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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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SimTCM: A human patient simulator with application to diagnostic accuracy studies of Chinese medicine

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Journal of Integrative Medicine 2015年第1期13卷 9-19页

作者： Arthur de Sá Ferreira Antonio Guilherme Pacheco Postgraduation Program in Rehabilitation Science Augusto Motta University Center Program of Scientific Computing

OBJECTIVE： The aim of this work is to develop and implement the SimTCM, an advanced computationa model that incorporates relevant aspects from traditional Chinese medicine （TCM） theory as well as advanced statistical and epidemiological techniques for simulation and analysis of human patients. METHODS： SimTCM presents five major attributes for simulation： representation of true and false profiles for any single pattern; variable count of manifestations for each manifestation profile; empirical distributions of patterns and manifestations in a disease-specific population; incorporation of uncertainty in clinical data; and the combination of the four examinations. The proposed model is strengthened by following international standards for reporting diagnostic accuracy studies, and incorporates these standards in its treatment of study population, sample size calculation, data collection of manifestation profiles, exclusion criteria and missing data handling, reference standards, randomization and blinding and test reproducibility. RESULTS： Simulations using data from patients diagnosed with hypertension and post-stroke sensory- motor impairments yielded no significant differences between expected and simulated frequencies of patterns （P = 0.22 or higher）. Time for convergence of simulations varied from 9.90 s （9.80, 10.27） to 28.31 s （26.33, 29.52）. The ratio iteration profile necessary for convergence varied between 1：1 and 5：1. CONCLUSION： This model is directly connected to forthcoming models in a large project to design and implement the Suite TCM： ProntTCM, SciTCM, DiagTCM, StudentTCM, Research TCM, HerbsTCM, AcuTCM, and DataTCM. It is expected that the continuity of the SuiteTCM project enhances the evidence-based practice of Chinese medicine.

关键词： medicine, Chinese traditional evidence-based medicine computer-assisted decision making rehabilitation medicine （TCM）

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Heavy Physics Contributions to Neutrinoless Double Beta Decay from QCD

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Physical Review Letters 2018年第17期121卷 172501-172501页

作者： A. Nicholson E. Berkowitz H. Monge-Camacho D. Brantley N. Garron C. C. Chang E. Rinaldi M. A. Clark B. Joó T. Kurth B. C. Tiburzi P. Vranas A. Walker-Loud Department of Physics and Astronomy University of North Carolina Chapel Hill North Carolina 27516-3255 USA Department of Physics University of California Berkeley California 94720 USA Institut für Kernphysik and Institute for Advanced Simulation Forschungszentrum Jülich 54245 Jülich Germany Department of Physics The College of William & Mary Williamsburg Virginia 23187 USA Nuclear Science Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Physics Division Lawrence Livermore National Laboratory Livermore California 94550 USA Theoretical Physics Division Department of Mathematical Sciences University of Liverpool Liverpool L69 3BX United Kingdom Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS) RIKEN 2-1 Hirosawa Wako Saitama 351-0198 Japan RIKEN-BNL Research Center Brookhaven National Laboratory Upton New York 11973 USA NVIDIA Corporation 2701 San Tomas Expressway Santa Clara California 95050 USA Scientific Computing Group Thomas Jefferson National Accelerator Facility Newport News Virginia 23606 USA NERSC Lawrence Berkeley National Laboratory Berkeley California 94720 USA Department of Physics The City College of New York New York New York 10031 USA Graduate School and University Center The City University of New York New York New York 10016 USA

Observation of neutrinoless double beta decay, a lepton number violating process that has been proposed to clarify the nature of neutrino masses, has spawned an enormous world-wide experimental effort. Relating nuclear decay rates to high-energy, beyond the standard model (BSM) physics requires detailed knowledge of nonperturbative QCD effects. Using lattice QCD, we compute the necessary matrix elements of short-range operators, which arise due to heavy BSM mediators, that contribute to this decay via the leading order π−→π+ exchange diagrams. Utilizing our result and taking advantage of effective field theory methods will allow for model-independent calculations of the relevant two-nucleon decay, which may then be used as input for nuclear many-body calculations of the relevant experimental decays. Contributions from short-range operators may prove to be equally important to, or even more important than, those from long-range Majorana neutrino exchange.

