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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Pececillo

Pececillo is a proxy-app for the open source Truchas metal processing code (LA-CC-15-097). It implements many of the physics models used in Truchas: free-surface, incompressible Navier-Stokes fluid dynamics (e.g., water waves); heat transport, material phase change, view factor thermal radiation; species advection-diffusion; quasi-static, elastic/plastic solid mechanics with contact; electomagnetics (Maxwell's equations). The models are simplified versions that retain the fundamental computational complexity of the Truchas models while omitting many non-essential features and modeling capabilities. The purpose is to expose Truchas algorithms in a greatly simplified context where computer science problems related to parallel performance on advanced architectures can be more easily investigated. While Pececillo is capable of performing simulations representative of typical Truchas metal casting, welding, and additive manufacturing simulations, it lacks many of the modeling capabilites needed for real applications.

Carlson, Neil↗
DOE OSTI2018

2018 Technology Innovation Program Heartbeat

Heartbeat responds to this problem by focusing instead on the physical behavior of the device being protected, under the hypothesis that malware infection will produce a measurable change in the power consumption state of a device that can be picked up by an outside detector. All code execution uses power, so the execution of malware—especially polymorphic variants—will leave a trace on a power consumption record. The Heartbeat system collects power trace measurements directly from the hardware and so is invisible to malware and resilient to internet service interruption. By collecting power measurement data only during the periodic invocation of a single or of several system functions, Heartbeat will address several challenges that plague current anomaly-based intrusion detection systems and is operational-context agnostic.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Exploiting Literature-Based Discovery to Study Effects of Bullying

Bullying represents an aggressive behavioral problem. It can create severe mental illness in children, adolescents, and adults. Researchers are interested in studying bullying to understand its long-term negative consequences and design better-targeted intervention programs. Broadly researchers in psychology and education have conducted studies on bullying without using advanced analytical features offered by the computer science domain. However, researchers could benefit from power of advanced analytics for an in-depth understanding of bullying. In this paper, we present graph-based advanced analytics by employing a literature-based discovery (LBD) approach to study the direct and indirect consequences of bullying from published literature. In addition, we describe a graph-based study on the evolution of research on bullying consequences in the last two decades. Our graph-based study reveals a summary view of associations of bullying with other diseases and disorders from published literature. Further, we employ literature reference information to quantify the strength of the graph-derived relationships. We demonstrate that LBD is a valuable approach to perform advanced analytics on bullying that helps us to understand the severity of its consequences.

Hasan, S M Shamimul↗
DOE OSTI2019

DE-SC0016684 - DOE Final Scientific and Technical Report

The Computing Research Association’s Committee on the Status of Women in Computing Research (CRA-W) aims to increase the number of women who participate and succeed in computer science research at every stage of the research pipeline. Achieving these objectives requires that an increasing number of women start and progress to the next stage. The CRA-W Grad Cohort Workshop is focused at the graduate level to maximize the likelihood participants will successfully complete their graduate degrees. Factors such as role models, skills, competence, confidence, mentoring, social support, and development of identity as a researcher help students to persist in graduate school, while factors such as sexism, racism, and dissatisfaction with their graduate program may cause them to leave. We, therefore, help first, second, and third year students build their skills, research strategies, and confidence; meet potential mentors from outside their institutions; develop connections with each other and with their research communities; and inform them about strategies for successful publishing, speaking, and advisor management strategies. By targeting early graduate students, we seek to help them stay in graduate school and in research.

Russell, Erik↗
DOE OSTI2019

On Holder-Brascamp-Lieb inequalities for torsion-free discrete Abelian groups

H\"older-Brascamp-Lieb inequalities provide upper bounds for a class of multilinear expressions, in terms of $L^p$ norms of the functions involved. They have been extensively studied for functions defined on Euclidean spaces. Bennett-Carbery-Christ-Tao have initiated the study of these inequalities for discrete Abelian groups and, in terms of suitable data, have characterized the set of all tuples of exponents for which such an inequality holds for specified data, as the convex polyhedron defined by a particular finite set of affine inequalities. In this paper we advance the theory of such inequalities for torsion-free discrete Abelian groups in three respects. The optimal constant in any such inequality is shown to equal $1$ whenever it is finite. An algorithm that computes the admissible polyhedron of exponents is developed. It is shown that nonetheless, existence of an algorithm that computes the full list of inequalities in the Bennett-Carbery-Christ-Tao description of the admissible polyhedron for all data, is equivalent to an affirmative solution of Hilbert's Tenth Problem over the rationals. That problem remains open. Applications to computer science will be explored in a forthcoming companion paper.

Christ, Michael↗
DOE OSTI2022

Model Choice Metrics to Optimize Profile-QSAR Performance

Predicting molecular activity against protein targets is difficult because of the paucity of experimental data. Approaches like multitask modeling and collaborative filtering seek to improve model accuracy by leveraging results from multiple targets, but are limited because different compounds are measured with different assays, leading to sparse data matrices. Profile-QSAR (pQSAR) 2.0 addresses this problem by fitting a series of partial least squares models for each target, using as features the predictions from single-task models on the remaining targets. Here, this method has been shown to produce better results than single task and multitask models. However, the factors determining the success of pQSAR 2.0 have as yet not been characterized. In this paper we examine the experimental conditions that lead to better pQSAR models. We limit the amount of data available to the method by retraining with decreasing amounts of data and explore the model’s ability to generalize to compounds that have never been assayed. Finally, we look at the properties of training data needed to demonstrate pQSAR improvement.

Biological and medical sciences, Computer science↗
DOE OSTI2024

Performance Improvements of Poincaré Analysis for Exascale Fusion Simulations

Understanding the time-varying magnetic field in a fusion device is critical for the successful design and construction of clean-burning fusion power plants. Poincaré analysis provides a powerful method for the visualization of magnetic fields in fusion devices. However, Poincaré plots can be very computationally expensive making it impractical, for example, to generate these plots in situ during a simulation. In this short paper, we describe a collaboration among computer science and physics researchers to develop a new Poincaré tool that provides a significant reduction in the time to generate analysis results.

Pugmire, Dave↗