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Artificial intelligence (AI) can be used to classify instruction-related activities from classroom videos. These AI models, however, are dependent on datasets that are used to train the model to recognize patterns and make predictions. Imbalances in datasets used for training—such as imbalances in the domain of mathematics featured in videos of classroom instruction—may bias a model’s performance, sometimes in unforeseen ways. In this study, we investigate whether an imbalanced training dataset with a disproportionate number of video recordings of lessons focused on Number and Operations and Algebra in elementary mathematics classrooms yielded differences in a model’s performance in other mathematical content domains. We analyze an AI model’s classification of 24 instructional activities and found a notable and unanticipated difference in the model’s performance for one of the mathematical content domains. 

Previous studies have shown that artificial intelligence can be used to classify instruction-related activities in classroom videos. The automated classi- fication of human activities, however, is vulnerable to biases in which the model performs substantially better or worse for different people groups. Although algo- rithmic bias has been highlighted as an important area for research in artificial intelligence in education, there have been few studies that empirically investigate potential bias in instruction-related activity recognition systems. In this paper, we report on an investigation of potential racial and skin tone biases in the automated classification of teachers’ activities in classroom videos. We examine whether a neural network’s classification of teachers’ activities differs with respect to teacher race and skin tone and whether differently balanced training datasets affect the performance of the neural network. Our results indicate that, under ordinary class- room lighting conditions, the neural network performs equally well regardless of teacher race or skin tone. Furthermore, our results suggest the balance of the training dataset with respect to teacher skin tone and race has a small—but not necessarily positive—effect on the neural network’s performance. Our study, how- ever, also suggests the importance of quality lighting for accurate classification of teacher-related instructional activities for teachers of color. We conclude with a discussion of our mixed findings, the limitations of our study, and potential directions for future research. 

The continental United States is well instrumented with facilities for mid‐latitude upper atmosphere research that operate on a continuous basis. In addition, citizen scientists provide a wealth of information when unusual events occur. We combine ionospheric total electron content (TEC) data from distributed arrays of GNSS receivers, magnetometer chains, and auroral observations obtained by citizen scientists, to provide a detailed view of the intense auroral breakup and westward surge occurring at the peak of the 10–11 May 2024 extreme geomagnetic storm. Over a 20‐min interval, vertical TEC (vTEC) increased at unusually low latitude (∼45°) and rapidly expanded azimuthally across the continent. Individual receiver/satellite data sets indicate sharp bursts of greatly elevated of vTEC (∼50 TECu). Intense red aurora was co‐located with the leading edge of the equatorward and westward TEC enhancements, indicating that the large TEC enhancement was created by extremely intense low‐energy precipitation during the rapid substorm breakup. 

$\text{DMRadio-}{\mathrm{m}}^3$is an experimental search for dark matter axions. It uses a solenoidal dc magnetic field to convert an axion dark-matter signal to an ac electromagnetic response in a coaxial copper pickup. The current induced by this axion signal is measured by dc SQUIDs. $\text{DMRadio-}{\mathrm{m}}^3$is designed to be sensitive to Kim-Shifman-Vainshtein-Zakharov (KSVZ) and Dine-Fischler-Srednicki-Zhitnisky (DFSZ) QCD axion models in the 10–200 MHz ( $41\mathrm{neV}/c^2–0.83\mathrm{\mu }\mathrm{eV}/c^2$) range, and to axions with $g_{a\gamma \gamma }=g_{a\gamma \gamma ,\mathrm{DFSZ}}\left(30\mathrm{MHz}\right)=1.87\times {10}^{-17}{\mathrm{GeV}}^{-1}$over 5–30 MHz as an extended goal. In this work, we present the electromagnetic modeling of the response of the experiment to an axion signal over the full frequency range of $\text{DMRadio-}{\mathrm{m}}^3$, which extends from the low-frequency, lumped-element limit to a regime where the axion Compton wavelength is only a factor of 2 larger than the detector size. With these results, we determine the live time and sensitivity of the experiment. The primary science goal of sensitivity to DFSZ axions across 30–200 MHz can be achieved with a $3\sigma$live scan time of 2.9 years. 

