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CAD CAM GOES TO SEA - THE SAAR-5 DESIGN AND CONSTRUCTION

NAVAL ENGINEERS JOURNAL 1992年第3期104卷 148-155页

作者： LINDGREN, JR SOLITARIO, WA MOORE, AP STREIFF, MA John R. Lindgren Jr:. is vice president for engineering at Ingalls Shipbuilding Inc. a Division of Litton Industries in Pascagoula Miss. He joined Ingalls in 1958 and has held various positions in the Engineering Division and participated in the design of numerous merchant ships drill rigs submarines and surface combatants and auxiliary support ships. Mr. Lindgren is a 1958 graduate of the University of Southwest Louisiana. His degree is in mechanical engineering and he is also a licensed professional engineer. William A. Solitario:is the director of advanced technology at Ingalls Shipbuilding Inc. in Pascagoula Miss. He received his B.S. degree in chemical engineering from the City University of New York and has 28 years experience in marine engineering and design. His current responsibilities include the direction of Ingalls' IRAD programs and several Navy-funded R&D programs to improve ship's performance and reduce ship's operating costs. He is a member of the Society of Naval Architects and Marine Engineers and past chairman of the Gulf Section East Area. Arnold P. Moore:is the director design engineering at Ingalls Shipbuilding where he is responsible for all new construction design and engineering activities. Prior to promotion to his current position Mr. Moore served as chief naval architect at Ingalls. He has 24 years experience in ship design construction and repair. Mr. Moore holds the professional degree of ocean engineer as well as a master's degree in naval architecture and marine engineering from MIT. He also earned a bachelor's degree in naval science from the U.S. Naval Academy and is a registered professional engineer. Mr. Moore served as an engineering duty officer in the U.S. Navy and is currently a captain in the Naval Reserve. He is a past chairman of the Gulf Section of the Society of Naval Architects and Marine Engineers and a member of the American Society of Naval Engineers and Sigma Xi. Michel A. Streiff:is the manager of CAD/CAM applications at Ingalls Shipbuilding Inc. His

The SA'AR-5 Corvette program is the first major warship construction to be entirely accomplished using a 3-dimensional, interference checked computer based design. This paper discusses the organization and approach used to create the design models which form the basis for interference checking as well as the source of extracted production data. The design or product model is the nucleus of the computer data base that defines the configuration of the entire ship. The data base includes geometry, weight, and material, as well as production control data. The ability of the computer to link such diverse information is the key to maintaining configuration control during the course of the design and construction. The ease with which formatted manufacturing data (both N.C. fabrication and installation) can be extracted enables the preparation of detailed packages containing the desired geometry as well as the associated material and sequencing data, thus assuring the producibility of the design. The SA'AR-5 design is CAD/CAM's state of the art in U.S. shipbuilding.

关键词： Warships

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Retraction: Effect of Types and Gap on Spark Plugs on Exhaust Emissions on the 110CC Double Wheel Vehicles and Its Dissemination Using Website (J. Phys.: Conf. Ser. 1114 012097)

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Journal of Physics: Conference Series 2018年第1期1114卷

作者： Tinus Ginting Hotler Manurung Astro Julida Harahap Erianto Ongko H Hartono Dahlan Abdullah Cut Ita Erliana Kundharu Saddhono S Sriadhi Supriyono Akbar Iskandar A A Saleh I Ketut Sudarsana Department of Mechanical Engineering Akademi Teknologi Industri Immanuel Indonesia Department of Informatics Akademi Teknologi Industri Immanuel Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Graduate Program Universitas Sebelas Maret Surakarta Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Department of Geography Education Universitas Prof Dr Hazairin SH Bengkulu Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Statistics Financial & Social Sciences Research Group Indonesia Department of Religious Education Institut Hindu Dharma Negeri Denpasar Denpasar Indonesia

This paper was retracted by IOP Publishing on 12 December 2018. This paper was published due to a technical error and was not intended to be included in this journal. Retraction published: 8 February 2019

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Effect of Types and Gap on Spark Plugs on Exhaust Emissions on the 110CC Double Wheel Vehicles and Its Dissemination Using Website

