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1. Vortex Fiber Nulling for Exoplanet Observations: First Direct Detection of M Dwarf Companions around HIP 21543, HIP 94666, and HIP 50319

   https://doi.org/10.3847/2041-8213/ad3619  

   Echeverri, Daniel ; Xuan, Jerry W. ; Monnier, John D. ; Delorme, Jacques-Robert ; Wang, Jason J. ; Jovanovic, Nemanja ; Horstman, Katelyn ; Ruane, Garreth ; Mennesson, Bertrand ; Serabyn, Eugene ; et al ( April 2024 , The Astrophysical Journal Letters)

   Vortex fiber nulling (VFN) is a technique for detecting and characterizing faint companions at small separations from their host star. A near-infrared (∼2.3μm) VFN demonstrator mode was deployed on the Keck Planet Imager and Characterizer (KPIC) instrument at the Keck Observatory and presented earlier. In this Letter, we present the first VFN companion detections. Three targets, HIP 21543 Ab, HIP 94666 Ab, and HIP 50319 B, were detected with host–companion flux ratios between 70 and 430 at and within one diffraction beamwidth (λ/D). We complement the spectra from KPIC VFN with flux ratio and position measurements from the CHARA Array to validate the VFN results and provide a more complete characterization of the targets. This Letter reports the first direct detection of these three M dwarf companions, yielding their first spectra and flux ratios. Our observations provide measurements of bulk properties such as effective temperatures, radial velocities, and$v\sin i$, and verify the accuracy of the published orbits. These detections corroborate earlier predictions of the KPIC VFN performance, demonstrating that the instrument mode is ready for science observations.

    

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2. STEM Hip-hop Pedagogy: A Meta-synthesis on Hip-hop Pedagogy STEM Interventions Tools for Underrepresented Minorities in K-12 Education

   https://doi.org/10.1145/3299815.3314431  

   Cummings, Robert ; Chambers, Brittany ; Reid, Amber ; Gosha, Kinnis ( January 2019 , Proceedings of the 2019 ACM Southeast Conference)

   Hip-hop pedagogy is the use of hip-hop artifacts and culture as resources to a curriculum or instruction. Applying hip-hop pedagogy to academic interventions has been suggested to effectively increase the value and connection to course content as well as the ability to re-envision content into ones that reflect underrepresented students of color’s lived experiences. As there is a gap in African American and non White Latino American performance in STEM compared to their White and Asian American counterparts, and a high demand in prospective STEM occupations (namely computer and information technology), hip-hop pedagogy can be leveraged to improve STEM identity to successively improve STEM performance. This paper represents the first stage of a case study to analyze and validate hip-hop pedagogical tools for computing and STEM instruction and outreach. This paper describes a qualitative meta-synthesis of hip-hop pedagogy and associated STEM outreach programs. Findings on hip-hop pedagogy in computing and STEM outreach interventions are discussed to aid in supporting and creating future hip-hop pedagogical interventions. 

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3. Anticipation and Delayed Estimation of Sagittal Plane Human Hip Moments using Deep Learning and a Robotic Hip Exoskeleton

   https://doi.org/10.1109/ICRA48891.2023.10161286  

   Molinaro, Dean D. ; Park, Ethan O. ; Young, Aaron J. ( May 2023 , IEEE)

   Estimating human joint moments using wearable sensors has utility for personalized health monitoring and generalized exoskeleton control. Data-driven models have potential to map wearable sensor data to human joint moments, even with a reduced sensor suite and without subject-specific calibration. In this study, we quantified the RMSE and R 2 of a temporal convolutional network (TCN), trained to estimate human hip moments in the sagittal plane using exoskeleton sensor data (i.e., a hip encoder and thigh- and pelvis-mounted inertial measurement units). We conducted three analyses in which we iteratively retrained the network while: 1) varying the input sequence length of the model, 2) incorporating noncausal data into the input sequence, thus delaying the network estimates, and 3) time shifting the labels to train the model to anticipate (i.e., predict) human hip moments. We found that 930 ms of causal input data maintained model performance while minimizing input sequence length (validation RMSE and R 2 of 0.141±0.014 Nm/kg and 0.883±0.025, respectively). Further, delaying the model estimate by up to 200 ms significantly improved model performance compared to the best causal estimators (p<0.05), improving estimator fidelity in use cases where delayed estimates are acceptable (e.g., in personalized health monitoring or diagnoses). Finally, we found that anticipating hip moments further in time linearly increased model RMSE and decreased R 2 (p<0.05); however, performance remained strong (R 2 >0.85) when predicting up to 200 ms ahead. 

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4. The Effect of Hip Assistance Levels on Human Energetic Cost Using Robotic Hip Exoskeletons

   https://doi.org/10.1109/LRA.2019.2890896  

   Kang, Inseung ; Hsu, Hsiang ; Young, Aaron ( April 2019 , IEEE Robotics and Automation Letters)
5. Powered Hip Exoskeleton Reduces Residual Hip Effort Without Affecting Kinematics and Balance in Individuals With Above-Knee Amputations During Walking

   https://doi.org/10.1109/TBME.2022.3211842  

   Ishmael, Marshall K. ; Gunnell, Andrew ; Pruyn, Kai ; Creveling, Suzi ; Hunt, Grace ; Hood, Sarah ; Archangeli, Dante ; Foreman, K. Bo ; Lenzi, Tommaso ( April 2023 , IEEE Transactions on Biomedical Engineering)