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On the “Preconditioning” Function Used in Planewave DFT Calculations and its Generalization

Communications in computational Physics 2015年第6期18卷 167-179页

作者： Yunkai Zhou James R.Chelikowsky Xingyu Gao Aihui Zhou Department of Mathematics Southern Methodist UniversityDallasTX 75275USA Center for Computational Materials Institute for Computational Engineering and Scienceand Departments of Physics and Chemical EngineeringUniversity of TexasAustinTX 78712USA HPCC Institute of Applied Physics and Computational MathematicsBeijing100094China LSEC Institute of Computational Mathematics and Scientific/Engineering ComputingAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China

The Teter,Payne,and Allan“preconditioning”function plays a significant role in planewave DFT *** function is often called the TPA *** present a detailed study of this“preconditioning”*** develop a general formula that can readily generate a class of“preconditioning”*** functions have higher order approximation accuracy and fulfill the two essential“preconditioning”purposes as required in planewave DFT *** general class of functions are expected to have applications in other areas.

关键词： Density functional theory planewave preconditioning function eigenvalue problem

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Generalized pseudopolar format algorithm for radar imaging with highly suboptimal aperture length

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science China(Information sciences) 2015年第4期58卷 63-77页

作者： HAN KuoYe WANG YanPing CHANG XiangKe TAN WeiXian HONG Wen Science and Technology on Microwave Imaging Laboratory (MITL) Institute of ElectronicsChinese Academy of Sciences (IECAS) University of Chinese Academy of Sciences (UCAS) Institute of Computational Mathematics and Scientific Engineering Computing Chinese Academy of Sciences (ICMSEC CAS)

Different from conventional spaceborne or airborne synthetic aperture radar(SAR) with optimal aperture length, an imaging radar with highly suboptimal aperture length acquires the data in short bursts by a geometry spreading over a large range. A polarlike or pseudopolar format grid is introduced to sample data close to the resolution, which presents the design of a separable kernel for efficient FFT *** proposed imaging algorithm formulates the reflectivity image of the target scene as an interpolation-free double image series expansion with two usual approximation-induced phase error terms being taken into account,whereby more generalized application scenarios with high frequency, large bandwidth or larger aperture length for imaging a target scene located within either the far-field or the near-field of the radar aperture are processable with high accuracy. In addition, convergence acceleration methods in computational mathematics are introduced to accelerate the convergence of the image series expansion, so as to make the algorithm more efficient. The proposed algorithm has been validated both qualitatively and quantitatively with an extensive collection of numerical simulations and field measurements of ground-based SAR(GB-SAR) data set.

关键词： pseudopolar format ε-algorithm convergence acceleration suboptimal imaging radar imaging synthetic aperture radar(SAR)

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Parkes Pulsar Timing Array constraints on ultralight scalar-field dark matter

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Physical Review D 2018年第10期98卷 102002-102002页

作者： Nataliya K. Porayko Xingjiang Zhu Yuri Levin Lam Hui George Hobbs Aleksandra Grudskaya Konstantin Postnov Matthew Bailes N. D. Ramesh Bhat William Coles Shi Dai James Dempsey Michael J. Keith Matthew Kerr Michael Kramer Paul D. Lasky Richard N. Manchester Stefan Osłowski Aditya Parthasarathy Vikram Ravi Daniel J. Reardon Pablo A. Rosado Christopher J. Russell Ryan M. Shannon Renée Spiewak Willem van Straten Lawrence Toomey Jingbo Wang Linqing Wen Xiaopeng You Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 D-53121 Bonn Germany School of Physics and Astronomy Monash University Clayton VIC 3800 Australia School of Physics University of Western Australia Crawley WA 6009 Australia OzGrav: Australian Research Council Centre of Excellence for Gravitational Wave Discovery Hawthorn VIC 3122 Australia Center for Theoretical Physics Department of Physics Columbia University New York New York 10027 USA Center for Computational Astrophysics Flatiron Institute New York New York 10010 USA CSIRO Astronomy and Space Science P.O. Box 76 Epping NSW 1710 Australia Sternberg Astronomical Institute Lomonosov Moscow State University Universitetskii pr. 13 Moscow 119234 Russia Kazan Federal University Kremlevskaya 18 Kazan 420008 Russia Centre for Astrophysics and Supercomputing Swinburne University of Technology P.O. Box 218 Hawthorn VIC 3122 Australia International Centre for Radio Astronomy Research Curtin University Bentley WA 6102 Australia Department of Electrical and Computer Engineering University of California at San Diego La Jolla California 92093 USA CSIRO Information Management and Technology PO Box 225 Dickson ACT 2602 Australia Jodrell Bank Center for Astrophysics University of Manchester Manchester M13 9PL UK Space Science Division Naval Research Laboratory Washington DC 20375 USA Cahill Center for Astronomy and Astrophysics MC 249-17 California Institute of Technology Pasadena California 91125 USA CSIRO Scientific Computing Australian Technology Park Locked Bag 9013 Alexandria NSW 1435 Australia Institute for Radio Astronomy & Space Research Auckland University of Technology Private Bag 92006 Auckland 1142 New Zealand Xinjiang Astronomical Observatory Chinese Academy of Sciences 150 Science 1-Street Urumqi Xinjiang 830011 China School of Physical Science and Technology Southwest University Chongqing 400715 China