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Journal of Physics: Conference Series 2018年第1期1114卷

Based on the formulation of the problem to be discussed by the writer is to know: the influence of 0.4 mm spark gap variation, and standard spark plug type, to CO and HC, the influence of 0.5 mm spark plug gap variation, and variation of platinum spark plug on CO and HC, influence of spark gap variation 0,6mm, and variation of iridium spark plug type to CO and HC emission Method used to obtain data needed to support the writer that is research method with experiment, literature study method or literature study The result of this research concludes that there is spark gap influence 0.4 mm, with variations of standard plug, platinum and iridium spark plugs against CO and HC emissions, where the lowest CO value is 0.27% and HC 98 ppm, 0.5 mm spark plug with lowest CO 0.15% and HC 46 ppm, 0.6 mm spark plug gap the lowest CO value of 0.19% and HC 24 ppm. The results of this research will be disseminated using the website

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Training of Physical Neural Networks

arXiv

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arXiv 2024年

作者： Momeni, Ali Rahmani, Babak Scellier, Benjamin Wright, Logan G. McMahon, Peter L. Wanjura, Clara C. Li, Yuhang Skalli, Anas Berloff, Natalia G. Onodera, Tatsuhiro Oguz, Ilker Morichetti, Francesco del Hougne, Philipp Le Gallo, Manuel Sebastian, Abu Mirhoseini, Azalia Zhang, Cheng Marković, Danijela Brunner, Daniel Moser, Christophe Gigan, Sylvain Marquardt, Florian Ozcan, Aydogan Grollier, Julie Liu, Andrea J. Psaltis, Demetri Alù, Andrea Fleury, Romain Lausanne Switzerland Microsoft Research 198 Cambridge Science Park CambridgeCB4 0AB United Kingdom Rain AI San Francisco United States Department of Applied Physics Yale University CT United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Max Planck Institute for the Science of Light Staudtstraße 2 Erlangen91058 Germany Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States FEMTO-ST Institute Optics Department CNRS University Bourgogne Franche-Comté Besançon25030 Cedex France Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom NTT Physics and Informatics Laboratories NTT Research Inc. Sunnyvale United States Lausanne Switzerland Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Univ Rennes CNRS IETR UMR 6164 RennesF-35000 France IBM Research Europe– Zurich Rüschlikon8803 Switzerland Department of Computer Science Stanford University United States Google DeepMind 1600 Amphitheatre Parkway Mountain ViewCA94043 United States Unité Mixte de Physique CNRS/Thales CNRS Thales Université Paris-Saclay Palaiseau France Laboratoire Kastler Brossel Sorbonne Université École Normale Supérieure Collège de France CNRS UMR 8552 Paris France Laboratoire Albert Fert CNRS Thales UniversitéParis-Saclay Palaiseau91767 France Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Lausanne Switzerland Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States

Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern artificial intelligence (AI). Could we train AI models 1000x larger than current ones? Could we do this and also have them perform inference locally and privately on edge devices, such as smartphones or sensors? Research over the past few years has shown that the answer to all these questions is likely "textityes, with enough research": PNNs could one day radically change what is possible and practical for AI systems. To do this will however require rethinking both how AI models work, and how they are trained – primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs at large scale, many methods including backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs, and so far no method has been shown to scale to the same scale and performance as the backpropagation algorithm widely used in deep learning today. However, this is rapidly changing, and a diverse ecosystem of training techniques provides clues for how PNNs may one day be utilized to create both more efficient realizations of current-scale AI models, and to enable unprecedented-scale models. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Observation of the Magnonic Dicke Superradiant Phase Transition

