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Title: POLYMER ELECTROLYTE MEMBRANES FOR ELECTROCHEMICAL ENERGY CONVERSION AND STORAGE SYSTEMS: FUEL CELLS AND REDOX FLOW BATTERIES
Creator: Yun, Sukhwan
Date: 2014, 2014-12
Description: Direct methanol fuel cells (DMFCs) and redox flow batteries (RFBs) are well-known electrochemical energy conversion/storage systems that...
Show moreDirect methanol fuel cells (DMFCs) and redox flow batteries (RFBs) are well-known electrochemical energy conversion/storage systems that utilize redox reactions to convert or store electricity. Ion exchange membranes (IEMs) are used in DMFCs and RFBs as electrolyte separators. The critical requirements for IEMs in these applications are high ionic conductivity, low electrolyte permeability, high stability, and low cost. Silsesquioxane (SQO)-based sulfonated poly(etheretherketone) composite membranes were synthesized. Morphological changes in the composite membranes resulting from the introduction of SQO were studied using small-angle x-ray scattering. A sharp decrease in proton conductivity with SQO loading (> 20 wt%) was attributed to morphological changes in the membrane, including agglomeration and inhomogeneous dispersion of SQO particles within the ionic domains. Anion exchange membranes (AEMs) based on quaternized cardo-poly(etherketone) (QPEK-C) were prepared and evaluated for all-vanadium RFB (VRFB) applications. The QPEK-C AEMs with different degrees of functionalization (0.9–1.6) exhibited sulfate ion conductivities ranging between 5.6 and 15.2 mS cm-1 at 30 oC. The AEM had a lower VO2+ permeability (2.8×10-8 cm2 s-), compared to that of Nafion® 212 (2.9±0.2 ×10-7 cm2 s-1), which was attributed to the Donnan exclusion effect. The mechanical strength of QPEK-C AEM degraded by 35% after exposure to a 1.5 M VO2+ solution for 1500 hours due to the oxidation of aromatic rings. A single-cell VRFB employing the AEM separator yielded current and energy efficiencies (at 30 mA cm-2) of 97-99% and 80-82 %, respectively. Enhanced sulfate ion conductivity (8.4 ± 0.2 mS cm-1) and decreased VO2+ permeability (0.53×10-9 cm2 s-1) were achieved by incorporating 20 wt% of n-(trimethoxysilylpropyl)-n,n,n-trimethylammonium additives into QPEK-C, (the pristine QPEK-C AEM yielded corresponding values of 4.5 ± 0.5 mS cm-1 and 1.09×10-9 cm2 s-1). About 99% coulombic efficiency was achieved with the VRFBs employing the composite AEM. However, a rapid reduction of the ionic conductivity down to the value of the pristine membrane was observed when the composite AEM was immersed in 1.5 M VO2+ solution for 3 days. Vanadium-cerium RFBs (V-Ce RFBs) evaluated with QPEK-C AEM separators yielded identical energy efficiency (84%) to corresponding RFBs evaluated with Nafion® 212. However, after over 20 charge-discharge cycles, the V-Ce RFB with the AEM separator yielded unchanged efficiency and capacity, while a 50% loss of capacity was observed with the Nafion® separator. This suggested that QPEK-C AEMs are promising candidates for RFB separators when different cations are used in the two electrolyte solutions, in that they act as efficient barriers that preclude the intermixing of the cations due to the Donnan exclusion effect.
Ph.D. in Chemical Engineering, December 2014
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Title: B-SPLINE BASED ROBUST FORMULATION IN TOPOLOGY OPTIMIZATION
Creator: Gu, Yu
Date: 2014, 2014-12
Description: In this thesis, we extend the B-spline based density representation to robust topology optimization. The B-spline based representation has...
Show moreIn this thesis, we extend the B-spline based density representation to robust topology optimization. The B-spline based representation has been shown to have the ability of preventing checker-boards. With separate analysis and design mesh, the B-spline based filter is also competitive on computation storage compared with usual density filter. However, the current B-spline based representation is not robust where gray transitions exist. The recent proposed robust formulation highlights the manufacturability that simulates under- or over- etching designs compared with intermediate design. The robustness is demonstrated through three test cases. The minimum length control for quadratic B-splines is analytically derived with respect to knot span size and threshold. Our study about the characteristics of rectangular domain of B-spline based representation, shows that the optimized designs are comparable to those by density filter.
M.S. in Mechanical and Aerospace Engineering, December 2014
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Title: DATA SHARING WITH PRIVACY AND SECURITY
Creator: Qian, Jianwei
Date: 2019
Description: Data is a non-exclusive resource and has synergistic effects. Open data sharing will enhance the utilization of big data’s value and...
