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Summary of the FESAC Transformative Enabling Capabilities Panel Report

FUSION SCIENCE AND TECHNOLOGY 2019年第3期75卷 167-177页

作者： Maingi, R. Lumsdaine, A. Allain, J. P. Chacon, L. Gourlay, S. A. Greenfield, C. M. Hughes, J. W. Humphreys, D. Izzo, V. McLean, H. Menard, J. E. Merrill, B. Rapp, J. Schmitz, O. Spadaccini, C. Wang, Z. White, A. E. Wirth, B. D. Princeton Plasma Phys Lab POB 451 Princeton NJ 08543 USA Oak Ridge Natl Lab Oak Ridge TN USA Univ Illinois Urbana IL 61801 USA Los Alamos Natl Lab Los Alamos NM USA Lawrence Berkeley Natl Lab Berkeley CA USA Gen Atom San Diego CA USA MIT 77 Massachusetts Ave Cambridge MA 02139 USA Univ Calif San Diego San Diego CA 92103 USA Lawrence Livermore Natl Lab Livermore CA USA Idaho Natl Lab Idaho Falls ID USA Univ Wisconsin Madison WI USA Univ Tennessee Knoxville TN USA

The U.S. Fusion Energy Sciences Advisory Committee was charged "to identify the most promising transformative enabling capabilities (TEC) for the U.S. to pursue that could promote efficient advance toward fusion energy, building on burning plasma science and technology." A subcommittee of U.S. technical experts was formed and received community input in the form of white papers and presentations on the charge questions. The subcommittee identified four "most promising transformative enabling capabilities": 1. advanced algorithms 2. high critical temperature superconductors 3. advanced materials and manufacturing 4. novel technologies for tritium fuel cycle control. In addition, one second-tier TEC, defined as a "promising transformative enabling capability," was identified: fast-flowing liquid-metal plasma-facing components. Each of these TECs presents a tremendous opportunity to accelerate fusion science and technology toward power production. Dedicated investment in these TECs for fusion systems is needed to capitalize on the rapid advances being made for a variety of nonfusion applications to fully realize their transformative potential for fusion energy.

关键词： Transformative enabling capabilities advanced algorithms high critical temperature superconductors advanced materials and manufacturing tritium fuel cycle control

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Perspectives on the FESAC transformative enabling capabilities: Priorities, plans, and Status

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FUSION ENGINEERING AND DESIGN 2020年 155卷 111529-111529页

作者： Lumsdaine, Arnold Maingi, Rajesh Field, Kevin G. Gourlay, Stephen Humphreys, David Katoh, Yutai Kessel, Charles Wang, Xiaorong Oak Ridge Natl Lab Oak Ridge TN 37830 USA Princeton Plasma Phys Lab POB 451 Princeton NJ 08543 USA Univ Michigan Ann Arbor MI 48109 USA Lawrence Berkeley Natl Lab Berkeley CA USA Gen Atom San Diego CA USA

In early 2017, the Fusion Energy Sciences Advisory Committee (FESAC), an advisory committee to the United States Department of Energy, was charged with identifying transformative enabling capabilities (TECs) "that could promote efficient advance toward fusion energy, building on burning plasma science and technology." A subcommittee with broad expertise was formed and sought feedback from scientific experts, including experts from outside the fusion community. Three workshops were conducted, and a report was approved by FESAC in 2018 that identified four of the "most promising" TECs: advanced algorithms, high-critical-temperature superconductors, advanced materials and manufacturing, and novel technologies for tritium fuel cycle control. In addition, one "promising" TEC was identified: fast flowing liquid metal plasma-facing components. This paper will give details on the promising TECs and an overview on considerations of these TECs in the United States since the publication of the report.

关键词： Fusion energy advanced algorithms Superconductors advanced manufacturing Tritium fuel cycle Liquid metals

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Compartmentalisation-based design automation method for power gridInspec keywordsOther keywords

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IET CYBER-PHYSICAL SYSTEMS: THEORY & APPLICATIONS 2017年第1期2卷 20-27页

作者： Vatanparvar, Korosh Fakhouri, Sani Siddika, Mst-Ayesha Al Faruque, Mohammad Abdullah Univ Calif Irvine Dept Elect Engn & Comp Sci Irvine CA 92697 USA City Univ Dept Comp Sci & Engn Dhaka Bangladesh

Power grid design and maintenance are conducted to solve the problems caused by load growth over time and to stay within the constraints of voltage drop, power factor, etc. Typically, solutions to these problems are optimised individually. Considering multiple problems simultaneously and applying different solutions require vast design space exploration. This exclusively needs advanced algorithms and complex global optimisation methods which are not easily-applicable in different scenarios. In the state-of-the-art methods, for solving multiple problems simultaneously, these individually optimised solutions are applied sequentially to the power grid. In this so-called uncoordinated method, the final solution may not be optimal solution considering all the variables, since it is considering the overlapping effect of the solutions on the power grid. To validate the compartmentalisation method, a detailed distribution grid has been modeled. After analysing the possible solutions and optimisation, power loss was reduced 45% and total cost decreased by 71%, compared to the uncoordinated method.

