Electronic Thesis or Dissertation

Organization and function of neuronal circuits require not only the interaction between the intrinsic components of the individual neurons but also the synaptic interactions that incorporate them into functional entities. Dendritic spines are the major sites for excitatory synaptic transmission, and are considered as the basic unit of information transfer in nervous system. Structural plasticity of dendritic spines is highly associated with functional plasticity, playing critical roles in learning and memory.
Here, we explored mechanisms underlying PKCα and structural plasticity of dendritic spines. We examined the spatiotemporal activation of actin regulators with 2pFLIM, including small GTPases Rac1, Cdc42 and Ras, in the presence or absence of PKCα during single-spine structural plasticity. Removal of PKCα expression in the postsynapse attenuated Rac1 activation during structural plasticity without affecting Ras or Cdc42 activity. Moreover, disruption of a PDZ binding domain within PKCα led to impaired Rac1 activation and deficits in structural spine remodeling. This work described that PKCα regulates the activation of Rac1, but not Ras or Cdc42, during sLTP of dendritic spines, and this modulation relies on PKCα’s PDZ-binding motif.

As artificial intelligence (AI), such as reinforcement learning (RL), has continued to grow, the introduction of AI for use in robotic arms in order to have them autonomously complete tasks has become an increasingly popular topic. Robotic arms have recently had a drastic spike in innovation, with new robotic arms being developed for a variety of tasks both menial and complicated. One robotic arm recently developed for everyday use in close proximity to the user is the Kinova Gen 3 Lite, but limited formal research has been conducted about controlling this robotic arm both with an AI and in general. Therefore, this thesis covers the implementation of Python programs in controlling the robotic arm physically as well as the use of a simulation to train an RL based AI compatible with the Kinova Gen 3 Lite. Additionally, the purpose of this research is to identify and solve the difficulties in the physical instance and the simulation as well as the impact of the learning parameters on the robotic arm AI. Similarly, the issues in connecting two Kinova Gen 3 Lites to one computer at once are also examined.
This thesis goes into detail about the goal of the Python programs created to move the physical robotic arm as well as the overall setup and goal of the robotic arm simulation for the RL method. In particular, the Python programs for the physical robotic arm pick up the object and place it at a different location, identifying a method to prevent the gripper from crushing an object without a tactile sensor in the process. The thesis also covers the effect of various learning parameters on the accuracy and steps to goal curves of an RL method designed to make a Kinova Gen 3 Lite grab an object in a simulation. In particular, a neural network implementation of RL method with one of the learning parameters changed in comparison to the optimal learning parameters. The neural network is trained using Python Anaconda to control a Kinova Gen 3 Lite robotic arm model for a simulation made in the Unity compiler.

As technology progresses, tasks involving object manipulation that were once conducted by humans are now being accomplished through robots. Specifically, robots carry out these goals through the utilization of different forms of artificial intelligence, including deep learning via a convolutional neural network. One robot made to accomplish this purpose is the ROS controlled TurtleBot3 Waffle Pi with an OpenMANIPULATOR-X robotic arm. This type of TurtleBot3 was developed with the express purpose of education and research but may not be limited to those two usages. Based on the current design of this classification of TurtleBot3, it may have multiple applications outside the testing environment, granting it further uses in a variety of tasks. The TurtleBot3 is easy to setup to fulfill the purposes for which the TurtleBot3 Waffle Pi was designed, and the exploration into further uses would allow for the discovery of alternatives to some tasks that normally require more work. For that reason, this thesis was conducted to determine the various uses of the TurtleBot3 with a robotic arm and if this robot can be used outside of a testing environment for various real-world tasks.