Show moreData is a non-exclusive resource and has synergistic effects. Open data sharing will enhance the utilization of big data’s value and tremendously boost economic growth and transparency. Data sharing platforms have emerged worldwide, but with very limited services. Security is one of the main reasons why most data are not commonly shared. This dissertation aims to tackle several security issues in building a trustworthy data sharing ecosystem. First, I reveal the privacy risks in data sharing by designing de-anonymization and privacy inference attacks. Second, I present an analysis of the relationship between the attacker's knowledge and the privacy risk of data sharing, and try quantifying and estimating the risk. Then, I propose anonymization algorithms to protect the privacy of participants in data sharing. Finally, I survey the status quo, privacy and security concerns, and opportunities in data trading. This dissertation involves various data types with a focus on graph data and speech data; it also involves various forms of data sharing including collection, publishing, query, and trading.
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Title: LOW-DOSE CARDIAC SPECT USING POST-FILTERING, DEEP LEARNING, AND MOTION CORRECTION
Creator: Song, Chao
Date: 2019
Description: Single photon emission computed tomography (SPECT) is an important technique in use today for the detection and evaluation of coronary artery...
Show moreSingle photon emission computed tomography (SPECT) is an important technique in use today for the detection and evaluation of coronary artery diseases. The image quality in cardiac SPECT can be adversely affected by cardiac motion and respiratory motion, both of which can lead to motion blur and non-uniform heart wall. In this thesis, we mainly investigate imaging de-noising algorithms and motion correction methods for improving the image quality in cardiac SPECT on both standard dose and reduced dose.First, we investigate a spatiotemporal post-processing approach based on a non-local means (NLM) filter for suppressing the noise in cardiac-gated SPECT images. Since in recent years low-dose studies have gained increased attention in cardiac SPECT owing to its potential radiation risk, to further improve the image quality on reduced dose, we investigate a novel de-noising method for low-dose cardiac-gated SPECT by using a three dimensional residual convolutional neural network (CNN). Furthermore, to reduce the negative effect of respiratory-binned acquisitions and assess the benefit of this approach in both standard dose and reduced dose using simulated acquisitions. Inspired by the success in respiratory correction, we investigate the potential benefit of cardiac motion correction for improving the detectability of perfusion defects. Finally, to combine the benefit of above two types of motion correction, dual-gated data acquisitions are implemented, wherein the acquired list-mode data are further binned into a number of intervals within cardiac and respiratory cycle according to the electrocardiography (ECG) signal and amplitude of the respiratory motion.
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Title: CITIZENSHIP PRESSURE, JOB STRESS, AND WORK-TO-FAMILY CONFLICT: THE MODERATING ROLE OF FLEXIBILITY IDIOSYNCRATIC DEALS
Creator: Ahmed, Shujaat Farah
Date: 2020
Description: Organizational expectations of employee performance have been expanding over time from traditional core task behaviors to include extra duties...
Show moreOrganizational expectations of employee performance have been expanding over time from traditional core task behaviors to include extra duties which may not be out of volition (Bolino, Turnley, Gilstrap, & Suazo, 2010). However, this extra work comes at a price, as employees are exhausted (Bolino et al., 2010) which can have health implications. Yet, no previous studies have examined the mechanism by which citizenship pressure is related to work interfering with family (WIF) conflict. Consequently, this study investigated an underlying mechanism, job stress, through which citizenship pressure was hypothesized to be related to work-family conflict from the work perspective, i.e., WIF conflict. This study also sought to identify the moderating role of flexibility idiosyncratic deals (i-deals) in the relationship of citizenship pressure with a) job stress, and b) WIF conflict. Data were collected across two waves with a time separation of one month in between waves. A total of 323 workers (mean age = 36.2) across industries in the United States participated in the study. Sixty-three percent identified as women, and 37% were men. Regression analyses were used to test the first three hypotheses. PROCESS was used to test the remainder of the hypotheses. Results for the regressions indicated that citizenship pressure was related at job stress and WIF conflict. Further, job stress was related to WIF conflict. Subsequently, the mediation hypothesis was significant. However, the moderation, and moderated mediation models were not statistically significant. I conducted post-hoc analyses to determine other possible significant paths in the model. The indirect effect of WIF conflict through the citizenship pressure and job stress link was statistically significant, thereby supporting an alternate mediation hypothesis. Perceived flexibility i-deals significantly moderated citizenship pressure and WIF conflict at time 1 only. The implications of this study are: managers should focus on their employees’ stressor experiences, as extra work beyond one’s specified job role is increasingly expected of employees. By doing so, pressure may be reduced through improving perceptions that employees can negotiate flexibility i-deals. This is especially important in an era of scarce resources, as pressure to go the extra mile is linked to a number of negative outcomes, such as increased WIF conflict and job stress.
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Title: DATA PRIVACY AND DEEP LEARNING IN THE MOBILE ERA: TRACEABILITY AND PROTECTION
Creator: Chen, Linlin
Date: 2020
Description: Privacy and deep learning have been two of the most exciting research trends in both academia and industry. On the one hand, big data rapidly...
