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  1. Free, publicly-accessible full text available February 1, 2023
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  4. Over the past two decades, educators have used computer-supported collaborative learning (CSCL) to integrate technology with pedagogy to improve student engagement and learning outcomes. Researchers have also explored the diverse affordances of CSCL, its contributions to engineering instruction, and its effectiveness in K-12 STEM education. However, the question of how students use CSCL resources in undergraduate engineering classrooms remains largely unexplored. This study examines the affordances of a CSCL environment utilized in a sophomore dynamics course with particular attention given to the undergraduate engineering students’ use of various CSCL resources. The resources include a course lecturebook, instructor office hours, amore »teaching assistant help room, online discussion board, peer collaboration, and demonstration videos. This qualitative study uses semi-structured interview data collected from nine mechanical engineering students (four women and five men) who were enrolled in a dynamics course at a large public research university in Eastern Canada. The interviews focused on the individual student’s perceptions of the school, faculty, students, engineering courses, and implemented CSCL learning environment. The thematic analysis was conducted to analyze the transcribed interviews using a qualitative data analysis software (Nvivo). The analysis followed a six step process: (1) reading interview transcripts multiple times and preliminary in vivo codes; (2) conducting open coding by coding interesting or salient features of the data; (3) collecting codes and searching for themes; (4) reviewing themes and creating a thematic map; (5) finalizing themes and their definitions; and (6) compiling findings. This study found that the students’ use of CSCL resources varied depending on the students’ personal preferences, as well as their perceptions of the given resource’s value and its potential to enhance their learning. For example, the dynamics lecturebook, which had been redesigned to encourage problem solving and note-taking, fostered student collaborative problem solving with their peers. In contrast, the professor’s example video solutions had much more of an influence on students’ independent problem-solving processes. The least frequently used resource was the course’s online discussion forum, which could be used as a means of communication. The findings reveal how computer-supported collaborative learning (CSCL) environments enable engineering students to engage in multiple learning opportunities with diverse and flexible resources to both address and to clarify their personal learning needs. This study strongly recommends engineering instructors adapt a CSCL environment for implementation in their own unique classroom context.« less
  5. Free, publicly-accessible full text available December 16, 2022
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  7. Abstract Superconductivity is among the most fascinating and well-studied quantum states of matter. Despite over 100 years of research, a detailed understanding of how features of the normal-state electronic structure determine superconducting properties has remained elusive. For instance, the ability to deterministically enhance the superconducting transition temperature by design, rather than by serendipity, has been a long sought-after goal in condensed matter physics and materials science, but achieving this objective may require new tools, techniques and approaches. Here, we report the transmutation of a normal metal into a superconductor through the application of epitaxial strain. We demonstrate that synthesizing RuOmore »2 thin films on (110)-oriented TiO 2 substrates enhances the density of states near the Fermi level, which stabilizes superconductivity under strain, and suggests that a promising strategy to create new transition-metal superconductors is to apply judiciously chosen anisotropic strains that redistribute carriers within the low-energy manifold of d orbitals.« less
    Free, publicly-accessible full text available December 1, 2022
  8. Bartoli, A ; Fusiello, A (Ed.)
    We propose an improved discriminative model prediction method for robust long-term tracking based on a pre-trained short-term tracker. The baseline pre-trained short-term tracker is SuperDiMP which combines the bounding-box regressor of PrDiMP with the standard DiMP classifier. Our tracker RLT-DiMP improves SuperDiMP in the follow- ing three aspects: (1) Uncertainty reduction using random erasing: To make our model robust, we exploit an agreement from multiple im- ages after erasing random small rectangular areas as a certainty. And then, we correct the tracking state of our model accordingly. (2) Ran- dom search with spatio-temporal constraints: we propose a robust ran- dommore »search method with a score penalty applied to prevent the prob- lem of sudden detection at a distance. (3) Background augmentation for more discriminative feature learning: We augment various backgrounds that are not included in the search area to train a more robust model in the background clutter. In experiments on the VOT-LT2020 bench- mark dataset, the proposed method achieves comparable performance to the state-of-the-art long-term trackers. The source code is available at: https://github.com/bismex/RLT-DIMP.« less
  9. Aims. Thanks to the high angular resolution, sensitivity, image fidelity, and frequency coverage of ALMA, we aim to improve our understanding of star formation. One of the breakthroughs expected from ALMA, which is the basis of our Cycle 5 ALMA-IMF Large Program, is the question of the origin of the initial mass function (IMF) of stars. Here we present the ALMA-IMF protocluster selection, first results, and scientific prospects. Methods. ALMA-IMF imaged a total noncontiguous area of ~53 pc 2 , covering extreme, nearby protoclusters of the Milky Way. We observed 15 massive (2.5 −33 × 10 3 M ⊙ ),more »nearby (2−5.5 kpc) protoclusters that were selected to span relevant early protocluster evolutionary stages. Our 1.3 and 3 mm observations provide continuum images that are homogeneously sensitive to point-like cores with masses of ~0.2 M ⊙ and ~0.6 M ⊙ , respectively, with a matched spatial resolution of ~2000 au across the sample at both wavelengths. Moreover, with the broad spectral coverage provided by ALMA, we detect lines that probe the ionized and molecular gas, as well as complex molecules. Taken together, these data probe the protocluster structure, kinematics, chemistry, and feedback over scales from clouds to filaments to cores. Results. We classify ALMA-IMF protoclusters as Young (six protoclusters), Intermediate (five protoclusters), or Evolved (four proto-clusters) based on the amount of dense gas in the cloud that has potentially been impacted by H  II region(s). The ALMA-IMF catalog contains ~700 cores that span a mass range of ~0.15 M ⊙ to ~250 M ⊙ at a typical size of ~2100 au. We show that this core sample has no significant distance bias and can be used to build core mass functions (CMFs) at similar physical scales. Significant gas motions, which we highlight here in the G353.41 region, are traced down to core scales and can be used to look for inflowing gas streamers and to quantify the impact of the possible associated core mass growth on the shape of the CMF with time. Our first analysis does not reveal any significant evolution of the matter concentration from clouds to cores (i.e., from 1 pc to 0.01 pc scales) or from the youngest to more evolved protoclusters, indicating that cloud dynamical evolution and stellar feedback have for the moment only had a slight effect on the structure of high-density gas in our sample. Furthermore, the first-look analysis of the line richness toward bright cores indicates that the survey encompasses several tens of hot cores, of which we highlight the most massive in the G351.77 cloud. Their homogeneous characterization can be used to constrain the emerging molecular complexity in protostars of high to intermediate masses. Conclusions. The ALMA-IMF Large Program is uniquely designed to transform our understanding of the IMF origin, taking the effects of cloud characteristics and evolution into account. It will provide the community with an unprecedented database with a high legacy value for protocluster clouds, filaments, cores, hot cores, outflows, inflows, and stellar clusters studies.« less
    Free, publicly-accessible full text available June 1, 2023