Doctor of Philosophy, University of Akron, 2022, Psychology-Industrial/Organizational

This dissertation studies the emergence of Transactive Memory Systems (TMS), specifically the shared knowledge structure component of TMS. To this end, the first part of the project provides in-depth discussions around the theoretical and methodological integration of the TMS and broader teams literatures, the alignment of theoretical and empirical TMS definitions, and the need to study communication in depth as the mechanism through which TMS develops. In the second part of the dissertation, the principles of these discussions were applied to build a computational model of TMS shared knowledge structure emergence in R. Each simulation ran for 100 iterations to study whether communication between agents regarding their areas of expertise resulted in the emergence of the TMS shared knowledge structure. Decision-making and deep learning theories were drawn on to predict that when agents did not have overlap in areas of expertise (had some overlap in areas of expertise), selecting to communicate with the team member thought most likely to be an expert in an information area led to more favorable (less favorable) outcomes than selecting which team member to communicate with randomly. The simulation was repeated for seven learning rates representing how readily agents changed their perceptions about their team members. Results indicated that query and response regarding expertise areas of agents consistently led to the emergence of a TMS shared knowledge structure across conditions. In most instances the pattern of emergence was marked by an initial period of rapid emergence followed by a decrease in the emergence rate. The results supported the hypotheses that when there is no expertise overlap, selecting maximally would lead to a more emerged shared knowledge structure than searching randomly, while the opposite would be the found when there was some expertise overlap. The latter finding challenges the assumption present in the TMS literature that searching for inf (open full item for complete abstract)

Committee: Joelle Elicker (Advisor); Andrea Snell (Committee Co-Chair); Matthew Juravich (Committee Member); James Diefendorff (Committee Member); Paul Levy (Committee Member) Subjects: Psychology

Doctor of Philosophy, The Ohio State University, 2021, Psychology

Populations and individuals diverge from each other in their cognitive abilities, and re- searchers have a great interest in characterizing and explaining these group and individual differences. Among research tools, behavioral tasks are widely adopted to assess cognitive abilities due to their simplicity and applicability. In behavioral tasks, descriptive statistics are commonly used as measurement indices for the cognitive abilities of interest. However, because these statistics have a limited ability to characterize the mechanisms underlying each task based on cognitive theories, they cannot fully explain the reasons that may cause group and individual differences. In this dissertation, I adopt hierarchical Bayesian approaches to model several behav- ioral tasks for cognition, with the aim to explore the mechanisms underlying the group and individual differences in populations tested by these tasks. I incorporate existing cognitive theories into the hierarchical Bayesian models, and use estimated parameters to characterize the cognitive abilities of interest. At the group difference level, I show that the hierarchi- cal Bayesian models can be used to identify the potential deficits in populations that have poorer task performance. At the individual level, I show that these models can reveal the behavioral patterns of each individual, and identify potential causes of individual differences. I built theory-based hierarchical Bayesian models to three behavioral tasks respectively: the progressive ratio task that measures motivation; the continuous performance task that measures sustained attention; and the memory updating task that measures working memory abilities. I show that these models have reasonable parameter recovery abilities and good fits to data. I apply these models to several empirical data sets. The progressive ratio task model is applied to a data set measuring motivation of people with and without schizophrenia (Wolf et al., 2014) and first-degree (open full item for complete abstract)

Committee: Trisha Van Zandt (Advisor); Jolynn Pek (Committee Member); Paul De Boeck (Committee Member); Mario Peruggia (Committee Member) Subjects: Psychology; Quantitative Psychology

