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Essays in Law and Economics
No full textMy dissertation focuses on issues related to the criminal justice system in the United States, especially with respect to indigent defense and policing.
First, I ask whether indigent defense attorneys secure better deals for same-race defendants. This is important since 80 percent of criminal defendants rely on assigned counsel for legal defense. To do this, I employ a difference-in-differences approach, exploiting the quasi-random assignment of court-appointed attorneys to cases in Travis County, Texas. Results indicate that while Black and White attorneys are similarly effective at securing dismissals for White defendants, Black attorneys are less effective than White attorneys at securing dismissals for Black defendants. Specifically, Black defendants who are represented by White rather than Black attorneys are 20-22 percent more likely to have their charges dismissed and 17-27 percent less likely to be incarcerated. Moreover, Black defendants who are represented by White attorneys are not more likely to re-offend in the future.
Second, I study the effect of police-involved shootings on gun violence and civilian cooperation with police. To distinguish between crime reporting and crime incidence, I use administrative data on 911 calls and ShotSpotter data from Minneapolis. Exploiting the variation in the timing and the distance to these incidents, I show that exposure to a police shooting increases gun-related crimes by 3-6 percent and reduces civilian crime reports to police by 4-6 percent.
Finally, I study the impact of internal affair investigations, the most common accountability system in policing, on police behavior. Using data from a large city where there is conditionally random assignment of officers to 911 calls, I employ regression discontinuity and difference-indifferences methods to distinguish the impact of investigations from confounding factors. Results indicate that increased oversight from internal investigations does not change an officer���s likelihood of making an arrest or using force. This is true across different types of allegations, including those that are sustained
Cricket Song Classification Using Transformers
No full textAs interest in studying animal sounds for biodiversity monitoring grows, the need for automatic methods to classify species based on their unique songs becomes crucial. This thesis presents an innovative approach to identifying various cricket species and genera by analyzing their audio recordings through advanced pretrained transformer models, specifically utilizing the AST (Audio-Spectrogram Transformer). The dataset includes 592 audio files for Gryllus species and 441 audio files for different genera and is meticulously curated to overcome challenges like uneven class distribution and variations in audio file durations. Customized label mapping and strategies such as undersampling and oversampling are applied to adapt the pretrained model to the specific classification task and balance the dataset respectively. The experimental setup includes three to four distinct datasets, each focusing on different subsets of cricket species and genera each. Training involves varying learning rates, with evaluation metrics encompassing validation and training accuracy, precision, recall, and F1-score. Further analysis is conducted to visualize the distribution of the data points. For this, first, Principal Component Analysis (PCA) is used to reduce the dimensionality of the features. Next, t-SNE visualization is used to provide insights into the spatial relationships between different species in the data space. This work differentiates itself from previous approaches that used CNNs, and it explores the capabilities of transformers in classifying cricket species and genus and aims to understand how these models perform in comparison. The transformer model achieved high accuracy rates of 95.31% for classifying Gryllus species and 94.27% for genus classification. This study has potential applications in education, conservation, agricultural pest management, and other ecological studies
High-Dimensional Analysis of the Linear-Quadratic Regulator Problem Using First Order Methods on GPU
No full textThere has been a growing desire to bridge the gap between the fields of machine learning and optimal control theory. While optimal control typically operates on known dynamical systems, machine learning uses large data sets based on sampled data. The differences in input data used have made it difficult to adapt optimal control concepts to machine learning applications. For example, a major challenge in utilizing a linear-quadratic regulator (LQR) is how computationally demanding it can be for high-dimensional systems. Solving the algebraic Riccati equation (ARE)
directly is time-consuming and is typically O(n��) complexity. Posing the problem as a Linear Matrix Inequality (LMI) is even worse, this is typically solved in O(n���) time.
