UWEC CERCA 2025

This research explores how various factors, particularly parenthood and employment, influence the cognitive dimensions of gendered well-being in Brazil, the Netherlands, and the United States. Using data from the World Values Survey Wave 7, we find that the impact of parenthood on women’s well-being varies across countries. For husbands, occupational status has a greater effect on happiness than fatherhood, while wives’ employment appears to have little to no significant relationship with their well-being. We discuss the implications of these findings and offer policy recommendations to address the motherhood dilemma, advance gender equality, and support female workforce participation.

This study investigates how financial security shapes the cognitive dimensions of well-being in China, India, and the United States. Utilizing data from the World Values Survey Wave 7, we find that different saving behaviors influence well-being—measured by happiness and life satisfaction—differently across countries. We examine the implications of these findings and propose policy measures to enhance financial literacy.

As the use of car dashboard cameras (dashcams) has increased, the availability of dashcam imagery has also increased. In recent years, dashcam imagery has been predominantly used in conjunction with computer vision techniques for autonomous vehicle systems. However, this research explores an alternative application of these technologies in the domain of public safety and security. Specifically, we apply object detection to dashcam imagery to address the challenge of identifying vehicles associated with active Amber Alerts. With the goal of aiding law enforcement in locating abducted children more efficiently, we employ the YOLO (You Only Look Once) object detection model, a state-of-the-art deep learning framework known for its real-time performance and accuracy. Our methodology involves training and fine-tuning the YOLO model on a custom dataset of dashcam footage, incorporating diverse environmental conditions such as varying lighting, weather, and traffic scenarios. Experimental results demonstrate that the model achieves high precision and recall rates in detecting target vehicles, validating its effectiveness for real-world deployment. This research highlights the potential of leveraging deep learning and computer vision techniques to address critical public safety challenges, offering a novel application of these technologies beyond their traditional use in autonomous driving. Our findings contribute to the growing body of work in computer science that seeks to harness AI for societal benefit.

Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer, accounting for over 90% of cases, and is characterized by aggressive growth, early metastasis, and resistance to therapy. A comprehensive understanding of the molecular mechanisms driving PDAC is essential for improving diagnosis, prognosis, and treatment. In this study, a multiomics approach was applied by analyzing both DNA methylation and RNA-sequencing datasets obtained from The Cancer Genome Atlas Pancreatic Adenocarcinoma project.The methylation dataset included significantly more tumor samples than normal samples, and a similar imbalance was observed in the RNA-seq dataset. This disparity posed a challenge for direct feature selection, as it could lead to a model biased toward tumor-associated features. To address this issue, six data imbalance correction techniques were evaluated and compared: Random Oversampling, Synthetic Minority Over-sampling Technique (SMOTE), and Adaptive Synthetic (ADASYN) for oversampling, along with Random Undersampling, Cluster Centroids, and AllKNN for undersampling. Identifying the most effective imbalance correction method is essential for improving feature selection accuracy and facilitating the discovery of novel genes associated with pancreatic ductal adenocarcinoma (PDAC). A deeper understanding of these oncogenes could contribute to the development of non-invasive diagnostic tests and personalized treatment strategies for PDAC.

Curling is a strategic ice sport that presents unique challenges for AI research due to its combination of complex decision-making and intricate physical dynamics. This project aims to develop a physics-based curling simulator to address these challenges, enabling accurate modeling of stone movement, ice conditions, and sweeping effects. Our approach involves utilizing an existing physics engine, MoJuCo, to simulate realistic curling interactions. We implemented physics models based on leading theories for basic curling shot selections. The simulator initially focuses on stone dynamics and shot selection, with more complex features such as sweeping effects being added in later iterations. A visualization web app displays shot outcomes and will eventually support AI training and data analysis.In addition to the simulation application for curling research, we developed a training module for both the physics of curling and interacting with the MoJuCo library. This module is designed to help new student learn about the complicated physics of curling. This module also helps students learn how to implement and maintain MuJuCo based features into the simulator.

Economic insecurity is a critical but often overlooked determinant of public health. This study investigates how financial instability contributes to poor health outcomes. Financial instability is characterized by unpredictability of income, employment insecurity, and limited access to basic
needs like food, clothing, housing and access to healthcare, all which affect physical and mental health outcomes. By analyzing the intersectionality of economic insecurity, healthcare access, and the role of social systems including healthcare policies and safety nets, this research aims to
identify key factors that negatively impact health and wellbeing and exacerbate these effects. This research primarily relies on quantitative data from closed ended survey questions, supplemented by qualitative data from the addition of three open ended survey questions. The survey explores an individual’s economic position, experience with healthcare accessibility and the perceptions of the effectiveness of social programs. The objective of the research is to discover in what ways economics affect health and to highlight systemic shortcomings in addressing these issues. The findings from this research can provide insight to those dedicated to
developing more effective interventions that would mitigate negative health consequences and improve healthcare accessibility.

Curling is a strategic team sport that presents unique challenges for artificial intelligence (AI) research, particularly in decision-making and physical simulation. However, a significant barrier to AI development in curling is the lack of structured and accessible datasets. This project aims to address this gap by leveraging standardized video feeds from Curling Stadium to generate datasets suitable for AI research.Our approach involves developing software that uses image detection models YOLO (You Only Look Once) and SAM (Segment Anything Model) to analyze YouTube videos of curling matches, tracking objects such as rocks and players to gather data on their positions and movements.The expected outcome of the larger project is a structured and scalable dataset that can be used for AI-based curling research, including game strategy analysis and predictive modeling. This project lays the foundation for broader AI applications in curling by automating data collection, enabling machine learning models to analyze strategic decision-making, and fostering human-AI collaboration in sports analytics.

The Internet of Things (IoT) encompasses a variety of systems and devices that enable data exchange across networks. With this interleaved connectivity comes an inherent vulnerability to attacks. Traditional intrusion detection in IoT environments has been primarily human-reliant, but modern malicious methods surpass manual approaches. Machine Learning (ML)-based Intrusion Detection Systems (IDS) show promise but require refinement to match human-monitored IDS effectiveness.This study involved a literature review of research involving the NetFlow dataset NF-ToN-IoT-v2, created in 2022 to enable ML-based IDS development. With balancing, the dataset includes approximately 16 million net-flows, with 63.99% attack and 36.01% benign. The data’s imbalanced nature was addressed through methods like down sampling to reduce training bias. A hyper-parameter tuning pipeline was used to optimize algorithm testing and cross-validation, especially for different data balancing methods.The algorithms tested based on previous research found during literature review include Naïve Bayes, Random Forest, K-Nearest Neighbor (KNN), Support Vector Machines (SVM), and XGBoost. Comparative analysis using confusion matrices and bar plots enabled the evaluation of algorithm effectiveness. Overall, this research highlights the potential of ML approaches in IoT IDS development, through leveraging NF-ToN-IoT-v2 to enhance detection accuracy and bridge the gap between human-monitored and ML-driven solutions.