Doctor of Philosophy, The Ohio State University, 2021, Psychology

Trait impulsivity, defined by actions taken without forethought and a consistent preference for immediate over delayed rewards, confers vulnerability to all externalizing spectrum disorders. This includes all disorders along the common developmental progression of attention-deficit/hyperactivity disorder (ADHD) in early childhood to conduct disorder (CD) and delinquency in later childhood and adolescence, to substance use disorders (SUDs) and antisocial personality disorder (ASPD) in adulthood. Such externalizing progression derives from complex interactions among individual-level vulnerabilities and environmental risk factors over time. Specifying how such mechanisms interact across development is a burgeoning area of research. Although trait-level mechanisms have long been studied, research linking trait-level to behavioral mechanisms is more limited. Furthermore, most existing research uses standard inferential approaches, which are not well suited for modeling complex relations among causal influences at different levels of analysis. In this dissertation, I describe how both (1) the methods used to make inference on individual difference correlations across levels of analysis, and (2) the statistical models used to infer how data within levels of analysis arise often fail to fully embody the substantive theories that researchers aim to test. I use my prior work on the “Reliability Paradox” (Haines et al., 2020a) to demonstrate (1), and my work on the Iowa Gambling Task (Haines, Vassileva, & Ahn, 2018) to demonstrate (2). I then discuss a third study (Haines et al., 2020b) that shows how joint generative models across levels of analysis (between behavioral and trait mechanisms, behavioral and neural mechanisms, etc.) can be used to better capture individual differences of theoretical interest.

Committee: Theodore Beauchaine (Advisor); Brandon Turner (Advisor); Patricia Van Zandt (Committee Member); Mona Makhija (Other) Subjects: Clinical Psychology; Cognitive Psychology; Developmental Psychology; Psychology

Doctor of Philosophy, The Ohio State University, 2020, Chemical Engineering

Quantitative Structure-Activity Relationships (QSARs) are empirical or semi-empirical models which correlate the structure of chemical compounds with their biological activities. QSAR analysis frequently finds application in drug development and environmental and human health protection. It is here that these models are employed to predict pharmacological endpoints for candidate drug molecules or to assess the toxicological potential of chemical ingredients found in commercial products, respectively. Fields such as drug design and health regulation share the necessity of managing a plethora of chemicals in which sufficient experimental data as to their application-relevant profiles is often lacking; the time and resources required to conduct the necessary in vitro and in vivo tests to properly characterize these compounds make a pure experimental approach impossible. QSAR analysis successfully alleviates the problems posed by these data gaps through interpretation of the wealth of information already contained in existing databases. This research involves the development of a novel QSAR workflow utilizing a local modeling strategy. By far the most common QSAR models reported in the literature are “global” models; they use all available training molecules and a single set of chemical descriptors to learn the relationship between structure and the endpoint of interest. Additionally, accepted QSAR models frequently use linear transformations such as principal component analysis or partial least squares regression to reduce the dimensionality of complex chemical data sets. To contrast these conventional approaches, the proposed methodology uses a locality-defining radius to identify a subset of training compounds in proximity to a test query to learn an individual model for that query. Furthermore, descriptor selection is utilized to isolate the subset of available chemical descriptors tailored specifically to explain the activity of each test compound. Finally, this (open full item for complete abstract)

Committee: James Rathman (Advisor); Bhavik Bakshi (Committee Member); Jeffrey Chalmers (Committee Member) Subjects: Chemical Engineering

Master of Science (M.S.), University of Dayton, 2018, Aerospace Engineering

This work seeks to provide a proof-of-concept for the use of variable-fidelity (VF) kriging to approximate the lift and drag values for a complex hypersonic flight vehicle. Otherwise known as aerodynamic database generation within the aerospace engineering community, the force or moment experienced by a vehicle due to airflow, as a function of independent inputs such as flight speed or attitude, is approximated via some mathematical form. In the case of this work, VF kriging is implemented such that the vehicle response is interpolated directly through the points of high-fidelity (HF) simulation data while the trends of the response approximation are guided by low-fidelity (LF) information. High-fidelity simulations are implemented via the Euler flow computational software package Cart3D. The low-fidelity information is given by supersonic-hypersonic small-disturbance theory implemented in a surface pressure estimation code, developed specifically for this work for completely arbitrary body shapes represented by unstructured, triangular-cell surface meshes. The major contribution is a framework that connects the two fidelity levels to VF kriging routines to produce lift and drag approximations of arbitrary complex vehicles under hypersonic flight conditions. Assessment of the quality of the approximations is given by the root-mean-square error (RMSE) between the VF kriging surrogates and high-fidelity simulations performed over the same independent input domain. Results in two dimensions show that the use of VF kriging, to produce an interpolant as a function of angle-of-attack and Mach number, increases surrogate accuracy by nearly an order of magnitude for lift and by over twenty times for drag, when compared to ordinary kriging without variable-fidelity modeling. Three-dimensional surrogates, with input of angle-of-attack and two independent elevon control surface deflections, show roughly two and four times more accuracy for lift and drag, respectively, compared (open full item for complete abstract)

