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This Innovative Practice Full Paper presents findings on the impact of framing Engineering as a prosocial career on high school students’ engineering identity formation. Engineers are often stereotyped as people who work alone and are primarily motivated by financial rewards. This stereotype may deter students who value altruism from pursuing engineering career pathways. In reality, many engineers work in collaborative, creative, interdisciplinary fields on problems that positively affect society. This work examined the impacts of framing engineering as altruistic on the engineering identity development of low socioeconomic status, predominantly Black high school students in an urban region of the Southern United States. The program consisted of a summer camp and academic year activities that included mentoring from underrepresented minority undergraduate engineering students. The program content was aligned to the US National Academy of Engineering’s Grand Challenges for Engineering (GCEs), a list of 14 critical challenges that society faces that will require engineering solutions to address. Each of these challenges highlights the exciting ways that a career in engineering allows students to serve their communities and improve the lives of others. A convergent, mixed-methods approach was used to understand how this program affected students’ perceptions of and interest in engineering. These results were compared to those for a traditional STEM Saturday informal education program with participants from the same demographic group. The altruistic framing resulted in students’ having a broader definition of engineering as well as increased interest in engineering as a potential career. 

The cause, or causes, of the Pleistocene megafaunal extinctions have been difficult to establish, in part because poor spatiotemporal resolution in the fossil record hinders alignment of species disappearances with archeological and environmental data. We obtained 172 new radiocarbon dates on megafauna from Rancho La Brea in California spanning 15.6 to 10.0 thousand calendar years before present (ka). Seven species of extinct megafauna disappeared by 12.9 ka, before the onset of the Younger Dryas. Comparison with high-resolution regional datasets revealed that these disappearances coincided with an ecological state shift that followed aridification and vegetation changes during the Bølling-Allerød (14.69 to 12.89 ka). Time-series modeling implicates large-scale fires as the primary cause of the extirpations, and the catalyst of this state shift may have been mounting human impacts in a drying, warming, and increasingly fire-prone ecosystem.

 

Darwin Core, the data standard used for sharing modern biodiversity and paleodiversity occurrence records, has previously lacked proper mechanisms for reporting what is known about the estimated age range of specimens from deep time. This has led to data providers putting these data in fields where they cannot easily be found by users, which impedes the reuse and improvement of these data by other researchers. Here we describe the development of the Chronometric Age Extension to Darwin Core, a ratified, community-developed extension that enables the reporting of ages of specimens from deeper time and the evidence supporting these estimates. The extension standardizes reporting about the methods or assays used to determine an age and other critical information like uncertainty. It gives data providers flexibility about the level of detail reported, focusing on the minimum information needed for reuse while still allowing for significant detail if providers have it. Providing a standardized format for reporting these data will make them easier to find and search and enable researchers to pinpoint specimens of interest for data improvement or accumulate more data for broad temporal studies. The Chronometric Age Extension was also the first community-managed vocabulary to undergo the new Biodiversity Informatics Standards (TDWG) review and ratification process, thus providing a blueprint for future Darwin Core extension development. 

Studies of horse evolution arose during the middle of the 19th century, and several hypotheses have been proposed for their taxonomy, paleobiogeography, paleoecology and evolution. The present contribution represents a collaboration of 19 multinational experts with the goal of providing an updated summary of Pliocene and Pleistocene North, Central and South American, Eurasian and African horses. At the present time, we recognize 114 valid species across these continents, plus 4 North African species in need of further investigation. Our biochronology and biogeography sections integrate Equinae taxonomic records with their chronologic and geographic ranges recognizing regional biochronologic frameworks. The paleoecology section provides insights into paleobotany and diet utilizing both the mesowear and light microscopic methods, along with calculation of body masses. We provide a temporal sequence of maps that render paleoclimatic conditions across these continents integrated with Equinae occurrences. These records reveal a succession of extinctions of primitive lineages and the rise and diversification of more modern taxa. Two recent morphological-based cladistic analyses are presented here as competing hypotheses, with reference to molecular-based phylogenies. Our contribution represents a state-of-the art understanding of Plio-Pleistocene Equus evolution, their biochronologic and biogeographic background and paleoecological and paleoclimatic contexts. 

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 

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 academia and industry.