It is widely accepted that dark matter contributes about a quarter of the critical mass-energy density in our Universe. The nature of dark matter is currently unknown, with the mass of possible constituents spanning nearly one hundred orders of magnitude. The ultralight scalar field dark matter, consisting of extremely light bosons with m∼10−22 eV and often called “fuzzy” dark matter, provides intriguing solutions to some challenges at sub-Galactic scales for the standard cold dark matter model. As shown by Khmelnitsky and Rubakov, such a scalar field in the Galaxy would produce an oscillating gravitational potential with nanohertz frequencies, resulting in periodic variations in the times of arrival of radio pulses from pulsars. The Parkes Pulsar Timing Array (PPTA) has been monitoring 20 millisecond pulsars at two- to three-week intervals for more than a decade. In addition to the detection of nanohertz gravitational waves, PPTA offers the opportunity for direct searches for fuzzy dark matter in an astrophysically feasible range of masses. We analyze the latest PPTA data set which includes timing observations for 26 pulsars made between 2004 and 2016. We perform a search in this data set for evidence of ultralight dark matter in the Galaxy using Bayesian and Frequentist methods. No statistically significant detection has been made. We, therefore, place upper limits on the local dark matter density. Our limits, improving on previous searches by a factor of 2 to 5, constrain the dark matter density of ultralight bosons with m≤10−23 eV to be below 6 GeV cm−3 with 95% confidence in the Earth neighborhood. Finally, we discuss the prospect of probing the astrophysically favored mass range m≳10−22 eV with next-generation pulsar timing facilities.

关键词： Dark matter Neutron stars & pulsars Electromagnetic radiation astronomy Gravitational wave detection

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Estimating communication skills based on multimodal information in group discussions

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Transactions of the Japanese Society for Artificial Intelligence 2016年第6期31卷

作者： Okada, Shogo Matsugi, Yoshihiro Nakano, Yukiko Hayashi, Yuki Huang, Hung-Hsuan Takase, Yutaka Nitta, Katsumi School of Computing Tokyo Institute of Technology Japan Department of Computational Intelligence and Systems Interdisciplinary Graduate School of Science and Engineering Tokyo Institute of Technology Japan Department of Computer and Information Science Seikei University Japan College of Sustainable System Sciences Osaka Prefecture University Japan Department of Information and Communication Science College of Information Science and Engineering Ritsumeikan University Japan

This paper focuses on developing a model for estimating communication skills of each participant in a group from multimodal (verbal and nonverbal) features. For this purpose, we use a multimodal group meeting corpus including audio signal data and head motion sensor data of participants observed in 30 group meeting sessions. The corpus also includes the communication skills of each participant, which is assessed by 21 external observers with the experience of human resource management. We extracted various kinds of features such as spoken utterances, acoustic features, speaking turns and the amount of head motion to estimate the communication skills. First, we created a regression model to infer the level of communication skills from these features using support vector regression to evaluate the estimation accuracy of the communication skills. Second, we created a binary (high or low) classification model using support vector machine. Experiment results show that the multimodal model achieved 0.62 in R2 as the regression accuracy of overall skill, and also achieved 0.93 as the classification accuracy. This paper reports effective features in predicting the level of communication skill and shows that these features are also useful in characterizing the difference between the participants who have high level communication skills and those who do not. © 2016, Japanese Society for Artificial Intelligence. All rights reserved.

