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1. Numerical cognition needs more and better distinctions, not fewer

   https://doi.org/10.1017/S0140525X21001163  

   Barth, Hilary; Shusterman, Anna (January 2021, Behavioral and Brain Sciences)

   Abstract We agree that the approximate number system (ANS) truly represents number. We endorse the authors' conclusions on the arguments from confounds, congruency, and imprecision, although we disagree with many claims along the way. Here, we discuss some complications with the meanings that undergird theories in numerical cognition, and with the language we use to communicate those theories. 

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2. Quantum approximate optimization of the long-range Ising model with a trapped-ion quantum simulator

   https://doi.org/10.1073/pnas.2006373117  

   Pagano, Guido; Bapat, Aniruddha; Becker, Patrick; Collins, Katherine S.; De, Arinjoy; Hess, Paul W.; Kaplan, Harvey B.; Kyprianidis, Antonis; Tan, Wen Lin; Baldwin, Christopher; et al (October 2020, Proceedings of the National Academy of Sciences)

   null (Ed.)

   Quantum computers and simulators may offer significant advantages over their classical counterparts, providing insights into quantum many-body systems and possibly improving performance for solving exponentially hard problems, such as optimization and satisfiability. Here, we report the implementation of a low-depth Quantum Approximate Optimization Algorithm (QAOA) using an analog quantum simulator. We estimate the ground-state energy of the Transverse Field Ising Model with long-range interactions with tunable range, and we optimize the corresponding combinatorial classical problem by sampling the QAOA output with high-fidelity, single-shot, individual qubit measurements. We execute the algorithm with both an exhaustive search and closed-loop optimization of the variational parameters, approximating the ground-state energy with up to 40 trapped-ion qubits. We benchmark the experiment with bootstrapping heuristic methods scaling polynomially with the system size. We observe, in agreement with numerics, that the QAOA performance does not degrade significantly as we scale up the system size and that the runtime is approximately independent from the number of qubits. We finally give a comprehensive analysis of the errors occurring in our system, a crucial step in the path forward toward the application of the QAOA to more general problem instances. 

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3. Drop rebound at low Weber number

   https://doi.org/10.1017/jfm.2025.10589  

   Gabbard, Chase T; Agüero, Elvis A; Cimpeanu, Radu; Kuehr, Katharina; Silver, Eli; Barotta, Jack-William; Galeano-Rios, Carlos A; Harris, Daniel M (September 2025, Journal of Fluid Mechanics)

   We study the rebound of drops impacting non-wetting substrates at low Weber number (We) through experiment, direct numerical simulation and reduced-order modelling. Submillimetre-sized drops are normally impacted onto glass slides coated with a thin viscous film that allows them to rebound without contact line formation. Experiments are performed with various drop viscosities, sizes and impact velocities, and we directly measure metrics pertinent to spreading, retraction and rebound using high-speed imaging. We complement experiments with direct numerical simulation and a fully predictive reduced-order model that applies natural geometric and kinematic constraints to simulate the drop shape and dynamics using a spectral method. At low We, drop rebound is characterised by a weaker dependence of the coefficient of restitution on We than in the more commonly studied high-We regime, with nearly We-independent rebound in the inertio-capillary limit, and an increasing contact time as We decreases. Drops with higher viscosity or size interact with the substrate longer, have a lower coefficient of restitution and stop bouncing sooner, in good quantitative agreement with our reduced-order model. In the inertio-capillary limit, low-We rebound has nearly symmetric spreading and retraction phases and a coefficient of restitution near unity. Increasing We or viscosity breaks this symmetry, coinciding with a drop in the coefficient of restitution and an increased dependence on We. Lastly, the maximum drop deformation and spreading are related through energy arguments, providing a comprehensive framework for drop impact and rebound at low We. 

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4. Student perceptions of and learning in makerspaces embedded in their undergraduate engineering preparation programs

   Nadelson, L. S. (April 2020, ASEE annual conference)

   Building upon our two years of research on the use of makerspaces in undergraduate engineering programs, we engaged in a large-scale data collection from students enrolled in undergraduate engineering preparation programs with affiliated makerspaces established for a minimum of three years. Using web searches, and other sources of information (e.g. references from other researchers or faculty members), we have identified 28 institutions that met our criteria. Working with a third party, we gathered over 574 responses from undergraduate engineering students with makerspace experiences spread across the 28 institutions. To gather our data, we created and validated an online survey with a combination of quantitative and qualitative items. We constructed a survey with subscales aligned with motivation to learn, growth mindset, learning goal orientation, knowledge of engineering as a profession, and belongingness and inclusion, as associated with work within makerspaces. We found significant positive correlations among the variables, positive levels of motivation, growth mindset, knowledge of engineering as a profession, and belongingness. We found differences in levels for gender, engineering majors, and student class standing. We discuss the implications for our findings in the context of undergraduate engineering student learning in makerspaces. 

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5. Student perceptions of and learning in makerspaces embedded in their undergraduate engineering preparation programs.

   Nadelson, L. S.; Villanueva, I.; Bouwma-Gearhart, J.; Soto, E.; Lenhart, C.; Youmans, K.; & Choi, Y. H. (January 2020, Zone 1 Conference of the American Society for Engineering Education)

   Building upon our two years of research on the use of makerspaces in undergraduate engineering programs, we engaged in a large-scale data collection from students enrolled in undergraduate engineering preparation programs with affiliated makerspaces established for a minimum of three years. Using web searches, and other sources of information (e.g. references from other researchers or faculty members), we have identified 28 institutions that met our criteria. Working with a third party, we gathered over 574 responses from undergraduate engineering students with makerspace experiences spread across the 28 institutions. To gather our data, we created and validated an online survey with a combination of quantitative and qualitative items. We constructed a survey with subscales aligned with motivation to learn, growth mindset, learning goal orientation, knowledge of engineering as a profession, and belongingness and inclusion, as associated with work within makerspaces. We found significant positive correlations among the variables, positive levels of motivation, growth mindset, knowledge of engineering as a profession, and belongingness. We found differences in levels for gender, engineering majors, and student class standing. We discuss the implications for our findings in the context of undergraduate engineering student learning in makerspaces. 

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