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Thalamic Modulation of Hippocampal Context Memories During Conditioned Fear Learning
No full textMaladaptive fear poses a significant public health burden, particularly in the forms of anxiety and stress-related disorders such as posttraumatic stress disorder (PTSD), anxiety disorders, and phobias. A popular treatment for these disorders is exposure therapy, which utilizes the 'extinction' of fear through repeated, unreinforced presentation of a stressful cue. However, while effective, these therapies are still susceptible to relapse, in part because extinction memories are highly context dependent. Understanding the neural mechanisms underlying the contextual control of fear suppression becomes particularly important. Recent work has implicated the thalamic nucleus reuniens (RE) in extinction by allowing for bidirectional communication between the prefrontal cortex (PFC), involved in higher-order cognition, and the hippocamps (HPC), which encodes context information. This dissertation examines the specific role of the RE in modulating HPC context information. I first show that the RE is not involved in the storage of extinction memories, and then show that context representations in the HPC can be used to determine appropriate responding to fearful cues. Finally, utilizing selective interference of context memory formation, I illustrate that the RE relies on HPC context memories in order to exert control over contextual processes. Collectively, these data reveal a role of the RE in the suppression of context-inappropriate behavior and, therefore, in encouraging context-appropriate responses to ambiguous cues
Assessment of Corrosion Prevention and Mitigation Techniques for Concrete Bridge Decks in Texas
No full textCorrosion in reinforced concrete is a self-accelerating process. Once rust forms on steel due to cracks and environment factors, it expands in size, causing larger cracks. The corrosion in reinforced concrete bridge decks has resulted in significant economic losses. To repair and maintain the durability of bridges, the Texas Department of Transportation spends millions of dollars every year. This study provides a comprehensive study of corrosion mitigation methods, such as high-performance concrete (HPC), calcium nitrite corrosion inhibitor (CNI), linseed oil, silane, and epoxy-coated rebar, for concrete bridge decks throughout Texas. To assess the performance of the mitigation methods, 61 concrete bridges across Texas were selected for both field evaluation and laboratory experiments using concrete cores extracted from the bridges. The field evaluation included visual inspection and non-destructive evaluation, such as infrared thermography, ground penetrating radar, ultrasonic tomography, half-cell potential, and corrosion rate mapping. In the laboratory, experiments included surface resistivity, bulk resistivity, ultrasonic pulse velocity, water absorption, chloride content measurement, carbonation depth measurement, coating adhesion testing, and electrochemical impedance spectroscopy. Using the results from both field and laboratory evaluations, a decision-making tool for selecting corrosion mitigation methods in reinforced concrete, based on different environment conditions of Texas, was developed. The tool seeks to contribute to the formulation of a comprehensive strategy to control corrosion and reduce corrosion-related financial losses as much as possible. This research aims to provide various approaches for evaluating the performance of different mitigation methods applied to concrete bridge decks, as well as to determine the durability and effectiveness of the mitigation methods that have been in use on real bridges for an extended period
Reinforcement Learning Model to Demystify the Limited Human Motor Learning Efficacy Due to the Sensory Mismatch
No full textVision and proprioception have fundamental sensory mismatches in delivering locational information, and such mismatches are critical factors limiting the efficacy of motor learning. However, it is still not clear how and to what extent this mismatch limits motor learning outcomes. To further the understanding of the effect of sensory mismatch on motor learning outcomes, a reinforcement learning algorithm and the simplified biomechanical elbow joint model were employed to mimic the motor learning process in a computational environment. By applying a reinforcement learning algorithm to the motor learning of elbow joint flexion task, simulation results successfully explained how visual-proprioceptive mismatch limits motor learning outcomes in terms of motor control accuracy and task completion speed. The larger the perceived angular offset between the two sensory modalities, the lower the motor control accuracy. Also, the more similar the peak reward amplitude of the two sensory modalities, the lower the motor control accuracy. In addition, simulation results suggest that insufficient exploration rate limits task completion speed, and excessive exploration rate limits motor control accuracy. Such a speed-accuracy trade-off shows that a moderate exploration rate could serve as another important factor in motor learning
Prototype of a Bi-Directional Digital Twin of an Industry 4.0 Smart Manufacturing Facility
