DSpace@RPI (Rensselaer Polytechnic Institute)
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Persona aware strategy towards building a comprehensive semantic data layer: a resource for marginalized us stem graduate students
No full textApril 2023School of ScienceThe long-standing problem of “what comes next,” i.e. the problem of finding an ideal career path, has been a barrier in attracting STEM students from marginalized communities. A Survey of Earned Doctorates conducted by NSF shows the disproportionate completion rates between marginalized and non-marginalized communities: An approximately 75\% increase in science and engineering doctorates as compared to an approximately 5\% increase among marginalized community members in the US in 2016. This dissertation aims at understanding and mitigating the factors affecting marginalized students in STEM. In this context, marginalized communities are defined as groups of students excluded based on ethnicity, race, linguistics, gender identity, age, physical ability, and/or immigration status. Even though marginalized students might have similar resources as their non-marginalized peers (including advisors, institute support programs, and various online resources), they are still not receiving the assistance they need to overcome their unique challenges. Apart from social barriers, marginalized students struggle to access the siloed, non-communicative, and incomplete reference points that speak to their experiences. I believe that the challenge of harmonizing these resources can be attacked by combining the currently used social science methodologies—surveys and interviews—with our own computer science techniques—web science and artificial intelligence tools—to provide a more concrete and trustworthy solution. This approach mitigates the lack of accessible reference points for marginalized US STEM graduate students based on the concepts which underpin social machines—“no one knows everything, but everyone knows something”—and semantics. The constant updating of resources makes them prone to combinatorial explosions and much less amenable to simple integration processes. The use of semantics via an ontology and a knowledge graph, therefore, becomes vital. This work focuses on building a persona aware semantic data layer to harmonize structured, semi-structured, and unstructured resources using knowledge graphs. The use of personas provides a mechanism to evaluate the sufficiency of the generated competency questions. These questions are then used to evaluate the ontologies and knowledge graphs without building an interface. The provided persona aware strategy helps ensure that such data layers could be well exploited to cater to multiple systems and applications. This work provides a pathway to develop a first of its kind, flexible, comprehensive resource for marginalized US graduate students to access the specific/general required information and connect with users who are concerned for their welfare or are interested in recruiting them. This will help facilitate entry of students from marginalized communities into more scientific fields, increasing the diversification of the graduate pool, and leading to more innovative, inclusive, and collaborative scientific progression.Ph
Spectral ct at ultra-high resolution via photon-counting and deep learning
No full textAugust 2023School of EngineeringComputed tomography (CT) plays an indispensable role in the clinical diagnosis and treatment of diseases. Enabled by recent dual-energy CT (DECT) advances, spectral CT allows anatomical and functional imaging with material characterization capabilities. Despite the great utilities, current CT technologies still suffer from insufficient spatial resolution and spectral fidelity for important clinical applications, especially in cardiovascular examination and temporal bone imaging. A breakthrough in high-fidelity spectral imaging at ultrahigh resolution (on the order of 50µm) would greatly benefit otology & neurotology, cardiology and other important clinical applications. The emerging X-ray photon-counting detectors (PCDs) make such imaging possible, with their incredibly small detector elements and impressive photon energy discrimination ability. Despite their promising potentials for medical CT, there are still major obstacles for clinical translation: (1) the imperfectness of current PCDs may cause severe spectral distortions by charge-sharing and pulse pileup effects, and hinder the fidelity of spectral imaging and limit the spatial resolution; (2) to keep image quality at a similar level for finer resolution, the radiation dose needed is proportional to the fourth power of the resolution increase which could be a huge concern; and (3) with drastically improved system resolution, the sensitivity to patient motion and geometry misalignment becomes more prominent and can be the bottleneck limiting the practical resolution. To overcome these challenges, our overall goal is to develop cutting-edge techniques and algorithms empowered by deep learning to pave the way for photon-counting spectral CT