关键词： Human resource management

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Machine Learning Unifies the Modelling of Materials and Molecules

arXiv

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arXiv 2017年

作者： Bartók, Albert P. De, Sandip Poelking, Carl Bernstein, Noam Kermode, James Csányi, Gábor Ceriotti, Michele Scientific Computing Department Science and Technology Facilities Council Rutherford Appleton Laboratory OxfordshireOX11 0QX United Kingdom Laboratory of Computational Science and Modelling Institute of Materials EPFL Lausanne Switzerland Department of Chemistry University of Cambridge CambridgeCB2 1EW United Kingdom Center for Materials Physics and Technology U.S. Naval Research Laboratory WashingtonDC20375 United States Warwick Centre for Predictive Modelling School of Engineering University of Warwick CoventryCV4 7AL United Kingdom Engineering Laboratory University of Cambridge

Determining the stability of molecules and condensed phases is the cornerstone of atomistic modelling, underpinning our understanding of chemical and materials properties and transformations. Here we show that a machine-learning model, based on a local description of chemical environments and Bayesian statistical learning, provides a unified framework to predict atomic-scale properties. It captures the quantum mechanical effects governing the complex surface reconstructions of silicon, predicts the stability of different classes of molecules with chemical accuracy, and distinguishes active and inactive protein ligands with more than 99% reliability. The universality and the systematic nature of our framework provides new insight into the potential energy surface of materials and molecules. Copyright © 2017, The Authors. All rights reserved.

关键词： Molecules

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Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care

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Nature communications 2019年第1期10卷 588页

作者： Lena Maier-Hein Matthias Eisenmann Annika Reinke Sinan Onogur Marko Stankovic Patrick Scholz Tal Arbel Hrvoje Bogunovic Andrew P Bradley Aaron Carass Carolin Feldmann Alejandro F Frangi Peter M Full Bram van Ginneken Allan Hanbury Katrin Honauer Michal Kozubek Bennett A Landman Keno März Oskar Maier Klaus Maier-Hein Bjoern H Menze Henning Müller Peter F Neher Wiro Niessen Nasir Rajpoot Gregory C Sharp Korsuk Sirinukunwattana Stefanie Speidel Christian Stock Danail Stoyanov Abdel Aziz Taha Fons van der Sommen Ching-Wei Wang Marc-André Weber Guoyan Zheng Pierre Jannin Annette Kopp-Schneider Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. l.maier-hein@dkfz.de. Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Centre for Intelligent Machines McGill University Montreal QC H3A0G4 Canada. Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna 1090 Vienna Austria. Science and Engineering Faculty Queensland University of Technology Brisbane QLD 4001 Australia. Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore MD 21218 USA. CISTIB - Center for Computational Imaging & Simulation Technologies in Biomedicine The University of Leeds Leeds Yorkshire LS2 9JT UK. Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center 6525 GA Nijmegen The Netherlands. Institute of Information Systems Engineering TU Wien 1040 Vienna Austria. Complexity Science Hub Vienna 1080 Vienna Austria. Heidelberg Collaboratory for Image Processing (HCI Heidelberg University 69120 Heidelberg Germany. Centre for Biomedical Image Analysis Masaryk University 60200 Brno Czech Republic. Electrical Engineering Vanderbilt University Nashville TN 37235-1679 USA. Institute of Medical Informatics Universität zu Lübeck 23562 Lübeck Germany. Division of Medical Image Computing (MIC) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Institute for Advanced Studies Department of Informatics Technical University of Munich 80333 Munich Germany. Information System Institute HES-SO Sierre 3960 Switzerland. Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC 3015 GD Rotterdam The Netherlands. Department of Computer Science University of Warwick Coventry CV4 7AL UK. Department of Radiation Oncology Massachusetts General Hospital Boston MA

In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.

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Batched Generation of Incomplete Sparse Approximate Inverses on GPUs

Batched Generation of Incomplete Sparse Approximate Inverses...

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Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA)

作者： Hartwig Anzt Edmond Chow Thomas Huckle Jack Dongarra Innovative Computing Laboratory University of Tennessee School of Computational Science and Engineering Georgia Institute of Technology Department of Informatics Technical University Munich University of Manchester Oak Ridge National Laboratory

ISBN: (纸本)9781509052233

Incomplete Sparse Approximate Inverses (ISAI) have recently been shown to be an attractive alternative to exact sparse triangular solves in the context of incomplete factorization preconditioning. In this paper we propose a batched GPU-kernel for the efficient generation of ISAI matrices. Utilizing only thread-local memory allows for computing the ISAI matrix with very small memory footprint. We demonstrate that this strategy is faster than the existing strategy for generating ISAI matrices, and use a large number of test matrices to assess the algorithm's efficiency in an iterative solver setting.