This thesis will examine the discrete-time LQR in the context of first order methods. These gradient-based methods provide an advantage in that they can be parallelized and run on GPUs. Multiple gradient-based algorithms will be proposed, and their performance will be compared with the traditional solutions for optimal LQR gains. The convergence rates of these gradients to the global optimum will be discussed and compared as the dimensionality of the problem increases. The ability to solve the LQR problem faster for high-dimensional systems may be useful for future large-scale optimal control problems in the aerospace field. Additionally, framing the LQR in terms of gradient-dominated policies may allow the LQR to be used in broader fields such as reinforcement learning
An Exploration of Theoretical and Methodological Typologies of Faith-Based Health Interventions
No full textChronic diseases and conditions continue to pose a significant public health challenge in the United States, affecting a large portion of the population. Unhealthy lifestyle behaviors contribute significantly to chronic disease development, emphasizing the need for effective multi-level health promotion and prevention strategies. Faith communities have emerged as key community partners in addressing health disparities and promoting public health initiatives, particularly concerning chronic diseases. However, despite their commendable efforts, there needs to be more clarity and standardization in defining and operationalizing faith-based health interventions. This conceptual ambiguity hinders the development of evidence-based interventions and limits their effectiveness. This study aims to address the need for more clarity within faith-based health interventions (FBHIs) pertaining to 1) definitions and operationalization, 2) intervention typologies, and 3) conceptual frameworks, guidelines, and models. This systematic review explores the current state of research, implementation, and evaluation of faith-based health intervention typologies and methodologies. Additionally, the theory utilization quality scale (TQS) and methodological utilization quality scale (MQS) were used to assess articles in this review. This review synthesized n=27 articles and highlighted a need for standardized terminology and conceptual frameworks in FBHIs, diverse participant samples, rigorous methodologies, and systematic analyses. While many interventions show promise in improving health outcomes, mixed findings underscore the necessity for robust research designs. The dissertation proposes a faith-based health intervention model and checklist for researchers and practitioners to address current gaps and leverage evidence-based and practice-based approaches
Influence of Dietary Saccharomyces cerevisiae Fermentation Product on Markers of Inflammation and Cartilage Metabolism in Young Exercising Horses Challenged with Intra-Articular Lipopolysaccharide
No full textThe objective was to evaluate dietary Saccharomyces cerevisiae fermentation product (SCFP) on joint inflammation and cartilage metabolism in exercising yearlings challenged with intra-articular lipopolysaccharide (LPS), hypothesizing SCFP (TruEquine��C, Diamond V Mills, Inc.) would ameliorate inflammation and increase cartilage metabolism. Thirty Quarter Horse yearlings were stratified by bodyweight (BW), age, sex, and assigned to (n =10/dietary treatment): control (0), 46, or 92 mg/kg BW/d SCFP. Treatments were top-dressed to 1% BW concentrate. Horses were stalled, offered ad libitum Coastal bermudagrass hay, and exercised for 30 min/d, 5 d/wk. Every 21 d, wither height (WH), hip height (HH), heart girth (HG), body length (BL), and BW were obtained. On d 46, horses underwent an LPS challenge with each radial carpal joint receiving 0.8 mL of a 0.5 ng LPS solution or sterile lactated Ringer���s solution (LRS). Synovial fluid was collected pre (h 0), and 6, 12, 24, and 336 h post-injection, and analyzed for prostaglandin E2 (PGE2), carboxypropeptide of type II collagen (CPII) and collagenase cleavage neopeptide (C2C) via ELISA, and chemokines (CCL2, and CCL11) and cytokines (TNF�� and IL-10) via multiplex platform. Rectal temperature (RT), heart rate (HR), respiration rate (RR), and carpal circumference (CC) were recorded prior to arthrocentesis. Data were analyzed using MIXED procedure of SAS. By d 56, growth parameters increased (P < 0.01) where control had a greater increase in BW than SCFP groups (P < 0.01). Clinical parameters were uninfluenced by diet (P ��� 0.29) but varied over time (P ��� 0.03). Treatments didn���t influence CPII, C2C, CPII:C2C (P ��� 0.46) or logPGE2, logCCL2, CCL11, and logIL-10 (P ��� 0.23). There was an interaction for CCL11 (P = 0.04) where control was greater than SCFP groups at h 6. Furthermore, logIL-10 had an interaction where 46 mg/kg was lower at h 12 compared to control and 92 mg/kg (P = 0.05). There was a treatment effect for TNF�� (P = 0.04) where 92 mg/kg was lower than 46 mg/kg and tended to be lower than control. Although SCFP didn���t influence cartilage metabolism or logPGE2, SCFP may ameliorate inflammatory cytokines and chemokines following an acute, intra-articular insult
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
Comprehensive Validation of Semi-Submersible Floater Dynamics: A Coupled CFD-FEM Approach with Iterative Wave Adjustment
No full textThis dissertation presents the development and application of a numerical wave tank based on an in-house Computational Fluid Dynamics (CFD) program, Fintie-Analytic Navier-Stokes (FANS). The CFD solver, featuring overset (Chimera) grid capability and dynamic memory allocation, enables efficient computation of multiple structured grid blocks with a large capacity for data interpolation between overset grids.
