- LocationRowe Center
- Websitehttps://events.uconn.edu/fyp/event/1315372-fye-peer-mentor-hub
More from Master Calendar
- Nov 139:00 AMLet's Talk with FumiStudents who may benefit from attending a Let's Talk: Mental Health Office Hours session include:Students who want help connecting to resources but are unsure where to begin Students who are looking for advice on a non-clinical issue Students who are unsure about therapy and are curious about what it is like to talk to a therapist Students who may have concerns about the mental health of a friend and seek advice on how to support their friend If a student is not an imminent risk, and is refusing your support in contacting our office, you may also consider contacting the UConn Student CARE Team (https://studentcareteam.uconn.edu/). This session is held by Fumi Sowah, LCSW (https://studenthealth.uconn.edu/person/olufumilayo-sowah/)
- Nov 139:00 AMMill River Community Engagement EventStudents will RSVP through the emails we directly send out. This is an Honors Event. See tags below for category information. #UHLevent11269
- Nov 139:00 AMSTEM Programs Virtual Office HourScience, Technology, Engineering, and Mathematics (STEM) fields are essential to U.S. economic competitiveness and growth. STEM-designated programs drive innovation and advancement, widening career prospects and strengthening the world economy. Those studying STEM develop a valuable quantitative and analytical skill set, elevating one's candidacy for well-paying, high-tech jobs.
- Nov 1310:00 AMConnecticut ¡Adelante! Info Session - Bilingual English/Spanish MSW Program Option
- Nov 1310:00 AMDoctoral Dissertation Oral Defense of Sarah SternStellar evolution and the synthesis of the elements are governed by key nuclear reactions, among which the fusion of 12C with an alpha particle to form 16O, denoted as the 12C(α,γ)16O reaction, is "of paramount importance". The ratio of carbon to oxygen produced during stellar helium burning, which is determined by the 12C(α,γ)16O reaction, allow us for example to predict the fate of massive stars, whether they end up as neutron stars or black holes. Despite five decades of study, this reaction's cross section remains poorly constrained at the astrophysically relevant energies. This thesis presents the development and implementation of a new method to measure the cross-section of the 12C(α,γ)16O reaction by measuring the time-reverse process – the 16O(γ,α)12C reaction – using a Time Projection Chamber (TPC) operated in intense γ-ray beams. The first-generation optical readout TPC (O–TPC) was constructed at UConn and used at the High Intensity γ source (HIγS) facility at Duke University. Building on these results, a next-generation electronic readout TPC (eTPC) was constructed and commissioned at the University of Warsaw, incorporating a fully digital electronic readout system for high-rate data acquisition. The eTPC was exposed to quasi-monoenergetic γ-rays from 8.51–13.9 MeV, corresponding to Ecm=1.4-4.8 MeV of the 12C(α,γ)16O reaction. A comprehensive analysis framework was developed to identify the 16O(γ,α)12C events and reconstruct their kinematics. This permitted angular distributions of the photo-dissociation events to be examined. The analyzed angular distributions yield results which are consistent with a fundamental prediction of quantum mechanics, a feat not seen in earlier data sets. The results demonstrate that this method can achieve accurate event reconstruction, clean event separation, accurate energy calibration, and angular resolution sufficient for astrophysical studies. This work establishes the validity of our new method for precision measurement of the 12C(α,γ)16O reaction through its time reverse process. This paves the way toward future measurements at lower energies with reduced uncertainty and improved extrapolation to stellar conditions.
- Nov 1310:00 AMDoctoral Dissertation Oral Defense of Yi WangThis dissertation focused on the design and development for fluorescent sensor array for the foodborne pathogenic bacterial and biofilm identification with machine learning techniques. it also includes the investigation of interfacial biofilm monitoring and quantification for better pathogenic biofilm control and food safety.