Plasma-based water purification involves the transport of reactive species across the gas–liquid interface. This process is limited by slow diffusion driven mass transport of reactive species across the interface. Additionally, the plasma gas–liquid contact area is typically limited, contributing to reduced dose delivery. These key factors make it difficult to scale up the treatment process to input flows of industrial interest. In this work, turbulence is explored as a means to introduce a fine grain structure, thus greatly increasing the interfacial surface area, leading to large property gradients and more efficient mass transport. Such a fine scale structure can also enhance the local electric field. The test apparatus explored in this work is the packed bed reactor that places thin water jets into contact with plasma. It is theorized that introducing turbulence, via increasing Reynolds number in such thin jets, may enhance the effective plasma dose at fixed plasma power. In this work, changes in the flow regime, from laminar to turbulent, of water jets in a packed bed water reactor (PBR) configuration are investigated experimentally. Methylene blue dye, a model contaminant, was tested in the PBR to demonstrate enhanced treatment via reduced treatment times. Plasma surface morphology around the jets noticeably changed with the flow regime, and turbulent flow demonstrated a faster hydrogen peroxide uptake, along with slower temperature, electrical conductivity, and a pH change in a batch treatment process, compared to laminar flow. The dye was destroyed significantly faster in the turbulent flow, indicating an increased effective plasma dose. 

Abstract Molecular dynamics (MD) has served as a powerful tool for designing materials with reduced reliance on laboratory testing. However, the use of MD directly to treat the deformation and failure of materials at the mesoscale is still largely beyond reach. In this work, we propose a learning framework to extract a peridynamics model as a mesoscale continuum surrogate from MD simulated material fracture data sets. Firstly, we develop a novel coarse-graining method, to automatically handle the material fracture and its corresponding discontinuities in the MD displacement data sets. Inspired by the weighted essentially non-oscillatory (WENO) scheme, the key idea lies at an adaptive procedure to automatically choose the locally smoothest stencil, then reconstruct the coarse-grained material displacement field as the piecewise smooth solutions containing discontinuities. Then, based on the coarse-grained MD data, a two-phase optimization-based learning approach is proposed to infer the optimal peridynamics model with damage criterion. In the first phase, we identify the optimal nonlocal kernel function from the data sets without material damage to capture the material stiffness properties. Then, in the second phase, the material damage criterion is learnt as a smoothed step function from the data with fractures. As a result, a peridynamics surrogate is obtained. As a continuum model, our peridynamics surrogate model can be employed in further prediction tasks with different grid resolutions from training, and hence allows for substantial reductions in computational cost compared with MD. We illustrate the efficacy of the proposed approach with several numerical tests for the dynamic crack propagation problem in a single-layer graphene. Our tests show that the proposed data-driven model is robust and generalizable, in the sense that it is capable of modeling the initialization and growth of fractures under discretization and loading settings that are different from the ones used during training. 

IMECE2022-88299 Midwest Engineered Systems Inc. has created a novel laser wire metal deposition process, ADDere manufacturing. ADDere has a much higher deposition rate than powder bed fusion, making it ideal for large components. In this project, the mechanical properties of ADDere printed materials were tested and compared to typical values found in ASM publications to show the quality of materials manufactured by the ADDere printing process. A detailed material analysis was performed on samples made from Ti-6Al-4V and 17-4 PH stainless steel. This work builds upon an earlier study of samples made from 17-4 PH that were produced using a single direction pattern. In this project, the 17-4 PH samples were printed in a cross hatched pattern, and testing results were compared to existing data from single direction samples of the previous research. The Ti-6Al-4V samples were created in two builds. One using the uni-directional method and the other with the crossed pattern. Testing specimens were removed from the samples using a water jet cutter and further machined into ASTM tensile bars and metallurgic mounts to perform a thorough material evaluation. The Ti-6Al-4V sample met the expected values in the ASM literature, and the cross hatched 17-4 PH exhibited a higher hardness and better microstructure than the single direction samples from the previous work. It was also observed that when the Ti64 samples were manufactured in the cross hatched pattern, the properties indicated slight improvement and more homogeneity than those printed in single layer direction. The obtained results indicate that ADDere’s printing process can produce highly refined materials that are customizable with their expected uses. This work showcases an excellent industry collaboration of an undergraduate research experience. 

Exploring dark matter via observations of extreme astrophysical environments -- defined here as heavy compact objects such as white dwarfs, neutron stars, and black holes, as well as supernovae and compact object merger events -- has been a major field of growth since the last Snowmass process. Theoretical work has highlighted the utility of current and near-future observatories to constrain novel dark matter parameter space across the full mass range. This includes gravitational wave instruments and observatories spanning the electromagnetic spectrum, from radio to gamma-rays. While recent searches already provide leading sensitivity to various dark matter models, this work also highlights the need for theoretical astrophysics research to better constrain the properties of these extreme astrophysical systems. The unique potential of these search signatures to probe dark matter adds motivation to proposed next-generation astronomical and gravitational wave instruments. Note: Contribution to Snowmass 2021 -- CF3. Dark Matter: Cosmic Probes