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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Clump survival and migration in VDI galaxies: an analytical model versus simulations and observations

ABSTRACT We address the nature of the giant clumps in high-z galaxies that undergo violent disc instability, distinguishing between long-lived and short-lived clumps. We study the evolution of long-lived clumps during migration through the disc via an analytical model tested by simulations and confront theory with CANDELS-HST observations. The clump ‘bathtub’ model, which considers gas and stellar gain and loss, involves four parameters: the accretion efficiency α, the star formation rate (SFR) efficiency ϵd, and the outflow mass-loading factors for gas and stars, η and ηs. The corresponding time-scales are comparable to the migration time, two-three orbital times. The accretion-rate dependence on clump mass, gas, and stars, allows an analytical solution involving exponential growing and decaying modes. For the fiducial parameter values there is a main evolution phase where the SFR and gas mass are constant and the stellar mass is rising linearly with time. This makes the inverse specific SFR an observable proxy for clump age. When η or ϵd are high, or α is low, the decaying mode induces a decline of SFR and gas mass till the migration ends. Later, the masses and SFR approach an hypothetical exponential growth with a constant specific SFR. The model matches simulations with different, moderate feedbacks, both in isolated and cosmological settings. The observed clumps agree with our predictions, indicating that the massive clumps are long-lived and migrating. A challenge is to model feedback that is non-disruptive in massive clumps but suppresses SFR to match the galactic stellar-to-halo mass ratio.

Dekel, Avishai↗
DOE OSTI2024

Some entanglement survives most measurements

To prepare quantum states and extract information, it is often assumed that one can perform a perfectly projective measurement. Such measurements can achieve an uncorrelated system and environment state. However, perfectly projective measurements can be difficult or impossible to perform in practice. Here, we investigate the limitations of repeated nonprojective measurements in preparing a quantum system. For an n-qubit system initially entangled with its environment and subsequently prepared with measurements, using a sequence of weak measurements, we show that some entanglement remains unless one of the measurement operators becomes perfectly projective through an extreme limiting process. Removing initial (unentangled) correlations between a system and its environment and the scenario where measurement outcomes are not tracked are also discussed. We present results for n-qubit and n-dimensional input states.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

RecJ3/4-aRNase J form a Ubl-associated nuclease complex functioning in survival against DNA damage in Haloferax volcanii

Nucleases are strictly regulated and often localized in the cell to avoid the uncontrolled degradation of DNA and RNA. Here, a new type of nuclease complex, composed of RecJ3, RecJ4, and aRNase J, was identified through its ATP-dependent association with the ubiquitin-like SAMP1 and AAA-ATPase Cdc48a. The complex was discovered in Haloferax volcanii , an archaeon lacking an RNA exosome. Genetic analysis revealed aRNase J to be essential and RecJ3, RecJ4, and Cdc48a to function in the recovery from DNA damage including genotoxic agents that generate double-strand breaks. The RecJ3:RecJ4:aRNase J complex (isolated in 2:2:1 stoichiometry) functioned primarily as a 3′−5′ exonuclease in hydrolyzing RNA and ssDNA, with the mechanism non-processive for ssDNA. aRNase J could also be purified as a homodimer that catalyzed endoribonuclease activity and, thus, was not restricted to the 5′−3′ exonuclease activity typical of aRNase J homologs. Moreover, RecJ3 and RecJ4 could be purified as a 560-kDa subcomplex in equimolar subunit ratio with nuclease activities mirroring the full RecJ3/4-aRNase J complex. These findings prompted reconstitution assays that suggested RecJ3/4 could suppress, alter, and/or outcompete the nuclease activities of aRNase J. Based on the phenotypic results, this control mechanism of aRNase J by RecJ3/4 is not necessary for cell growth but instead appears important for DNA repair. IMPORTANCE Nucleases are critical for various cellular processes including DNA replication and repair. Here, a dynamic type of nuclease complex is newly identifiedidentifiedidentifiedin the archaeon Haloferax volcanii , which is missing the canonical RNA exosome. The complex, composed of RecJ3, RecJ4, and aRNase J, functions primarily as a 3′−5′ exonuclease and was discovered through its ATP-dependent association with the ubiquitin-like SAMP1 and Cdc48a. aRNase J alone forms a homodimer that has endonuclease function and, thus, is not restricted to 5′−3′ exonuclease activity typical of other aRNase J enzymes. RecJ3/4 appears to suppress, alter, and/or outcompete the nuclease activities of aRNase J. While aRNase J is essential for growth, RecJ3/4, Cdc48a, and SAMPs are important for recovery against DNA damage. These biological distinctions may correlate with the regulated nuclease activity of aRNase J in the RecJ3/4-aRNaseJ complex.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

A Novel N Staging System for Predicting Survival in Patients with Medullary Thyroid Cancer

Introduction: Despite the crucially prognostic value of lymph node metastasis (LNM) in patients with medullary thyroid cancer (MTC), only the LNM compartment alone was reflected in the 8th edition of the American Joint Committee on Cancer (AJCC) system. Objective: This study aimed to incorporate the metastatic lymph node number and metastatic lymph node ratio to generate a more accurate and appropriate N staging system for patients with MTC based on recursive partitioning analysis. Design, Setting, and Patients: Two cohorts were included in the analysis, including 1374 MTC patients from the Surveillance, Epidemiology, and End Results database as the derivation cohort, and 164 patients from Fudan University Shanghai Cancer Center as the validation cohort. The predictive performance of the alternative proposed N staging system was compared with that of the 8th AJCC system by using the Harrell concordance index (C-index) and the area under the receiver operating characteristic curve (AUC). Results: In the derivation cohort, the C-index and the AUC at 10 years were 0.778 and 0.789, respectively, for the novel N staging system, and 0.749 and 0.741, respectively, for the 8th AJCC N staging system. Similar trends were also observed in the validation cohort. The proposed N staging system had a better prognostic performance. Conclusion: With some improvements, the novel N staging system for MTC suggested from this research may be assessed for potential adoption in the next edition of the AJCC N staging system.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Experiments and modelling of adhesive failure initiation of an epoxy underfill: a study of electronic packaging survivability

