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Effects of Early-Life Acetaminophen and Interleukin-1B Exposures on Anxiety, Motor, and Play Behaviors in Long-Evans Rats
Get PDFAcetaminophen (APAP) is the leading antipyretic and analgesic drug administered to infants and pregnant women. Nevertheless, epidemiological evidence has implicated both early-life APAP (ELA) and early-life inflammation (ELI) as risk-factors for neurodevelopmental disorders, including Autism Spectrum Disorder (ASD). Moreover, both APAP-induced toxicity and high levels of inflammation may be more prevalent among males, which may make them more susceptible to neurodevelopmental effects of these exposures. Rodent studies of ELA and ELI to date support epidemiological findings in humans, with each respective exposure altering social, anxiety, and motor behaviors. Additionally, previous studies have shown sex-specific effects of both exposures, with males being more prone to social alterations than females. Given that both ELA and ELI have effects on dopaminergic signaling, this dissertation aimed to examine whether these exposures alter social play and anxiety (i.e., DA-dependent behaviors) in rats. Long-Evans rats were injected subcutaneously with IL1B+Veh, Veh+APAP, IL1B+APAP, or Veh+Veh during postnatal days 9 (P9), P11, and P13. Anxiety and motor behaviors were quantified during adolescence and adulthood using open field assays. Social play was quantified during adolescence in a social interaction test with an unfamiliar partner. IL1B treatment led to lower fecal boli count in adulthood, indicating decreased anxiety. APAP-treated rats showed a decrease in activity levels in adolescence, but an increase in adulthood. These results suggest that IL1B and APAP have varying impacts on anxiety behaviors and activity levels, depending on the stage of development during which these behaviors were assayed. Moreover, IL1B x APAP interactions showed that APAP and IL1B increased total duration of social approach. However, IL1B+APAP-treated rats showed total duration of approach comparable to control rats. These findings suggest that the behavioral effects of IL1B and APAP are not synergistic. Moreover, sex-specific effects of IL1B were found as IL1B-treated females showed more dramatic differences in exploratory behaviors (i.e., frequency in the center) and average bout duration of play compared to IL1B-treated males. Furthermore, APAP decreased clockwise rotations in females, not males. As such, these findings also suggest that females may be more vulnerable to IL1B- and APAP-induced behavioral changes compared to males when exposed during infancy
Building Energy Management: A Data-Driven Approach Using Clustering and Load Forecasting
Get PDFThe increase of smart meters in the grid has led to the generation of a vast amount of high dimensional energy data with improving temporal resolution. During analysis, relying on short samples like a day or week of data, could lead to wrong conclusion due to seasonal dynamics and customer behavior variations. To effectively utilize the vast amount of information, it must be compressed into a low-dimensional representation. This thesis explores the state-of-the-art dimensionality reduction techniques for a high-dimensional, non-linear energy dataset and proposes a novel deep learning based method to address the limitation of existing approaches. The proposed method exploits LSTM based Variational Autoencoders to generate an encoded representation. By utilizing this method, a 8760-dimensional dataset can be condensed into 10 dimensions, which can be further reduced by PCA to a 2D representation for visualization purposes. The effectiveness of this dimensionality reduction technique is demonstrated through its application to a large number of residential buildings, followed by the implementation of clustering algorithms on the reduced dataset. With this foundation, the thesis explores the application of clustering algorithms for energy consumption forecasting at the individual building level. Accurate load forecasting is crucial for both utilities and consumers in smart grid environments, enabling users choose a more appropriate electricity consumption scheme and resource optimization for utilities. However, forecasting load for individual building is challenging compared to the aggregated load because of high volatility and uncertainty in the load profile patterns. Several machine learning and deep learning models have been developed in the past but such exploration either produced high error or required individual model for each building, which is not feasible for large number of buildings. To address this challenge, a novel deep learning ar chitecture is developed, utilizing the aforementioned clustering algorithm to group buildings with similar consumption patterns. The forecasting model is based on a Sequence-to-Sequence architecture, with separate models developed for each cluster. Other experiments are conducted using baseline LSTM, non-clustered Seq2Seq, and Seq2Seq+KMeans models. These are performed for comparison with the proposed model. For evaluation, Mean Absolute Percentage Error is used. In summary, this thesis contributes to the field of smart grid analysis by providing innovative solutions for dimensionality reduction and load forecasting, enabling more efficient and accuracy energy management strategies in context of complex power system
Climate Change , House Prices and Inequality in the United States
Get PDFThis dissertation investigates the multifaceted relationship between climate change, housing values, and inequality in the United States using county-level data from 2005 to 2022. In Chapter 1, we examine the heterogeneous impact of climate change on house prices, employing fixed-effect panel data analysis, Difference-in-Differences (DiD) with Propensity Score Matching (PSM), and fixed-effect panel quantile regressions. Findings indicate that climate change significantly impacts housing values, but this effect is not uniform. Low-value homes experience substantial price discounts due to heightened climate risk perception, while high-value homes are largely unaffected. This heterogeneity suggests that climate change poses a greater financial threat to middle- and low-income households, who are more likely to own low-value homes. Implications for policymakers include regulating climate risk behavior in lending to prevent systemic vulnerabilities similar to those seen in past financial crises.
