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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Quantum skew divergence

In this paper, we study the quantum generalisation of the skew divergence, which is a dissimilarity measure between distributions introduced by Lee in the context of natural language processing. We provide an in-depth study of the quantum skew divergence, including its relation to other state distinguishability measures. Finally, we present a number of important applications: new continuity inequalities for the quantum Jensen-Shannon divergence and the Holevo information, and a new and short proof of Bravyi's Small Incremental Mixing conjecture.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Artificial Intelligence a D and D Enabler - 20552

The paper addresses the development of a specific search engine dedicated to Decommissioning and Dismantling (D and D) projects. The solution combines advanced model based system engineering methodologies and a data centric approach. It is based on an appropriate use of the last Natural Language processing techniques, the most innovative artificial intelligence algorithms and open source framework to handle large volume and diversity of data met in the case of a nuclear infrastructure we have to dismantle. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Specifying structural constraints of architectural patterns in the ARCHERY language

ARCHERY is an architectural description language for modelling and reasoning about distributed, heterogeneous and dynamically reconfigurable systems in terms of architectural patterns. The language supports the specification of architectures and their reconfiguration. This paper introduces a language extension for precisely describing the structural design decisions that pattern instances must respect in their (re)configurations. The extension is a propositional modal logic with recursion and nominals referencing components, i.e., a hybrid µ-calculus. Its expressiveness allows specifying safety and liveness constraints, as well as paths and cycles over structures. Refinements of classic architectural patterns are specified.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Automated Radiology Report Summarization Using an Open-Source Natural Language Processing Pipeline

Diagnostic radiologists are expected to review and assimilate findings from prior studies when constructing their overall assessment of the current study. Radiology information systems facilitate this process by presenting the radiologist with a subset of prior studies that are more likely to be relevant to the current study, usually by comparing anatomic coverage of both the current and prior studies. It is incumbent on the radiologist to review the full text report and/or images from those prior studies, a process that is time-consuming and confers substantial risk of overlooking a relevant prior study or finding. This risk is compounded when patients have dozens or even hundreds of prior imaging studies. Our goal is to assess the feasibility of natural language processing techniques to automatically extract asserted and negated disease entities from free-text radiology reports as a step towards automated report summarization. We compared automatically extracted disease mentions to a gold-standard set of manual annotations for 50 radiology reports from CT abdomen and pelvis examinations. The automated report summarization pipeline found perfect or overlapping partial matches for 86% of the manually annotated disease mentions (sensitivity 0.86, precision 0.66, accuracy 0.59, F1 score 0.74). The performance of the automated pipeline was good, and the overall accuracy was similar to the interobserver agreement between the two manual annotators.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

A large-scale analysis of bioinformatics code on GitHub

In recent years, the explosion of genomic data and bioinformatic tools has been accompanied by a growing conversation around reproducibility of results and usability of software. However, the actual state of the body of bioinformatics software remains largely unknown. Here, the purpose of this paper is to investigate the state of source code in the bioinformatics community, specifically looking at relationships between code properties, development activity, developer communities, and software impact. To investigate these issues, we curated a list of 1,720 bioinformatics repositories on GitHub through their mention in peer-reviewed bioinformatics articles. Additionally, we included 23 high-profile repositories identified by their popularity in an online bioinformatics forum. We analyzed repository metadata, source code, development activity, and team dynamics using data made available publicly through the GitHub API, as well as article metadata. We found key relationships within our dataset, including: certain scientific topics are associated with more active code development and higher community interest in the repository; most of the code in the main dataset is written in dynamically typed languages, while most of the code in the high-profile set is statically typed; developer team size is associated with community engagement and high-profile repositories have larger teams; the proportion of female contributors decreases for high-profile repositories and with seniority level in author lists; and, multiple measures of project impact are associated with the simple variable of whether the code was modified at all after paper publication. In addition to providing the first large-scale analysis of bioinformatics code to our knowledge, our work will enable future analysis through publicly available data, code, and methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

An introduction to LIME 1.0 and its use in coupling codes for multiphysics simulations.