at an ultra-high spatial resolution to enter clinical practice, and come up with a clinical micro-CT (CMCT) prototype design with an initial emphasis on temporal bone ultra-high resolution imaging. Towards the goal, this thesis is organized around following specific aims targeting aforementioned challenges: (1) develop a deep learning-based photon counting data calibration approach for high-fidelity spectral imaging; (2) develop and optimize advanced geometric calibration, motion correction, interior tomography, and dose-saving image reconstruction methods dedicated for high-quality ultra-high resolution imaging at minimized radiation dose; (3) design and prototype a CMCT system with simulation tools according to clinical needs for temporal bone imaging (∼ 50µm), and demonstrating the clinical feasibility and imaging capabilities in phantoms or animal models both numerically and physically. A set of techniques have been developed with simulation and validated on real data upon achieving these aims, which will be a giant leap forward pushing the dream of CMCT into reality.Ph
Explorations of an ai infused paradigm for education: tippae
No full textMay 2023School of ScienceBoth international and domestic data indicates that the US public education system is failing to teach its students mathematics. Not only this, but this data indicates that long-standing issues in the education system, such as performance gaps between socio-economic strata, have not only been growing worse with time in this domain but have been severely accelerated and exacerbated by Covid19. In this dissertation, I advance the creation of a paradigm of artificial agents designed to utilize several core pedagogical properties to tackle this education crisis. These advancements take the form of the creation and investigation of two novel formalizations that are paramount to the foundational properties of the paradigm as a whole. The first of these formalizations is the Artificial Agent Identity Teleportation Theory (AIT Theory for short) for migratable artificial intelligences which organizes the current state of the art on migratable agent identities into a unifying framework in computational logic and axiomizes the relationship between dynamic embodiment and presentation of identity. The second of these formalizations is the Logico-Mathematical Item Difficulty Theory (LID Theory for short) which captures a measure of the inherent difficulty of test items by examining the question's cognitive features through the medium of analysing the solution proof in computational logic. After introducing these formalizations, this work investigates the potential practical feasibility in utilizing them in real-time application environments through the engineering and analysis of prototype core functionalities of the paradigm being advanced. These core functionalities were built using logicist artificial intelligences capable of utilizing automated reasoning over the novel theories. The first of these core functionalities, the ability to take idealized perceptual information about an environment and recognize identity teleportation from one embodiment to another, can be executed in less than three seconds. The second core functionality, the ability to take in the information of fourth grade standardized testing math problems and estimate a numerical score of difficulty, executes in less than a second per problem across a wide range of subjects and difficulty levels. The results of these investigations reveal that real-time applications that exhibit correct behavior as defined by the novel theories are, indeed, practically feasible.Ph
Seize the data: addressing research challenges among children with autism spectrum disorder using statistical and machine learning techniques
No full textAugust 2022School of EngineeringAutism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental condition that is estimated to affect about 1 in 44 children in the United States. While the etiology of ASD continues to be an area of intense investigation, the use of machine learning and multivariate statistical methods hold considerable promise in approaching clinically relevant questions regarding the nature of this condition. These techniques have shown great potential to identify underlying patterns in metabolomic and environmental data that have significance for ASD diagnosis and behavioral severity prediction. Nonetheless, biomarkers are not currently utilized in clinical settings to aid in diagnosis, and the degree to which various cellular/metabolic pathways converge to behavioral symptoms is poorly understood. This thesis seeks to leverage statistical learning and systems biology techniques on datasets derived from children with ASD to achieve three main goals. Firstly, this work analyzes urinary elemental measurements taken from a cohort of children and their mothers to identify possible environmental and physiological differences that could underscore areas pertinent to ASD etiology and mechanisms of action. Next, metabolomic differences observed between children with ASD and their typically