In Chapter 2, we explore the role of the housing market as a channel through which climate change exacerbates inequality. Using proxies for income inequality (Gini coefficient) and wealth inequality (upper-to-lower house value ratio), this analysis reveals that climate change disproportionately increases economic disparities by impacting low-value housing. Methodologically, we employ DiD with PSM and fixed-effect panel data analysis to capture the causal impact of natural events on inequality. Results show that when house prices are controlled, the significant relationship between climate change and inequality diminishes, underscoring the housing market’s pivotal role in this dynamic. Policy recommendations include targeted interventions such as subsidies and insurance incentives to support housing stability in climate-vulnerable, low-income areas
Bank Board Diversity, ESG Activities, and Bank Risk-Taking
Get PDFIn the past decade, there has been growing interest in the influence of diverse boards on risk management, particularly with regard to environmental risk management. However, limited evidence exists on the role of diverse boards in policy decisions, especially the adoption of green credit policy in the banking sector. Drawing on critical mass theory, this study investigates the importance of board gender diversity and the critical mass of women directors that could make a positive impact of green credit policy adoption for global banks. Data was collected for 540 banks operating in 34 countries, covering ten years (2013-2022). The study employed various regression techniques, including panel probit and pooled OLS on unbalanced panel data. The results suggest that banks with a critical mass of four (4) women directors on boards are more likely to adopt green credit policy. We confirm the dynamism in critical mass for women directors through various robust analyses. Our results also hold after controlling for endogeneity and are robust when we use alternative proxies. These results have important policy implications for the global banking sector. We advocate for careful examination of the appropriate number of women representation for policy decisions in the banking sector which could vary across the globe due to a diverse range of exogenous factors. The study contributes to the literature on board diversity and provides insights into how diversity can improve green credit practices for the banking industry. The concept of financial inclusion has evolved beyond the discourse on financial stability and social development, now encompassing not only low-cost retail deposits of large banks that generate arbitrage gains but also banks situated in technologically advanced areas. This study investigates the consequences of the role driven by Fintech-based financial inclusion (FFI) in influencing the relationship between bank risk-taking and ESG (environmental, social, and governance) activities. Our findings show that ESG activity reduces bank risk-taking, suggesting that increased involvement in ESG activities by banks will lead to a reduction in risk. Additionally, FFI acts as a mediator in the connection between bank risk and ESG
Innovative Rate Design as a Free Market Solution to Climate, Resiliency, and Economic Challenges
Get PDFA century-old problem of electricity rate design is cost-shifting between ratepayers (Wellinghoff, J. & Tong, J., 2015). A much newer cost-shifting example of great and increasing importance happens whenever ratepayer generated “renewable energy” is sold to the grid—all too often, this is accused of being unfairly rewarded (Ritchie, 2016). ProRate resolves both these concerns and ProRate can actually be derived simply from the premise of avoiding “all” cost shifts between ratepayers (Katz, CLEPm Rewards to Arrest Demand Cost-Shifting, 2019; Katz, CLEP5 Rewards to Arrest Energy Cost-Shifting, 2019). Another major problem with the Old Utility Model is the lack of price signals (Electric Choice, 2012; Duncan, 2017; Faruqui, Hledik, & Palmer, 2012; Milliner, 2019). ProRate utilizes time-varying rates for both energy and demand to eliminate cost-shifting onto others and provides fair compensation to locally generated and/or locally stored electricity—both to improve grid reliability, reduce instantaneous demand needs, and, importantly, reduce the carbon footprint of all ratepayers (Price Electric, 2015). In this paper, I suggest ProRate adoption, strategies, as well as address implementation challenges that when addressed alongside giving ratepayers access to net metering and the wholesale marketplace of energy (MISO), gives ratepayers benefits. I demonstrate that these benefits include economic, environmental, and enhancing moral agency of ratepayers. Finally, I suggest where future research can be optimally directed, while giving blueprints and tools to help demonstrate a future pilot’s successful adoption
