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No skill is more important for a student of mechanics than the ability to draw a complete and accurate free-body diagram (FBD). A good FBD facilitates proper accounting of forces when writing the balances that lead to governing equations in statics, solid mechanics, and dynamics. Because this skill is essential, educational approaches that improve the ability of students to draw correct FBDs are critical for maximizing the potential of the next generation of engineers. Traditionally, learning to draw FBDs involves classroom instruction followed by homework practice consisting of problems drawn from a textbook. Homework as practice does not serve all students well, because it does not scaffold the process of drawing FBDs in terms of distinct tasks (e.g., isolating the body, considering support reactions) nor does it offer immediate feedback, which students often need to avoid falling into the same error repeatedly. To address these shortcomings, we embarked on the design, implementation, and testing of a mobile application (app) that offers an alternative venue for FBD practice. The app provides students with asynchronous opportunities for training, varied tasks that target specific FBD issues, and several levels of immediate feedback. We hypothesize that the gamified environment and puzzle-based gameplay will improve student skill and self-efficacy in drawing FBDs, particularly for women, who may feel less confident in their spatial skills. Data collected to describe student experiences may also provide additional insight into how to improve FBD instruction generally. In this paper, we detail the process for designing and implementing the app and provide initial data regarding student impressions and use. The app was piloted in Fall 2022 in a large Introduction to Statics course as a non-graded study activity; all students except one (n=97) participated in an evaluation of its design features and user experiences. Approximately half (54%) of students indicated they had played half or more of the available games. When commenting about how the FBD app did, or did not, help their learning, 49% of respondents appreciated that the app allowed additional opportunities for practice. Students used these opportunities to further develop several skills, such as visualizing the system and setting up accurate diagrams, which strengthened their confidence and reviewed key concepts. While describing the value of practicing through the app, 21% of students called out how the app provided feedback. They specifically mentioned the positive experiences of receiving feedback that is immediate, that explains boundary connections, and that deepens learning after mistakes are made. These and other findings from the pilot study are discussed with corresponding next steps for development. 

No skill is more important for a student of mechanics than the ability to draw a complete and accurate free-body diagram (FBD). A good FBD facilitates proper accounting of forces when writing the balances that lead to governing equations in statics, solid mechanics, and dynamics. Because this skill is essential, educational approaches that improve the ability of students to draw correct FBDs are critical for maximizing the potential of the next generation of engineers. Traditionally, learning to draw FBDs involves classroom instruction followed by homework practice consisting of problems drawn from a textbook. Homework as practice does not serve all students well, because it does not scaffold the process of drawing FBDs in terms of distinct tasks (e.g., isolating the body, considering support reactions) nor does it offer immediate feedback, which students often need to avoid falling into the same error repeatedly. To address these shortcomings, we embarked on the design, implementation, and testing of a mobile application (app) that offers an alternative venue for FBD practice. The app provides students with asynchronous opportunities for training, varied tasks that target specific FBD issues, and several levels of immediate feedback. We hypothesize that the gamified environment and puzzle-based gameplay will improve student skill and self-efficacy in drawing FBDs, particularly for women, who may feel less confident in their spatial skills. Data collected to describe student experiences may also provide additional insight into how to improve FBD instruction generally. In this paper, we detail the process for designing and implementing the app and provide initial data regarding student impressions and use. The app was piloted in Fall 2022 in a large Introduction to Statics course as a non-graded study activity; all students except one (n=97) participated in an evaluation of its design features and user experiences. Approximately half (54%) of students indicated they had played half or more of the available games. When commenting about how the FBD app did, or did not, help their learning, 49% of respondents appreciated that the app allowed additional opportunities for practice. Students used these opportunities to further develop several skills, such as visualizing the system and setting up accurate diagrams, which strengthened their confidence and reviewed key concepts. While describing the value of practicing through the app, 21% of students called out how the app provided feedback. They specifically mentioned the positive experiences of receiving feedback that is immediate, that explains boundary connections, and that deepens learning after mistakes are made. These and other findings from the pilot study are discussed with corresponding next steps for development.   

