Shuttle Program Loads Integration: Going From Concept to Operations and Staying Successful

From the beginning of the Shuttle Program to its end, integrated loads and dynamics analyses and tests have been critical in shaping the vehicle design and operational decisions for NASA and its customers. Starting with scaled models and simple mathematical simulations of the structural dynamics, engineers defined the required structural stiffness and predicted the limit loads for each element of the system. Early structural tests provided reasonable confidence that the models and predictions were good. The first launch of the Space Shuttle brought surprises, though, when the ignition overpressure event caused a forward fuel tank support strut to buckle, among several unexpected effects. The launch pad and other ground equipment became an integral part of the system integration, especially where the acoustic and pressure environments of ignition and lift-off were concerned. Following the Challenger accident, operating limits were changed in response to new understandings of how the integrated system performed. Controlling loads while maximizing performance was a key tenet of the Performance Enhancement design process, which enabled construction of the International Space Station. During the return to flight after the Columbia accident, engineers grew to understand that loads during the roll maneuver were also important to the vehicle s structural margin and life. At this point the crawler transport from the Vehicle Assembly Building to the launch pad also became a part of the integrated loads analysis. Even in the last years of the Space Shuttle Program, new data still provided interesting insights into this complicated and fascinating spaceship. This paper will present some examples of the important findings by the team of specialists that supported the Integrated Loads and Dynamics Panel for the Space Shuttle Program

The Habitable Exoplanet Observatory (HabEx) Mission Concept Study Final Report

The Habitable Exoplanet Observatory, or HabEx, has been designed to be the Great Observatory of the 2030s. For the first time in human history, technologies have matured sufficiently to enable an affordable space-based telescope mission capable of discovering and characterizing Earthlike planets orbiting nearby bright sunlike stars in order to search for signs of habitability and biosignatures. Such a mission can also be equipped with instrumentation that will enable broad and exciting general astrophysics and planetary science not possible from current or planned facilities. HabEx is a space telescope with unique imaging and multi-object spectroscopic capabilities at wavelengths ranging from ultraviolet (UV) to near-IR. These capabilities allow for a broad suite of compelling science that cuts across the entire NASA astrophysics portfolio. HabEx has three primary science goals: (1) Seek out nearby worlds and explore their habitability; (2) Map out nearby planetary systems and understand the diversity of the worlds they contain; (3) Enable new explorations of astrophysical systems from our own solar system to external galaxies by extending our reach in the UV through near-IR. This Great Observatory science will be selected through a competed GO program, and will account for about 50% of the HabEx primary mission. The preferred HabEx architecture is a 4m, monolithic, off-axis telescope that is diffraction-limited at 0.4 microns and is in an L2 orbit. HabEx employs two starlight suppression systems: a coronagraph and a starshade, each with their own dedicated instrument

The Habitable Exoplanet Observatory (HabEx) Mission Concept Study Final Report

The Habitable Exoplanet Observatory, or HabEx, has been designed to be the Great Observatory of the 2030s. For the first time in human history, technologies have matured sufficiently to enable an affordable space-based telescope mission capable of discovering and characterizing Earthlike planets orbiting nearby bright sunlike stars in order to search for signs of habitability and biosignatures. Such a mission can also be equipped with instrumentation that will enable broad and exciting general astrophysics and planetary science not possible from current or planned facilities. HabEx is a space telescope with unique imaging and multi-object spectroscopic capabilities at wavelengths ranging from ultraviolet (UV) to near-IR. These capabilities allow for a broad suite of compelling science that cuts across the entire NASA astrophysics portfolio. HabEx has three primary science goals: (1) Seek out nearby worlds and explore their habitability; (2) Map out nearby planetary systems and understand the diversity of the worlds they contain; (3) Enable new explorations of astrophysical systems from our own solar system to external galaxies by extending our reach in the UV through near-IR. This Great Observatory science will be selected through a competed GO program, and will account for about 50% of the HabEx primary mission. The preferred HabEx architecture is a 4m, monolithic, off-axis telescope that is diffraction-limited at 0.4 microns and is in an L2 orbit. HabEx employs two starlight suppression systems: a coronagraph and a starshade, each with their own dedicated instrument.Comment: Full report: 498 pages. Executive Summary: 14 pages. More information about HabEx can be found here: https://www.jpl.nasa.gov/habex

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

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

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders