Building on prior research, this study extends and improves an algorithm originally designed to identify flood-related ontology concepts, evaluating its applicability across multiple fields. The primary objectives are to: (1) demonstrate the algorithm's effectiveness in various domains (urban system, flood with urban system, and flood with fashion) and (2) illustrate how community detection enables each cluster’s “centroid” to anchor the ontology hierarchy and represent core topics. The improved algorithm shows greater efficiency, completing in 0.35 seconds compared to 0.6 seconds for the original algorithm on datasets with up to 1,000 rows, with both algorithms delivering consistent results. The findings confirm the algorithm's adaptability across different domains and demonstrate the use of cluster centroids, derived through community detection, as representatives of key topics in domain-specific ontologies. This approach enhances information retrieval, supports coherent data integration across systems, and provides a framework to aid decision-making and analysis in complex domains.

Concerns on Castration is an in-depth analysis of the United States laws on castration. There are no modern resources that one can consult to understand what the laws say nor which states feature castration laws. The paper provides the context for castration laws in the United States, which states have adopted them, the contents of those laws, their use, and then questions as to their constitutionality. The final prong of the paper - constitutionality - also asks whether or not castration is a treatment or a punishment. Deciding either way significantly affects Eighth Amendment Cruel and Unusual analyses. This contribution is meant to increase awareness about the practice and to better inform legislators in their endeavors to impose just punishments.

Sexual violence (SV) against adult women is common, with over 50% experiencing SV involving physical contact in their lifetime. Survivors face long-term effects on their sexual health and well-being, including reduced sexual assertiveness, difficulties in communication, increased sexual shame, and risky sexual behaviors. Unfortunately, existing intervention fail to address survivors’ specific sexual health needs such as these. Furthermore, survivors are less likely to have access to comprehensive sex education (CSE) and often face barriers in their experiences with sex education. CSE has been shown to improve sexual self-efficacy, reduce shame, and enhance sexual self-perception, all of which contribute to better sexual health outcomes. My thesis examines the effects of a targeted, six-hour sex education program on survivors’ sexual shame, self-efficacy, and self-concept. Workshops are held at local sexual assault centers and on GSU campus, with participants engaging in group activities and mindfulness exercises. Surveys are completed before and after the program to measure outcomes. Findings from this research will provide a better understanding of the sexual well-being of adult female survivors, inform future interventions, and help practitioners working with survivors. By the conference date, three workshops will have been conducted, and preliminary data will be available. My presentation will provide an overview of the curriculum, methods, and any preliminary findings, supported by pictures from workshops conducted prior to the study. Attendees will also participate in an interactive sex education activity from the intervention, providing insight into the program’s holistic approach and impact.

Artificial intelligence (AI) has been highlighted as promoting significant improvements in firm operations. Healthcare is a key domain in which operational improvements are a core objective. However, it is evident that the adoption of AI across hospitals is uneven. This begs the question: What influences hospitals to adopt AI? This is a crucial question because of the huge resources needed to adopt AI. While researchers have been focused on understanding the factors driving AI adoption across various industries, thus far, there has been limited identification of the factors influencing hospitals to adopt AI. In this study, we identified these factors. We used the Behavioral Theory of Firms to rationalize the hospital characteristics that influence AI adoption. Based on our analysis of data from hospitals in the USA in 2022, we discovered that hospitals invest in AI because they had a culture of investing in technology in the past. Also, hospitals registered with the American Hospital Association (AHA) tend to invest in AI more than those not registered with AHA. Our study further suggests that the number of beds in a hospital is positively related to AI adoption. Our study explains AI adoption in healthcare through the Behavioral Theory of Firms lens and adds to our understanding of what we know about AI adoption in healthcare.