Abstract We present observations that suggest the X-line of guide-field magnetic reconnection is not necessarily orthogonal to the plane in which magnetic reconnection is occurring. The plane of magnetic reconnection is often referred to as theL–Nplane, whereLis the direction of the reversing and reconnecting magnetic field andNis normal to the current sheet. The X-line is often assumed to be orthogonal to theL–Nplane (defined as theM-direction) in the majority of theoretical studies and numerical simulations. The four-satellite Magnetospheric Multiscale (MMS) mission, however, observes a guide-field magnetic reconnection event in Earth’s magnetotail in which the X-line may be oblique to theL–Nplane. This finding is somewhat opportune as two of the MMS satellites at the sameNlocation report nearly identical observations with no significant time delays in the electron diffusion region (EDR) even though they have substantial separation inL. A minimum directional derivative analysis suggests that the X-line is between 40° and 60° fromM, adding support that the X-line is oblique. Furthermore, the measured ion velocity is inconsistent with the apparent motion of the MMS spacecraft in theL-direction through the EDR, which can be resolved if one assumes a shear in theL–Nplane and motion in theM-direction. A nonorthogonal X-line, if somewhat common, would call for revisiting theory and simulations of guide-field magnetic reconnection, reexamination of how the reconnection electric field is supported in the EDR, and reconsidering the large-scale geometry of the X-line. 

Neutrinoless double beta decay is one of the most sensitive probes for new physics beyond the Standard Model of particle physics. One of the isotopes under investigation is ${}^{136}\mathrm{Xe}$, which would double beta decay into ${}^{136}\mathrm{Ba}$. Detecting the single ${}^{136}\mathrm{Ba}$daughter provides a sort of ultimate tool in the discrimination against backgrounds. Previous work demonstrated the ability to perform single atom imaging of Ba atoms in a single-vacancy site of a solid xenon matrix. In this paper, the effort to identify signal from individual barium atoms is extended to Ba atoms in a hexa-vacancy site in the matrix and is achieved despite increased photobleaching in this site. Abrupt fluorescence turn-off of a single Ba atom is also observed. Significant recovery of fluorescence signal lost through photobleaching is demonstrated upon annealing of Ba deposits in the Xe ice. Following annealing, it is observed that Ba atoms in the hexa-vacancy site exhibit antibleaching while Ba atoms in the tetra-vacancy site exhibit bleaching. This may be evidence for a matrix site transfer upon laser excitation. Our findings offer a path of continued research toward tagging of Ba daughters in all significant sites in solid xenon. Published by the American Physical Society2024 

Electron-neutrino charged-current interactions with xenon nuclei were modeled in the nEXO neutrinoless double- $\beta$decay detector ( $\sim 5$metric ton, 90% ${}^{136}\mathrm{Xe}$, 10% ${}^{134}\mathrm{Xe}$) to evaluate its sensitivity to supernova neutrinos. Predictions for event rates and detectable signatures were modeled using the Model of Argon Reaction Low Energy Yields (MARLEY) event generator. We find good agreement between MARLEY’s predictions and existing theoretical calculations of the inclusive cross sections at supernova neutrino energies. The interactions modeled by MARLEY were simulated within the nEXO simulation framework and were run through an example reconstruction algorithm to determine the detector’s efficiency for reconstructing these events. The simulated data, incorporating the detector response, were used to study the ability of nEXO to reconstruct the incident electron-neutrino spectrum and these results were extended to a larger xenon detector of the same isotope enrichment. We estimate that nEXO will be able to observe electron-neutrino interactions with xenon from supernovae as far as 5–8 kpc from Earth, while the ability to reconstruct incident electron-neutrino spectrum parameters from observed interactions in nEXO is limited to closer supernovae. Published by the American Physical Society2024 

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.