Committee: Markus Rumpfkeil (Advisor); Jose Camberos (Committee Member); Raymond Kolonay (Committee Member) Subjects: Aerospace Engineering; Applied Mathematics

Doctor of Philosophy, The Ohio State University, 2017, Electrical and Computer Engineering

Electron-plasmonic devices are of strong interest for terahertz applications. In this work, we develop rigorous computational tools using finite difference time domain (FDTD) methods for accurate modeling of these devices. Existing full-wave-hydrodynamic models already combine Maxwell's and hydrodynamic electron-transport equation for multiphysical hybrid modeling. However, these multilevel methods are time-consuming as dense mesh is required for plasmonic modeling. Therefore, they are not suited for design and optimization. To address this issue, we propose new iterative ADI-FDTD-hydrodynamic hybrid coupled model. The new implementations provide time-efficient, yet accurate, modeling of these devices. It is demonstrated that for a typical simulation, up to 50% reduction in simulation-time is achieved with a nominal 3% error in calculations. Using the new tool-set, we investigate several devices that operate using the properties of 2D electron gas (2DEG). We provide one of the first multiphysical numerical analyses of these devices, giving accurate estimates of their terahertz performance. The developed tool allows simulation of arbitrary 2DEG based terahertz devices, providing useful and intuitive 2D field information. This has allowed understanding of the operation and radiation principles of these devices. Specifically, we examine the known plasma-wave instability in short-channel high electron mobility transistors (HEMTs) that leads to terahertz emissions at cryogenic temperatures. We also examine terahertz emitters that exploit resonant tunneling induced negative differential resistance (NDR) in HEMTs. Finally, using this tool we numerically demonstrate the existence of acoustic and optical-plasmonic modes within 2DEG bilayer systems in HEMTs. Methods for exciting and controlling these modes are also discussed enabling new physics among bilayer devices.

Committee: John Volakis (Advisor); Siddharth Rajan (Advisor); Kubilay Sertel (Committee Member); Teixeira Fernando (Committee Member); Niru Nahar (Committee Member); Karin Musier-Forsyth (Committee Member) Subjects: Electrical Engineering; Plasma Physics

Doctor of Philosophy, Case Western Reserve University, 2017, Materials Science and Engineering

The construction of mesoscale assemblies using a bottom-up approach is an emerging research area in recent years, while understanding the interactions that control the organization of the constructing building blocks turns out to be a prerequisite for effective assembly design. The intrinsic electronic structures of materials determine their optical properties, which give rise to long-range interactions including electrostatic and van der Waals (vdW) components. And electrostatic along with vdW interactions, are the fundamental interactions that drive mesoscale assembly. In this research, the long range interactions of inorganic material and bio-molecules are investigated using both experiment and computation optical methods, and then a functional mesoscale assembly based on an inorganic substrate and biomolecular particles are built. In terms of the inorganic material, the full spectral optical properties and van der Waals-London interaction of bulk SiC crystal is investigated with a combination of vacuum ultraviolet spectroscopy and Liftshiz theory-based Hamaker coefficient calculation, and compared with OLCAO electronic structure calculations of the band structure and ab initio optical properties of SiC. Cylindrical biomolecules, DNA, is selected as a biomolecular example. The effect of nitrogen base polarizability on the optical properties and electronic structures of double-strand DNA are studied by optical characterization and ab initio density functional theory modeling; then G-quadruplex DNA consisting of human telomere sequence are characterized by spectroscopy, static light scattering and ab initio modeling, and compared with corresponding double-strand DNA, giving rise to the structural dependent electronic structures and pH dependent pair-wise interactions. Finally, the first semi-ordered, layered, mesoscale self-assembly capable of photon management comprised of plant viruses is created and characterized. Anti-reflection and photon-trapping properti (open full item for complete abstract)

Committee: Roger French Dr. (Advisor); Nicole Steinmetz Dr. (Committee Member); Alp Sehirlioglu Dr. (Committee Member); Rudolf Podgornik Dr. (Committee Member); Hongping Zhao Dr. (Committee Member) Subjects: Materials Science

Doctor of Philosophy (PhD), Wright State University, 2016, Human Factors and Industrial/Organizational Psychology PhD