Show morePrivacy and deep learning have been two of the most exciting research trends in both academia and industry. On the one hand, big data rapidly expedite lots of data orientated applications, especially like deep learning services. With the tremendous value exhibited by the data, the privacy of data subjects who generate the data, has also raised much attention. Meanwhile more regulations and legislation have been enacted or enforced, intending to enforce the companies and organizations to strictly comply with the personal privacy protection while collecting or utilizing their data. All these moves will substantially change the ways to train the deep learning models and provide AI services, and in some ways might hinder the development of deep learning if not coming up with some sophisticated mechanisms. On the other hand, deep learning has been showing incredibly promising performance in a variety of areas like face recognition, voice recognition, recommendation & advertising, autonomous driving, medical imaging, etc.. This keeps us thinking will deep learning also in turn influence privacy and be leveraged to compromise privacy. Meanwhile we also observe that mobile devices become so ubiquitous that more shares of data are generated on mobile devices, and mostly those data are both extremely sensitive for data subjects as well as extremely valuable for developing deep learning. We shouldn’t neglect the impact of mobile devices on both privacy and deep learning.In this thesis I explore the research on the interactions between privacy and deep learning, especially with the mobile devices being involved in. Specifically I work on: 1). How does privacy change the way we use the data when building deep learning models, and present the mechanism for privacy protection towards deep learning. 2). How does deep learning in turn make privacy more vulnerable to be compromised, and demonstrate the privacy compromise by facilitating deep learning to trace the source mobile devices and link the personal identities.
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Title: Integrity based landmark generation: A method to generate landmark configurations that guarantee mobile robot localization safety
Creator: Chen, Yihe
Date: 2020
Description: From the bronze-age city Nineveh to the modern metropolitan like Tokyo, traffic shape cities and profoundly affect the life of people. Similar...
Show moreFrom the bronze-age city Nineveh to the modern metropolitan like Tokyo, traffic shape cities and profoundly affect the life of people. Similar to how the wide-spreading of automobile had modified the modern cities in early 20th century, we are now standing on the eve of yet another traffic revolution. With the vast spreading of autonomous/semi- autonomous robotics application, it is important for the urban designers to design or retrofit urban environment that is safe and friendly to the autonomous robots; As more robots are deployed in life-critical situations, such as autonomous passenger vehicles, it is imperative to consider their safety, and in particular, their localization safety. While it would be ideal to guarantee safety in any environment without having to physically modify said environment, this is not always possible and one may have add landmarks or active beacons to reach an acceptable level of safety for landmark-based localization. Localization safety is assessed using integrity, the primary safety metric used in open-sky aviation applications that has been recently applied to mobile robots and can ac- count for the impact of rarely occurring, undetected faults. Conventional integrity monitor- ing method has high dependency on GPS system, while the traditional Global Navigation Satellite System - Inertia Measurement Unit (GNSS-IMU) based localization does not ap- plied in the metropolitan areas due to the signal blocking and multi-pathing problem caused by high-rise structures. Thus, this dissertation concentrates on the feature based integrity monitoring method. This dissertation formulates environmental localization safety problem as a system- atic optimization problem: given the robot’s trajectory and the current landmark map, add the minimal number of new landmarks at certain location such that the integrity risk along the trajectory is below a given safety threshold. This dissertation proposes two algorithms to solve the problem: Integrity-based Landmark Generator (I-LaG) and Fast I-LaG. I-LaG adds fewer landmarks but it is relatively computationally expensive; Fast I-LaG is less com- putationally intensive at the expense of more landmarks. Both simulation and experimental results are presented.
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Title: PREPARATION OF MG3SB2-XBIX THERMOELECTRIC MATERIAL AND THEIR LASER SINTERING BEHAVIOR
Creator: Xiao, Xudong
Date: 2020
Description: In the introduction part, a novel thermoelectric material, Zintl compounds were introduced due to its potential high thermoelectric...
Show moreIn the introduction part, a novel thermoelectric material, Zintl compounds were introduced due to its potential high thermoelectric performance in low-temperature applications as thermoelectric devices. Recent researches focused on Mg3Sb2-based Zintl Compounds was summarized in this article, and the general methods and its limitations to fabricate Mg3Sb2-based thermoelectric modules were introduced. Thus, a novel selective laser melting process was introduced, and it has enormous potential to fabricate Mg3Sb2-based thermoelectric modules in commercial applications. What’s more, the challenges of selective laser melting method were also discussed in the articles. For developing selective laser melting as the mature method to fabricate Mg3Sb2-based thermoelectric modules, many works and researches need to be done. In my project, the powder of Mg3.1(Sb0.3Bi0.7)1.99Te0.01 thermoelectric material was synthesized by using the mechanical alloying process and the proper particle size and distribution of powder using for selective laser melting was obtained by increasing the time of mechanical alloying. The thermoelectric properties of the powder prepared by mechanical alloying were measured, and it shows the powder still in a good performance after a long-time mechanical alloying particle reduction process. Finally, the obtained powder was treated by different parameters of continued wave fiber laser. The morphology and composition of the sintered area were analyzed to better understanding the process of laser sintering. More work needs to be done for using a selective laser melting method to fabricate Mg3Sb2-based thermoelectric modules in the future.