LIME is a small software package for creating multiphysics simulation codes. The name was formed as an acronym denoting 'Lightweight Integrating Multiphysics Environment for coupling codes.' LIME is intended to be especially useful when separate computer codes (which may be written in any standard computer language) already exist to solve different parts of a multiphysics problem. LIME provides the key high-level software (written in C++), a well defined approach (with example templates), and interface requirements to enable the assembly of multiple physics codes into a single coupled-multiphysics simulation code. In this report we introduce important software design characteristics of LIME, describe key components of a typical multiphysics application that might be created using LIME, and provide basic examples of its use - including the customized software that must be written by a user. We also describe the types of modifications that may be needed to individual physics codes in order for them to be incorporated into a LIME-based multiphysics application.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2016

Assessment of online public opinions on large infrastructure projects: A case study of the Three Gorges Project in China

Public opinion becomes increasingly salient in the ex post evaluation stage of large infrastructure projects which have significant impacts to the environment and the society. However, traditional survey methods are inefficient in collection and assessment of the public opinion due to its large quantity and diversity. Recently, Social media platforms provide a rich data source for monitoring and assessing the public opinion on controversial infrastructure projects. This paper proposes an assessment framework to transform unstructured online public opinions on large infrastructure projects into sentimental and topical indicators for enhancing practices of ex post evaluation and public participation. The framework uses web crawlers to collect online comments related to a large infrastructure project and employs two natural language processing technologies, including sentiment analysis and topic modeling, with spatio-temporal analysis, to transform these comments into indicators for assessing online public opinion on the project. Based on the framework, we investigate the online public opinion of the Three Gorges Project on China's largest microblogging site, namely, Weibo. Assessment results present spatial-temporal distributions of post intensity and sentiment polarity, reveals major topics with different sentiments and summarizes managerial implications, for ex post evaluation of the world's largest hydropower project. The proposed assessment framework is expected to be widely applied as a methodological strategy to assess public opinion in the ex post evaluation stage of large infrastructure projects. - Highlights: • We developed a framework to assess online public opinion on large infrastructure projects with environmental impacts. • Indicators were built to assess post intensity, sentiment polarity and major topics of the public opinion. • We took the Three Gorges Project (TGP) as an example to demonstrate the effectiveness proposed framework. • We revealed spatial-temporal patterns of post intensity and sentiment polarity on the TGP. • We drew implications for a more in-depth understanding of the public opinion on large infrastructure projects.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Automatic Determination of the Need for Intravenous Contrast in Musculoskeletal MRI Examinations Using IBM Watson’s Natural Language Processing Algorithm

Magnetic resonance imaging (MRI) protocoling can be time- and resource-intensive, and protocols can often be suboptimal dependent upon the expertise or preferences of the protocoling radiologist. Providing a best-practice recommendation for an MRI protocol has the potential to improve efficiency and decrease the likelihood of a suboptimal or erroneous study. The goal of this study was to develop and validate a machine learning-based natural language classifier that can automatically assign the use of intravenous contrast for musculoskeletal MRI protocols based upon the free-text clinical indication of the study, thereby improving efficiency of the protocoling radiologist and potentially decreasing errors. We utilized a deep learning-based natural language classification system from IBM Watson, a question-answering supercomputer that gained fame after challenging the best human players on Jeopardy! in 2011. We compared this solution to a series of traditional machine learning-based natural language processing techniques that utilize a term-document frequency matrix. Each classifier was trained with 1240 MRI protocols plus their respective clinical indications and validated with a test set of 280. Ground truth of contrast assignment was obtained from the clinical record. For evaluation of inter-reader agreement, a blinded second reader radiologist analyzed all cases and determined contrast assignment based on only the free-text clinical indication. In the test set, Watson demonstrated overall accuracy of 83.2% when compared to the original protocol. This was similar to the overall accuracy of 80.2% achieved by an ensemble of eight traditional machine learning algorithms based on a term-document matrix. When compared to the second reader’s contrast assignment, Watson achieved 88.6% agreement. When evaluating only the subset of cases where the original protocol and second reader were concordant (n = 251), agreement climbed further to 90.0%. The classifier was relatively robust to spelling and grammatical errors, which were frequent. Implementation of this automated MR contrast determination system as a clinical decision support tool may save considerable time and effort of the radiologist while potentially decreasing error rates, and require no change in order entry or workflow.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Proposing New RadLex Terms by Analyzing Free-Text Mammography Reports