关键词： distribution networks maintenance engineering optimisation load (electric) power grid design power grid maintenance load growth physical characteristics electrical characteristics design space exploration advanced algorithms complex global optimisation methods uncoordinated method compartmentalisation-based design automation method optimum solution distribution grid model power loss

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Computational Methods for Simulating Some Typical Problems in Computational Geosciences

Computational Methods for Simulating Some Typical Problems i...

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5th International Conference on Computational Methods (ICCM)

作者： Zhao, Chongbin Cent S Univ Computat Geosci Res Ctr Changsha 410083 Hunan Peoples R China

The main purpose of this paper is to present computational methods for simulating some typical problems in the emerging computational geoscience field. Due to remarkable differences between engineering systems and Earth ones, existing computational methods, which are designed for solving engineering problems, cannot be directly used to solve geoscience problems without any modification. However, the fundamental philosophy of developing computational methods is applicable to the computational simulation of both geoscience and engineering problems. Because of their inherent approximation, computational methods must be verified before putting into application. After several computational methods and algorithms, which are developed for simulating some typical problems in the emerging computational geoscience field, are briefly introduced, a typical geoscience problem, known as the chemical dissolution-front instability problem in ore-forming systems of supercritical Zhao numbers, is selected to demonstrate how computational methods can be used to solve geoscience problems.

关键词： Computational methods advanced algorithms computational geosciences coupled systems chemical reactions

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Computational Methods for Simulating Some Typical Problems in Computational Geosciences

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INTERNATIONAL JOURNAL OF COMPUTATIONAL METHODS 2016年第2期13卷

作者： Zhao, Chongbin Cent S Univ Computat Geosci Res Ctr Changsha 410083 Hunan Peoples R China

关键词： Computational methods advanced algorithms computational geosciences coupled systems chemical reactions

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A secure distributed logistic regression protocol for the detection of rare adverse drug events

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2013年第3期20卷 453-461页

作者： El Emam, Khaled Samet, Saeed Arbuckle, Luk Tamblyn, Robyn Earle, Craig Kantarcioglu, Murat CHEO Res Inst Ottawa ON K1H 8L1 Canada Univ Ottawa Dept Paediat Ottawa ON Canada Mem Univ Newfoundland Fac Med eHlth Res Unit St John NF A1C 5S7 Canada McGill Univ Dept Epidemiol Biostat & Occupat Hlth Montreal PQ Canada Inst Clin Evaluat Sci Toronto ON Canada Univ Texas Dallas Dallas TX 75230 USA

Background There is limited capacity to assess the comparative risks of medications after they enter the market. For rare adverse events, the pooling of data from multiple sources is necessary to have the power and sufficient population heterogeneity to detect differences in safety and effectiveness in genetic, ethnic and clinically defined subpopulations. However, combining datasets from different data custodians or jurisdictions to perform an analysis on the pooled data creates significant privacy concerns that would need to be addressed. Existing protocols for addressing these concerns can result in reduced analysis accuracy and can allow sensitive information to leak. Objective To develop a secure distributed multi-party computation protocol for logistic regression that provides strong privacy guarantees. Methods We developed a secure distributed logistic regression protocol using a single analysis center with multiple sites providing data. A theoretical security analysis demonstrates that the protocol is robust to plausible collusion attacks and does not allow the parties to gain new information from the data that are exchanged among them. The computational performance and accuracy of the protocol were evaluated on simulated datasets. Results The computational performance scales linearly as the dataset sizes increase. The addition of sites results in an exponential growth in computation time. However, for up to five sites, the time is still short and would not affect practical applications. The model parameters are the same as the results on pooled raw data analyzed in SAS, demonstrating high model accuracy. Conclusion The proposed protocol and prototype system would allow the development of logistic regression models in a secure manner without requiring the sharing of personal health information. This can alleviate one of the key barriers to the establishment of large-scale post-marketing surveillance programs. We extended the secure protocol to account for co

关键词： Ethical study methods privacy enhancing the conduct of biological/clinical research and trials assuring information system security and personal privacy anonymization privacy and security health services medical informatics pharmacoepidemiology medical education advanced algorithms personal health records and self-care systems languages and computational methods

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A practical approach to achieve private medical record linkage in light of public resources

