By assembling the countless work put in by past and current AAPASC community members, this guidebook aims to serve as a foundation for future AAPASC executive boards and organizations. The goal for this project is to be a living document, maintained and changed by future generations of AAPI student leadership to adapt to the needs and goals of their university experience, but also to provide a connection to the learned experience of the past.

I built a database for ESG Intelligence (ESGi) Group, a consulting firm that advises law firms on implementing good environmental, social, and governance (ESG) practices internally as well as how law firms can better serve their clients with respect to ESG. This paper explores my decision-making process for the design of the database and the challenges I ran into while creating and populating the database. I turned a list of things that ESGi Group wanted to track into an entity relationship diagram (ERD), which I eventually turned into a relational database in MySQL. I further defined the contents of the database by mapping the ERD into a relational model, normalizing the relational model, and creating an attribute domain table. I coded the database in SQL, collected data in an excel spreadsheet (downloaded from AMLAW 200 and NLJ 500 purchased data, manually searching individual firm websites, and scraping law.com in R), and then inserted the data into the database. I ran into issues with data completeness due to the lacking regulation of firm transparency about ESG reporting, but this project succeeded in proof of concept rather than implementation. I also discuss security and privacy considerations, and ESGi Group’s possible options for further development of this project in the future.

Historically, young people have spearheaded environmental movements, demanding equitable involvement in decision-making processes that impact their future. Despite their active participation, barriers such as inadequate knowledge, lack of empowerment, and diminished hope often hinder meaningful engagement and impact. This study addresses these challenges by implementing a series of educational workshops designed to equip youth with the necessary tools to effectively influence climate policy and urban planning so that they can feel more hopeful about the future in the face of climate change. Utilizing both qualitative and quantitative methods, this research evaluates how different methods of arts-based educational engagement impact workshop participants' knowledge, empowerment, and optimism regarding their ability to inspire environmental change. The findings aim to contribute to the discourse on effective youth engagement in environmental justice, advocating for strategies that equip youth with the tools they need to foster sustainable community development and hope for the future.

The automated transcription of Greek manuscripts is a current research goal in the digital humanities. Pre-processing manuscript images is an important part of any computer based transcription pipeline. However, pre-processing for ancient manuscripts specifically has not been highly developed. The result of this project is a noiseless pre-processing method that keeps diacritics. Further, text line segmentation is automated for manuscripts without annotation.

Current industrial production of petrochemicals releases CO2 as a byproduct into the atmosphere, contributing to climate change. The sustainable alternative, microbial carbon capture, has primarily focused on phototrophs that have naturally occurring carbon fixation pathways, but are slow-growing, difficult to genetically engineer, and require sunlight, which limits their large-scale production capacity. Using a heterotroph such as Escherichia coli allows for chemical production at high titers, rates, and yields (TRY) while being fast growing and easy to genetically engineer. Under fermentation conditions, the carboxylases in E. coli fix inorganic carbon in the reductive branch of the TCA cycle, producing industrially relevant chemical precursors such as succinate. However, the carboxylase’s access to CO2 is limited by the conditions surrounding it; most of the inorganic carbon inside the cell is in the form of bicarbonate. Increasing the local concentration of CO2 near the carboxylase may improve the kinetics of the pathway. To do this, a fusion protein that colocalizes carbonic anhydrase and phosphoenolpyruvate carboxykinase (Pck) was created. However, since strains expressing this fusion protein did not grow above OD600 = 1 under fermentation conditions, further design optimization and investigation is needed.

The Women’s Power and Influence Index uses publicly available information to rank companies based on their gender policies, with the thought that public rankings trigger the behavioral and policy changes that move us in the direction of gender equity and pay parity. This project employs survey analysis to take a closer look at four of the criteria the WPI uses to score companies - maternity leave, childcare, harassment and discrimination training, and professional development. Our work evaluates survey responses to determine optimal policies for each of the four criteria with the hope that in future iterations of the Index, these policies can be incorporated into the scoring methods as a standard against which respective company policies can be compared.

Convergent Case Management (CCM) is a mandatory reentry program offered in Arizona state-run prison units. This study evaluates the success of this program through semi-structured interviews with correctional officers and incarcerated men and women at two Arizona prison units. These results are contextualized within the history of rehabilitative program evaluation from the "nothing works" paradigm of the 1970s through contemporary Good Lives Model and desistance thinking.

Extensive feminist work has discussed the various forms of oppression that are enacted on marginalized genders by men to reify their masculine identity. Ecofeminists, who posit that oppression against women, animals, and the environment is interconnected, have expanded the feminist conception of who is oppressed by masculinity to animals and the environment. Recreational hunting plays a quintessential role in many men’s normative gender development and is directly exploitative towards nature and animals; ecofeminists have shown that it operates within a framework that objectifies and exploits women. This project employs an ecofeminist lens to discuss the following: How some justifications for hunting rely on the notion that men are inherently violent, the link between compulsory heterosexuality and hunting, hunting’s contribution to the masculine identity, and the early conservation movement’s relationship to hunting. I also analyzed a recent issue of a hunting magazine for evidence of the discussed themes to provide further evidence to the growing body of ecofeminist scholarship.

I study some comparative statics implications of disappointment-averse preferences for optimal portfolios. Specifically, I find that risk-averse disappointment-averse investors increase investment in a risky asset as a result of a monotone likelihood ratio improvement in the asset’s distribution, a subset of First Order Stochastic improvements. This gives a testable implication between the disappointment aversion model, and alternatives, including expected utility. I also discuss previously noted implications for disappointment aversion in helping explain the equity premium puzzle.

This study presents a comparative analysis of machine learning models on their ability to determine match outcomes in the English Premier League (EPL), focusing on optimizing prediction accuracy. The research leverages a variety of models, including logistic regression, decision trees, random forests, gradient boosting machines, support vector machines, k-nearest neighbors, and extreme gradient boosting, to predict the outcomes of soccer matches in the EPL. Utilizing a comprehensive dataset from Kaggle, the study uses the Sport Result Prediction CRISP-DM framework for data preparation and model evaluation, comparing the accuracy, precision, recall, F1-score, ROC-AUC score, and confusion matrices of each model used in the study. The findings reveal that ensemble methods, notably Random Forest and Extreme Gradient Boosting, outperform other models in accuracy, highlighting their potential in sports analytics. This research contributes to the field of sports analytics by demonstrating the effectiveness of machine learning in sports outcome prediction, while also identifying the challenges and complexities inherent in predicting the outcomes of EPL matches. This research not only highlights the significance of ensemble learning techniques in handling sports data complexities but also opens avenues for future exploration into advanced machine learning and deep learning approaches for enhancing predictive accuracy in sports analytics.