关键词： Lattice QCD Lattice field theory Neutrinoless double beta decay

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Discounted Markov Decision Processes via Time Aggregation

Discounted Markov Decision Processes via Time Aggregation

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European Control Conference

作者： Edilson F. Arruda Marcelo D. Fragoso Industrial Engineering Program Alberto Luiz Coimbra Institute - Graduate School and Research in Engineering Federal University of Rio de Janeiro Rio de Janeiro RJ Brazil Center for Systems and Control National Laboratory for Scientific Computing Petropolis RJ Brazil

ISBN: (纸本)9781509025923

This paper applies two-phase time aggregation to solve discounted Markov decision processes (MDP). This procedure, recently proposed for average cost MDPs, is extended here discounted MDPs with a view at easing the computational burden associated to finding a quality solution within a reasonable time frame. Numerical examples are presented to illustrate the results.

关键词： Discounted Markov Decision Processes Time Aggregation

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Searching For Gravitational Waves From Cosmological Phase Transitions With The NANOGrav 12.5-year dataset

arXiv

引用

arXiv 2021年

作者： Arzoumanian, Zaven Baker, Paul T. Blumer, Harsha Bécsy, Bence Brazier, Adam Brook, Paul R. Burke-Spolaor, Sarah Charisi, Maria Chatterjee, Shami Chen, Siyuan Cordes, James M. Cornish, Neil J. Crawford, Fronefield Cromartie, H. Thankful DeCesar, Megan E. Demorest, Paul B. Dolch, Timothy Ellis, Justin A. Ferrara, Elizabeth C. Fiore, William Fonseca, Emmanuel Garver-Daniels, Nathan Gentile, Peter A. Good, Deborah C. Hazboun, Jeffrey S. Holgado, A. Miguel Islo, Kristina Jennings, Ross J. Jones, Megan L. Kaiser, Andrew R. Kaplan, David L. Kelley, Luke Zoltan Key, Joey Shapiro Laal, Nima Lam, Michael T. Lazio, T. Joseph W. Lee, Vincent S.H. Lorimer, Duncan R. Luo, Jing Lynch, Ryan S. Madison, Dustin R. McLaughlin, Maura A. Mingarelli, Chiara M.F. Mitridate, Andrea Ng, Cherry Nice, David J. Pennucci, Timothy T. Pol, Nihan S. Ransom, Scott M. Ray, Paul S. Shapiro-Albert, Brent J. Siemens, Xavier Simon, Joseph Spiewak, Renée Stairs, Ingrid H. Stinebring, Daniel R. Stovall, Kevin Sun, Jerry P. Swiggum, Joseph K. Taylor, Stephen R. Turner, Jacob E. Vallisneri, Michele Vigeland, Sarah J. Witt, Caitlin A. Zurek, Kathryn M. X-Ray Astrophysics Laboratory NASA Goddard Space Flight Center Code 662 GreenbeltMD20771 United States Department of Physics and Astronomy Widener University One University Place ChesterPA19013 United States Department of Physics and Astronomy West Virginia University P.O. Box 6315 MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States Department of Physics Montana State University BozemanMT59717 United States Cornell Center for Astrophysics and Planetary Science Department of Astronomy Cornell University IthacaNY14853 United States Cornell Center for Advanced Computing Cornell University IthacaNY14853 United States CIFAR Azrieli Global Scholars Program CIFAR Toronto Canada Department of Physics and Astronomy Vanderbilt University 2301 Vanderbilt Place NashvilleTN37235 United States Station de Radioastronomie de Nancay Observatoire de Paris Universite PSL CNRS Universite d’Orleans Nancay18330 France FEMTO-ST Institut de recherche Department of Time and Frequency UBFC CNRS ENSMM Besancon25030 France Laboratoire de Physique et Chimie de l’Environment et de l’Espace LPC2E UMR7328 Universite d’Orleans CNRS Orleans45071 France Department of Physics and Astronomy Franklin & Marshall College P.O. Box 3003 LancasterPA17604 United States Department of Physics Lafayette College EastonPA18042 United States George Mason University FairfaxVA22030 United States U.S. Naval Research Laboratory WashingtonDC20375 United States National Radio Astronomy Observatory 1003 Lopezville Rd. SocorroNM87801 United States Department of Physics Hillsdale College 33 E. College Street HillsdaleMI49242 United States Eureka Scientific Inc. 2452 Delmer Street Suite 100 OaklandCA94602-3017 United States Infinia ML 202 Rigsbee Avenue DurhamNC27701 United States Department of Astronomy University of Maryland College ParkMD20742

We search for a first-order phase transition gravitational wave signal in 45 pulsars from the NANOGrav 12.5 year dataset. We find that the data can be modeled in terms of a strong first order phase transition taking place at temperatures below the electroweak scale. However, we do not observe any strong preference for a phase-transition interpretation of the signal over the standard astrophysical interpretation in terms of supermassive black holes mergers;but we expect to gain additional discriminating power with future datasets, improving the signal to noise ratio and extending the sensitivity window to lower frequencies. An interesting open question is how well gravitational wave observatories could separate such signals. © 2021, CC BY.

