Engineering topics

Dauelsberg, Lori Rose

Publications and source records attributed to Dauelsberg, Lori Rose.

3 records

DOE OSTI2024

Discovery of 15 A-F Main Sequence Pulsating Star Candidates

Delta Scuti stars, spanning spectral types A to F with masses between 1.5 and 2.5 solar masses, exhibit a complex mix of radial and non-radial pressure modes. This study focuses on 15 Delta Scuti, Gamma Doradus, and Hybrid candidate stars using TESS satellite photometry data with a time series of exposures ranging from 20 to 600 seconds across different sky regions. Employing the prewhitening technique via fast Fourier transforms, significant frequencies are extracted from light curves. Three approximation methods are used to estimate critical stellar parameters such as density, radius, and mass. This research also verifies the variability of these stars, which are currently not classified as variable in databases like SIMBAD.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Research on Remote and Hybrid Scientific Work: A Literature Review

This literature review is divided into two parts. The first part looks at new developments and emerging research specifically related to remote work since the start of the COVID-19 pandemic. This encompasses research and reporting on the impacts of COVID-19 on the workplace, more speculative writing on the possible future of remote and hybrid work, and impacts of remote work during COVID-19 for diversity, equity, and inclusion. The second part focuses on more fundamental research done on remote collaboration and remote work tools prior to COVID-19, which addresses in more detail how different types, aspects, or stages of work can best be supported using virtual collaboration tools. In both sections, we review literature that directly focuses on remote scientific collaboration, which is somewhat limited, as well as the broader literature on remote and hybrid work, which is relevant to a wide variety of workplaces, including scientific ones.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2021

EpiCast: Simulating Epidemics with Extreme Detail

In early 2020, COVID-19 swept the globe. Governments attempted to “flatten the curve” through business shutdowns and stay-at-home orders, but the United States was hit hard. By the end of March, mere months after the virus first emerged in humans 7,000 miles away, the U.S. had recorded 192,300 cases and 5,300 deaths. While this unprecedented disaster sent shockwaves through every level of society and clouded an uncertain future, state and local governments turned to computational and mathematical epidemiology researchers to help formulate intervention strategies to limit the spread of the disease. Traditional forecasting models provided a reasonable understanding of how the near future was likely to look, but local policy makers and public health communities still struggled to understand how potential mitigations ought to be implemented. Decision makers needed a way to measure the impact of their policy choices—they needed better technology. EpiCast answered the call, bringing urgently needed answers to policymakers grappling with how to adjust school and business schedules. EpiCast is modeling software that generates a synthetic, representative population to simulate infectious disease transmission in the United States with extreme detail and granularity. The software models human behavior combined with community-specific information to provide a fine-grained preview of the effect of potential mitigation strategies for decision makers.

60 APPLIED LIFE SCIENCES↗