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  1. Free, publicly-accessible full text available May 13, 2023
  2. Free, publicly-accessible full text available October 1, 2023
  3. This work in progress paper describes results from a NSF Research in the Formation of Engineers grant. The overarching objective of this research is to understand how framing engineering as an altruistic profession affects the engineering identity development of low socioeconomic status (SES) African American 8th - 10th grade students from an urban area within a predominantly rural Southern state. While there has been significant focus on increasing STEM knowledge and career interests for underrepresented minority (predominantly African American) low SES students from rural regions of these states, less focus has been paid to engineering specifically and to urban areas in this region. Little is known about how the intersections of race, poverty, local environment, and regional culture affect this group’s perceptions of potential engineering career pathways. This research seeks to understand the effects of different interventions on students’ self-efficacy and interest in engineering. In the first part, the effects of an existing Saturday STEM program were investigated. In the second part, the effects of a camp and mentoring program which highlights the positive societal impacts of engineering are being investigated. This paper highlights the structure of these programs and findings to date
  4. Abstract

    Significant investments to upgrade and construct large-scale scientific facilities demand commensurate investments in R&D to design algorithms and computing approaches to enable scientific and engineering breakthroughs in the big data era. Innovative Artificial Intelligence (AI) applications have powered transformational solutions for big data challenges in industry and technology that now drive a multi-billion dollar industry, and which play an ever increasing role shaping human social patterns. As AI continues to evolve into a computing paradigm endowed with statistical and mathematical rigor, it has become apparent that single-GPU solutions for training, validation, and testing are no longer sufficient for computational grand challenges brought about by scientific facilities that produce data at a rate and volume that outstrip the computing capabilities of available cyberinfrastructure platforms. This realization has been driving the confluence of AI and high performance computing (HPC) to reduce time-to-insight, and to enable a systematic study of domain-inspired AI architectures and optimization schemes to enable data-driven discovery. In this article we present a summary of recent developments in this field, and describe specific advances that authors in this article are spearheading to accelerate and streamline the use of HPC platforms to design and apply accelerated AI algorithms in academiamore »and industry.

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  5. Georeferencing is the process of aligning a text description of a geographic location with a spatial location based on a geographic coordinate system. Training aids are commonly created around the georeferencing process to disseminate community standards and ideas, guide accurate georeferencing, inform users about new tools, and help users evaluate existing geospatial data. The Georeferencing for Research Use (GRU) workshop was implemented as a training aid that focused on the creation and research use of geospatial coordinates, and included both data researchers and data providers, to facilitate communication between the groups. The workshop included 23 participants with a wide background of expertise ranging from students (undergraduate and graduate), professors, researchers and educators, scientific data managers, natural history collections personnel, and spatial analyst specialists. The conversations and survey results from this workshop demonstrate that it is important to provide opportunities for biocollections data providers to interact directly with the researchers using the data they produce and vice versa.