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Abstract Robotic systems often struggle to adapt to dynamic, unstructured environments due to top-down design constraints based on human assumptions. Inspired by biological morphogenesis, this study introduces a cellular plasticity model based on Turing patterns, enabling multi-cellular robots to self-organize their cell phenotypes in response to environmental stimuli. The model leverages reaction-diffusion dynamics to capture key cellular plasticity phenomena observed in muscle cells, neurons, and stem cells. Analytical analysis explores equilibrium points, stability, and conditions for emergent Turing patterns, while simulations examine parametric influences on system behavior. Physical experiments with the Loopy platform demonstrate that its cells dynamically self-organize mechanical properties in response to behavioral and environmental demands. This response enables Loopy to achieve similar performance to empirically optimized static parameters in obstacle-free environments and outperform the static configuration in an environment with limited space. This work advances morphogenetic robotics, presenting a scalable framework for decentralized, dynamic adaptation in unmodeled environments. 

Quantitative reasoning is an essential learning objective of physics instruction. The Physics Inventory for Quantitative Literacy (PIQL) is a published assessment tool that has been developed for calculus-based physics courses to help instructors evaluate whether their students learn to reason this way. However, the PIQL is not appropriate for the large population of students taking physics who are not enrolled in, or have not completed, calculus. To address this need, we have developed the General Equation-based Reasoning inventory of QuaNtity (GERQN). The GERQN is an of the PIQL and is appropriate for most physics students; the only requirement is that students have taken algebra, so they are familiar with the use of variables, negative quantities, and linear functions. In this paper, we present the development and validation of the GERQN, and a short discussion on how the GERQN can be used by instructors to help their students learn. 

The Physics Inventory of Quantitative Literacy (PIQL) has been used to measure the development of students’ physics quantitative literacy in calculus-based introductory physics courses. Despite its effectiveness, issues persist regarding time constraints and potential memorization of items. We propose to split the PIQL into two shorter but statistically equivalent exams (PIQLets) in order to avoid these problems. Using a data set collected with the full PIQL, we created 480 theoretical PIQLet pairs containing different combinations of items. We provide evidence for the similarity of PIQLet pairs by calculating score differences, and comparing the distribution of item parameters calculated using item response theory. Our results demonstrate the feasibility of this approach for defining an equivalent pair of PIQLets using a limited data set from a single university. Additional analyses using a broader and more diverse data set will be required for more broadly applicable results. 

For this year’s career issue, LCGC North America teamed up with the American Chemical Society Subdivision on Chromatography and Separations Chemistry to ask the analytical chemistry community what skills new employees in the field need to succeed. In this report, we analyze the survey results and explore how they can inform the future of analytical chemistry curriculum development.