An analytic Directional Wave Simulation (DWS) program is coupled with the Navier-Stokes solver at the wave maker location. Numerical wave parameters, including wave elevation and velocity are transmitted from the DWS block to the CFD domain via overlapping grids. To achieve a calibrated numerical wave spectrum at target location, an iterative wave adjustment method is developed, utilizing a 4-wave decomposition scheme based on harmonics separation theory.
The free surface in the CFD domain is captured with the level set method. 5th-order Weighted Essentially Non-Oscillatory (WENO) and 2nd-order Alternative Direction Implicit (ADI) schemes are employed for spatial and time discretization of level set governing equations. To mitigate wave reflection at domain boundaries, a forcing zone method using damping source terms in the governing equations is introduced.
A nonlinear Finite Element Method (FEM) mooring model named MOORING3D is developed to investigate hydrodynamic responses of mooring systems. Coupled with the FANS program, this model explores the global performance of moored floating structures under various environmental conditions. A six-degrees-of-freedom (6-DOF) motion solver is integrated into the FANS program to update the motion of the floater.
A robust verification procedure based on the least-squares Richardson extrapolation method is introduced to estimate the discretization uncertainties of the numerical simulations. The convergence study in this research focuses on spatial and temporal discretization uncertainties.
Data from a comparative study at 2020 ISOPE conference is utilized to assess the wave generation and iterative wave adjustment method. Model tests from this comparative study investigate the nonlinear interactions of steep focused waves with a fixed cylinder. Wave elevations at target locations and wave slamming loads on the cylinder are compared with the experimental measurement and other numerical solutions for validation. The results highlight the positive effect of the iterative adjustment method on highly nonlinear numerical wave generation.
Verification and Validation (V&V) studies are conducted on the coupled CFD-FEM program using a Floating Offshore Wind Turbine (FOWT) platform model. Convergence studies include pitch free decay and regular wave tests, which are parts of the OC5 project (Offshore Code Comparison Collaboration, Continued, with Correlation project). The numerical wave profile and 3-DOF responses of the platform are validated against model test measurement and other numerical solutions. The agreement between the coupled numerical solution and the experiment validates the method.
The coupled numerical solution is employed to investigate hydrodynamic responses of the plat-form under highly nonlinear irregular wave conditions, addressing the importance of calibrating the wave to the target spectrum. Short-duration extreme wave cases and a long-duration 3-hour irregular wave case are conducted to comprehensively evaluate the integrated solver���s performanc
Technical, Economic, and Life Cycle Assessment of Membrane-Based Air-Cooling Systems
No full textThe escalating demand for space cooling in Qatar, projected to triple by 2050, underscores the urgent need to shift from traditional refrigerant-based systems to more sustainable air-cooling technologies. This transition aims to mitigate the significant contributions to global warming, with a potential increase in global temperatures by 0.44��C by the century's end. Current research in membrane-based air-cooling systems, which combine adiabatic evaporative cooling with isothermal dehumidification, shows promise for enhanced efficiency. However, existing studies often overlook key operational energy requirements, such as the energy for low-temperature condensation, leading to underestimating system costs and efficiency. Moreover, environmental assessments of these systems frequently neglect the construction phase and criteria beyond electricity consumption. Experimentally, the focus has been on developing thin-film composites and nanocomposites on unaltered support membranes, which, despite increasing selectivity, do not significantly improve overall permeance. This limitation increases the need for membrane area, raising costs for membrane-based cooling solutions.