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Title: IMPROVING KNOWLEDGE OF MICROBIAL DYNAMICS ON BUILDING MATERIALS UNDER HIGH MOISTURE CONDITIONS
Creator: ZHAO, DAN
Date: 2020
Description: Most buildings experience some kind of high moisture event(s) throughout their life cycles, often resulting from water leaks or migration of...
Show moreMost buildings experience some kind of high moisture event(s) throughout their life cycles, often resulting from water leaks or migration of water vapor through the enclosure. Dampness and moisture in buildings leads to fungal growth and is associated with adverse human health outcomes. Although the dynamics of fungal growth on buildings materials has been investigated for decades, few studies have integrated modern chemical or microbiological analytical methods (e.g., DNA sequencing, qPCR, etc.) to understand microbial dynamics on materials held at high humidity conditions. Moreover, most mold growth prediction models remain relatively simplistic and rely solely on empirical data for visible mold growth. To bridge some of these gaps, this research aims to improve understanding of microbial growth and community dynamics on building materials under high moisture conditions and to improve our ability to predict microbial growth and community dynamics under a variety of conditions. Five distinct but overlapping research objectives are used to achieve these goals, including: (1) evaluating the growth of microorganisms on wetted building materials and identifying relationships between specific microbial taxa, metabolites, and environmental variables; (2) identifying inherent material chemistry drivers of fungal growth susceptibility and their relation to microbial community structure; (3) exploring how fluctuating moisture exposures impact bacterial and fungal growth and dynamics on building materials; (4) investigating microbial interactions using isolated communities on a single material; and (5) evaluating and improving existing mathematical mold growth models.
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Title: ENHANCED OPTICAL TOMOGRAPHY IN DIFFUSE MEDIA USING OPTICAL GATING OF EARLY PHOTONS
Creator: Ghosh, Aishwarya
Date: 2020
Description: Tissue biopsies, where a volume of tissue is removed from a patient, typically through needle extraction, provides critical information about...
Show moreTissue biopsies, where a volume of tissue is removed from a patient, typically through needle extraction, provides critical information about the cellular and molecular aspects of an individual patient’s health and/or disease. However, current pathological assessments of tissue biopsies evaluate less than 1% of the volume of the tissue (e.g., one to a few 5-micron slices are sectioned out of the biopsy and stained/processed for microscopic analysis). Since the bulk of tissue biopsy is carried out through optical imaging (absorption or fluorescence), a more 3D, “whole-biopsy” view is conceivably possible with optical projection tomography (OPT). The challenge with OPT has been that for clinically relevant sized biopsies, most photons undergo multiple scattering events that lead to loss of spatial resolution that makes accurate pathological analysis intractable. In my MS thesis, I worked on the development of an enhanced OPT system that employs optical gating based on non-linear up-conversion of infrared ultrashort laser pulses to isolate “early-arriving” photons that experience significantly less scatter than the bulk of photons transiting a scattering biological sample. Considering the complexity of such a system, the entirety of my MS thesis work was spent constructing and testing the femtosecond optical gated OPT system and though I was unable to validate its operation in biological samples, simulations suggest that the properties we were able to achieve could allow high resolution optical imaging in 0.1-1 cm-diameter specimens.
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Title: THREE ESSAYS IN ENTREPRENEURIAL FINANCE AND COMMODITY MARKETS
Creator: Jia, Jian
Date: 2020
Description: This dissertation includes three essays with a series of empirical investigations in areas of entrepreneurial finance and commodity markets.In...
Show moreThis dissertation includes three essays with a series of empirical investigations in areas of entrepreneurial finance and commodity markets.In the first essay, I study the impact of General Data Protection Regulation (GDPR) on investment in new and emerging technology firms. My findings indicate negative post-GDPR effect after its 2018 rollout on EU ventures, relative to their US counterparts, but no such effects following its 2016 enactment.In the second essay, I examine how investors’ tendency to prefer investing in local ventures interacts with the effects of the GDPR on venture investment in EU. I demonstrate that GDPR’s enactment and rollout differentially affect investors as a function of their proximity to ventures. Specifically, I show that GDPR’s rollout in 2018 has a negative effect on EU venture investment and the effects are higher when ventures and lead investors are not in the same country or union. The relationship manifests in the number of deals per month and in the amount invested per deal, and is particularly pronounced for newer and data-related ventures.In the third essay, I formulate two claims about spot and futures return prediction in industrial metal futures market. These claims lead to testable hypotheses, and provide theory-based restrictions for the coefficients of spot and futures return regression. I investigate six industrial metals and find empirical support for my hypotheses. The in-sample and out-of-sample evidence shows that financial variables, proxies for global economic activities, and the basis predict futures and spot price returns consistently with my hypotheses. Furthermore, my out-of-sample trading experiments document economic significance of the restrictions.
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Title: NANOMATERIALS FOR ADVANCED BATTERY CATHODES
Creator: Moazzen, Elahe
Date: 2020
Description: Cathode materials are key components that directly determine the power density of a battery. One of the most effective ways of developing high...
