29 research outputs found

    Etiquette for medical students email communication with faculty members: a single-institution study

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    This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Abstract Background Email is widely used as a means of communication between faculty members and students in medical education because of its practical and educational advantages. However, because of the distinctive nature of medical education, students inappropriate email etiquette may adversely affect their learning as well as faculty members perception of them. Little data on medical students competency in professional email writing is available; therefore, this study explored the strengths and weaknesses of medical students email etiquette and factors that contribute to professional email writing. Methods A total of 210 emails from four faculty members at Seoul National University College of Medicine were collected. An evaluation criteria and a scoring rubric were developed based on the various email-writing guidelines. The rubric comprised 10 items, including nine items for evaluation related to the email components and one item for the assessment of global impression of politeness. Three evaluators independently assessed all emails according to the criteria. Results Students were identified as being 61.0 % male and 52.8 % were in the undergraduate-entry program. The sum of each component score was 62.21 out of 100 and the mean value for global impression was 2.6 out of 4. The results demonstrated that students email etiquettes remained low-to-mediocre for most criteria, except for readability and honorifics. Three criteria, salutation (r=0.668), closing (r=0.653), and sign-off (r=0.646), showed a strong positive correlation with the global impression of politeness. Whether a student entered a graduate-entry program or an undergraduate-entry program significantly contributed to professional email writing after other variables were controlled. Conclusions Although students in the graduate-entry program demonstrated a relatively superior level of email etiquette, the majority of medical students did not write emails professionally. Educating all medical students in email etiquette may well contribute to the improvement of student–faculty relationships as well as their email writing

    Deep Underground Neutrino Experiment (DUNE), Far Detector Technical Design Report, Volume I Introduction to DUNE

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    International audienceThe preponderance of matter over antimatter in the early universe, the dynamics of the supernovae that produced the heavy elements necessary for life, and whether protons eventually decay—these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our universe, its current state, and its eventual fate. The Deep Underground Neutrino Experiment (DUNE) is an international world-class experiment dedicated to addressing these questions as it searches for leptonic charge-parity symmetry violation, stands ready to capture supernova neutrino bursts, and seeks to observe nucleon decay as a signature of a grand unified theory underlying the standard model. The DUNE far detector technical design report (TDR) describes the DUNE physics program and the technical designs of the single- and dual-phase DUNE liquid argon TPC far detector modules. This TDR is intended to justify the technical choices for the far detector that flow down from the high-level physics goals through requirements at all levels of the Project. Volume I contains an executive summary that introduces the DUNE science program, the far detector and the strategy for its modular designs, and the organization and management of the Project. The remainder of Volume I provides more detail on the science program that drives the choice of detector technologies and on the technologies themselves. It also introduces the designs for the DUNE near detector and the DUNE computing model, for which DUNE is planning design reports. Volume II of this TDR describes DUNE's physics program in detail. Volume III describes the technical coordination required for the far detector design, construction, installation, and integration, and its organizational structure. Volume IV describes the single-phase far detector technology. A planned Volume V will describe the dual-phase technology

    Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

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    International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/cc charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1±0.6\pm0.6% and 84.1±0.6\pm0.6%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

    Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

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    International audienceLiquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation

    Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora