We discuss a new set of ~ 500 numerical n-body calculations designed to constrain the masses and bulk densities of Styx, Nix, Kerberos, and Hydra. Comparisons of different techniques for deriving the semimajor axis and eccentricity of the four satellites favor methods relying on the theory of Lee & Peale (2006), where satellite orbits are derived in the context of the restricted three body problem (Pluto, Charon, and one massless satellite). In each simulation, we adopt the nominal satellite masses derived in Kenyon & Bromley (2019b), multiply the mass of at least one satellite by a numerical factor f >= 1, and establish whether the system ejects at least one satellite on a time scale <= 4.5~Gyr. When the total system mass is large (f >> 1), ejections of Kerberos are more common. Systems with lower satellite masses (f ~ 1) usually eject Styx. In these calculations, Styx often signals an ejection by moving to higher orbital inclination long before ejection; Kerberos rarely signals in a useful way. The n-body results suggest that Styx and Kerberos are more likely to have bulk densities comparable with water ice, rho_SK <= 2 g/cm^3, than with rock. A strong upper limit on the total system mass, M_SNKH <= 9.5 x 10^19 g, also places robust constraints on the average bulk density of the four satellites, rho_SNKH <= 1.4 g/cm^3. These limits support models where the satellites grow out of icy material ejected during a major impact on Pluto or Charon.
This study of the role and impact of the subject selector in academic libraries is unique and long overdue. We focused on the Pac-12 university libraries, a representative sample of nationwide academic libraries. The strength of our investigation is this small, focused sample size and unique statistical analysis of subject specialists. There is a wide variety among these libraries with respect to the hiring requirements for MLIS, the MLIS with an additional advanced-subject master’s degree, and those libraries who hire non-MLIS librarians. This investigation has the possibility of promoting greater awareness for the future of subject specialists in academic libraries.
Objective: In 2018, the Network of the National Libraries of Medicine (NNLM) launched a national sponsorship program to support U.S. public library staff in completing the Medical Library Association’s (MLA) Consumer Health Information Specialization (CHIS). The primary objective of this research project was to determine if completion of the sponsored specialization was successful in improving public library staff ability to provide consumer health information and whether it resulted in new services, programming, or outreach activities at public libraries. Secondary objectives of this research were to determine motivation for and benefits of the specialization and to determine the impact on sponsorship on obtaining and continuing the specialization.
Methods: To evaluate the sponsorship program, we developed and administered a 16-question online survey via REDCap in August 2019 to 224 public library staff that were sponsored during the first year of the program. We measured confidence and competence in providing consumer health information using questions aligned with the eight Core Competencies for Providing Consumer Health Information Services . Additionally, the survey included questions about new consumer health information activities at public libraries, public library staff motivation to obtain the specialization, and whether it led to immediate career gains. To determine the overall value of the NNLM sponsorship, we measured whether funding made it more likely for participants to complete or continue the specialization.
Results: Overall, 136 participants (61%) responded to the survey. Our findings indicated that the program was a success: over 80% of participants reported an increase in core consumer health competencies, with a statistically significant improvement in mean competency scores after completing the specialization. Ninety percent of participants have continued their engagement with NNLM, and over half offered new health information programs and services at their public library. All respondents indicated that completing the specialization met their expectations, but few reported immediate career gains. While over half of participants planned to renew the specialization or obtain the more advanced, Level II specialization, 72% indicated they would not continue without the NNLM sponsorship.
Conclusion: Findings indicate that NNLM sponsorship of the CHIS specialization was successful in increasing the ability of public library staff to provide health information to their community. and This dataset represents the de-identified raw results of a 16-question, online survey (via REDCap) collected in August 2019 to 224 public library staff who were sponsored for a Consumer Health Information Specialization (CHIS). The purpose of the study was to determine whether the sponsorship program had an impact on public library staff to provide consumer health information.
Weather-related research often requires synthesizing vast amounts of data that need archival solutions that are both economical and viable during and past the lifetime of the project. Public cloud computing services (e.g., from Amazon, Microsoft, or Google) or private clouds managed by research institutions are providing object data storage systems appropriate for long-term archives of such large geophysical data sets. , Current Status:
Our research group no longer needs to maintain archives of High Resolution Rapid Refresh (HRRR) model output at the University of Utah since complete publicly-accessible archives of HRRR model output are now available from the Google Cloud Platform and Amazon Web Services (AWS) as part of the NOAA Open Data Program.
Google and AWS store the HRRR model output in GRIB2 format, a file type that efficiently stores hundreds of two-dimensional variable fields for a single valid time. Despite the highly compressible nature of GRIB2 files, they are often on the order of several hundred MB each, making high-volume input/output applications challenging due to the memory and compute resources needed to parse these files.
With support from the Amazon Sustainability Data Initiative, our group is now creating and maintaining HRRR model output in an optimized format, Zarr, in a publicly-accessible S3 bucket- hrrrzarr. HRRR-Zarr contains sets for each model run of analysis and forecast files sectioned into 96 small chunks for every variable. The structure of the HRRR-Zarr files are designed to allow users the flexibility to access only the data they need through selecting subdomains and parameters of interest without the overhead that comes from accessing numerous GRIB2 files.
This effort began in 2015 to illustrate the use of a private cloud object store developed by the Center for High Performance Computing (CHPC) at the University of Utah. We began archiving thousands of two-dimensional gridded fields (each one containing over 1.9 million values over the contiguous United States) from the High-Resolution Rapid Refresh (HRRR) data assimilation and forecast modeling system. The archive has been used for retrospective analyses of meteorological conditions during high-impact weather events, assessing the accuracy of the HRRR forecasts, and providing initial and boundary conditions for research simulations. The archive has been accessible interactively and through automated download procedures for researchers at other institutions that can be tailored by the user to extract individual two-dimensional grids from within the highly compressed files. Over a thousand users have voluntarily registered to use the HRRR archive at the University of Utah.
Our archive has grown to over 130 Tbytes of model output but we no longer need to continue that effort since the GRIB2 files are available now via Google and AWS. As mentioned above, we now provide much of the same information in an alternative format that is appropriate particularly for machine-learning applications.