When people look through the environment their eyes are guided in part by what they have recently seen. This phenomenon, referred to as visual priming, is studied in the laboratory through manipulations of stimulus repetition. Typically, in search tasks, response times are speeded when the same target is repeated relative to when it is changed (e.g., Maljkovic & Nakayama, 1994). Although priming is thought to be based on a memory mechanism in the visual system, there is a debate in the literature as to whether such a mechanism is driven by relatively early (e.g., feature-based accounts) or later (e.g., episodic memory accounts) processing. Across three experiments, this dissertation utilized a computational modeling framework (Systems Factorial Technology; Townsend & Nozawa, 1995) to directly compare early and later accounts of priming and determine when visual priming is processed within the visual system in both feature and conjunctive search tasks. Specifically, priming was assessed in terms of its temporal relation (i.e., parallel or serial) to a relatively early process (the processing of conspicuity) and a relatively later process (the processing of Rewards, Experiment 1a; the processing of Word Cues, Experiments 1b and 2) in the visual system. The results suggest that the priming manipulation is processed in parallel with the conspicuity and word cue manipulations within both singleton (Experiments 1a and 1b) and conjunctive (Experiment 2) search. This supports accounts of priming as an early process and suggest that models of priming as a later process within feature or conjunctive search should be rejected. Further, these results also provide evidence to suggest word cues are processed at early stages of visual processing. This supports models of visual processing that suggest high-level representations can modulate the earliest levels of the visual system. Together, these findings provide some of the strongest evidence about the temporal process (open full item for complete abstract)

Committee: Joseph Houpt Ph.D. (Committee Chair); Assaf Harel Ph.D. (Committee Member); Scott Watamaniuk Ph.D. (Committee Member); Alan Pinkus Ph.D. (Committee Member) Subjects: Cognitive Psychology; Experimental Psychology; Psychology

Doctor of Philosophy, The Ohio State University, 2015, Biophysics

Many processes in biological systems are dependent upon complex regulatory schemes, often arranged in scale-free networks. Because of the difficulties in studying the nuanced behavior of such complicated systems, understanding the interactions between the individual nodes is essential to grasp the system as a whole. A highly productive example is the mechanism controlling tryptophan biosynthesis in Bacillus species. Thirty years of investigation into this system has resulted in paradigmatic examples of biosensors, response-amplification schemes, and closed-loop control. Despite this thorough inquest, many important processes within the TRAP system are still poorly understood; several are investigated with a variety of experimental techniques and analytical approaches in this work. One is the feedback rescue mechanism provided by the protein Anti-TRAP (AT) – although a crystal structure of a TRAP-AT complex is available, this static representation has proved to be misleading. Here, we show that the TRAP-Anti-TRAP interaction is actually quite complex, because the components the polydentate nature of each component leads to the formation of large heteropolymers at physiological ratios. These complexes are studied extensively with a broad range of structural, thermodynamic, and kinetic experiments. In addition to the complicated behavior they display in combination, the individual components themselves display fascinating properties that are tied to their regulatory function. Because TRAP is a homopolymer of eleven identical subunits, its activation by binding up to eleven tryptophans provides an unparalleled opportunity to examine binding cooperativity. The nature of this thermodynamic coupling mechanism is investigated in this work, leading to the realization that the presence of just a few bound tryptophan molecules causes profound changes in TRAP long before a majority of its active sites are occupied. AT, which also exists as a homopolymer of three identical (open full item for complete abstract)

Committee: Mark Foster Ph.D (Advisor); Venkat Gopalan Ph.D (Committee Member); Richard Swenson Ph.D (Committee Member); Charles Bell Ph.D (Committee Member) Subjects: Biochemistry; Biophysics; Molecular Physics; Molecules; Polymers

Master of Sciences, Case Western Reserve University, 0, EMC - Aerospace Engineering

Flame spread over solid fuels with simple geometries has been extensively studied in the past, but few have investigated the effects of complex fuel geometry. This study uses numerical modeling to analyze the flame spread and burning of wavy (corrugated) thin solids and the effect of varying the wave amplitude. Sensitivity to gas phase chemical kinetics is also analyzed. Fire Dynamics Simulator is utilized for modeling. The simulations are two-dimensional Direct Numerical Simulations including finite-rate combustion, first-order pyrolysis, and gray gas radiation. Changing the fuel structure configuration has a significant effect on all stages of flame spread. Corrugated samples exhibit flame shrinkage and break-up into flamelets, behavior not seen for flat samples. Increasing the corrugation amplitude increases the flame growth rate, decreases the burnout rate, and can suppress flamelet propagation after shrinkage. Faster kinetics result in slightly faster growth and more surviving flamelets. These results qualitatively agreement with experiments.

Committee: James T'ien (Committee Chair); Joseph Prahl (Committee Member); Yasuhiro Kamotani (Committee Member) Subjects: Aerospace Engineering; Fluid Dynamics; Mechanical Engineering

Master of Science, The Ohio State University, 2008, Aeronautical and Astronautical Engineering

Two new turbulence models have been implemented in the turbomachinery flow simulation code TURBO. This paper focuses on the validation and implementation of the shear stress transportation (SST) model and the detached eddy simulation (DES) model. The models are validated against experimental data as well as results from the current two-equation, low Reynolds number, K-E model. Validation is conducted on a circular cylinder and the NASA transonic compressor rotor, Rotor 35. Cp and St predictions are examined for the cylinder while operating range and performance figures are examined for Rotor 35.Upon validation, the models are examined for robust performance with regards to the tip-gap modeling of Rotor 35. Currently TURBO standard grids utilize a periodic loss-less tip region. Grid spacing near this region is explored by introducing clustering at the blade tip. A vena-contracta approach and griding of the tip-gap region are also explored.

Committee: Jen-Ping Chen PhD (Advisor); Meyer Benzakein PhD (Committee Member) Subjects: Engineering; Fluid Dynamics

Master of Science in Engineering, University of Akron, 2010, Biomedical Engineering

Space travel has placed humans in an interesting physiological situation that makes it necessary to secure the health of the astronauts. In space, due to the lack of gravity, there is a fluid shift toward the upper body that results in a decrease in plasma volume. As a result, there is a significant drop in red blood cell mass over the flight period, which could result in space flight anemia. To monitor this change, ultrasound must be used, since it is the most trusted and only flight surgeon approved imaging/detecting modality for space flight. Continued research into the use of ultrasound for monitoring hematocrit levels can improve the lives of humans both in space and on Earth. A physical means to examine the viability of a cross-correlation detection method for ultrasound (originally demonstrated for optical light scattering) that minimizes multiple scattering effects [23] was demonstrated by conducting a Young's two-pinhole experiment. This was implemented using a pinhole mask on the receiving transducer to affect cross-correlation. The resulting interference pattern should have a period predicted by the pinhole size, spacing, and frequency of the ultrasound signal. Interference patterns were produced for a series of masks with different pinhole sizes and pinhole separations. The fringe patterns were analyzed, with the measured period compared to the predicted period, and the 300/700(pinhole diameter/separation) mask was determined as the most optimal. A two-dimensional computer model was developed using the Comsol Multiphysics software package (Comsol AB.). The model was created to analyze the physical cross correlation method and help explain the experimental results, accounting for some of the effects not captured by the analytical model. The simulations showed that the masks with smaller pinholes (~100μm) had periods that were not consistent with the analytic predictions, indicating the presence of effects that were not properly modeled analytically. One o (open full item for complete abstract)

Committee: Bruce Taylor Dr. (Advisor); Stanley Rittgers Dr. (Committee Member); Dale Mugler Dr. (Committee Member) Subjects: Biomedical Research

Doctor of Philosophy, The Ohio State University, 2013, Aero/Astro Engineering

This dissertation describes model reduction techniques for the computation of aerodynamic heat flux and pressure loads for multi-disciplinary analysis of hypersonic vehicles. NASA and the Department of Defense have expressed renewed interest in the development of responsive, reusable hypersonic cruise vehicles capable of sustained high-speed flight and access to space. However, an extensive set of technical challenges have obstructed the development of such vehicles. These technical challenges are partially due to both the inability to accurately test scaled vehicles in wind tunnels and to the time intensive nature of high-fidelity computational modeling, particularly for the fluid using Computational Fluid Dynamics (CFD). The aim of this dissertation is to develop efficient and accurate models for the aerodynamic heat flux and pressure loads to replace the need for computationally expensive, high-fidelity CFD during coupled analysis. Furthermore, aerodynamic heating and pressure loads are systematically evaluated for a number of different operating conditions, including: simple two-dimensional flow over flat surfaces up to three-dimensional flows over deformed surfaces with shock-shock interaction and shock-boundary layer interaction. An additional focus of this dissertation is on the implementation and computation of results using the developed aerodynamic heating and pressure models in complex fluid-thermal-structural simulations. Model reduction is achieved using a two-pronged approach. One prong focuses on developing analytical corrections to isothermal, steady-state CFD flow solutions in order to capture flow effects associated with transient spatially-varying surface temperatures and surface pressures (e.g., surface deformation, surface vibration, shock impingements, etc.). The second prong is focused on minimizing the computational expense of computing the steady-state CFD solutions by developing an efficient surrogate CFD model. The develop (open full item for complete abstract)