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2013年第2期20卷 285-292页

作者： Kuzu, Mehmet Kantarcioglu, Murat Durham, Elizabeth Ashley Toth, Csaba Malin, Bradley Univ Texas Dallas Dept Comp Sci Dallas TX 75230 USA Vanderbilt Univ Dept Biomed Informat Nashville TN 37203 USA Vanderbilt Univ Dept Elect Engn & Comp Sci Nashville TN 37203 USA

Objective Integration of patients' records across resources enhances analytics. To address privacy concerns, emerging strategies such as Bloom filter encodings (BFEs), enable integration while obscuring identifiers. However, recent investigations demonstrate BFEs are, in theory, vulnerable to cryptanalysis when encoded identifiers are randomly selected from a public resource. This study investigates the extent to which cryptanalysis conditions hold for (1) real patient records and (2) a countermeasure that obscures the frequencies of the identifying values in encoded datasets. Design First, to investigate the strength of cryptanalysis for real patient records, we build BFEs from identifiers in an electronic medical record system and apply cryptanalysis using identifiers in a publicly available voter registry. Second, to investigate the countermeasure under ideal cryptanalysis conditions, we compose BFEs from the identifiers that are randomly selected from a public voter registry. Measurement We utilize precision (ie, rate of correct re-identified encodings) and computation efficiency (ie, time to complete cryptanalysis) to assess the performance of cryptanalysis in BFEs before and after application of the countermeasure. Results Cryptanalysis can achieve high precision when the encoded identifiers are composed of a random sample of a public resource (ie, a voter registry). However, we also find that the attack is less efficient and may not be practical for more realistic scenarios. By contrast, the proposed countermeasure made cryptanalysis impractical in terms of precision and efficiency. Conclusions Performance of cryptanalysis against BFEs based on patient data is significantly lower than theoretical estimates. The proposed countermeasure makes BFEs resistant to known practical attacks.

关键词： Languages and computational methods advanced algorithms personal health records and self-care systems privacy security record linkage ethical study methods statistical analysis of large datasets methods for integration of information from disparate sources distributed systems assuring information system security and personal privacy

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Automated extraction of ejection fraction for quality measurement using regular expressions in Unstructured Information Management Architecture (UIMA) for heart failure

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第5期19卷 859-866页

作者： Garvin, Jennifer H. DuVall, Scott L. South, Brett R. Bray, Bruce E. Bolton, Daniel Heavirland, Julia Pickard, Steve Heidenreich, Paul Shen, Shuying Weir, Charlene Samore, Matthew Goldstein, Mary K. SLC VA Healthcare Syst IDEAS Ctr Salt Lake City UT USA Univ Utah Sch Med Div Epidemiol Salt Lake City UT USA Univ Utah Sch Med Dept Biomed Informat Salt Lake City UT USA VA Palo Alto Hlth Care Syst Palo Alto CA USA Stanford Univ Ctr Primary Care & Outcomes Res PCOR Stanford CA 94305 USA SLC VA Healthcare Syst GRECC Salt Lake City UT USA

Objectives Left ventricular ejection fraction (EF) is a key component of heart failure quality measures used within the Department of Veteran Affairs (VA). Our goals were to build a natural language processing system to extract the EF from free-text echocardiogram reports to automate measurement reporting and to validate the accuracy of the system using a comparison reference standard developed through human review. This project was a Translational Use Case Project within the VA Consortium for Healthcare Informatics. Materials and methods We created a set of regular expressions and rules to capture the EF using a random sample of 765 echocardiograms from seven VA medical centers. The documents were randomly assigned to two sets: a set of 275 used for training and a second set of 490 used for testing and validation. To establish the reference standard, two independent reviewers annotated all documents in both sets;a third reviewer adjudicated disagreements. Results System test results for document-level classification of EF of <40% had a sensitivity (recall) of 98.41%, a specificity of 100%, a positive predictive value (precision) of 100%, and an F measure of 99.2%. System test results at the concept level had a sensitivity of 88.9% (95% CI 87.7% to 90.0%), a positive predictive value of 95% (95% CI 94.2% to 95.9%), and an F measure of 91.9% (95% CI 91.2% to 92.7%). Discussion An EF value of <40% can be accurately identified in VA echocardiogram reports. Conclusions An automated information extraction system can be used to accurately extract EF for quality measurement.