Epoxy underfills can be implemented in electronic packaging to enhance solder joint reliability of surface mounted components. However, it is important for an engineer to have a failure criterion that can be used for failure predictions and redesign of electronic assemblies. For this study, data from epoxy bond failure in mock electronic part assemblies were correlated to finite element analyses to predict adhesive failure initiation. Experiments were performed to determine failure loads for various loading locations and nonlinear viscoelastic analyses were performed for the same loading locations to determine a maximum principal strain failure parameter. Predictions showed that a maximum principal strain failure parameter defined from one test could be used as an indicator of adhesive failure of an epoxy bond undergoing other modes of loading. Failure initiation predictions matched experimental data using a maximum principal strain failure parameter for an epoxy bond undergoing mixed modes of loading for both unfilled and alumina oxide filled 828DEA epoxy. Such experimental setup is deemed appropriate for future epoxy testing.

42 ENGINEERING↗
DOE OSTI2024

Survival and Age at Maturity in Head-Started Wood Turtles ( Glyptemys insculpta ) with Implications for Population Recovery

A small relict population of Glyptemys insculpta (Wood Turtle) was discovered on a protected area in New Jersey in 2006. Marking and radio-tracking of the old founder individuals helped to determine movement patterns and habitat use. Monitoring of nesting females revealed that nesting habitat and nest success was limited due to invasive plants, human landscape alteration, and Procyon lotor (Raccoon) depredation. We initiated habitat restoration including creation of protected nesting areas, mowing in winter, invasive plant removal, and adjacent landowner education. Here, we direct-released hatchlings from protected nests from 2006 to 2015, yet only a few were detected in subsequent years. Between 2011 and 2023, some or all of each hatchling cohort were head-started indoors at a high school for 9 months. We have continuously radio-tracked all head-starts from the 2011 cohort and portions of the 2012–2014 cohorts. Head-started turtles found their own food, established home ranges, and hibernated communally with founder adults. Subsidized Raccoons, mowers, automobiles, and flooding events—all human-instigated—were the causes of mortality. The first males and first females from the 2011 head-start cohort reached maturity in 2017 and 2019, respectively, at ages 6–8, younger than expected by 4–5 years. Successful reproduction by head-starts was confirmed by viable hatchlings produced in 2019, 2020, and 2023. Head-starting can pull a relict population out of the nose-dive to extirpation when used in conjunction with habitat-restoration practices, but it must be conducted with persistence and continuity over at least the number of years it takes for the earliest cohorts to reach maturity and begin producing offspring of their own.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Eject, crash, or survive: Using machine learning to predict orbital instability of exoplanetary systems

Astronomers throughout history, including titans like Kepler and Newton, have tackled planetary dynamics and orbital instability. Despite strides taken in research, understanding the evolution of planetary orbits remains an intricate, computationally expensive, and analytically unsolved problem. I apply machine learning classification methods to numerical simulations of planetary systems in order to predict the long-term fate of the planet - whether the planet remains in a stable orbit or not. My method uses the first 41.1 years (≤ 500 orbits) of data from a planet’s simulation to calculate 17 dynamically-motivated metrics; I trained my classifier on these features to predict a planet’s stability after 107 years. At 84.33%, my classifier was comparable in accuracy to pre-existing literature, despite using significantly less computational power than most other methods. In my research, I found that the standard deviation of eccentricity, mass ratios for neighboring planets, and semi-major axis ratio with the outer planet neighbor to be the most predictive features of instability. I propose reasons for the importance of these features, their role in planetary dynamics, as well as possible explanations for why some planets were misclassified. By understanding the important metrics of instability and reasons for misclassification, we can begin to understand more about system architectures, orbital motion and dynamics, and the formation and evolution of the exoplanetary systems. This is applicable in our own Solar System, and with exoplanet discovery missions such as TESS, this research becomes especially relevant in understanding the new exoplanetary systems we discover.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Eject, crash, or survive: Using machine learning to predict orbital instability of exoplanetary systems

Astronomers throughout history, including titans like Kepler and Newton, have tackled planetary dynamics and orbital instability. Despite strides taken in research, understanding the evolution of planetary orbits remains an intricate, computationally expensive, and analytically unsolved problem. I apply machine learning classification methods to numerical simulations of planetary systems in order to predict the long-term fate of the planet - whether the planet remains in a stable orbit or not. My method uses the first 41.1 years (≤ 500 orbits) of data from a planet’s simulation to calculate 17 dynamically-motivated metrics; I trained my classifier on these features to predict a planet’s stability after 107 years. At 84.33%, my classifier was comparable in accuracy to pre-existing literature, despite using significantly less computational power than most other methods. In my research, I found that the standard deviation of eccentricity, mass ratios for neighboring planets, and semi-major axis ratio with the outer planet neighbor to be the most predictive features of instability. I propose reasons for the importance of these features, their role in planetary dynamics, as well as possible explanations for why some planets were misclassified. By understanding the important metrics of instability and reasons for misclassification, we can begin to understand more about system architectures, orbital motion and dynamics, and the formation and evolution of the exoplanetary systems. This is applicable in our own Solar System, and with exoplanet discovery missions such as TESS, this research becomes especially relevant in understanding the new exoplanetary systems we discover.

79 ASTRONOMY AND ASTROPHYSICS↗