developing counterparts are evaluated in blood, urine, and feces to expand the repertoire of potential clinically relevant biomarkers. Reliable biochemical biomarkers can pave the way for less subjectivity in diagnosis and for earlier detection, which can allow for better treatment outcomes and improve access to resources for caregivers. The interplay between prominent potential biomarkers and behavioral/comorbid symptoms are also identified to provide context for the development of targeted intervention and treatment strategies. Finally, the analysis of a microbiota transplant therapy (MTT) study performed on a cohort of children with ASD and gastrointestinal issues is presented. The effectiveness of this treatment in ameliorating both GI and severe behavioral symptoms is presented, along with its environmental and metabolomic implications.Ph
Semantics-based Framework for Incentivized Research Data Sharing
No full textWe present a framework for incentivized research data sharing using an ontology called the Data Sharing Ontology (DSO). The DSO captures the semantics of academic research data sharing and provides an operational specification for data sharing between researchers. The DSO includes a two-part incentive mechanism to confirm citations and reward reproducible research methods. The proposed solution is demonstrated using a dataset-sharing decentralized application use case. The paper's contributions provide a scalable technique for creating, curating, publishing, and consuming web-based, structured, and reusable datasets, including semantically annotated knowledge graphs
Reasoning with cognitive likelihood for artificially-intelligent agents: formalization & implementation
No full textMay 2023School of ScienceHuman beings routinely encounter situations containing informal, non-quantitative uncertainty. Consider for example the following scenario: Driving toward a four-way intersection, you stop at a red light. Eventually, the light turns green, but you perceive a driver approaching from your left, their light having turned red moments ago, and subsequently perceive their car accelerate. What can we say about this situation? It certainly seems likely that the driver will drive straight through the light. Of course, it's entirely possible that the driver will change their trajectory at the last second and slam on the brakes. How can we quantify this uncertainty (assuming this is what we desired)? We could compute a probability over all recorded instances of drivers accelerating toward red lights and either going through or stopping. But clearly humans don’t engage in anything like this computation when they reason about other drivers on the road. We use likelihoods to express qualities (as opposed to quantities e.g. probabilities) of the uncertainty of beliefs. In this way, one may reason that "I believe it's highly likely that the driver will drive through the red light" and subsequently come to the conclusion that, despite having the legal right-of-way, one should wait to avoid an accident. Autonomous agents, in order to effectively interact with humans that reason this way, will need to possess and exploit the ability to model reasoning with notions of qualitative uncertainty. The present dissertation introduces Cognitive Likelihood, a framework for reasoning with uncertain beliefs. The framework is implemented within a novel logic -- the Inductive Deontic Cognitive Event Calculus (IDCEC) -- which includes a formal grammar and semantics which dictate how agents can reason within the framework. These formalisms are implemented in an automated reasoner called ShadowAdjudicator in order to enable the automatic generation of IDCEC proofs. We present the novel algorithm underlying ShadowAdjudicator which enables this automated proof discovery. Finally, we demonstrate how these contributions can be utilized to solve autonomous driving problems and to adjudicate arguments regarding a notorious probability puzzle, the Monty Hall Problem.Ph
Investigating undergraduate student perceptions of engineering judgment
No full textMay 2023School of EngineeringEngineering judgment remains an elusive concept with many varying interpretations and descriptions. However, while there is no consensus on an explicit definition, there is agreement among researchers and practicing engineers alike that it is a vital skill for the field of civil engineering. Engineers, as a broad profession, are required to make judgment calls in their day to day work. In the case of civil engineers specifically, their decisions affect the infrastructure society relies on. As a result, their judgments are critical, because any mistake can directly result in a lower standard of living for the masses: Clean water, effective transportation, and safe structures are just the tip of the iceberg for services civil engineers provide. This judgment skill begins developing during the education and training undergraduates receive before entering the workforce. As a result, undergraduates’ perceptions of engineering judgment is an area of interest. Educators can employ numerous learning strategies, but ultimately the student’s takeaways are what matter. To analyze student perceptions on engineering judgment, this work employs a qualitative study utilizing the constant comparative method and documents the findings through a thematic analysis. Once this initial analysis was complete, the emergent themes were compared to existing literature to further the investigation and identify areas of interest. The emergent themes of multi-factored decision making and collaboration lead to the generation of hypotheses for future analysis of student development, as well as improved teaching strategies to better support the development of engineering judgement in undergraduate education.M
