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Context. One hypothesis for runaway stars (RSs) is that they are ejected from star clusters with high velocities relative to the cluster center-of-mass motion. There are two competing mechanisms for their production: supernova-based ejections in binaries, where one companion explodes, leaves no remnant, and launches the other companion at the instantaneous orbital velocity, and the disintegration of triples (or higher-order multiples), which produces a recoiled runaway binary (RB) and an RS. Aims. We search for RS candidates using data from the Gaia DR3 survey with a focus on triple disintegration since in this case the product is always a binary and a single star that should be moving in opposite directions. Methods. We created a systematic methodology to look for candidate RS-RB runaway pairs produced from the disintegration of bound three-body systems formed from single-binary interactions based on momentum conservation and causality. The method we use is general and can be applied to any cluster with a 5D kinematic data set. We used our criteria to search for these pairs in a 150 pc circular field of view surrounding the open cluster M67, which we used as a benchmark cluster to test the robustness of our method. Results. Our results reveal only one RS-RB pair that is consistent with all of our selection criteria out of an initial sample of 10⁸pairs. 

In this paper, we present findings of the entering mathematics identities and visions of high-quality mathematics teaching of Black freshman undergraduate preservice elementary teachers. Evidence suggests that race and racialized experiences impacted both their MIs and their vision for mathematics teaching, highlighting the need to attend to these constructs as we support the preparation of Black teachers who can flourish in their humanity and brilliance. 

In this paper, we present findings of the entering mathematics identities and visions of high-quality mathematics teaching of Black freshman undergraduate preservice elementary teachers. Evidence suggests that race and racialized experiences impacted both their MIs and their vision for mathematics teaching, highlighting the need to attend to these constructs as we support the preparation of Black teachers who can flourish in their humanity and brilliance. 

Many higher education institutions in the United States provide mathematics tutoring services for undergraduate students. These informal learning experiences generally result in increased final course grades (Byerly & Rickard, 2018; Rickard & Mills, 2018; Xu et al., 2014) and improved student attitudes toward mathematics (Bressoud et al., 2015). In recent years, research has explored the beliefs and practices of undergraduate and, sometimes graduate, peer tutors, both prior to (Bjorkman, 2018; Johns, 2019; Pilgrim et al., 2020) and during the COVID19 pandemic (Gyampoh et al., 2020; Mullen et al., 2021; Van Maaren et al., 2021). Additionally, Burks and James (2019) proposed a framework for Mathematical Knowledge for Tutoring Undergraduate Mathematics adapted from Ball et al. (2008) Mathematical Knowledge for Teaching, highlighting the distinction between tutor and teacher. The current study builds on this body of work on tutors’ beliefs by focusing on mathematical sciences graduate teaching assistants (GTAs) who tutored in an online setting during the 2020-2021 academic year due to the COVID-19 pandemic. Specifically, this study addresses the following research question: What were the mathematical teaching beliefs and practices of graduate student tutors participating in online tutoring sessions through the mathematics learning center (MLC) during the COVID-19 pandemic? 

Key Points We compared tools for describing streamflow timeseries, including streamflow metrics, wavelet, and Fourier analysis Each method indicated streamflow data are structured: variability at short timescales is negatively correlated with long timescales Globally, dams were less correlated with streamflow regime than catchment size and climate were 

Abstract Considering the growing interest in magnetic materials for unconventional computing, data storage, and sensor applications, there is active research not only on material synthesis but also characterisation of their properties. In addition to structural and integral magnetic characterisations, imaging of magnetisation patterns, current distributions and magnetic fields at nano- and microscale is of major importance to understand the material responses and qualify them for specific applications. In this roadmap, we aim to cover a broad portfolio of techniques to perform nano- and microscale magnetic imaging using superconducting quantum interference devices, spin centre and Hall effect magnetometries, scanning probe microscopies, x-ray- and electron-based methods as well as magnetooptics and nanoscale magnetic resonance imaging. The roadmap is aimed as a single access point of information for experts in the field as well as the young generation of students outlining prospects of the development of magnetic imaging technologies for the upcoming decade with a focus on physics, materials science, and chemistry of planar, three-dimensional and geometrically curved objects of different material classes including two-dimensional materials, complex oxides, semi-metals, multiferroics, skyrmions, antiferromagnets, frustrated magnets, magnetic molecules/nanoparticles, ionic conductors, superconductors, spintronic and spinorbitronic materials.