After years of development, the RadLex terminology contains a large set of controlled terms for the radiology domain, but gaps still exist. We developed a data-driven approach to discover new terms for RadLex by mining a large corpus of radiology reports using natural language processing (NLP) methods. Our system, developed for mammography, discovers new candidate terms by analyzing noun phrases in free-text reports to extend the mammography part of RadLex. Our NLP system extracts noun phrases from free-text mammography reports and classifies these noun phrases as “Has Candidate RadLex Term” or “Does Not Have Candidate RadLex Term.” We tested the performance of our algorithm using 100 free-text mammography reports. An expert radiologist determined the true positive and true negative RadLex candidate terms. We calculated precision/positive predictive value and recall/sensitivity metrics to judge the system’s performance. Finally, to identify new candidate terms for enhancing RadLex, we applied our NLP method to 270,540 free-text mammography reports obtained from three academic institutions. Our method demonstrated precision/positive predictive value of 0.77 (159/206 terms) and a recall/sensitivity of 0.94 (159/170 terms). The overall accuracy of the system is 0.80 (235/293 terms). When we ran our system on the set of 270,540 reports, it found 31,800 unique noun phrases that are potential candidates for RadLex. Our data-driven approach to mining radiology reports can identify new candidate terms for expanding the breast imaging lexicon portion of RadLex and may be a useful approach for discovering new candidate terms from other radiology domains.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Integrating Natural Language Processing and Machine Learning Algorithms to Categorize Oncologic Response in Radiology Reports

A significant volume of medical data remains unstructured. Natural language processing (NLP) and machine learning (ML) techniques have shown to successfully extract insights from radiology reports. However, the codependent effects of NLP and ML in this context have not been well-studied. Between April 1, 2015 and November 1, 2016, 9418 cross-sectional abdomen/pelvis CT and MR examinations containing our internal structured reporting element for cancer were separated into four categories: Progression, Stable Disease, Improvement, or No Cancer. We combined each of three NLP techniques with five ML algorithms to predict the assigned label using the unstructured report text and compared the performance of each combination. The three NLP algorithms included term frequency-inverse document frequency (TF-IDF), term frequency weighting (TF), and 16-bit feature hashing. The ML algorithms included logistic regression (LR), random decision forest (RDF), one-vs-all support vector machine (SVM), one-vs-all Bayes point machine (BPM), and fully connected neural network (NN). The best-performing NLP model consisted of tokenized unigrams and bigrams with TF-IDF. Increasing N-gram length yielded little to no added benefit for most ML algorithms. With all parameters optimized, SVM had the best performance on the test dataset, with 90.6 average accuracy and F score of 0.813. The interplay between ML and NLP algorithms and their effect on interpretation accuracy is complex. The best accuracy is achieved when both algorithms are optimized concurrently.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Clinical Natural Language Processing for Radiation Oncology: A Review and Practical Primer

Natural language processing (NLP), which aims to convert human language into expressions that can be analyzed by computers, is one of the most rapidly developing and widely used technologies in the field of artificial intelligence. Natural language processing algorithms convert unstructured free text data into structured data that can be extracted and analyzed at scale. In medicine, this unlocking of the rich, expressive data within clinical free text in electronic medical records will help untap the full potential of big data for research and clinical purposes. Recent major NLP algorithmic advances have significantly improved the performance of these algorithms, leading to a surge in academic and industry interest in developing tools to automate information extraction and phenotyping from clinical texts. Thus, these technologies are poised to transform medical research and alter clinical practices in the future. Radiation oncology stands to benefit from NLP algorithms if they are appropriately developed and deployed, as they may enable advances such as automated inclusion of radiation therapy details into cancer registries, discovery of novel insights about cancer care, and improved patient data curation and presentation at the point of care. However, challenges remain before the full value of NLP is realized, such as the plethora of jargon specific to radiation oncology, nonstandard nomenclature, a lack of publicly available labeled data for model development, and interoperability limitations between radiation oncology data silos. Successful development and implementation of high quality and high value NLP models for radiation oncology will require close collaboration between computer scientists and the radiation oncology community. Here, we present a primer on artificial intelligence algorithms in general and NLP algorithms in particular; provide guidance on how to assess the performance of such algorithms; review prior research on NLP algorithms for oncology; and describe future avenues for NLP in radiation oncology research and clinics.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2013