Exploration of a novel technique for waste heat recovery through molecular dynamics: influence of wettability and electric field on water and water-based nanofluids
No full textAugust 2022School of EngineeringMost of the energy produced globally comes by way of a heat engine. The Carnot principle places a limit as to how thermodynamically efficient a heat engine can be. There is no heat engine that can be 100% thermodynamically efficient and as such a substantial proportion of all heat supplied to a heat engine is lost as waste heat. Waste heat therefore is a large energy source ready to be properly utilized. Herein, a novel approach for converting waste heat to electricity is discussed. It involves the use of the liquid to vapor phase change of a material dielectric (water) or electrolyte (nanofluid) in the embodiment of a capacitor for direct thermal to electrostatic energy conversion. While this method of waste heat recovery could potentially be added to the ever-expanding portfolio of energy conversion techniques, a number of aspects must be addressed before it can be brought into practice. Water was seen as an ideal dielectric phase change material given its high relative permittivity ratio when in the liquid form as compared to its vapor form. However, given its short voltage holdoff time the phase change of water would need to occur rapidly. This brings up concerns of explosive boiling. Herein, molecular dynamics analysis into the explosive boiling behavior of thin water films gave more insight into how the interaction between the surface and liquid affected explosive boiling onset time. A Lennard-Jones potential with one interaction site and a Morse potential with three interaction sites between water and solid substrate were used. It was found generally that a stronger interaction between water film and substrate led to faster explosive boiling onset times but an increase in the number of interaction sites delayed explosive boiling, even at the same wettability (contact angle).
Understanding changes in the density and enthalpy of vaporization of a liquid dielectric such as water in the presence of an electric field is of importance due to the electrostatic nature of the waste heat conversion method under consideration. Specifically, if both density and enthalpy of vaporization are increased, the thermodynamic efficiency of the waste heat conversion method under consideration is decreased. Electric field effects are explored herein via molecular dynamics using two water models, the TIP4P-Ew and SWM4-NDP. The SWM4-NDP model is polarizable while the TIP4P-Ew model is not, which allows for a determination of the importance of model polarizability (i.e. variation in water model dipole moment) on these two properties of water when subjected to an electric field. Herein it was found that both water models respond similarly in terms of density and vaporization enthalpy variance upon the introduction of an electric field. Comparison was also made to the pressure induced by the electric field (electrostriction pressure) by way of a density comparison and it was found that the predicted electrostriction pressure overestimates the pressure experienced by water.
Water by itself has a high enthalpy of vaporization, which limits the efficiency of the newly proposed conversion method. Research both experimental and through simulation has shown that the vaporization enthalpy of nanofluids can be engineered via nanoparticle size and material selection. An avenue less explored is manipulating the enthalpy of vaporization by altering the interaction strength between the nanoparticles and the base fluid. In practice this could be achieved through the addition of coatings to the nanoparticles to alter their wettability to the base fluid. This was explored by using a Lennard-Jones potential and Morse potential to model the interaction between base fluid (water) and the nanoparticle. For nanoparticles 2nm in diameter and at weight percentages up to 6%, the change in vaporization enthalpy due to alterations of the interaction strength between nanoparticle and base fluid was not significant (less than a 1% difference) when compared to the effect of altering the weight percentage of nanoparticles in the nanofluid or introducing an electric field. However, the effect of wettability may still become important at other nanoparticle concentrations and sizes.