No full textThis thesis presents a pioneering exploration into the development and practical application of a bi-directional digital twin prototype within the context of Industry 4.0 smart manufacturing. Bridging the gap between the physical and digital realms, this research addresses the emerging need for advanced digital replication and interaction mechanisms capable of enhancing operational efficiency and educational processes in modern manufacturing environments. By integrating technologies like Siemens TIA Portal, NetToPLCSIM, and OPC UA Server, with a sophisticated communication framework, the study establishes a seamless, real-time bi-directional communication between a physical model and its digital counterpart. The results demonstrate the system's capability to accurately mirror actions and movements across the physical and digital domains, highlighting its potential to revolutionize manufacturing processes, predictive maintenance, and training methodologies. This work not only contributes to the theoretical understanding of digital twin technologies but also showcases a tangible implementation, paving the way for future research and the broadening of digital twin applications across various sectors of the industrial domain
Development and Verification of a Nuclear Forensics Methodology for the Attribution of Plutonium Using Data Science Methods
No full textAn advantage the global community has in preventing nuclear terrorism is the difficulty for a nonstate actor to procure special nuclear material (SNM). The regulation of SNM is essential in stymying adversaries. A nuclear forensics methodology, able to determine the provenance of SNM, like plutonium (Pu), will aid the international community in deterring nuclear smuggling. If Pu is recovered outside of regulatory control, an attribution capability would help inform conventional investigators. In both cases of theft and state hand-off of Pu, a guarantee that an offending party would be discovered and punished could force preemptive abandonment of any planned misdeeds.
The goal of this research was to develop and verify a nuclear forensics methodology for attributing unknown separated Pu samples using machine learning techniques. The methodology needed to be capable of identifying the following three attributes: the reactor-type that produced the Pu sample, the burnup of the irradiated uranium fuel that produced the Pu sample, and the time since irradiation (TSI). The methodology also needed to be robust enough to attribute samples that contain a mixture of Pu from multiple different reactor sources. A set of isotope ratios was used as the forensics signature and the training of the machine learning models utilized data from a library of Monte Carlo reactor neutronic and fuel burnup simulations. Lastly, the methodology needed to be validated by demonstrating that it could successfully attribute physical Pu samples.
Research proceeded in three main parts. First, machine learning models suitable for this application were identified, and were then trained and tested for attributing single reactor type Pu samples to assess feasibility. Second, the methodology was validated with a Pu sample separated from low enriched uranium dioxide (LEUO2) irradiated in a thermal neutron flux spectrum. Third, the machine learning methodology was adapted to attribute samples that were sourced from multiple reactor types. Additionally, a method for estimating the machine learning models��� prediction uncertainty that considered the Pu sample���s measurement uncertainty was investigated. Ultimately, all main objectives were successfully achieved. This is the first example in open literature of a methodology for attributing mixed reactor type Pu samples
The White Atlantic: Finding South Africa in the American South, 1954-1966
No full textThe Citizens��� Council of America advanced what some historians call a ���segregationist foreign policy.��� ���Massive resistance��� to domestic desegregation during the 1950s and '60s pushed the Council to concern itself with foreign affairs, particularly the United Nations' criticism of South African apartheid. Council publications and television broadcasts show that the Council opportunistically reframed its rhetoric to match South Africa���s defense of apartheid as a system dedicated to local self-determination and combating communism. The Citizens��� Council mirrored this strategy, promoting segregation to stave off communist inroads and reinforce states��� rights, meaning self-determination
John Bickham field notebook: AK21501-AK22000.pdf
No full textBound book, each page corresponds to a karyotype slide data.Data pages for AK22001-AK22500 corresponding to unique identifiers of specimens/samples examined for biological research. Specimens are primarily housed at Texas A&M University; Biodiverstiy Research and Teaching Collection
A Guarantee for Confusion: The Impact of Texas��� Guaranteed Tuition Legislation on Tuition and Fees
No full textIncreases in tuition and fees annually charged to students have resulted in public universities and colleges under scrutiny from state legislators. There are as many approaches to increases in tuition as there are states. States like Texas, Illinois, and Oklahoma have implemented legislation requiring public institutions in their respective states to freeze tuition for students. Unlike other states, Texas has placed the requirement on institutions vs. a requirement placed upon students. This research examines the impact on average tuition and average enrollment for Texas public four-year institutions subjected to that state���s guaranteed tuition legislation, using difference-in-differences, a quasi-experimental method. Panel data from 2008 to 2019 obtained from 531 public four-year universities in the United States via the Integrated Postsecondary Education Data System (IPEDS) were used to address two research questions: (1) At public four-year institutions, does the implementation of a required state-level guaranteed tuition policy option for students lead to changes in annual tuition charges of the aggregate amount of tuition paid by students over four years? (2) At public four-year institutions, does the implementation of a state-level guaranteed tuition policy option lead to changes in enrollment for students?