关键词： Sparse matrices Kernel Instruction sets Linear systems Jacobian matrices Approximation algorithms Context

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Authorship Correction: International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study

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Journal of medical Internet research 2021年第11期23卷 e34625页

作者： Griffin M Weber Harrison G Zhang Sehi L'Yi Clara-Lea Bonzel Chuan Hong Paul Avillach Alba Gutiérrez-Sacristán Nathan P Palmer Amelia Li Min Tan Xuan Wang William Yuan Nils Gehlenborg Anna Alloni Danilo F Amendola Antonio Bellasi Riccardo Bellazzi Michele Beraghi Mauro Bucalo Luca Chiovato Kelly Cho Arianna Dagliati Hossein Estiri Robert W Follett Noelia García Barrio David A Hanauer Darren W Henderson Yuk-Lam Ho John H Holmes Meghan R Hutch Ramakanth Kavuluru Katie Kirchoff Jeffrey G Klann Ashok K Krishnamurthy Trang T Le Molei Liu Ne Hooi Will Loh Sara Lozano-Zahonero Yuan Luo Sarah Maidlow Adeline Makoudjou Alberto Malovini Marcelo Roberto Martins Bertrand Moal Michele Morris Danielle L Mowery Shawn N Murphy Antoine Neuraz Kee Yuan Ngiam Marina P Okoshi Gilbert S Omenn Lav P Patel Miguel Pedrera Jiménez Robson A Prudente Malarkodi Jebathilagam Samayamuthu Fernando J Sanz Vidorreta Emily R Schriver Petra Schubert Pablo Serrano Balazote Byorn Wl Tan Suzana E Tanni Valentina Tibollo Shyam Visweswaran Kavishwar B Wagholikar Zongqi Xia Daniela Zöller Isaac S Kohane Tianxi Cai Andrew M South Gabriel A Brat Department of Biomedical Informatics Harvard Medical School Boston MA United States. BIOMERIS (BIOMedical Research Informatics Solutions) Pavia Italy. Clinical Research Unit Botucatu Medical School São Paulo State University Botucatu Brazil. Division of Nephrology Department of Medicine Ente Ospedaliero Cantonale Lugano Switzerland. Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy. Information Technology Department Azienda Socio-Sanitaria Territoriale di Pavia Pavia Italy. Unit of Internal Medicine and Endocrinology Istituti Clinici Scientifici Maugeri SpA SB IRCCS Pavia Italy. Massachusetts Veterans Epidemiology Research and Information Center Veterans Affairs Boston Healthcare System Boston MA United States. Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy. Department of Medicine Massachusetts General Hospital Boston MA United States. Department of Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA United States. Health Informatics Hospital Universitario 12 de Octubre Madrid Spain. Department of Learning Health Sciences University of Michigan Medical School Ann Arbor MI United States. Department of Biomedical Informatics University of Kentucky Lexington KY United States. Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine Philadelphia PA United States. Institute for Biomedical Informatics University of Pennsylvania Perelman School of Medicine Philadelphia PA United States. Department of Preventive Medicine Northwestern University Chicago IL United States. Institute for Biomedical Informatics University of Kentucky Lexington KY United States. Medical University of South Carolina Charleston SC United States. Department of Computer Science Renaissance Computing Institute University of North Carolina at Chapel Hill Chapel Hill NC United States. Departm

[This corrects the article DOI: 10.2196/31400.].

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Track 1-A Study of Nanodroplet Diffusion on Smooth Surfaces

Track 1-A Study of Nanodroplet Diffusion on Smooth Surfaces

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2016年大连国际微纳流体和微流控芯片大会(2016 International Conference of Microfluidics, Nanofluidics and Lab-On-A-Chip

作者： Chu Li Jizu Huang Zhigang Li Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China

In this study, we investigate how nanodroplets diffuse on smooth surfaces through molecular dynamics (MD) simulations and theoretical *** dynamics simulations show that nanodroplet surface diffusion is different from that of single molecules and solid *** dependence of nanodroplet diffusion coefficient on temperature undergoes a transition from linear to nonlinear as the surface wettability is weakened due to the coupling of temperature and surface *** also develop a simple relation for the diffusion coefficient by using the contact angle and contact radius of the *** works well for a wide range of surface wettabilities and different sized nanodroplets, as confirmed by MD simulations.

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