Show moreCathode materials are key components that directly determine the power density of a battery. One of the most effective ways of developing high power density cathodes is bringing them into the nano-scale world, which results in many expected and unexpected properties. Some of the desired characteristics include faster charge/discharge kinetics, improved capacity retention and structural stability due to the higher surface to volume ratio and shorter ion diffusion paths. In this dissertation a number of uniquely designed nano-sized cathode materials and nanocomposites are developed and investigated for alkaline aqueous and lithium ion battery applications. Nickel hydroxide (Ni(OH)2), which is one of the most important cathode materials in alkaline batteries, suffers from low conductivity, which usually leads to inefficient discharge and incomplete utilization of the material. A series of Ni(OH)2/Co(OH)2 core/shell nanoplatelets were synthesized and systematically investigated as cathode materials. Structure-property correlations revealed that electrochemical behavior and reversibility of Co(OH)2 redox conversion depended non-linearly on the average shell thickness, with the best performance (99.6% of theoretical capacity of the composite material) achieved at shell thickness of 1.9 ± 0.3 nm. Two fundamental phenomena were suggested to be responsible for the superior performance: templated shell deposition and galvanic coupling of core and shell materials.Manganese (IV) oxide (MnO2), which is another practical cathode that has a great potential to be utilized for a variety of energy storage systems, still has some major challenges including reversible cycling in rechargeable batteries. One of the most crucial challenges is the fact that polymorphs of MnO2 have different electrochemical activities as aqueous and Li-ion battery cathodes. However, most synthetic samples contain a mixture of polymorphs, which makes the structure-property correlations more complicated. This dissertation reports on systematic studies correlating synthesis, thermal and mechanical processing, and composite formation with polymorph composition, electrochemical performance and ion intercalation mechanisms. Among all the results, several main conclusions were reached: 1) Through control of the synthesis parameters and post-processing, desired phase compositions and nanoparticle morphologies, which optimize MnO2 performance in aqueous alkaline electrolyte, can be achieved. Nanoparticles with higher fraction of the akhtenskite polymorph showed higher reversible capacities in LiOH electrolyte (~210 mAh g-1), with stable performance for over 50 cycles. The effects of sub-nanoparticle organization of MnO2 polymorphs by thermal treatment without any morphology change on cycling performance, phase activation, and charge/discharge mechanisms in LiOH electrolyte as well as the detailed mechanism of the polymorph conversion during annealing were studied and for the first time, demonstrating that the electrochemical activity of MnO2 material strongly depends not only on the lattice structure of individual polymorphs but also on the sub-nanoparticle polymorph architecture and interphases.2) Several processing strategies, including thermal and mechanical processing, and composite fabrication were utilized to develop functional MnO2 cathodes for Li-ion batteries. Improvements in capacity and cycling performance were correlated to the presence of the pyrolusite phase of MnO2 and the crystallite size. Composite fabrication by graphene oxide wrapping also provided significant performance improvements through polymorph composition control and improved conductivity.
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Title: SALMONELLA SURVIVAL AND TRANSCRIPTOMIC RESPONSE ON FRESH-CUT CANTALOUPE FLESH WITH AND WITHOUT ORGANIC ACID PRETREATMENT
Creator: Zhou, Xinyi
Date: 2020
Description: Outbreaks of Salmonella enterica associated with fresh-cut melons are becoming more frequent in recent years. Antimicrobial activity of...
Show moreOutbreaks of Salmonella enterica associated with fresh-cut melons are becoming more frequent in recent years. Antimicrobial activity of organic acids on fresh-cut melons have been previously studied. However, little is known about the molecular mechanism behind the antimicrobial activity of organic acid.Four strains of S. enterica were utilized: Newport 36796 and 339652, and Typhimurium LT2 and 46249. Both high and low inoculation levels were performed. For low level, each strain was individually cultured and spot-inoculated onto separate 100 g untreated fresh-cut cantaloupe samples resulting in 4 log-CFU/g. For high level, samples were first submerged into 2% citric acid or malic acid for 1 minute or left as untreated control. Cantaloupe were spot inoculated with one of four strains which resulted in 7-log CFU/g. All inoculated samples were air-dried for 1 h then stored at 4°C for 7 d in deli containers. Enumeration was conducted at 0, 1, 3, 5, and 7 d. Duplicate samples were used in each of three independent trials and results were analyzed by Student’s t-test, p≤0.05. Samples for sequencing were prepared using the TruSeq Stranded mRNA kit and run on a MiSeq according to the manufacturer instructions.For low inoculation level, population of all four strains increased significantly from 0 to 3 d. The two cantaloupe outbreak-related strains (Newport 339652 and Typhimurium 46249) increased significantly between 0 and 7 d from 3.44±0.11 to 3.76±0.13 and 3.36±0.12 to 3.78±0.19 log CFU/g, respectively. For high inoculation level, the population on the untreated cantaloupe was 6.55±0.18 log CFU/g at 7 d, whereas it was significantly lower on the citric and malic acid-treated cantaloupes (6.26 ± 0.09 log CFU/g and 6.07 ± 0.18 log CFU/g). After 1 d, S. enterica genes were downregulated up to 437.4-fold compared and upregulated up to 23.2-fold. The notable downregulated genes encoded proteins related to catalyzing metabolism (L-aspartate oxidase) and also related to nutrient uptake (PstC).The results of this study can aid in understanding population dynamics of S. enterica on fresh-cut cantaloupes and efficacy of malic and citric acids. The results can also aid in understanding mechanism underlying S. enterica survival on fresh-cut cantaloupes.