In all, the studies presented here further the understanding of phase change and thermodynamic properties of water and water based nanofluids under an electrostatic field which will help inform the development of a novel approach to waste heat conversion. The reduction of waste heat will improve energy sustainability outlooks.Ph
Loaded language and conspiracy theorizing
No full textMay 2023School of Humanities, Arts, and Social SciencesLoaded language is an umbrella term for words, phrases, and overall rhetorical strategies that have strong emotional implications and intent to sway others. Belief in conspiracy theories is tied to a range of strong emotions (van Prooijen and Douglas, 2018). Accordingly, language with strong emotional and persuasive content may be expressed by people experiencing the strong emotions associated with conspiracy theorizing. In this research, I examined multiple types of loaded language in three studies: (1) a comparison of loaded language on two online parenting forums for and against vaccination, (2) a comparison of loaded language on the subreddits r/conspiracy, r/science, and r/wallstreetbets, and (3) an evaluation of loaded language in a dataset from the alt-tech social networking platform Parler on January 6, 2021, when the U.S. Capitol was attacked as a consequence of conspiracy theories regarding the legitimacy of the 2020 Presidential Election. Results show that loaded language, whose usage is linked to the cognitive motivations underlying belief in conspiracy theories, is a linguistic marker of conspiracy theorizing.Ph
Ambient pressure synthesis and characterization of strongly correlated oxides
No full textMay 2022School of EngineeringStrongly correlated oxides are an important class of materials with a vast array of intriguing optical, electrical, electrochemical, dielectric, and catalytic properties which makes them extremely attractive for a variety of applications, including supercapacitors, batteries, fuel cells, sensors, catalysts, solar cells, and smart windows. Their unique properties arise from the strong coupling between the d band electrons as well as electron-lattice interactions. One well-studied but not well-understood property is the insulator-to-metal transition (IMT) that can occur due to temperature, doping, and other external factors, which can be exploited for many advanced optoelectronic applications. Therefore, the central aim of this dissertation is to use a suite of electrochemical, electronic, and spectroscopic techniques to elucidate the defect-property-function correlation that underpins the metal-insulator phase transition seen in two types of correlated oxides: i) Mott-Hubbard insulators, such as VO2; and ii) charge-transfer insulators, such as rare-earth doped nickelates (RNiO3). In the first study (Chapter 3), the effects of oxygen over-stoichiometry on the band structure and IMT in VO2 are presented. Systematic modulation of VO1.86 to VO2.44 shows that charge fluctuation in the metallic phase of VO2 forms electron (e) and hole (h+) pairs, which leads to delocalized V3+ and V5+ states. As a result, the IMT is linked to changes in the V-O bond length, localization of V3+ e at V5+ sites, formation of V4+-V4+ dimers, and removal of π^* screening electrons. In addition, we show that phase transitions are linked to the lattice V3+/V5+ concentration and that electronic transitions are regulated by charge fluctuation, charge redistribution, and structural transition.
In the second study (Chapter 4), a three-step phase transition in VO2 is presented as a result of high charge injection. Using two-dimensional crystalline platelets, electron doping nearly two orders of magnitude larger than previously reported is achieved using Li+. As a result of this massive charge injection, a three-step insulator-to-metal-to-insulator-to-metal transition and switch in the electrical polarity from n-type to p-type is reported for the first time. A “lattice redox model” is proposed to aid in explaining the origin of the thermal-, electrochemical-, and compositional-induced IMT, which involves V redox-induced band filling, structural distortion, and electron effects.
In the third study (Chapter 5), an aging technique is presented to stabilize the Ni3+ cations in rare-earth doped nickelates at ambient pressure. A solid-state synthesis method is used followed by two types of aging: i) a slow ambient-air aging for 6-8 months or ii) an accelerated aging at a higher temperature of 650°C for 1-3 weeks. This technique was used to successfully synthesize bulk SmNiO3 and NdNiO3. Rietveld refinement showed the composition of NdNiO3 and SmNiO3 to be as high as 96 wt% and 43 wt% after aging, respectively. Sharp IMT are seen in both SmNiO3 and NdNiO3 after aging of 2-3 orders of magnitude, which is similar to high-pressure synthesized samples reported in the literature.Ph