This research employs two primary methodologies to address the challenges of space cooling in Qatar, focusing on enhancing the efficiency and environmental sustainability of membrane dehumidification systems. A comprehensive simulation of various system configurations initially underpins these systems' exhaustive technoeconomic and environmental life-cycle assessment (LCA), benchmarked against conventional cooling technologies. Utilizing the Recipe2016 methodology for both Midpoint and Endpoint analyses, this LCA reveals a significant reduction (50-66%) in human health and ecotoxicity impacts compared to traditional AC systems while highlighting the minimal (<1%) environmental impact of the construction phase. Subsequent technoeconomic analysis identifies membrane cost as a critical factor in the viability of condenser-based membrane air-cooling systems. This underscores the necessity for membranes with a water vapor permeance (WVP) above 20,000 GPU. To address this, novel mixed matrix membranes containing hydrophilic nanomaterials into a polyethersulfone matrix were designed and fabricated using phase inversion to significantly enhance permeance without compromising mechanical stability. Among these MMM, the membrane containing 0.2 wt.% sulfonated graphene oxide with exceptional WVP of ~ 25,000, now protected by a US provisional patent, was coated with a thin active Pebax layer to enhance water vapor air selectivity. Economic assessment based on this SGO-TFN revealed a minimum loss of 15% saving in the total annualized relative to conventional systems, with a 60% increase in equipment manufacturing costs
Hydro-Mechanical Study of Two Shrink-Swell Soils Under Hydrating and Shearing
No full textShrink-swell soils are a type of soil that expands and contracts in response to changes in moisture content. The soil is widely spread across the world and has become a prevalent geotechnical practice problem. The uneven shrink and swell behavior of shrink-swell soil causes damage to infrastructure and cost millions to repair. Most mechanical tests on shrink-swell soils have been conducted under oedometer conditions which restrict lateral movement. A limitation of such approach is that lateral soil strains are possible in practical problems. Triaxial tests, under controlled shear and confinement conditions, can represent better actual soil conditions. Very limited research has been conducted involving soil shearing and soaking behaviors under triaxial conditions, and they were related to low to intermediate expansive soils. Our research is pioneering in this area, exploring this feature of soil behavior of high to very high expansive clays under triaxial conditions. This study focuses on examining the behavior of unsaturated shrink-swell soils during shearing and soaking, aiming to offer reference soil data for civil structures like foundations and retaining walls built on these soils. It specifically investigates two types of shrink-swell soils, Texas natural soil and MX-80 bentonite, using an updated triaxial test protocol in the laboratory. The triaxial test results for Texas natural soil are utilized to validate the Barcelona Basic Model (BBM) in numerical modeling. Additionally, this dissertation includes a numerical study exploring the effect of horizontal swelling pressure on retaining wall structures in shrink-swell soil environments. The goal is to apply theoretical understanding of shrink-swell soils to address real-world geotechnical challenges. The behaviors of two shrink-swell soils under shearing and soaking are discussed and compared. The effects of stress level (particular deviatoric stress) on unsaturated shrink-swell soil volume change and final shear strength when subjected to soaking are investigated. The study also concludes how varying swelling properties of soils affect their behavior under shearing and soaking in different triaxial test conditions. BBM successfully replicates the behavior of Texas natural soil in terms of deviator stress and volume change under triaxial conditions. These insights contribute to setting benchmarks for shearing and soaking shear strength of shrink-swell soils in triaxial conditions and expanding the database available for the Texas region, aiding in foundation design. Moreover, the BBM helps to understand the distribution of horizontal swelling pressure on retaining walls, leading to simplified methods for inclusion in retaining wall design standards
A Novel ML-based Approach for the Prediction of the Oceanic Heat Flux in a Slab Ocean Model Coupled to a Physics-Based Model of the Atmosphere
No full textA slab-ocean model is a thermodynamic model of the ocean mixed layer. It provides a prognostic variable for the sea surface temperature (SST), and when coupled to a model of the atmospheric circulation, it allows for two-way ocean-atmosphere interactions at a low computational cost. The standard formulation of a slab ocean model accounts for the spatially varying thermal effects of the oceanic circulation by a prescribed two-dimensional static estimate of the oceanic heat flux field. A downside to using such a static estimate is that it cannot capture the effects of changes in the ocean circulation. This work presents a methodology to introduce a temporarily changing two-dimensional oceanic heat flux field in a slab ocean model. It also introduces a novel machine learning-based approach to dynamically evolve this field. The approach is tested on the low resolution atmospheric global circulation model SPEEDY, which has an optional slab ocean component. This component is modified to implement the proposed methodology. It is first demonstrated that the static estimate of the oceanic heat flux can be further improved by an iterative method. Then, it is shown that with the temporally varying estimate of the oceanic heat flux the model produces more realistic sea surface temperature variability than with the static estimate. Finally, it is demonstrated that the machine learning-based approach can be used to replace the prescribed estimates with a dynamically evolving field of the oceanic heat flux