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Title: Silkworm silk - CNT composite fibers: fabrication, characterization and application in cell stimulation for tissue regeneration
Creator: Zheng, Shuyao
Date: 2019
Description: Silk fibroin, a structural protein of the Bombyx mori cocoons, is widely studied for biomedical applications because of its strong mechanical...
Show moreSilk fibroin, a structural protein of the Bombyx mori cocoons, is widely studied for biomedical applications because of its strong mechanical property, biocompatibility, low degradation rate and ease of processing. In this study, we incorporated functionalized CNT in silkworm silk proteins to generate biocomposite fibers by electrospinning. The electro-spun (E-spun) fibers were unidirectionally aligned with a diameter at the sub-micrometer scale, mimicking the locally oriented ECM proteins in native tissues. The addition of a minute amount of CNT not only reinforced the stability and the mechanical strength of the flexible scaffolds, but also rendered the fibers electrical conductivity to not only facilitate the E-spun fiber formation but also grant the fibers an additional functionality that can be utilized for cell stimulation. It is known that endogenous electric field is present in the human body at various locations and plays critical physiological roles through its effect on cell proliferation, migration and activation mediated by its interference with growth factor secretion and intracellular signaling pathways. Electrical stimulation (ES) has been widely investigated as a potential method to regulate cell functions for the treatment of a number of diseases. It has been reported that in dermal fibroblasts, ES induces the secretion of FGF-1 and FGF-2 which promotes cell proliferation, contraction, migration, and activation/differentiation into myofibroblasts for increased collagen synthesis in favor of tissue repair. In this work, the modulation effect of the aligned E-spun SWS-CNT fibers was examined by their application in inducing polarization and activation of fibroblasts with cellular deficit, like those from pelvic organ prolapse (POP) patients. These cells are characterized by low productivity of collagen with abnormally high COLI/COLIII ratio, leading to a loose and fragile collagen network, decreased integration among the tissue constituents and reduced tissue strength. Electrical stimulation boosted the collagen productivity by 20 folds in cells on silk-CNT than on pure silk due to silk-CNT’s high electrical conductivity. The developed approach can be potentially utilized to remedy the dysfunctional fibroblasts for therapeutic treatment of diseases and health conditions associated with collagen disorder.
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Title: IDENTIFICATION OF BAX∆2 FRAMESHIFTING REGION VIA DUAL LUCIFERASE ANALYSIS
Creator: Reiner, Katherine
Date: 2020
Description: The antitumor protein Bax is susceptible to microsatellite instability (MSI) mutations that alter its open reading frame by changing Baxs’...
Show moreThe antitumor protein Bax is susceptible to microsatellite instability (MSI) mutations that alter its open reading frame by changing Baxs’ microsatellite of eight guanines (G8) to seven guanines (G7). This mutation results in a frameshift that is corrected by alternative splicing, making Bax∆2. Evidence shows that non-MSI mutated full length Bax∆2 (Bax∆2 G8) can be found in tissue. However, the extra guanine in Bax∆2 should result in premature termination of protein synthesis. Therefore, we believe that Bax∆2 is capable of +1 frameshifting to correct the out of frame sequence caused by splicing. The dual luciferase assay system is a useful tool for measuring frameshifting and in this study, we cloned full length Bax∆2 G8 into a dual luciferase vector to analyze frameshifting. Using this method, we found that the full length Bax∆2 G8 sequence has 3.5% frameshifting activity. To further determine whether the frameshifting occurs in or near the G8 microsatellite, we focused on several truncated constructs containing the first three exons. The results from dual luciferase assay showed that frameshifting activity was high in the constructs containing the G8 microsatellite but diminished when the G8 microsatellite region was removed. Surprisingly, constructs containing exon 4 and 5, which are away from the predicted frameshifting region, also showed frameshifting activity. One possibility to explain these results is that mRNA structures, which are critical to frameshifting, could be altered by construct truncation and consequently lead to artificial frameshifting. Thus, using truncated constructs may not be a viable option for testing frameshifting activity. To maintain mRNA integrity, point mutations within the full sequence, could be a better option to identify the frameshifting site.
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Title: PURIFICATION AND ANALYSIS OF BAXΔ2 PROTEIN AGGREGATES FROM MAMMALIAN CELLS
Creator: Wang, Xiling
Date: 2020
Description: BaxΔ2 is a unique isoform of the proapoptotic protein Bax that does not target mitochondria. The proapoptotic function of BaxΔ2 is through...
