Peer mentorship is a mutually beneficial relationship that allows two individuals who are at approximately the same experience level to interact with one another with the goal of providing personal, professional, or both types of support. It has been found that peer mentorship within academic settings have generally positive retention, persistence, and student experience outcomes for both mentors and mentees. While peer mentoring research and initiatives are growing, very few instances exist of determining student perceived needs regarding peer mentorship. As such, at a western institution in the United States, students were surveyed to self-report their perceived peer mentorship needs. This survey occurred during Fall 2021, just after the onset of the COVID-19 pandemic. Out of 223 participants, 79 students indicated that they currently had a peer mentor at the time the survey was administered. Students were given both a definition and examples of peer mentorship before indicating they had a peer mentor. Their mentors may have been formally assigned through an existing program at the college of engineering of interest or informally obtained through their own efforts. These 79 participants were asked what additional support they wish their peer mentor could provide. Through phenomenological analysis of open-ended responses, common avenues for additional support were determined. These findings allowed for development of recommendations for shaping the future implementation of more targeted and beneficial peer mentoring initiatives. The recommendations include providing flexibility in peer mentorship, training on resources and events, and a variety of peer mentoring opportunities early and consistently.

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Before the availability of isotopic data from chemical reprocessing of spent nuclear fuels, burnup code normalization was done using data from small burnup samples. Chemical reprocessing plant isotopic data are an additional source of burnup data reliably measured to permit normalization of calculational code processes to real data. By applying isotopic correlation techniques to this source of data and to data from burnup calculations, it appears that procedures are available a). to detect code input errors, b).to detect calculational biases in both U and Pu isotopes and c). to diagnose calculational weaknesses. A means whereby the maximum use ,of dissolution data is realized is given. 6 1 2 1 C o m. p a r i s o n , o f Y a n k e e Rowe C o r e V D i s s o l u t i on .Ba..tch Da.ta and C o r e V C a l c u l a t e d , D a t a S 3