Berry phase and Hannay’s angle in the Born–Oppenheimer hybrid systems

In this paper, we investigate the Berry phase and Hannay’s angle in the Born–Oppenheimer (BO) hybrid systems and obtain their algebraic expressions in terms of one form connection. The semiclassical relation of Berry phase and Hannay’s angle is discussed. We find that, besides the usual connection term, the Berry phase of quantum BO composite system also contains a novel term brought forth by the coupling induced effective gauge potential. This quantum modification can be viewed as an effective Aharonov–Bohm effect. Moreover, the similar phenomenon is founded in Hannay’s angle of classical BO composite system, which indicates that the Berry phase and Hannay’s angle possess the same relation as the usual one. An example is used to illustrate our theory. This scheme can be used to generate artificial gauge potentials for neutral atoms. Besides, the quantum–classical hybrid BO system is also studied to compare with the results in full quantum and full classical composite systems. -- Highlights: •We have derived the Berry phase and Hannay’s angle in BO hybrid systems. •The Berry phase contains a novel term brought by the effective gauge potential. •This mechanism can be used to generate artificial gauge potentials for neutral atoms. •The relation between Hannay’s angles and Berry phases is established.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

MPI + OpenACC: Accelerating radiation transport mini-application, minisweep, on heterogeneous systems

According to the article, architectures are rapidly evolving, and exascale machines are expected to offer billion-way concurrency. We need to rethink algorithms, languages and programming models among other components in order to migrate large scale applications and explore parallelism on these machines. Although directive-based programming models allow programmers to worry less about programming and more about science, expressing complex parallel patterns in these models can be a daunting task especially when the goal is to match the performance that the hardware platforms can offer. One such pattern is wavefront. This paper extensively studies a wavefront-based miniapplication for Denovo, a production code for nuclear reactor modeling. We parallelize the Koch–Baker–Alcouffe (KBA) parallel-wavefront sweep algorithm in the main kernel of Minisweep (the miniapplication) using CUDA 9.0, OpenMP 4.0 (SIMD) and OpenACC 2.6. Our OpenACC implementation running on NVIDIA’s next-generation Volta GPU boasts an 85.06x speedup over serial code, which is larger than CUDA’s 83.72x speedup over the same serial implementation. We also explore the scalability of our solution using MPI to decompose our simulation domain, allowing us to run on many nodes and accelerators present in state-of-the-art HPC systems. Our parallelization effort across platforms also motivated us to define an abstract parallelism model that is architecture independent, with a goal of creating software abstractions that can be used by applications employing the wavefront sweep motif.

Computer Science↗
DOE OSTI2013

Public participation in environmental impact assessment: why, who and how?

Even a cursory glance at the literature on environmental impact assessment (EIA) reveals that public participation is being considered as an integral part of the assessment procedure. Public participation in EIA is commonly deemed to foster democratic policy-making and to render EIA more effective. Yet a closer look at the literature unveils that, beyond this general assertion, opinions of the precise meaning, objectives and adequate representation of public participation in EIA considerably diverge. Against this background, in this article we aim to provide a comprehensive overview of the academic debate on public participation in EIA concerning its meaning, objectives and adequate level of inclusiveness. In so doing, we hope to stimulate a more focused debate on the subject, which is key to advancing the research agenda. Furthermore, this paper may serve as a starting point for practitioners involved in defining the role of public participation in EIA practice. -- Highlights: • There is little reflection on the meaning, objectives and adequate level of inclusiveness of public participation in EIA. • We provide a comprehensive overview of the academic debate on public participation in EIA concerning the meaning, objectives and adequate level of inclusiveness. • Theoretical claims put forth by scholars are contrasted with empirical evidence. • Overview shall stimulate a more focused debate on the subject. • This paper may serve as a starting point for practitioners involved in defining the role of public participation in EIA.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Multi-criteria group decision-making based sustainability measurement of wastewater treatment processes