The Desegregation of Schools in Thibodaux, Louisiana: 1954-1970
Get PDFThe study of school systems in Thibodaux, the seat of Lafourche Parish, adds to research on school desegregation in Southern rural communities. This thesis highlights the untold story of the Black community\u27s resistance to segregation in Thibodaux and efforts by white officials to maintain a segregated school system. Black resistance included a petition filed in 1955 and the Edward Hill v. Lafourche Parish School Board (1967) case. Partial integration began in 1966, but the parish did not establish a unitary system until the 1968-1969 school year. This research focuses on the Lafourche Parish public school system from first through twelfth grades. This thesis investigates the process of desegregating schools in Thibodaux from 1954 to 1970 by analyzing the Lafourche Parish School Board records, court case records, newspapers, and oral history interviews
Super Mario Evolution by the Augmentation of Topology
Get PDFThis paper describes the creation and development of an implementation of the NeuroEvolution of Augmenting Topologies (NEAT) architecture to train an agent to play Super Mario Brothers. Building off of a basic implementation of NEAT, this thesis project shows the process of refining the fitness calculation that ranks the networks in the population and also defines the creation and application of a dataset to train the agent. The use of a dataset to train an agent is a novel idea in the world of reinforcement learning because, generally, reinforcement learning trains an agent to complete a singular task like the pole balancing problem. Training an agent to play something as complex as a video game, however, requires that an agent is exposed to as many different situations that occur within the game as possible. The goal of this thesis project is to create an agent that has a robust general understanding of how to play the game, such that it is able to react to new situations that were not seen in training. The results of this thesis project show that this generalized understanding is possible via neuroevolution, when given enough training time, a properly designed fitness calculation, and a properly applied dataset of scenarios
Blind Spot
Get PDFThe subject matter for my paintings and photographs comes from observing the suburban landscape of my New Orleans neighborhood. My work contends this seemingly mundane environment teems with beauty, the sublime, and interactions between the human world and nature. My paintings use vibrant colors and unexpected light sources to challenge the viewer’s relationship to the plants, animals, houses, and fences we see every day. Some of the photographs I take are the source imagery for my paintings and other images are reserved for my book of photography. The book, like my paintings are images taken from when I am out walking my dog. What I see and record is a story that can be simultaneously ordinary and odd.
Places like our neighborhoods, though utterly familiar, can be alien and contain great wonder when seen with fresh eyes. I believe these places can tell us something about ourselves and how we impact our environment. This body of work is my attempt at conveying this idea
Muslim International Students’ Perception of Islamophobia in Their Immigration Journey to the U.S.: A Case Study
Get PDFThis study serves as an impetus for universities to address improving pre-arrival services and procedures for Muslim international students and a resource for practitioners and lawmakers to examine current policies specifically through the lens of Critical Race and Critical Muslim Theory. Policymakers can examine the effects of Islamophobia on international students and how it affects higher education, communities, and the economies
Caribbean Christmas
Get PDFA simple Hallmark style Christmas script taking place in the Caribbean. Cole has developed an app used for the education of young students and moves to the Eastern Caribbean island of Saint Joseph, but his boss desires to use the app for data collection, threatening Cole\u27s budding relationship and his new friends in community