关键词： Gravity waves

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The NANOGrav 12.5-year data set: Search for non-Einsteinian polarization modes in the gravitational-wave background

arXiv

引用

arXiv 2021年

作者： Arzoumanian, Zaven Baker, Paul T. Blumer, Harsha Bécsy, Bence Brazier, Adam Brook, Paul R. Burke-Spolaor, Sarah Charisi, Maria Chatterjee, Shami Chen, Siyuan Cordes, James M. Cornish, Neil J. Crawford, Fronefield Cromartie, H. Thankful DeCesar, Megan E. DeGan, Dallas M. Demorest, Paul B. Dolch, Timothy Drachler, Brendan Ellis, Justin A. Ferrara, Elizabeth C. Fiore, William Fonseca, Emmanuel Garver-Daniels, Nathan Gentile, Peter A. Good, Deborah C. Hazboun, Jeffrey S. Holgado, A. Miguel Islo, Kristina Jennings, Ross J. Jones, Megan L. Kaiser, Andrew R. Kaplan, David L. Kelley, Luke Zoltan Key, Joey Shapiro Laal, Nima Lam, Michael T. Lazio, T. Joseph W. Lorimer, Duncan R. Liu, Tingting Luo, Jing Lynch, Ryan S. Madison, Dustin R. McEwen, Alexander McLaughlin, Maura A. Mingarelli, Chiara M.F. Ng, Cherry Nice, David J. Olum, Ken D. Pennucci, Timothy T. Pol, Nihan S. Ransom, Scott M. Ray, Paul S. Romano, Joseph D. Sardesai, Shashwat C. Shapiro-Albert, Brent J. Siemens, Xavier Simon, Joseph Siwek, Magdalena S. Spiewak, Renée Stairs, Ingrid H. Stinebring, Daniel R. Stovall, Kevin Sun, Jerry P. Swiggum, Joseph K. Taylor, Stephen R. Turner, Jacob E. Vallisneri, Michele Vigeland, Sarah J. Wahl, Haley M. Witt, Caitlin A. X-Ray Astrophysics Laboratory NASA Goddard Space Flight Center Code 662 GreenbeltMD20771 United States Department of Physics and Astronomy Widener University One University Place ChesterPA19013 United States Department of Physics and Astronomy West Virginia University P.O. Box 6315 MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States Department of Physics Montana State University BozemanMT59717 United States Cornell Center for Astrophysics and Planetary Science Department of Astronomy Cornell University IthacaNY14853 United States Cornell Center for Advanced Computing Cornell University IthacaNY14853 United States CIFAR Azrieli Global Scholars Program CIFAR Toronto Canada Department of Physics and Astronomy Vanderbilt University 2301 Vanderbilt Place NashvilleTN37235 United States Station de Radioastronomie de Nancay Observatoire de Paris Universite PSL CNRS Universite d'Orleans Nancay18330 France FEMTO-ST Institut de Recherche Department of Time and Frequency UBFC CNRS ENSMM Besancon25030 France Laboratoire de Physique et Chimie de l'Environment et de l'Espace LPC2E UMR7328 Universite d'Orleans CNRS Orleans45071 France Department of Physics and Astronomy Franklin & Marshall College P.O. Box 3003 LancasterPA17604 United States Department of Physics Lafayette College EastonPA18042 United States George Mason University FairfaxVA22030 United States U.S. Naval Research Laboratory WashingtonDC20375 United States Department of Physics Oregon State University CorvallisOR97331 United States National Radio Astronomy Observatory 1003 Lopezville Rd. SocorroNM87801 United States Department of Physics Hillsdale College 33 E. College Street HillsdaleMI49242 United States Eureka Scientific Inc. 2452 Delmer Street Suite 100 OaklandCA94602-3017 United States School of Physics and Astronomy Rochester Institu

