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Program Cost for Post-Master’s (APRN prepared) to DNPTuition is based on a cohort model for the 2024 cohort. Students must complete the program with their cohort to remain eligible for cohort pricing. Total program cost: The MSN (APRN-prepared) to Doctor of Nursing Practice program is 35 continuous semester hours, from summer 2024 through spring of 2026, with a total program cost of $41,790. Additional coursework may be required based on coursework analysis (must meet the AACN DNP Essentials

**Effects of target race and gender on perceptions of mental illness 3. Kasidy L. Javernick and Patricia J. Morales **Perceptions of Psychology: Comparing Views Across Introductory and Advanced Psychology Majors and Other Natural Science Majors 4. Daniel D. Thomas **Beyond Policing: Analysis of Response Models and Their Psychological Impact on Mental Health Crisis Interventions 5. Ellie Smith and Amy Ruiz **Improving Child Anxiety: The Impact of Art, Play, and Music Therapy Interventions 6. Samantha F

comment to EPC for its review. Proposals for new degrees, new majors, new minors, concentrations, and programs that require action by the Board of Regents require submission of a cost analysis. In addition, EPC shall present such proposals to the faculty, which will decide whether to recommend them to the Board of Regents. Descriptions of proposed curricular changes, course numbers, titles, credit hours, and synopses of proposed degree requirement changes shall be published by EPC in the 30-day Notice

2021, a paper that he wrote on alliance-building and Northern Irish women during World War II was published by the University of Alabama’s Crimson Historical Review. Greyson’s History capstone paper—which discussed Holocaust education in East Germany—is in review with journals at the University of Southern California, Vanderbilt University, and the University of Wisconsin–Madison. In the Fall of 2021, Greyson began graduate studies in Data Analytics at Boston University. Abbie Welch '19Abbie Welch

and efficient machine learning technique k-means clustering to categorize over 60 years worth of popular music. Using data from the Billboard Top 100 charts, we were able to train our machine learning model with over 17,000 different songs and their audio features (acousticness, danceability, loudness, liveness, instrumentalness, energy, and speechiness). Clustering songs with similar features allows us to make predictions by comparing a user’s favorite tracks to our clusters and picking