Pond aquaculture accounts 65% of global finfish production. A major factor limiting pond aquaculture productivity is fluctuating oxygen levels, which are heavily influenced by atmospheric conditions and primary productivity. Being able to predict DO concentrations by measuring environmental parameters would be beneficial to improving the industry’s efficiencies. The data collected included pond DO, water temperature, air temperature, atmospheric pressure, wind speed/direction, solar irradiance, rainfall, pond Chl-a concentrations as well as water color images. Pearson’s correlations and stepwise regressions were used to determine the variables’ connection to DO and their potential usefulness for a prediction model. It was determined that sunlight levels play a crucial role in DO fluctuations and crashes because of its influence on pond heating, primary productivity, and pond stratification. It was also found that image data did have correlations to certain weather variables and helped improve prediction strength.

This study demonstrates the relationship between intergenerational trauma and domestic space, specifically focusing on how Holocaust survivors’ homes became extensions of their traumatized psyches that their children “inhabited.” Based on my analysis of literature and oral histories of the second generation, my project employs the theory of postmemory to demonstrate how the spatial and temporal conditions of survivor-family homes, along with the domestic practices and objects contained therein, rendered these domestic milieus spaces of traumatic contagion. Postmemorial structures often functioned as spaces that afforded few illusions of familial permanency, thereby familiarizing survivors’ children with an intimate and pervading fear of external threat at a young age, which challenged or precluded feelings of parental protection and refuge within the domestic. I discuss the ways by which the second generation’s inherited perceptions of space—along with their inherited perception of matter and time— structured and structure their perceptions of their domestic lives. This study explores how, in turn, postmemorial structures shaped and shape the second generation’s inherited perceptions of space, matter, and time. As survivors’ traumas were registered in the very space of their homes, their homes functioned as material archives of their Holocaust pasts, creating domestic environments that commonly also wounded their children. In addition to survivors’ unspoken traumas, their spoken narratives of the Holocaust were also imbued in the space of postmemorial structures to such an extent that these homes became the very “framework” or “architecture” of their psychosocial lives. I argue that insofar as survivor-family homes were imaginatively transformed by survivors’ children into the sites of their parents’ traumas—whether they were concentration camps, ghettoes, places of hiding, etc.—their domestic spaces became central technologies that catalyzed and perpetuated the intergenerational transmission of Holocaust trauma and embodied experience. I further argue that the ways by which they describe their home lives constitute indirect expressions of their belated relationships to the Holocaust.

Tidal flat refers to the sediment-rich environment along the seashore, which is alternatively exposed or inundated during tidal cycles. It is widely recognized as not only the sentinel of coastal environment change, but also the safeguard for beachfront communities. It is necessary to comprehensively understand the wellness of tidal flat environments, especially for the United States (US), which has the eighth longest coastline throughout the world. Aiming at the dynamics of tidal flats, this dissertation firstly proposed a monitoring framework from three levels, including the pixel, object, and lifecycle. In addition, eleven events were defined to describe the dynamic activities throughout the lifecycles, which were captured, represented, and analyzed by utilizing graph theory. The Everglades in the southeastern corner of Florida Peninsula was selected to test this approach, which verifies an effective way to track, represent, and analyze the dynamic activities of tidal flats. Secondly, this dissertation mapped the distributions of tidal flats in the conterminous US, which provides a reliable dataset on a large spatiotemporal scale for future use. A random forest classification model was proposed, which uses 30 predictor variables to describe the spectral change patterns between the satellite images acquired in subsequent time steps. On the other hand, a total of 58,735 ground truth samples were collected under five classes, including permanent water, tidal flats, barren grounds, vegetated lands, and artificial surfaces. These sample points were randomly divided into two parts: 80% of them were used to train the random forest model, and the rest 20% were used to validate the results.