We search NANOGrav's 12.5-year data set for evidence of a gravitational wave background (GWB) with all the spatial correlations allowed by general metric theories of gravity. We find no substantial evidence in favor of the existence of such correlations in our data. We find that scalar-transverse (ST) correlations yield signal-to-noise ratios and Bayes factors that are higher than quadrupolar (tensor transverse, TT) correlations. Specifically, we find ST correlations with a signal-to-noise ratio of 2.8 that are preferred over TT correlations (Hellings and Downs correlations) with Bayesian odds of about 20:1. However, the significance of ST correlations is reduced dramatically when we include modeling of the Solar System ephemeris systematics and/or remove pulsar J0030+0451 entirely from consideration. Even taking the nominal signal-to-noise ratios at face value, analyses of simulated data sets show that such values are not extremely unlikely to be observed in cases where only the usual TT modes are present in the GWB. In the absence of a detection of any polarization mode of gravity, we place upper limits on their amplitudes for a spectral index of γ = 5 and a reference frequency of fyr = 1yr-1. Among the upper limits for eight general families of metric theories of gravity, we find the values of A95%TT = (9.7±0.4)×10-16 and A95%ST = (1.4±0.03)×10-15 for the family of metric spacetime theories that contain both TT and ST modes. © 2021, CC BY-NC-SA.

关键词： Gravity waves

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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

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Author Correction: Federated learning enables big data for rare cancer boundary detection

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Nature communications 2023年第1期14卷 436页

作者： Sarthak Pati Ujjwal Baid Brandon Edwards Micah Sheller Shih-Han Wang G Anthony Reina Patrick Foley Alexey Gruzdev Deepthi Karkada Christos Davatzikos Chiharu Sako Satyam Ghodasara Michel Bilello Suyash Mohan Philipp Vollmuth Gianluca Brugnara Chandrakanth J Preetha Felix Sahm Klaus Maier-Hein Maximilian Zenk Martin Bendszus Wolfgang Wick Evan Calabrese Jeffrey Rudie Javier Villanueva-Meyer Soonmee Cha Madhura Ingalhalikar Manali Jadhav Umang Pandey Jitender Saini John Garrett Matthew Larson Robert Jeraj Stuart Currie Russell Frood Kavi Fatania Raymond Y Huang Ken Chang Carmen Balaña Jaume Capellades Josep Puig Johannes Trenkler Josef Pichler Georg Necker Andreas Haunschmidt Stephan Meckel Gaurav Shukla Spencer Liem Gregory S Alexander Joseph Lombardo Joshua D Palmer Adam E Flanders Adam P Dicker Haris I Sair Craig K Jones Archana Venkataraman Meirui Jiang Tiffany Y So Cheng Chen Pheng Ann Heng Qi Dou Michal Kozubek Filip Lux Jan Michálek Petr Matula Miloš Keřkovský Tereza Kopřivová Marek Dostál Václav Vybíhal Michael A Vogelbaum J Ross Mitchell Joaquim Farinhas Joseph A Maldjian Chandan Ganesh Bangalore Yogananda Marco C Pinho Divya Reddy James Holcomb Benjamin C Wagner Benjamin M Ellingson Timothy F Cloughesy Catalina Raymond Talia Oughourlian Akifumi Hagiwara Chencai Wang Minh-Son To Sargam Bhardwaj Chee Chong Marc Agzarian Alexandre Xavier Falcão Samuel B Martins Bernardo C A Teixeira Flávia Sprenger David Menotti Diego R Lucio Pamela LaMontagne Daniel Marcus Benedikt Wiestler Florian Kofler Ivan Ezhov Marie Metz Rajan Jain Matthew Lee Yvonne W Lui Richard McKinley Johannes Slotboom Piotr Radojewski Raphael Meier Roland Wiest Derrick Murcia Eric Fu Rourke Haas John Thompson David Ryan Ormond Chaitra Badve Andrew E Sloan Vachan Vadmal Kristin Waite Rivka R Colen Linmin Pei Murat Ak Ashok Srinivasan J Rajiv Bapuraj Arvind Rao Nicholas Wang Ota Yoshiaki Toshio Moritani Sevcan Turk Joonsang Lee Snehal Prabhudesai Fanny Morón Jacob Mandel Konstantinos Kamnitsas Ben Glocker Luke V M Dixon Matthew Williams Peter Zamp Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA. Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Informatics Technical University of Munich Munich Bavaria Germany. Intel Corporation Santa Clara CA USA. Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany. Clinical Cooperation Unit Neuropathology German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Neuropathology Heidelberg University Hospital Heidelberg Germany. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany. Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany. Neurology Clinic Heidelberg University Hospital Heidelberg Germany. Department of Radiology & Biomedical Imaging University of California San Francisco San Francisco CA USA. Symbiosis Center for Medical Image Analysis Symbiosis International University Pune Maharashtra India. Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Bangalore Karnataka India. Department of Radiology School of Medicine and Public Health University of Wisconsin Madison WI USA. Department of Medical Physics School of Medicine and Public Health University of Wisconsin Madison WI USA. Leeds Teaching Hospitals Trust Department of Radiology Leeds UK. Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA. Catalan Institute of Oncology Badalona Spain. Consorci MAR Parc de Salut de Barcelona Catalonia Spain. Department of Radiology (IDI