The analysis found the Texas legislation did not significantly increase the average annual tuition charged to in-state students. On average, the legislation did result in a .96 decrease in the average required fees. The analysis also found there were positive changes in enrollment after the imposition of the guaranteed tuition plan legislation. The findings of this study provide insight into the consequences associated Texas��� guaranteed tuition plan legislation. For state policymakers, this study suggests the way in which a guaranteed tuition plan is structured is impactful. For institutional leadership, this study suggests room for improvement in how these plans are described and marketed to students and their families
Quantitative Analysis of Strain Response Measured by Low-Frequency Distributed Acoustic Sensing During Hydraulic Fracturing
No full textThis master's thesis investigates the pivotal role of strain measurements in hydraulic fracturing operations, employing Low-Frequency Distributed Acoustic Sensing (LF-DAS) technology to monitor strain changes during treatments. A significant gap in the existing research is addressed by systematically analyzing strain decay beyond the fracture domain corridor. The thesis investigates the impact of parent-well depletion and completion design on hydraulic fracture geometry, employing a decline factor of the strain decay curve as a key analytical tool. This analysis is supported by a geomechanics model, providing a comprehensive understanding of the dataset. Furthermore, the study conducts a comprehensive analysis of Hydraulic Fracture Test Site-2 (HFTS-2), considering the maximum cumulative strain change and decline factor of the strain decay curves. The thesis outlines a well-structured workflow for processing and analyzing LF-DAS cross-well strain data
Reliability and Economics of Distribution Systems with Edge-Level Distributed Energy Resources
No full textElectrical power distribution systems are experiencing a pivotal transformation due to the increasing integration of edge-level behind-the-meter (BTM) distributed energy resources (DERs). This transformation introduces challenges in the planning and operation of distribution systems. Primary challenges include reliable delivery of electricity to the end user while maintaining the utility���s financial viability. This dissertation introduces a multi-level hierarchical framework that bridges the gap in the current distribution system reliability assessment by incorporating the complexities and stochastic nature of end-user DERs. This framework is adaptable to distribution systems with varying levels of DER penetration and addresses the technological diversity and unpredictability inherent in DERs, making it a significant advancement over existing methodologies. This work developed a modular general-purpose end-user reliability model that forms the basis for developing reliability assessment methods and revenue impact analysis. A bottom-up probabilistic approach is presented that integrates the end-user with BTM DER into the reliability assessment. A notable innovation in this work is the application of probabilistic distributions to quantify the end-user BTM DER penetration and integrate them into the probabilistic approach to assess distribution system reliability in various DER penetration scenarios. Economic impact assessment forms another crucial dimension of this dissertation, encompassing a comprehensive exploration of the implications of end-user BTM DER integration for utility revenue, customer costs, and overall system economics. The dissertation examines the cost-benefit dynamics of DERs and the influence of regulatory policies such as Net Energy Metering (NEM), quantifying the economic impacts under various DER adoption scenarios. The dissertation employs reliability test cases and simulation analyses to study the effectiveness of the developed framework and assessment methodologies. This dissertation contributes to the field of power systems by providing methods and tools for managing the challenges and opportunities presented by end-user BTM DER in system planning and integration