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Abstract This paper outlines the potential gains for Constructionist research and praxis in modelling that might be obtained by recognising the power of the Patch—a humble computational being in the NetLogo modelling environment that has been overshadowed by its more popular fellow agent, the Turtle. To contextualise this opportunity, I describe how Constructionist modelling has thrived by promoting forms of learning that rely on learners’ identifying with agents. I argue that patches are a neglected agent type in this multi‐agent modelling tradition, and that the possibilities for learners to adopt the patch perspective in support of exploratory forms of modelling and aesthetic expression have been under‐researched. Nevertheless, I show there are a variety of powerful ways for learners––both individually and in groups––to identify with patches. I describe ongoing research showing how taking an aesthetic approach to patches has the potential to support individuals and groups in powerful forms of learning with and about multi‐agent modelling. Practitioner notesWhat is already known about this topicTurtles (movable agents in Logo and Constructionist environments descended from Logo) can be ‘transitional objects’ that provide learners a way to make powerful ideas their own.These agents can be powerful ‘objects‐to‐think‐with’ in large part because they encourage learners to identify with them in a form of learning known as ‘syntonic learning’.Expressive activities that draw on learners’aestheticinterests can support their learning with and about computational representations.Multi‐agent modelling is a powerful extension of Logo‐based learning environments that provides access to powerful ideas about complex systems and their emergent properties.In the multi‐agent setting, individual learners and/or groups of learners can identify syntonically with agents to provide entry points for reasoning about complexity.What this paper addsPatches (non‐movable agents in the NetLogo modelling environment) are under‐represented in the research on multi‐agent modelling, and the potential for learners to adopt the patches’ perspective has been neglected.An aesthetically driven approach to patches can ground students’ understanding of their expressive value.Participatory activities in which learners play the role of patches (called ‘Stadium Card’ activities) can ground the patch perspective, so that learners can achieve a form of syntonicity and/or collectively adopt the perspective of patches in the aggregate.Participatory activities that blend intrinsic and extrinsic perspectives on the patch grid can further enhance learners’ facility with programming for patches and their understanding of patches’ collective expressive power.Implications for practice and/or policyBalancing the focus between turtles and patches can enrich the modelling toolbox of learners new to agent‐based modelling.Patchesdocapture important aspects of individual and collective experience, and so can be good objects‐to‐think‐with, especially when conceptualising phenomena at a larger scale.The expressive potential of the patch grid is an important topic for computer science as well (eg, through 2D cellular automata). This is a rich context for learning in itself, which can be made accessible to groups of learners through physical or virtual participatory role‐play.Moreover, physical or virtual grids of people‐patches may exhibit novel aggregate computational properties that could in turn become interesting areas for research in computer science.
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Probing the active sites of oxide encapsulated electrocatalysts with controllable oxygen evolution selectivity
Electronic and ionic conductivity of an oxide overlayer can dictate the active site location, which can increase OER selectivity over competing reactions.
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- Award ID(s):
- 2011967
- PAR ID:
- 10590656
- Publisher / Repository:
- Royal Society of Chemistry
- Date Published:
- Journal Name:
- EES Catalysis
- Volume:
- 2
- Issue:
- 4
- ISSN:
- 2753-801X
- Page Range / eLocation ID:
- 953 to 967
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Key points The lack of formal training in peer review can be detrimental to the publishing activities of early career researchers, including graduate students and postdoctoral fellows.Lack of recognition of the work of early career researchers (ECRs) in review means that journals cannot easily identify them as potential reviewers in the future.Participation as peer‐reviewers increases understanding in early career researchers of their own writing and review process.ECRs can be highly motivated to volunteer for journal editorial work—Journal of Emerging Investigatorsencourages this by offering many different roles.Publishers that utilize and train early career researchers as peer‐reviewers can prevent poor reviewer practices.more » « less
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ObjectiveThis study aims to improve workers’ postures and thus reduce the risk of musculoskeletal disorders in human-robot collaboration by developing a novel model-free reinforcement learning method. BackgroundHuman-robot collaboration has been a flourishing work configuration in recent years. Yet, it could lead to work-related musculoskeletal disorders if the collaborative tasks result in awkward postures for workers. MethodsThe proposed approach follows two steps: first, a 3D human skeleton reconstruction method was adopted to calculate workers’ continuous awkward posture (CAP) score; second, an online gradient-based reinforcement learning algorithm was designed to dynamically improve workers’ CAP score by adjusting the positions and orientations of the robot end effector. ResultsIn an empirical experiment, the proposed approach can significantly improve the CAP scores of the participants during a human-robot collaboration task when compared with the scenarios where robot and participants worked together at a fixed position or at the individual elbow height. The questionnaire outcomes also showed that the working posture resulted from the proposed approach was preferred by the participants. ConclusionThe proposed model-free reinforcement learning method can learn the optimal worker postures without the need for specific biomechanical models. The data-driven nature of this method can make it adaptive to provide personalized optimal work posture. ApplicationThe proposed method can be applied to improve the occupational safety in robot-implemented factories. Specifically, the personalized robot working positions and orientations can proactively reduce exposure to awkward postures that increase the risk of musculoskeletal disorders. The algorithm can also reactively protect workers by reducing the workload in specific joints.more » « less
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Abstract Adaptive plasticity is expected to evolve when informative cues predict environmental variation. However, plastic responses can be maladaptive even when those cues are informative, if prediction mistakes are shared across members of a generation. These fitness costs can constrain the evolution of plasticity when initial plastic mutants use of cues of only moderate reliability. Here, we model the barriers to the evolution of plasticity produced by these constraints and show that dispersal across a metapopulation can overcome them. Constraints are also lessened, though not eliminated, when plastic responses are free to evolve gradually and in concert with increased reliability. Each of these factors be viewed as a form of bet-hedging: by lessening correlations in the fates of relatives, dispersal acts as diversifying bet-hedging, while producing submaximal responses to a cue can be understood as a conservative bet-hedging strategy. While poor information may constrain the evolution of plasticity, the opportunity for bet-hedging may predict when that constraint can be overcome. Abstract Populations may make bad predictions when when using partially reliable cues to track changing environments (left). These mistakes can render plasticity deleterious (s < 0); right) when cue reliability is low, but dispersal among demes spreads out the effects of mistakes and allows the evolution of adaptive plasticity.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/D4EY00074A
