Implementation Protocols: The Hidden In Plain Sight Project Asa Olsen; Dana Culley; Karole-Anne Alviso; Jasmine Montgomery; Joshua Mackie; Eliann Carr, PhD; Ellensburg Police Department Project Mentor(s): Cody Stoddard, PhD Children of all ages struggle with many different influences leading to negative behaviors. Whether drugs, alcohol, mental health, etc., it is your job as a parent to be there for them and to help them through these tough processes and experiences, ensuring they feel safe and heard. The Hidden In Plain Sight Project is designed to help guide and inform parents how to help their children. The project uses visual aids, such as a mock bedroom and mock backpacks, to help show parents modern methods how children hide their poor decision making, for example the “core four” behaviors found most common in middle schools and high schools: alcohol, nicotine, marijuana, and mental health. Through our program, we explore partnerships throughout the community to present these topics to the parents and then strategize how to have tough conversations with their children to help better their family relationship, rather than overreacting when confronted with their child’s poor decision making and resorting to punitive actions that could harm the relationship. We had several presentations within the Ellensburg community that have shown tremendous success in bolstering parents’ preparedness to have difficult conversations with their children and increasing their awareness of concerning indicators. The intent of implementing the Hidden In Plain Sight Project is to establish measurable outcomes to compare juvenile related crime trends to show whether or not this program may mitigate common issues in our community. Presentation Type: Poster Presentation (May 21, 9:30am–3:00pm) Keywords: community education, family relationships, youth safety SOURCE Form ID: 124 Mathematics Comparing and Predicting the Mortality Rates Between Men and Women in the US Halle Byerly, Brandon Hudock Project Mentor(s): Yvonne Chueh, PhD The goal of this project is to predict future mortality rates and analyze differences between men and women at age 55 over the past 90 years. We use data from the Human Mortality Database, which includes variables such as births, population, life expectancy, age, probability of death, central death rate, and average length of survival for males and females from 1933 to 2024. To model mortality trends, we apply linear regression, ridge regression, decision trees, random forests, and boosting. These models are trained on earlier data and evaluated on more recent data to assess their predictive performance. Model validation is conducted using LOOCV, k-fold cross-validation, and bootstrapping. Results will be presented through mortality trend graphs, age-specific mortality curves, gender difference plots, and variable importance analyses. Presentation Type: Poster Presentation (May 21, 9:30am–3:00pm) Keywords: Mathematics, Actuarial Science SOURCE Form ID: 202
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