Show moreBaxΔ2 is a unique isoform of the proapoptotic protein Bax that does not target mitochondria. The proapoptotic function of BaxΔ2 is through forming cytotoxic aggregates in the cytosol. The cytotoxicity of BaxΔ2 is known as associated with the BH3 killing domain and the C-terminus, which recruits caspase 8. BaxΔ2 proteins without C-terminal form large cytosolic protein aggregates unable to induce caspase 8-dependent cell death. Since abnormal cytosolic protein aggregates often contain complexes of proteins that involved in many diseases, we would like to purify BaxΔ2 aggregates and examine their components. In this study, we expressed GFP-tagged BaxΔ2(Δ6) in the Bax-negative HCT116 cell line and purified the aggregates via different digestion processes. We found that most aggregates were trapped into a DNA pellet after cell lysis. Digestion with DNase could release the aggregates, which were susceptible to detergent solvent. The yield of purification is very low and needed improvement. The results from Western Blot showed that, in addition to BaxΔ2 proteins, stress granule protein TIAR was also potentially in the aggregates. Identification of the components inside aggregates will help us to understand the mechanism of BaxΔ2 cytotoxicity.
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Title: Machine Learning (ML) for Extreme Weather Power Outage Forecasting in Power Distribution Networks
Creator: Bahrami, Anahita
Date: 2023
Description: The Midwest region experiences a diverse range of severe weather conditions throughout the year. During the warmer months, thunderstorms,...
Show moreThe Midwest region experiences a diverse range of severe weather conditions throughout the year. During the warmer months, thunderstorms, heavy rain, lightning, tornadoes, and high winds pose a threat, while the colder season brings ice storms, snowstorms, high winds, and sleet storms, all of which can cause significant damage to the environment, properties, transportation systems, and power grids. The average climate in the Midwest is influenced by factors such as latitude, solar input, water systems' typical positions and movements, topography, the Great Lakes, and human activities. The combination of these conditions during different seasons contributes to the development of various types of storms. Therefore, it is crucial to predict the impacts of such atmospheric events on distribution and transmission lines, enabling utilities to assess and implement preventive measures and strategies to minimize the economic losses associated with these disasters. Additionally, the accurate classification of storm modes through an automated system allows operators to study trends in relation to climate change and implement necessary strategies to ensure grid reliability and resilience.In recent years, a significant number of power outages have occurred due to extreme ice formation on transmission and distribution networks, posing a threat to the power grid's resilience and reliability. To prepare power providers for snowstorms, extensive research has been conducted on snow accretion on power lines. Over the past two decades, many scientists have turned to machine learning (ML) algorithms for predicting ice accretion on overhead conductors, as ML models demonstrate superior accuracy compared to statistical forecasting models when it comes to forecasting challenging and fine-grained problems. However, most existing models primarily focus on predicting ice formation on power lines and fail to forecast the resulting damage to the distribution network. Therefore, this project proposes a model for predicting power outages caused by snow and ice storms in the distribution network. The goal is to aid in the planning process for disaster response and ensure the resilience and reliability of the power grid. The proposed outage prediction model incorporates statistical and machine learning techniques, taking into account features related to weather conditions, storm events, and information about the power network feeders.
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Title: Application of Blockchain and Artificial Intelligence Methods in Power System Operation and Control
Creator: Farhoumandi, Matin
Date: 2023
Description: The proliferation of distributed energy resources (DERs) and the large-scale electrification of transportation infrastructure are driving...
Show moreThe proliferation of distributed energy resources (DERs) and the large-scale electrification of transportation infrastructure are driving forces behind the ongoing evolution for transforming traditionally passive consumers into prosumers (both consumers and producers) in a coordinated system of power distribution network (PDN) and urban transportation network (UTN). In this new paradigm, peer-to-peer (P2P) energy trading is a promising energy management strategy for dynamically balancing the supply and demand in electricity markets. In this thesis, we propose the applications of artificial intelligence technology to power system operation and control. First, blockchain (BC) is applied to electric vehicle charging station (EVCS) operations to optimally transact energy in a hierarchical P2P framework. In the proposed framework, a decentralized privacy-preserving clearing mechanism is implemented in the transactive energy market (TEM) in which BC’s smart contracts are applied in a coordinated PDN and UTN operation. The effectiveness of the proposed TEM and its solution approach are validated via numerical simulations which are performed on a modified IEEE 123-bus PDN and a modified Sioux Falls UTN. Second, machine learning and deep learning methods are applied to short-term forecasting of non-conforming net load (STFNL). STFNL plays a vital role in enhancing the secure and efficient operation and control of power systems. However, power system consumption is affected by a variety of external factors and thus includes high levels of variations. These variations cause STFNL to be a challenging task as more DERs are integrated into the power grid. This thesis proposes two commonly used machine learning and deep learning methods, i.e., ensemble bagged and long short-term memory, for STFNL. The advantages, features and applications of these methods are expanded in a proposed fusion forecasting model that improves the STFNL accuracy. Additionally, data engineering and preprocessing options are used to increase the accuracy of the proposed fusion model. A comparative study based on practical load data is performed to demonstrate that the proposed fusion methodology can reach a relatively higher forecasting accuracy with lower error indices. Index Terms—Blockchain, deep learning and machine learning, electric vehicle charging stations, non-conforming net load forecasting, peer-to-peer transactive energy, power distribution and transportation networks, distributed energy resources, behind-the-meter supply resources.
