Comparison of two methods in the estimation of vertical jump height

Vertical jumps are vital aspects in many sports. Many technologies are available to determine and calculate jump height. One such portable and easy-to-use technology is an Inertial Measurement Unit (IMU) that uses accelerometers, gyroscopes and magnetometers. The purpose of this study was to compare vertical jump heights calculated from the data captured with an IMU versus true jump height calculated using a gold standard 3-Dimensional Motion Capture system. Ten subjects completed five jumps for six different conditions including vertical counter-movement jumps and jumps involving rotations on the ground and using a trampoline. An average Pearson correlation coefficient of 0.87 was found between the IMU and motion capture for all conditions. Condition correlations ranged from 0.76 to 0.94. Bland-Altman analyses showed that the IMU underestimated the vertical jump height compared to the motion capture by 5.0 to 9.2 cm across all conditions. Results suggest an IMU can be used to measure jump height in a laboratory setting with a reasonable accuracy, even during vertical jumps that include rotations

Revealing components of the galaxy population through nonparametric techniques

The distributions of galaxy properties vary with environment, and are often multimodal, suggesting that the galaxy population may be a combination of multiple components. The behaviour of these components versus environment holds details about the processes of galaxy development. To release this information we apply a novel, nonparametric statistical technique, identifying four components present in the distribution of galaxy H$\alpha$ emission-line equivalent-widths. We interpret these components as passive, star-forming, and two varieties of active galactic nuclei. Independent of this interpretation, the properties of each component are remarkably constant as a function of environment. Only their relative proportions display substantial variation. The galaxy population thus appears to comprise distinct components which are individually independent of environment, with galaxies rapidly transitioning between components as they move into denser environments.Comment: 12 pages, 10 figures, accepted for publication in MNRA

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages