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The existing curriculum and models for civil engineering graduate programs assume that graduating Ph.D. students will primarily pursue career opportunities in research or academia. However, the number of civil engineering Ph.D. graduate students continues to increase, while the number of opportunities in academia for civil engineers remains stagnant. As a result, it is becoming increasingly apparent that the civil engineering graduate programs must be reevaluated to assist students entering industry after graduation. As part of a larger research study funded through the NSF Innovations in Graduate Education (IGE), we aim to answer the following research questions: 1) How can a research-to-practice model assist students in preparing for a transportation engineering career outside of academia?, 2) What impacts does the research-to-practice graduate model have on the development of transportation engineering doctoral students’ professional identity?, 3) How does the cognitive apprenticeship framework prepare doctoral students for professional practice in transportation engineering?, and 4) What influences does the research-to-practice model have on doctoral students’ motivation toward degree completion? As part of the first phase for the project, two surveys were developed: a graduate engineering student motivation survey based on Expectancy-Value-Theory, and an instrument based on the Cognitive Apprenticeship framework. The motivation survey was based on an instrument designed and validated by Brown & Matusovich (2013) which aimed to measure undergraduate engineering students' motivation towards obtaining an engineering degree. The survey prompts were reviewed and rewritten to reflect the change in context from undergraduate to graduate school. Revised survey prompts were reviewed with a group of graduate engineering students through a think aloud protocol and changes to the instrument were made to ensure consistency in interpretation of the prompts (Rodriguez-Mejia and Bodnar, 2023). The cognitive apprenticeship instrument was derived from the Maastricht Clinical Teaching Questionnaire (MCTQ), originally designed to offer clinical educators feedback on their teaching abilities, as provided by medical students during their clerkship rotations (Stalmeijer et al., 2010). To tailor it to the context of engineering graduate students, the MCTQ's 24 items were carefully examined and rephrased. A think aloud was conducted with three civil engineering graduate students to determine the effectiveness and clarity of the cognitive apprenticeship instrument. Preliminary results show that minimal clarification is needed for some items, and suggestions to include items which address support from their mentors. The other part of the project assessment involves students completing monthly reflections to obtain their opinions on specific events such as seminars or classes, and identify their perceptions of their identity as professionals, scientists, or researchers. Preliminary results suggest that the students involved place an emphasis on developing critical thinking and planning skills to become an engineering professional, but de-emphasize passion and enjoyment. This paper will report on initial findings obtained through this first phase of the IGE project. 

Background: The National Science Foundation (NSF) and other organizations have spent millions of dollars each year supporting well-designed educational innovations that positively impact the undergraduate engineering students who encounter them. However, many of these pedagogical innovations never experience widespread adoption. To further the ability of innovation developers to advance engineering education practice and achieve sustained adoption of their innovations, this paper explores how one community-based model, engineering education guilds, fosters propagation across institutions and individuals. Engineering education guilds seek to work at the forefront of educational innovation by creating networks of instructor change-agents who design and implement a particular innovation in their own context. The guilds of interest are the Consortium to Promote Reflection in Engineering Education (CPREE) and the Kern Entrepreneurial Engineering Network (KEEN). With these guilds as exemplars, this study’s purpose is (1) to articulate how the approaches of engineering education guilds align with existing literature on supporting sustained adoption of educational innovations and (2) to identify how these approaches can advance the science, technology, engineering and math (STEM) education community’s discussion of propagation practices through the use of the Designing for Sustained Adoption Assessment Instrument (DSAAI). The DSAAI is a conceptual framework based on research in sustained adoption of pedagogical innovations. It has previously been used in the form of a rubric to analyze dissemination and propagation plans of NSF educational grant recipients and was shown to predict the effectiveness of those propagation plans. Results: Through semi-structured interviews with two leaders from each guild, we observed strong alignment between the structures of CRPEE and KEEN and evidence-based sustained adoption characteristics. For example, both guilds identified their intended audience early in their formation, developed and implemented extensive plans for engaging and supporting potential adopters, and accounted for the complexity of the higher education landscape and their innovations in their propagation plans. Conclusions: Our results suggest that guilds could provide another approach to innovation, as their structures can be aligned with evidence-based methods for propagating pedagogical innovations. Additionally, while the DSAAI captures many of the characteristics of a welld-esigned propagation strategy, there are additional components that emerged as successful strategies used by the CPREE and KEEN guild leaders. These strategies, including having mutual accountability among adopters and connecting adoption of innovations to faculty reward structures in the form of recognition and funding should be considered as educational innovators work to encourage adoption of their innovations. 

Engineering education guilds, such as the Consortium to Promote Reflection in Engineering Education (CPREE) and the Kern Entrepreneurial Engineering Network (KEEN), seek to work at the forefront of educational innovation by creating networks of instructor change agents who design and implement a particular innovation in their own context to further the professional formation of engineers (PFE). While many of the innovations facilitated by CPREE and KEEN have been published extensively, it is unclear how successful the propagation of reflection and entrepreneurial mindset has been in the engineering education community. The major aim of this project is to characterize these two engineering education guilds with respect to their dissemination/propagation plans and, in the future, quantify the propagation of the innovations championed by CPREE and KEEN. The research questions we seek to answer in this paper are: (1) What are the planned dissemination/propagation approaches of well-established engineering education guilds? and (2) To what extent do their characteristics align with the Designing for Sustained Adoption Assessment Instrument (DSAAI)? The DSAAI was developed in 2016 to provide education developers, grant writing consultants, and funding agencies with a tool for assessing the propagation plans of researchers developing educational change strategies. To answer these questions, we conducted semi-structured interviews with the leaders of CPREE and KEEN. The transcriptions of the interviews will be used to create within-case reports for each guild. The within-case reports will consist of a rich description of the pedagogical innovation as well as the history of the guild and its goals. Using the DSAAI, we will qualitatively code the techniques that each guild is using to facilitate widespread adoption as well as the extent to which they are following a dissemination or propagation paradigm. Lastly, thematic analysis will be used to capture emerging themes that arise from the interviews. 

Abstract SBND is the near detector of the Short-Baseline Neutrino program at Fermilab. Its location near to the Booster Neutrino Beam source and relatively large mass will allow the study of neutrino interactions on argon with unprecedented statistics. This paper describes the expected performance of the SBND photon detection system, using a simulated sample of beam neutrinos and cosmogenic particles. Its design is a dual readout concept combining a system of 120 photomultiplier tubes, used for triggering, with a system of 192 X-ARAPUCA devices, located behind the anode wire planes. Furthermore, covering the cathode plane with highly-reflective panels coated with a wavelength-shifting compound recovers part of the light emitted towards the cathode, where no optical detectors exist. We show how this new design provides a high light yield and a more uniform detection efficiency, an excellent timing resolution and an independent 3D-position reconstruction using only the scintillation light. Finally, the whole reconstruction chain is applied to recover the temporal structure of the beam spill, which is resolved with a resolution on the order of nanoseconds. 

Abstract The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20% increase in the efficiency of sub-1 cm vertex reconstruction across all neutrino flavours. 

The determination of the direction of a stellar core collapse via its neutrino emission is crucial for the identification of the progenitor for a multimessenger follow-up. A highly effective method of reconstructing supernova directions within the Deep Underground Neutrino Experiment (DUNE) is introduced. The supernova neutrino pointing resolution is studied by simulating and reconstructing electron-neutrino charged-current absorption on ${}^{40}\mathrm{Ar}$and elastic scattering of neutrinos on electrons. Procedures to reconstruct individual interactions, including a newly developed technique called “brems flipping,” as well as the burst direction from an ensemble of interactions are described. Performance of the burst direction reconstruction is evaluated for supernovae happening at a distance of 10 kpc for a specific supernova burst flux model. The pointing resolution is found to be 3.4 degrees at 68% coverage for a perfect interaction-channel classification and a fiducial mass of 40 kton, and 6.6 degrees for a 10 kton fiducial mass respectively. Assuming a 4% rate of charged-current interactions being misidentified as elastic scattering, DUNE’s burst pointing resolution is found to be 4.3 degrees (8.7 degrees) at 68% coverage. Published by the American Physical Society2025 

Abstract This paper introduces a novel track-length extension fitting algorithm for measuring the kinetic energies of inelastically interacting particles in liquid argon time projection chambers (LArTPCs). The algorithm finds the most probable offset in track length for a track-like object by comparing the measured ionization density as a function of position with a theoretical prediction of the energy loss as a function of the energy, including models of electron recombination and detector response. The algorithm can be used to measure the energies of particles that interact before they stop, such as charged pions that are absorbed by argon nuclei. The algorithm's energy measurement resolutions and fractional biases are presented as functions of particle kinetic energy and number of track hits using samples of stopping secondary charged pions in data collected by the ProtoDUNE-SP detector, and also in a detailed simulation. Additional studies describe the impact of thedE/dxmodel on energy measurement performance. The method described in this paper to characterize the energy measurement performance can be repeated in any LArTPC experiment using stopping secondary charged pions. 

The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase strategy toward the implementation of this leading-edge, large-scale science project. The 2023 report of the US Particle Physics Project Prioritization Panel (P5) reaffirmed this vision and strongly endorsed DUNE Phase I and Phase II, as did the European Strategy for Particle Physics. While the construction of the DUNE Phase I is well underway, this White Paper focuses on DUNE Phase II planning. DUNE Phase-II consists of a third and fourth far detector (FD) module, an upgraded near detector complex, and an enhanced 2.1 MW beam. The fourth FD module is conceived as a "Module of Opportunity", aimed at expanding the physics opportunities, in addition to supporting the core DUNE science program, with more advanced technologies. This document highlights the increased science opportunities offered by the DUNE Phase II near and far detectors, including long-baseline neutrino oscillation physics, neutrino astrophysics, and physics beyond the standard model. It describes the DUNE Phase II near and far detector technologies and detector design concepts that are currently under consideration. A summary of key R&D goals and prototyping phases needed to realize the Phase II detector technical designs is also provided. DUNE's Phase II detectors, along with the increased beam power, will complete the full scope of DUNE, enabling a multi-decadal program of groundbreaking science with neutrinos. 

ProtoDUNE Single-Phase (ProtoDUNE-SP) is a 770-ton liquid argon time projection chamber that operated in a hadron test beam at the CERN Neutrino Platform in 2018. We present a measurement of the total inelastic cross section of charged kaons on argon as a function of kaon energy using 6 and $7\mathrm{GeV}/c$beam momentum settings. The flux-weighted average of the extracted inelastic cross section at each beam momentum setting was measured to be $380\pm 26\mathrm{mbarns}$for the $6\mathrm{GeV}/c$setting and $379\pm 35\mathrm{mbarns}$for the $7\mathrm{GeV}/c$setting. Published by the American Physical Society2024 

The Module-0 Demonstrator is a single-phase 600 kg liquid argon time projection chamber operated as a prototype for the DUNE liquid argon near detector. Based on the ArgonCube design concept, Module-0 features a novel 80k-channel pixelated charge readout and advanced high-coverage photon detection system. In this paper, we present an analysis of an eight-day data set consisting of 25 million cosmic ray events collected in the spring of 2021. We use this sample to demonstrate the imaging performance of the charge and light readout systems as well as the signal correlations between the two. We also report argon purity and detector uniformity measurements and provide comparisons to detector simulations.