There are various processes which can be used for wastewater treatment (WT), and the selection of the most sustainable one among different processes is a hard task. This study aims at helping the decision-makers (DM) to address this by developing an intuitionistic fuzzy set (IFS) theory based group multi-attribute decision analysis (MADA) method. Ten criteria in environment, economy, society-politic, and technology dimensions were employed to achieve sustainability measurement (SM) of WT processes. The multi-criteria sustainability assessment method developed in this study allow different experts to attend the SM, and enable the participants to employ the natural language/words to depict their intuitionistic opinions. Accordingly, the proposed method can achieve group SM under uncertainties. An illustrative case including four processes for wastewater treatment, namely Anaerobic-Anoxic-Oxic (AAO) process, Triple Oxidation Ditch (TOD) process, Anaerobic single-ditch oxidation (ASD) process, and Sequencing batch reactor activated sludge process (SBR), has been studied, and the results reveal that this method can determine sustainability sequence of different WT processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Self-assembly, condensation, and order in aqueous lyotropic chromonic liquid crystals crowded with additives

Dense multicomponent systems with macromolecules and small solutes attract a broad research interest as they mimic the molecularly crowded cellular interiors. The additives can condense and align the macromolecules, but they do not change the degree of covalent polymerization. We chose a lyotropic chromonic liquid crystal with reversibly and non-covalently assembled aggregates as a much softer system, reminiscent of 'living polymers', to demonstrate that small neutral and charged additives cause condensation of aggregates with ensuing orientational and positional ordering and nontrivial morphologies of phase separation, such as tactoids and toroids of the nematic and hexagonal columnar phase coexisting with the isotropic melt. Scanning transmission X-ray microscopy (STXM) with near edge X-ray absorption fine structure (NEXAFS) analysis as well as fluorescent microscopy demonstrates segregation of the components. The observations suggest that self-assembly of chromonic aggregates in the presence of additives is controlled by both entropy effects and by specific molecular interactions and provide a new route to the regulated reversible assembly of soft materials formed by low-molecular weight components.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Backhopping effect in magnetic tunnel junctions: Comparison between theory and experiment

We report on magnetic switching and backhopping effects due to spin-transfer-torque in magnetic tunnel junctions. Experimental data on current-induced switching in junctions with a MgO tunnel barrier reveal random back-and-forth switching between magnetization states, which appears when the current direction favors the parallel magnetic configuration. The effect depends on the barrier thickness t{sub b} and is not observed in tunnel junctions with very thin MgO tunnel barriers, t{sub b} < 0.95 nm. The switching dependence on bias voltage and barrier thickness is explained in terms of the macrospin model, with the magnetization dynamics described by the modified Landau-Lifshitz-Gilbert equation. Numerical simulations indicate that the competition between in-plane and out-of-plane torque components can result in a non-deterministic switching behavior at high bias voltages, in agreement with experimental observations. When the barrier thickness is reduced, the overall coupling between the magnetic layers across the barrier becomes ferromagnetic, which suppresses the backhopping effect.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

High-speed spiral imaging technique for an atomic force microscope using a linear quadratic Gaussian controller

This paper demonstrates a high-speed spiral imaging technique for an atomic force microscope (AFM). As an alternative to traditional raster scanning, an approach of gradient pulsing using a spiral line is implemented and spirals are generated by applying single-frequency cosine and sine waves of slowly varying amplitudes to the X and Y-axes of the AFM’s piezoelectric tube scanner (PTS). Due to these single-frequency sinusoidal input signals, the scanning process can be faster than that of conventional raster scanning. A linear quadratic Gaussian controller is designed to track the reference sinusoid and a vibration compensator is combined to damp the resonant mode of the PTS. An internal model of the reference sinusoidal signal is included in the plant model and an integrator for the system error is introduced in the proposed control scheme. As a result, the phase error between the input and output sinusoids from the X and Y-PTSs is reduced. The spirals produced have particularly narrow-band frequency measures which change slowly over time, thereby making it possible for the scanner to achieve improved tracking and continuous high-speed scanning rather than being restricted to the back and forth motion of raster scanning. As part of the post-processing of the experimental data, a fifth-order Butterworth filter is used to filter noises in the signals emanating from the position sensors and a Gaussian image filter is used to filter the images. A comparison of images scanned using the proposed controller (spiral) and the AFM PI controller (raster) shows improvement in the scanning rate using the proposed method.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