In the present dissertation, we discuss the development of a stereoselective method for the production of phosphorus compounds that utilizes a phospha-Michael addition reaction. Separately, the design and synthesis of compounds that contain an all-carbon bridged bicyclic scaffold is reported; these compounds were used in initial SAR studies in different in vivo models. In Chapter one is presented a mechanistic framework to develop a highly diastereoselective method catalyzed by phase transfer chemistry leading to phosphinate compounds. In this method, phosphinate nucleophiles were added to various alkenyl ketones as Michael acceptors using crown ethers as phase transfer agents to obtain highly diastereoselective products with the generation of a carbon-based quaternary centers. A closed transition state mechanism is proposed to describe the diastereoselectivity observed in the reactions that is consistent with product outcome as established by X-ray crystallography. Analysis using the 31P NMR technique is also reported to ascertain the diastereomeric ratios in product formation. Using products obtained with the newly developed method, we disclose for the first time a novel phospha-heterocycle with high control of stereochemistry. Relative stereochemistry of the phosphorus containing heterocycle was reported using 2D NMR analysis. In Chapter two focus is placed on the use of acrylates as Michael acceptors in both the diastereoselective and enantioselective studies of phospha-Michael addition. In the asymmetric method development, screening of various chiral catalysts and development of HPLC method to quantify the enantiopurity of products obtained under reaction conditions are reported. The role of crown ether catalysts towards diastereoselectivity is reported.

We examined how adult attachment styles influence human perception of support provision. We invited 119 couples to the lab, where they performed an exploration task. We also used pre- and post-exploration measures, including assessing adult attachment styles and partners' perception of support provided during the task. Three independent coders watched the videos of couples interacting and rated partners' support provision behavior. We utilized West and Kenny's (2011) truth and bias model to compare judgments (partners' perception of support received during the exploration task) with so-defined truth (combined rankings from coders). We used regression analysis to investigate how attachment orientation moderates the perception of support provision. On average, individuals tended to over-perceive helpfulness and under-perceive intrusiveness. Attachment avoidance was not a significant predictor of directional bias for helpfulness and intrusiveness. Results for the second exploratory hypothesis show those higher on attachment anxiety to have a weaker bias in underperceiving intrusiveness.

Achieving a consensus among a large number of nodes has always been a challenge for any decentralized system. Consensus algorithms are the building blocks for any decentralized network that is susceptible to malicious activities from authorized and unauthorized nodes. Proof-of-Work is one of the first modern approaches to achieve at least a 51% consensus, and ever since many new consensus algorithms have been introduced with different approaches of consensus achievement. These decentralized systems, also called blockchain systems, have been implemented in many applications such as supply chains, medical industry, and authentication. However, it is mostly used as a cryptocurrency foundation for token exchange. For these systems to operate properly, they are required to be robust, scalable, and secure. This dissertation provides a different approach of using consensus algorithms for allowing information sharing among nodes in a secured fashion while maintaining the security and immutability of the consensus algorithm. The consensus algorithm proposed in this dissertation utilizes a trust parameter to enforce cooperation, i.e., a trust value is assigned to each node and it is monitored to prevent malicious activities over time. This dissertation also proposes a new solution, named localized consensus, as a method that allows nodes in small groups to achieve consensus on information that is only relevant to that small group of nodes, thus reducing the bandwidth of the system. The proposed models can be practical solutions for immense and highly dynamic environments with validation through trust and reputation values. Application for such localized consensus can be communication among autonomous vehicles where traffic data is relevant to only a small group of vehicles and not the entirety of the system.

We consider a portfolio optimization problem in stochastic volatility jump-diffusion model. The model is a mispriced Lévy market that contains informed and uninformed investors. Contrarily to the uninformed investor, the informed investor knows that a mispricing exists in the market. The stock price follows a jump-diffusion process, the mispricing and volatility are modelled by Ornstein-Uhlenbeck (O-U) process and Cox-Ingersoll-Ross (CIR) process, respectively. We only present results for the informed investor whose goal is to maximize utility from terminal wealth over a finite investment horizon under the power utility function. We employ methods of stochastic calculus namely Hamilton-Jacobi-Bellman equation, instantaneous centralized moments of returns and three-level Crank-Nicolson method. We solve numerically the partial differential equation associated with the optimal portfolio. Under the power utility function, analogous results to those obtain in the jump-diffusion model under logarithmic utility function and deterministic volatility are obtained.