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Hybrid Monte Carlo and continuum modeling of electrolytes with concentration-induced dielectric variations

引用

Physical Review E 2016年第5期94卷 053312-053312页

作者： Xiaofei Guan Manman Ma Zecheng Gan Zhenli Xu Bo Li School of Mathematical Sciences Tongji University 1239 Siping Road Shanghai 200092 China School of Mathematical Sciences and Institute of Natural Sciences Shanghai Jiao Tong University Shanghai 200240 China Department of Mathematics University of Michigan Ann Arbor Michigan 48109 USA School of Mathematical Sciences Institute of Natural Sciences and MoE Key Lab of Scientific and Engineering Computing Shanghai Jiao Tong University Shanghai 200240 China Department of Mathematics and Graduate Program in Quantitative Biology University of California San Diego California 92093-0112 USA

The distribution of ions near a charged surface is an important quantity in many biological and material processes, and has been therefore investigated intensively. However, few theoretical and simulation approaches have included the influence of concentration-induced variations in the local dielectric permittivity of an underlying electrolyte solution. Such local variations have long been observed and known to affect the properties of ionic solution in the bulk and around the charged surface. We propose a hybrid computational model that combines Monte Carlo simulations with continuum electrostatic modeling to investigate such properties. A key component in our hybrid model is a semianalytical formula for the ion-ion interaction energy in a dielectrically inhomogeneous environment. This formula is obtained by solving for the Green's function Poisson's equation with ionic-concentration-dependent dielectric permittivity using a harmonic interpolation method and spherical harmonic series. We also construct a self-consistent continuum model of electrostatics to describe the effect of ionic-concentration-dependent dielectric permittivity and the resulting self-energy contribution. With extensive numerical simulations, we verify the convergence of our hybrid simulation scheme, show the qualitatively different structures of ionic distribution due to the concentration-induced dielectric variations, and compare our simulation results with the self-consistent continuum model. In particular, we study the differences between weakly and strongly charged surfaces and multivalencies of counterions. Our hybrid simulations conform particularly the depletion of ionic concentrations near a charged surface and also capture the charge inversion. We discuss several issues and possible further improvement of our approach for simulations of large charged systems.

关键词： Charge Dielectric properties Colloids Green's function methods Monte Carlo methods

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Exploring low internal reorganization energies for silicene nanoclusters

arXiv

引用

arXiv 2017年

作者： Pablo-Pedro, Ricardo Lopez-Rios, Hector Mendoza-Cortes, Jose-L Kong, Jing Fomine, Serguei van Voorhis, Troy Dresselhaus, Mildred S. Department of Chemistry Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Instituto de Investigaciones en Materiales Universidad Nacional Autónoma de México Apartado Postal 70-360 CU Coyoacán Ciudad de México04510 Mexico Department of Chemical & Biomedical Engineering Florida A&M University Florida State University Joint College of Engineering Tallahassee FL32310 United States Scientific Computing Department Materials Science and Engineering Program High Performance Material Institute Condensed Matter Theory National High Magnetic Field Laboratory Florida State University TallahasseeFL32310 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

High-performance materials rely on small reorganization energies to facilitate both charge separation and charge transport. Here, we performed DFT calculations to predict small reorganization energies of rectangular silicene nanoclusters with hydrogen-passivated edges denoted by H-SiNC. We observe that across all geometries, H-SiNCs feature large electron affinities and highly stabilized anionic states, indicating their potential as n-type materials. Our findings suggest that fine-tuning the size of H-SiNCs along the "zigzag" and "armchair" directions may permit the design of novel n-type electronic materials and spinctronics devices that incorporate both high electron affinities and very low internal reorganization energies. Copyright © 2017, The Authors. All rights reserved.

关键词： Silicene

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