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Title: Migration of Silver from Silver Zeolite/Low-Density Polyethylene Films into Food Stimulants
Creator: Sayeed, Maryam
Date: 2023
Description: Zeolites are naturally occurring or synthetic crystalline microporous aluminosilicate structures with remarkable catalytic, adsorption, and...
Show moreZeolites are naturally occurring or synthetic crystalline microporous aluminosilicate structures with remarkable catalytic, adsorption, and ion-exchange properties. Their unique framework of pores, channels, and cages with precise dimensions makes them an excellent fit for ion exchange and storage. Silver-exchanged zeolite (Ag/Y) composites may be incorporated into polymer matrices to create antimicrobial packaging materials. The slow release of Ag from nanosilver-enabled polymer nanocomposites (PNCs) may inhibit the growth of bacteria and other pathogens on the film’s surface, improving food quality and reducing food waste. However, the migration of Ag ions from the film into food matrices is of great concern as it could expose humans to high concentrations of a heavy metal from dietary sources. The amount of migration depends on various factors, including the potential form of Ag and its concentration in the film, the film thickness, and the storage conditions.The primary objective of this study is to investigate the effect of the form of Ag bound to the zeolite on the migration behavior of Ag from Ag/Y incorporated low-density polyethylene (LDPE) films. For Ag/Y-incorporated LDPE PNCs with distinct Ag species, the Ag migration into the water and Squirt (a commercial soft drink) was at least four times higher from films containing zeolites exchanged with ionic Ag versus zeolites exchanged with nanoparticulate Ag. Similarly, migration into 9 wt % aqueous Domino sugar (granulated sucrose) solution was seven times higher in the ionic silver-incorporated film than in the nanoparticulate Ag film. This study suggests that it is important to consider the form of Ag in silver-exchanged zeolite while producing packaging materials since the potential form of Ag in the PNCs might significantly affect Ag migration behavior.
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Title: Defense-in-Depth for Cyber-Secure Network Architectures of Industrial Control Systems
Creator: Arnold, David James
Date: 2024
Description: Digitization and modernization efforts have yielded greater efficiency, safety, and cost-savings for Industrial Control Systems (ICS). To...
Show moreDigitization and modernization efforts have yielded greater efficiency, safety, and cost-savings for Industrial Control Systems (ICS). To achieve these gains, the Internet of Things (IoT) has become an integral component of network infrastructures. However, integrating embedded devices expands the network footprint and softens cyberattack resilience. Additionally, legacy devices and improper security configurations are weak points for ICS networks. As a result, ICSs are a valuable target for hackers searching for monetary gains or planning to cause destruction and chaos. Furthermore, recent attacks demonstrate a heightened understanding of ICS network configurations within hacking communities. A Defense-in-Depth strategy is the solution to these threats, applying multiple security layers to detect, interrupt, and prevent cyber threats before they cause damage. Our solution detects threats by deploying an Enhanced Data Historian for Detecting Cyberattacks. By introducing Machine Learning (ML), we enhance cyberattack detection by fusing network traffic and sensor data. Two computing models are examined: 1) a distributed computing model and 2) a localized computing model. The distributed computing model is powered by Apache Spark, introducing redundancy for detecting cyberattacks. In contrast, the localized computing model relies on a network traffic visualization methodology for efficiently detecting cyberattacks with a Convolutional Neural Network. These applications are effective in detecting cyberattacks with nearly 100% accuracy. Next, we prevent eavesdropping by applying Homomorphic Encryption for Secure Computing. HE cryptosystems are a unique family of public key algorithms that permit operations on encrypted data without revealing the underlying information. Through the Microsoft SEAL implementation of the CKKS algorithm, we explored the challenges of introducing Homomorphic Encryption to real-world applications. Despite these challenges, we implemented two ML models: 1) a Neural Network and 2) Principal Component Analysis. Finally, we hinder attackers by integrating a Cyberattack Lockdown Network with Secure Ultrasonic Communication. When a cyberattack is detected, communication for safety-critical elements is redirected through an ultrasonic communication channel, establishing physical network segmentation with compromised devices. We present proof-of-concept work in transmitting video via ultrasonic communication over an Aluminum Rectangular Bar. Within industrial environments, existing piping infrastructure presents an optimal solution for cost-effectively preventing eavesdropping. The effectiveness of these solutions is discussed within the scope of the nuclear industry.
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