERIAL FOR NEXT GENERATION LITHIUM-ION BATTERIES
- Creator
- He, Qianran
- Date
- 2019
- Description
-
Silicon has attracted huge attention in the last decade as the anode material for Li-ion batteries because it has a theoretical capacity ∼10...
Show moreSilicon has attracted huge attention in the last decade as the anode material for Li-ion batteries because it has a theoretical capacity ∼10 times that of graphite. However, the practical application of Si is hindered by three major challenges: large volume expansion during cycling (∼300%), low electrical conductivity, and instability of the SEI layer caused by repeated volume changes of the Si material. Our study focused on novel design and synthesis of Si anodes that can solve all the key problems of Si anodes simultaneously. The Si micro-reactors we designed and synthesized contain well-designed internal structures, including (i) nanoscale Si building blocks, (ii) the engineered void space, and (iii) a conductive carbon shell. Because of these internal structures and nitrogen doped carbon shell, these sub micrometer-sized Si particles are termed as Si micro-reactors and denoted as Si@void@C(N). According to our electrochemical results, the as-synthesized Si micro-reactors could live up to 1000 charge/discharge cycles at high current densities (up to 8 A/g) while still providing a higher specific capacity than the state-of-the-art carbonaceous anodes. Our investigation shows that the unique design of Si@void@C(N) has a relatively low specific surface area (SSA) which significantly reduces the undesired surface side reactions and increases ICE to 91%, while the engineered voids with nano-channel shape inside the structure can accommodate Si volume expansion and keep the structure and SEI layer stable. Furthermore, the porous N-doped carbon shell along with nano-channeled voids allows rapid lithiation of the Si micro-reactor without Li plating during ultrafast charging. As a result, Si@void@C(N) exhibits ultrafast charging capability with high ICE, superior specific capacity and long cycle life.
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- Title
- HETEROGENEOUS CATALYST FOR ALKANE DEHYDYGENATION AND IMPLEMENTING TO SOLID OXIDE FUEL CELL
- Creator
- Xu, Yunjie
- Date
- 2019
- Description
-
In the past decade, shale gas has become the most import source of natural gas in the United States. Large amounts of light alkanes in shale...
Show moreIn the past decade, shale gas has become the most import source of natural gas in the United States. Large amounts of light alkanes in shale gas, such as methane, ethane, and propane are available as an industrial source of chemicals through the catalyzed, on-purpose light alkane dehydrogenation to olefins. Therefore, it is obvious there is a benefit to developing catalysts to directly convert shale gas to olefins. However, alkane dehydrogenation and non-oxidative methane coupling are thermodynamically unfavorable reactions at low temperatures. The energy requirements make these reactions less attractive for shale gas utilization. In principle, consuming the hydrogen product with a fuel cell can drive the thermodynamically unfavorable reaction by reducing the hydrogen partial pressure in the anode and by heat generating by the fuel cell, while also generating electricity in the process. Moreover, catalyst integration with fuel cell can facilitate the transfer of charge in anode which is rate determine step in the fuel cell. This thesis will focus on catalyst development for alkane dehydrogenation and exploring a way to integrate these catalysts with fuel cells.Chapters 2, 3 and 4 focus on designing, characterizing, and studying catalysts for non-oxidative coupling of methane (NOCM) and propane dehydrogenation (PDH). PtM (M is a transition metal) alloys were found to efficiently decrease the desorption energy of olefin products and avoid deeper C-H bond activation compared to metallic Pt. Based on the previous study of single cobalt on silica, a novel synthesis of PtCo3 was developed to further increase the activity of the PDH reaction. The Pt bimetallic catalyst made by novel synthesis route was proven to be one of several types of alloy. It was observed that extremely high conversion of PDH and high selectivity of target olefin were catalyzed by PtCo3/SiO2. Ga, as another promotor to replace Co, was also investigated. As expected, PtGa3 alloy was formed by a similar synthesis, and it showed extraordinary stability and activity for propane dehydrogenation. A Mo-Pt dual-metal catalyst was found to catalyze methane coupling even though Pt-Mo bimetallic alloys do not form. We hypothesize that Pt catalyzed C-H bond cleavage of CH4 to form methyl radical, and a MoOC species, formed by MoO3 reacting with CH4, could effectively facilitate methyl radical coupling to form larger alkanes and alkenes. Pt-Mo dual-metal catalyst had higher catalytic activity for methane coupling than a physical mixture of Pt and Mo and genuine PtMo alloy. Chapter 5 details our efforts to transplant PtM catalysts from silica support to target fuel cell material--(La,Sr)(Cr,Fe)O3 as a support. Different catalyst structures were observed, and, in this case, second transition metals become a barrier to prevent Pt aggregation. When using propane as fuel for fuel cell, we observed electrochemical redox reactions occurred via electrochemical analysis. However, the resistance of cell is comparatively high and limited overall system performance. Chapter 6 details a study of the impact of the electrode oxide phase on overall cell performance. In this case, we conducted a fundamental study of degradation of cathode material, (La,Sr)(Co,Fe)O3. We found that raw material and cells can degrade even under room temperature. Thus, the storage of raw powder and fabricated cells is critical for performance studies. This also indicates that our high cell resistance in previous electrochemical measurements could come from the insulating compound formation during storage. Some directions for future research on catalyst integration and electrochemical testing are outlined.
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