Committee: Jack McNamara (Advisor); Thomas Eason III (Committee Member); Jeffrey Bons (Committee Member); Mo-How Herman Shen (Committee Member); Mei Zhuang (Committee Member) Subjects: Aerospace Engineering

Master of Science (M.S.), University of Dayton, 2024, Aerospace Engineering

Traditional conceptual-level aerodynamic analysis is limited to empirical and/or inviscid models due to considerations of computational cost and complexity. There is a distinct desire to incorporate higher-fidelity analysis into the conceptual-design process as early as possible. This work seeks to enable the use of high-fidelity data by developing and applying multi-fidelity surrogate models that can efficiently predict the underlying response of a system with high accuracy. To that end, a novel form of the multi-fidelity polynomial chaos expansion (PCE) method is introduced, extending the surrogate modeling technique to accept three distinct fidelities of input. The PCE implementation is evaluated for a series of analytical test functions, showing excellent accuracy in creating multi-fidelity surrogate models. Aerodynamic analysis of a generic hypersonic vehicle (GHV) is performed using three codes of increasing fidelity: CBAERO (panel code), Cart3D (Euler), and FUN3D (RANS). The multi-fidelity PCE technique is used to model the aerodynamic responses of the GHV over a broad, five-dimensional input domain defined by Mach number, dynamic pressure, angle of attack, and left and right control surface settings. Mono-, bi-, and tri-fidelity PCE surrogates are generated and evaluated against a high-fidelity “truth” database to assess the global error of the surrogates focusing on the prediction of lift, drag, and pitching moment coefficients. Both monofidelity and multi-fidelity surrogates show excellent predictive capabilities. Multi-fidelity PCE models show significant promise, generating aerodynamic databases anchored to RANS fidelity at a fraction of the cost of direct evaluation.

Committee: Markus Rumpfkeil (Advisor); Jose Camberos (Committee Member); Timothy Eymann (Committee Member) Subjects: Aerospace Engineering

Doctor of Philosophy, The Ohio State University, 2024, Linguistics

Decades of psycholinguistics research have shown that human sentence processing is highly incremental and predictive. This has provided evidence for expectation-based theories of sentence processing, which posit that the processing difficulty of linguistic material is modulated by its probability in context. However, these theories do not make strong claims about the latent probability distribution of the human comprehender, which poses key research questions about its characteristics. Computational language models that define a conditional probability distribution are helpful for answering these questions, as they can be trained to embody different predictive processes and yield concrete surprisal predictors (i.e. negative log probabilities) that can be evaluated against measures of processing difficulty collected from human subjects. This dissertation evaluates Transformer-based large language models, which are artificial neural networks trained to predict the next word on massive amounts of text, as expectation-based models of sentence processing. Experiments reveal a robust and systematic divergence between the predictive processing of large language models and that of human subjects, the degree of which increases reliably with their number of parameters and amount of training data. This strong effect indicates that human sentence processing is not driven by the predictions made by these large-scale machine learning models, and highlights a fundamental shortcoming of large language models as models of human cognition. This dissertation additionally elucidates this discrepancy between humans and large language models. A series of analyses shows that large language models generally underpredict human-like processing difficulty by making 'superhumanly accurate' predictions of upcoming words, which may be a manifestation of the extensive real-world knowledge gleaned from large sets of training examples that are not available to humans. The learning trajectorie (open full item for complete abstract)

Committee: William Schuler (Committee Chair); Michael White (Committee Member); Micha Elsner (Committee Member); Tal Linzen (Committee Member) Subjects: Computer Science; Linguistics