关键词： Natural language processing (NLP) heart failure left ventricular ejection fraction (EF) Improving healthcare workflow and process efficiency applied informatics Improving government and community policy relevant to informatics and health quality process modeling and hypothesis generation Informatics Enhancing the conduct of biological/clinical research and trials applications that link biomedical knowledge from diverse primary sources (includes automated indexing) visualization of data and knowledge uncertain reasoning and decision theory languages and computational methods statistical analysis of large datasets advanced algorithms discovery and text and data mining methods other methods of information extraction automated learning human-computer interaction and human-centered computing cognitive study (including experiments emphasizing verbal protocol analysis and usability) knowledge representations knowledge acquisition and knowledge management delivering health information and knowledge to the public processing and display analysis image representation controlled terminologies and vocabularies ontologies knowledge bases ejection fraction machine learning simulation of complex systems (at all levels: molecules to work groups to organizations) developing/using clinical decision support (other than diagnostic) and guideline systems detecting disease outbreaks and biological threats

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Utility of gene-specific algorithms for predicting pathogenicity of uncertain gene variants

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第2期19卷 207-211页

作者： Crockett, David K. Lyon, Elaine Williams, Marc S. Narus, Scott P. Facelli, Julio C. Mitchell, Joyce A. Univ Utah Sch Med Dept Biomed Informat Salt Lake City UT USA Univ Utah Sch Med Dept Pathol Salt Lake City UT USA Intermt Healthcare Clin Genet Inst Salt Lake City UT USA Univ Utah Ctr High Performance Comp Salt Lake City UT USA

The rapid advance of gene sequencing technologies has produced an unprecedented rate of discovery of genome variation in humans. A growing number of authoritative clinical repositories archive gene variants and disease phenotypes, yet there are currently many more gene variants that lack clear annotation or disease association. To date, there has been very limited coverage of gene-specific predictors in the literature. Here the evaluation is presented of "gene-specific" predictor models based on a naive Bayesian classifier for 20 gene-disease datasets, containing 3986 variants with clinically characterized patient conditions. The utility of gene-specific prediction is then compared with "all-gene" generalized prediction and also with existing popular predictors. Gene-specific computational prediction models derived from clinically curated gene variant disease datasets often outperform established generalized algorithms for novel and uncertain gene variants.

关键词： Amino acid properties gene variant classification machine learning phenotype prediction bioinformatics gene variants classification gene disease database developing/using computerized provider order entry designing usable (responsive) resources and systems methods for integration of information from disparate sources high-performance and large-scale computing distributed systems agents software engineering: architecture data exchange communication integration across care settings (inter- and intra-enterprise) system implementation and management issues languages computational methods statistical analysis of large datasets advanced algorithms identifying genome and protein structure and function detecting disease outbreaks and biological threats visualization of data and knowledge

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A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2011年第5期18卷 601-606页

作者： Jiang, Min Chen, Yukun Liu, Mei Rosenbloom, S. Trent Mani, Subramani Denny, Joshua C. Xu, Hua Vanderbilt Univ Sch Med Dept Biomed Informat Nashville TN 37232 USA Vanderbilt Univ Sch Med Dept Med Nashville TN 37232 USA

Objective The authors' goal was to develop and evaluate machine-learning-based approaches to extracting clinical entities including medical problems, tests, and treatments, as well as their asserted status from hospital discharge summaries written using natural language. This project was part of the 2010 Center of Informatics for Integrating Biology and the Bedside/Veterans Affairs (VA) natural-language-processing challenge. Design The authors implemented a machine-learning-based named entity recognition system for clinical text and systematically evaluated the contributions of different types of features and ML algorithms, using a training corpus of 349 annotated notes. Based on the results from training data, the authors developed a novel hybrid clinical entity extraction system, which integrated heuristic rule-based modules with the ML-base named entity recognition module. The authors applied the hybrid system to the concept extraction and assertion classification tasks in the challenge and evaluated its performance using a test data set with 477 annotated notes. Measurements Standard measures including precision, recall, and F-measure were calculated using the evaluation script provided by the Center of Informatics for Integrating Biology and the Bedside/VA challenge organizers. The overall performance for all three types of clinical entities and all six types of assertions across 477 annotated notes were considered as the primary metric in the challenge. Results and discussion Systematic evaluation on the training set showed that Conditional Random Fields outperformed Support Vector Machines, and semantic information from existing natural-language-processing systems largely improved performance, although contributions from different types of features varied. The authors' hybrid entity extraction system achieved a maximum overall F-score of 0.8391 for concept extraction (ranked second) and 0.9313 for assertion classification (ranked fourth, but not statistically

关键词： Natural language processing machine learning named entity recognition clinical text processing NLP ethical study methods Text generation from discrete data Classical experimental and quasi-experimental study methods (lab and field) Developing and refining EHR Data standards (including image standards) Controlled terminologies and vocabularies Human–computer interaction and human-centered computing Surveys and needs analysis Qualitative/ethnographic field study policy Legal historical Automated learning Translational research—application of biological knowledge to clinical care Uncertain reasoning and decision theory Linking the genotype and phenotype languages computational methods advanced algorithms discovery text and data mining methods Knowledge representations Knowledge bases

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