arXiv

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arXiv 2024年

作者： Kim, Dasom Dasgupta, Sohail Ma, Xiaoxuan Park, Joong-Mok Wei, Hao-Tian Luo, Liang Doumani, Jacques Li, Xinwei Yang, Wanting Cheng, Di Kim, Richard H.J. Everitt, Henry O. Kimura, Shojiro Nojiri, Hiroyuki Wang, Jigang Cao, Shixun Bamba, Motoaki Hazzard, Kaden R.A. Kono, Junichiro Applied Physics Graduate Program Smalley–Curl Institute Rice University HoustonTX77005 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Ames National Laboratory AmesIA50011 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States Department of Physics International Center of Quantum and Molecular Structures Materials Genome Institute Shanghai University Shanghai200444 China Department of Physics National University of Singapore 117551 Singapore Department of Physics and Astronomy Iowa State University AmesIA50011 United States Smalley–Curl Institute Rice University HoustonTX77005 United States DEVCOM Army Research Laboratory-South HoustonTX77005 United States Institute for Materials Research Tohoku University Sendai980-8577 Japan Department of Physics Yokohama National University Yokohama240-8501 Japan Department of Physics and Astronomy University of California DavisCA95616 United States Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States

Two-level atoms coupled with single-mode cavity photons are predicted to exhibit a quantum phase transition when the coupling strength exceeds a critical value, entering a phase in which atomic polarization and photonic field are finite even at zero temperature and without external driving. However, this phenomenon, the superradiant phase transition (SRPT), is forbidden by a no-go theorem due to the existence of the diamagnetic term in the Hamiltonian. Here, we present spectroscopic evidence for a magnonic SRPT in ErFeO3, where the role of the photonic mode (two-level atoms) in the photonic SRPT is played by an Fe3+ magnon mode (Er3+ spins). The absence of the diamagnetic term in the Fe3+–Er3+ exchange coupling ensures that the no-go theorem does not apply. Terahertz and gigahertz magnetospectroscopy experiments revealed the signatures of the SRPT – a kink and a softening, respectively, of two spin–magnon hybridized modes at the critical point. Copyright © 2024, The Authors. All rights reserved.

关键词： Phase transitions

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Zeeman polaritons as a platform for probing Dicke physics in condensed matter

arXiv

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arXiv 2024年

作者： Kritzell, T. Elijah Doumani, Jacques Asano, Tobias Yamada, Sota Tay, Fuyang Xu, Hongjing Yan, Han Katayama, Ikufumi Takeda, Jun Nevidomskyy, Andriy Nojiri, Hiroyuki Bamba, Motoaki Baydin, Andrey Kono, Junichiro Applied Physics Graduate Program Smalley-Curl Institute Rice University 6100 Main Street HoustonTX77005 United States Department of Electrical and Computer Engineering Rice University 6100 Main Street HoustonTX77005 United States Department of Physics Yokohama National University 79-5 Tokiwadai Hodogaya-ku Kanagawa Yokohama240-8501 Japan Department of Physics and Astronomy Rice University 6100 Main Street HoustonTX77005 United States Smalley-Curl Institute Rice University 6100 Main Street HoustonTX77005 United States Institute for Multidisciplinary Sciences Yokohama National University 79-5 Tokiwadai Hodogaya-ku Kanagawa Yokohama240-8501 Japan Institute for Materials Research Tohoku University 2-1-1 Katahira Aoba-ku Miyagi Sendai980-8577 Japan Rice Advanced Materials Institute Rice University 6100 Main Street HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University 6100 Main Street HoustonTX77005 United States

The interaction of an ensemble of two-level atoms and a quantized electromagnetic field, described by the Dicke Hamiltonian, is an extensively studied problem in quantum optics. However, experimental efforts to explore similar physics in condensed matter typically employ bosonic matter modes (e.g., phonons, magnons, and plasmons) that are describable as simple harmonic oscillators, i.e., an infinite ladder of equally spaced energy levels. Here, we examine ultrastrong coupling between a coherent light mode and an ensemble of paramagnetic spins, a finite-multilevel system, in Gd3Ga5O12. The electron paramagnetic resonance of Gd3+ ions is tuned by a magnetic field into resonance with a Fabry–Pérot cavity mode, resulting in the formation of spin–photon hybrid states, or Zeeman polaritons. We observe that the light–matter coupling strength, measured through the vacuum Rabi splitting, decreases with increasing temperature, which can be explained by the temperature-dependent population difference between the lower and higher-energy states, a trait of a finite-level system. This finding demonstrates that a spin–boson system is more compatible with the Dicke model and has advantages over boson–boson systems for pursuing experimental realizations of phenomena predicted for ultrastrongly coupled light–matter hybrids. Copyright © 2024, The Authors. All rights reserved.