Doctor of Philosophy, The Ohio State University, 2024, Psychology

Decision making is not instantaneous. Instead, it is a dynamic process with information collected and aggregated over time. In decisions with multiple attributes (i.e., dimensions) and multiple options, the dynamics can be reflected in the weighting of the attributes and considerations of the options. For example, when choosing whether to travel and risk getting COVID or to stay home and be safe, people who are risk averse may put more weight on safety; when recommending a restaurant for a friend, some restaurants may come more easily to mind than others. The main objective of my research is to uncover the decision processes in different choice domains. Specifically, I use dynamic choice models, combined with measures of how people search through options visually or in memory, to explain choice outcomes and decision times. From the visual search perspective, one empirical finding is that people are more likely to choose the option that they have looked at more. Little is known about how these two distinct features of the choice process interact. I proposed a computational model to account for attentional towards both options and attributes. I then used five eye-tracking datasets (two- alternative, two-attribute preferential tasks) from different choice domains to test the model. I found very stable option-level and attribute-level attentional discount factors across datasets, though non-fixated options are consistently discounted more than non-fixated attributes. Additionally, I found that people generally discount the non-fixated attribute of the non-fixated option in a multiplicative way, and so that feature is consistently discounted the most. Finally, I also found that gaze allocation reflects attribute weights, with more gaze to higher-weighted attributes. In summary, this work uncovers an intricate interplay between attribute weights, gaze processes, and preferential choice. From the memory search perspective, I investigated how category fluency (i.e., how e (open full item for complete abstract)

Committee: Duane Wegener (Committee Chair); Jason Coronel (Committee Member); Roger Ratcliff (Committee Member); Ian Krajbich (Advisor) Subjects: Psychology

Master of Science in Mechanical Engineering (MSME), Wright State University, 2024, Mechanical Engineering

Conventional turbulence models often predict behaviors opposite as to what is observed in flows subject to rotation. In this type of flow scenario, rotation typically induces turbulence suppression. To address this limitation, a modification to the Spalart Allmaras Model with Rotation Correction (SA-R) was proposed to enhance the original Spalart Allmaras Model's sensitivity to rotation and curvature. To test the validity and accuracy of this modification, two cases were investigated. The first case involved an axisymmetric rotating pipe. A Reynolds Number of 37,000 was implemented and the initial and boundary conditions established by Zaets et. al. were utilized. Initially non-rotating, the flow transitioned to full rotation at N=0.6 at 9 m. Results demonstrated strong alignment with experimental data, showcasing improvements over the SA , SA-R, SARC, and SA-R23 models. In the second case, a vortex, surrounded by irrotational flow, was studied. This case used a Reynolds number of 10^5, and implemented the initial and boundary conditions outlined by Spalart and Garbaruk. While the modified model showed improvement over the SA model, it still displayed slight circulation overshoot, a behavior considered unphysical. However, it notably reduced the magnitude of eddy viscosity. The SARC model did produce a laminar state solution. Other vortex parameters also indicated circulation overshoot of the modified SA-R model. Overall, the modified SA-R model showed significant improvement for rotational flow scenarios and holds potential for further refinement to improve accuracy.

Committee: George Huang Ph.D., P.E. (Advisor); José Camberos Ph.D., P.E. (Committee Member); Mitch Wolff Ph.D. (Committee Member) Subjects: Fluid Dynamics

Doctor of Philosophy (PhD), Wright State University, 2023, Human Factors and Industrial/Organizational Psychology PhD

Instead of characterizing transfer from short-term memory to long-term memory as the relocation of information from one structural system to another, I propose a theory that conceives of transfer as the learning processes that act on and transform the representations of the information itself. Dynamic Associative Memory posits that recently encoded memories are supported by active maintenance and the relevance of the current context. Over time, the current context becomes less relevant; therefore, the brain must learn contextually invariant associations between memories so that they may support themselves. I instantiated my theory in the ACT-R cognitive architecture and created a new module to automate and fully integrate attentional refreshing into the architecture. The DAM module extends ACT-R's spreading activation to allow activation to be shared among related items in declarative memory. It implements a novel associative learning process based on causal inference that stochastically generates new memory traces for associations between items proportionate to the causal power of one item to predict the other. I also developed another module to provide ACT-R models with a principled method for updating temporal context, and I proposed similarity functions for quantifying the contextually invariant relatedness of hierarchical relationships and the contextually mediated relatedness of features. I ran three simulation studies, systematically manipulating cognitive load, encoding instructions, and the repetition and semantic content of the to-be-remembered items, to investigate the fitness and predictions of the new model. Recall of elaborated words was better than unelaborated words, which were recalled better than non-words. Recall of lists composed of items with less semantic content benefited more from repetition. The model failed to reproduce the benchmark cognitive load effect in immediate recall, but the effect returned in delayed recall, suggesting that (open full item for complete abstract)