关键词： Phonons

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Project 1640 observations of brown dwarf GJ 758 B: Near-infrared spectrum and atmospheric modeling

arXiv

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arXiv 2017年

作者： Nilsson, R. Veicht, A. Giorla Godfrey, P.A. Rice, E.L. Aguilar, J. Pueyo, L. Roberts, L.C. Oppenheimer, R. Brenner, D. Luszcz-Cook, S.H. Bacchus, E. Beichman, C. Burruss, R. Cady, E. Dekany, R. Fergus, R. Hillenbrand, L. Hinkley, S. King, D. Lockhart, T. Parry, I.R. Sivaramakrishnan, A. Soummer, R. Vasisht, G. Zhai, C. Zimmerman, N.T. Astrophysics Department American Museum of Natural History Central Park West at 79th Street New YorkNY10024 United States Department of Astronomy Stockholm University AlbaNova University Center Roslagstullsbacken 21 StockholmSE-106 91 Sweden Department of Astronomy California Institute of Technology 1200 E. California Blvd PasadenaCA91125 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States Department of Engineering Science & Physics College of Staten Island 2800 Victory Blvd. Staten IslandNY10314 United States Department of Physics and Astronomy Johns Hopkins University BaltimoreMD21218 United States Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Dr. PasadenaCA91109 United States Institute of Astronomy Cambridge University Madingley Road CambridgeCB3 0HA United Kingdom NASA Exoplanet Science Institute California Institute of Technology PasadenaCA91125 United States Caltech Optical Observatories California Institute of Technology PasadenaCA91125 United States Department of Computer Science Courant Institute of Mathematical Sciences New York University 715 Broadway New YorkNY10003 United States School of Physics University of Exeter Stocker Road ExeterEX4 4QL United Kingdom

The nearby Sun-like star GJ 758 hosts a cold substellar companion, GJ 758 B, at a projected separation of 30 au, previously detected in high-contrast multi-band photometric observations. In order to better constrain the companion’s physical characteristics, we acquired the first low-resolution (R ∼ 50) near-infrared spectrum of it using the high-contrast hyperspectral imaging instrument Project 1640 on Palomar Observatory’s 5 m Hale telescope. We obtained simultaneous images in 32 wavelength channels covering the Y , J, and H bands (∼952–1770 nm), and used data processing techniques based on principal component analysis to efficiently subtract chromatic background speckle-noise. GJ 758 B was detected in four epochs during 2013 and 2014. Basic astrometric measurements confirm its apparent northwest trajectory relative to the primary star, with no clear signs of orbital curvature. Spectra of SpeX/IRTF observed T dwarfs were compared to the combined spectrum of GJ 758 B, with χ2 minimization suggesting a best fit for spectral type T7.0±1.0, but with a shallow minimum over T5–T8. Fitting of synthetic spectra from the BT-Settl13 model atmospheres gives an effective temperature Teff = 741 ± 25 K and surface gravity log g = 4.3 ± 0.5 dex (cgs). Our derived best-fit spectral type and effective temperature from modeling of the low-resolution spectrum suggest a slightly earlier and hotter companion than previous findings from photometric data, but do not rule out current results, and confirm GJ 758 B as one of the coolest sub-stellar companions to a Sun-like star to date. Copyright © 2017, The Authors. All rights reserved.