Committee: Ion Juvina Ph.D. (Committee Chair); Joseph Houpt Ph.D. (Committee Member); Glenn Gunzelmann Ph.D. (Committee Member); Valerie Shalin Ph.D. (Committee Member); Herbert Colle Ph.D. (Committee Member) Subjects: Cognitive Psychology

Doctor of Philosophy, The Ohio State University, 2023, Biomedical Sciences

Natural Killer (NK) cells are innate immune cells which recognize a variety of ligands on target cells. Presence or absence of certain ligands on target cells can lead to signaling cascades in NK cells which can result in killing of target cells, production of cytokines, and proliferation of NK cells. Additionally, NK cell phenotypes can change as they undergo differentiation in response to stimulation. Though we have some understanding of each of these processes, modern tools such as mass cytometry and mathematical modeling give us unprecedented ability to reconstruct entire signaling networks, profile NK cell phenotype changes for populations of cells, and relate signaling with the enhanced cell cycle that accompanies rapid proliferation in response to stimulation. By constructing mathematical models of each of these processes, we can understand NK cell responses to stimulation at multiple scales, and relate these scales for improved insights. This work demonstrates a computational tool that models single-cell signaling networks from time-stamped mass cytometry data of signaling proteins, models NK cell population dynamics as they respond to mouse cytomegalovirus (MCMV) infection, and analyzes how NK cell signaling can be intertwined with cell cycle. We find that linear assumptions of single-cell signaling kinetics may be acceptable in many systems, that the NK response to MCMV is characterized by a highly proliferative subset midway through the maturation process, and that NKG2D-stimulated NK cells may have preferential activation in early stages of cell cycle. Taken together, these works present a comprehensive multi-scale view of NK cell signaling and development in response to stimulation.

Committee: Jayajit Das (Advisor); William Carson (Committee Member); Qin Ma (Committee Member) Subjects: Bioinformatics; Biology

Doctor of Philosophy, University of Toledo, 2023, Mechanical Engineering

Shape Memory Effect (SME) and Superelasticity (SE) are key characteristics of shape memory alloy (SMA) materials. SME allows the material to return to its original shape after heating, while superelasticity enables recovery from significant inelastic deformation. NiTiHf is a highly promising SMA, known for its elevated SME and SE performances. Designing and controlling NiTiHf SMA properties as desired poses challenges due to its dependence on many factors. Three core characteristics define SMA materials: transformation temperatures (TTs), thermal hysteresis (TH), and actuation strain (AS). TTs are crucial design properties that determine the activation threshold for SME and SE effects. TH, resulting from TTs differences, reflects the energy loss during each SME action. AS represents the amount of recoverable strain during each SME actuation. Traditional approaches to designing NiTiHf TTs, TH, and AS have relied solely on experimental studies, which have not yielded comprehensive results and can be impractical due to high costs and time requirements. Cost-effective modeling approaches, including physics-based and data-driven methods, expedite material design and process optimization. Machine learning (ML) modeling, equipped with strong regression analyses, significantly reduces the need for experimental trials to optimize alloy design. Physics-based modeling, considering underlying physical principles, plays a critical role as error compensation tools. In this study, both data-driven and physics-based modeling were utilized to overcome the high-dimensional dependency of NiTiHf TTs and AS on various factors and the limited understanding of governing physics. The input parameters for the machine learning models included elemental composition, thermal treatments, and common post-processing steps used in NiTiHf fabrication. This feature selection incorporated a majority of accessible information from the literature on NiTiHf TTs and AS, making use of all essential proce (open full item for complete abstract)

Committee: Mohammad Elahinia (Committee Chair); Ala Qattawi (Committee Chair); Othmane Benafan (Committee Member); Behrang Poorganji (Committee Member); Meysam Haghshenas (Committee Member) Subjects: Mechanical Engineering