关键词： Principal component analysis

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Galaxy clusters at z∼1 imaged by ALMA with the Sunyaev-Zel’dovich effect

arXiv

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arXiv 2022年

作者： Kitayama, Tetsu Ueda, Shutaro Okabe, Nobuhiro Akahori, Takuya Hilton, Matt Hughes, John P. Ichinohe, Yuto Kohno, Kotaro Komatsu, Eiichiro Lin, Yen-Ting Miyatake, Hironao Oguri, Masamune Sifón, Cristóbal Takakuwa, Shigehisa Takizawa, Motokazu Tsutsumi, Takahiro van Marrewijk, Joshiwa Wollack, Edward J. Department of Physics Toho University Chiba Funabashi274-8510 Japan No. 1 Section 4 Roosevelt Road Taipei10617 Taiwan Institute of Astronomy National Tsing Hua University Hsinchu30013 Taiwan Department of Physics Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Astrophysical Science Center Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Core Research for Energetic Universe Hiroshima University Department of Physics Hiroshima University 1-3-1 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Physics Program Graduate School of Advanced Science and Engineering Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Mizusawa VLBI observatory National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Operation Division SKA Observatory Jodrell Bank Lower Withington MacclesfieldSK11 9DL United Kingdom Astrophysics Research Centre University of KwaZulu-Natal Westville Campus Durban4041 South Africa School of Mathematics Statistics & Computer Science University of KwaZulu-Natal Westville Campus Durban4041 South Africa Wits Centre for Astrophysics School of Physics University of the Witwatersrand Private Bag 3 Johannesburg2050 South Africa Department of Physics and Astronomy Rutgers The State University of New Jersey PiscatawayNJ08854-8019 United States Department of Physics Rikkyo University 3-34-1 Nishi-Ikebukuro Toshima-ku Tokyo171-8501 Japan Institute of Astronomy School of Science The University of Tokyo 2-21-1 Osawa Mitaka Tokyo181-0015 Japan Research Center for the Early Universe School of Science The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-0033 Japan Max-Planck-Institut für Astrophysik Karl-Schwarzschild Str. 1 GarchingD-85741 Germany The University of Tokyo Institutes for Advanced Study The University of Tokyo 5-1-5 Kashiwanoha Chiba277-8583 Japan Nagoya University Nagoya464-8

We present high angular-resolution measurements of the thermal Sunyaev-Zel’dovich effect (SZE) toward two galaxy clusters, RCS J2319+0038 at z = 0.9 and HSC J0947−0119 at z = 1.1, by the Atacama Large Millimeter/submillimeter Array (ALMA) in Band 3. They are supplemented with available Chandra X-ray data, optical data taken by Hyper Suprime-Cam on Subaru, and millimeter-wave SZE data from the Atacama Cosmology Telescope. Taking into account departures from spherical symmetry, we have reconstructed non-parametrically the inner pressure profile of two clusters as well as electron temperature and density profiles for RCS J2319+0038. This is one of the first such measurements for an individual cluster at z >∼ 0.9. We find that the inner pressure profile of both clusters is much shallower than that of local cool-core clusters. Our results consistently suggest that RCS J2319+0038 hosts a weak cool core, where radiative cooling is less significant than in local cool cores. On the other hand, HSC J0947−0119 exhibits an even shallower pressure profile than RCS J2319+0038 and is more likely a non-cool-core cluster. The SZE centroid position is offset by more than 140 h−170kpc from the peaks of galaxy distribution in HSC J0947−0119, suggesting a stronger influence of mergers in this cluster. We conclude that these distant clusters are at a very early stage of developing the cool cores typically found in clusters at lower redshifts. Copyright © 2022, The Authors. All rights reserved.

关键词： Millimeter waves

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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

arXiv

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Corrigendum to "A Gene Module-Based eQTL Analysis Prioritizing Disease Genes and Pathways in Kidney Cancer"

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Computational and structural biotechnology journal 2018年 16卷 196页

作者： M Q Yang D Li W Yang Y Zhang J Liu W Tong Joint Bioinformatics Graduate Program Department of Information Science George W. Donaghey College of Engineering and Information Technology University of Arkansas at Little Rock USA. University of Arkansas for Medical Sciences 2801 S. University Ave Little Rock AR 72204 USA. School of Computer Science Carnegie Mellon University 5000 Forbes Ave Pittsburgh PA 15213 USA. Department of Statistics Harvard University Cambridge MA 02138 USA. Divisions of Bioinformatics and Biostatistics National Center for Toxicological Research US Food and Drug Administration 3900 NCTR Road Jefferson AR 72079 USA.

[This corrects the article DOI: 10.1016/***.2017.09.003.].

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