检索结果-内蒙古大学图书馆

Ambient-pressure CVD of graphene on low-index Ni surfaces using methane: A combined experimental and first-principles study

引用

Physical Review Materials 2018年第7期2卷 073404-073404页

作者： Daniela L. Mafra Jimena A. Olmos-Asar Fabio R. Negreiros Alfonso Reina Ki Kang Kim Mildred S. Dresselhaus Jing Kong Gary J. Mankey Paulo T. Araujo Department of Electrical Engineering and Computer Sciences Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Centro de Ciências Naturais e Humanas Universidade Federal do ABC Santo André 09210-580 SP Brazil INFIQC CONICET Departamento de Química Teórcia y Computacional Facultad de Ciencias Químicas Universidad Nacional de Córdoba X5000HUA Argentina Department of Materials Science and Engineering Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Energy and Materials Engineering Dongguk University-Seoul Seoul 04620 Republic of Korea Department of Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Physics and Astronomy The University of Alabama Tuscaloosa Alabama 35487 USA Center for Materials for Information Technology (MINT Center) The University of Alabama Tuscaloosa Alabama 35401 USA Natural Sciences Institute Graduate Program in Physics - Federal University of Para Belem PA 66075-110 Brazil

The growth of large area single-layer graphene (1-LG) is studied using ambient pressure chemical vapor deposition on single-crystal Ni(111), Ni(110), and Ni(100). By varying both the furnace temperature in the range of 800–1100 °C and the gas flow through the growth chamber, uniform, high-quality 1-LG is obtained for Ni(111) and Ni(110) single crystals and for Ni(100) thin films. Surprisingly, only multilayer graphene growth could be obtained for single-crystal Ni(100). The experimental results are analyzed to determine the optimum combination of temperature and gas flow. Characterization with optical microscopy, Raman spectroscopy, and optical transmission support our findings. Density-functional theory calculations are performed to determine the energy barriers for diffusion, segregation, and adsorption, and model the kinetic pathways for formation of different carbon structures on the low-index surfaces of Ni.

关键词： Growth Synthesis 2-dimensional systems Carbon-based materials Atomic force microscopy Chemical vapor deposition Density functional calculations Density functional theory Raman spectroscopy

来源：评论

学校读者我要写书评

暂无评论

Robot navigation according to the characteristics of pedestrian flow

Robot navigation according to the characteristics of pedestr...

引用

Annual Conference of Industrial Electronics Society

作者： Nozomi Nishino Ryo Tsugita Daisuke Chugo Sho Yokota Satoshi Muramatsu Hiroshi Hashimoto Graduate School of Science and Technology Kwansei Gakuin University Sanda Hyogo Japan School of Information Science and Technology Tokai University Hiratsuka Kanagawa Japan School of Science and Engineering Toyo University Kawagoe Saitama Japan Master Program of Innovation for Design and Engineering Advanced Institute of Industrial Technology Shinagawa Tokyo Japan

In recent years, service robots have advanced toward having to the ability to move autonomously in an environment in which they co-exist with humans. Thus, the robots must engage in obstacle avoidance to arrive at their goal point. In our previous research, we developed the artificial potential based method that the robot to plans an effective path simulating pedestrian characteristics by gently changing its moving distance. The repulsive potential changes depend on the pedestrian's characteristics. Using our idea, the robot can plan a path without including unnecessary potential changes, according to the pedestrian's route estimation range. Thus, our idea realizes both an effective path and pedestrian avoidance. In this method, the characteristics of the pedestrian are constant. However, pedestrian's walking characteristics are different depending on the environment that pedestrian's walking direction is various in free space model like shopping mall, in contrast, they are determined along the route in corridor model like an office. Therefore, the pedestrian's characteristic model should be changed depend on environment. Because the number of the avoiding-path case is unlimited, we propose the pedestrian potential model is applicable for any environments in this study. In the proposed method, the robot determines the pedestrian's position and plans the avoiding-path, which is reflected in the pedestrian's repulsive potential spread. The value of spread is calculated and changes depending on the pedestrian's position in real-time. We confirmed the proposed method is efficient by comparing experiment with the previous method, where the spread potential constant. The proposed method's effectiveness was confirmed through experiment with the prototype robot.

关键词： Robots Prototypes Navigation Estimation Mobile communication

来源：评论

学校读者我要写书评

暂无评论

A consensus protocol for functional connectivity analysis in the rat brain (vol 26, pg 673, 2023)

引用

NATURE NEUROSCIENCE 2023年第6期26卷 1127-1128页

作者： Grandjean, Joanes Desrosiers-Gregoire, Gabriel Anckaerts, Cynthia Angeles-Valdez, Diego Ayad, Fadi Barriere, David A. Blockx, Ines Bortel, Aleksandra Broadwater, Margaret Cardoso, Beatriz M. Celestine, Marina Chavez-Negrete, Jorge E. Choi, Sangcheon Christiaen, Emma Clavijo, Perrin Colon-Perez, Luis Cramer, Samuel Daniele, Tolomeo Dempsey, Elaine Diao, Yujian Doelemeyer, Arno Dopfel, David Dvorakova, Lenka Falfan-Melgoza, Claudia Fernandes, Francisca F. Fowler, Caitlin F. Fuentes-Ibanez, Antonio Garin, Clement M. Gelderman, Eveline Golden, Carla E. M. Guo, Chao C. G. Henckens, Marloes J. A. G. Hennessy, Lauren A. Herman, Peter Hofwijks, Nita Horien, Corey Ionescu, Tudor M. Jones, Jolyon Kaesser, Johannes Kim, Eugene Lambers, Henriette Lazari, Alberto Lee, Sung-Ho Lillywhite, Amanda Liu, Yikang Liu, Yanyan Y. Lopez-Castro, Alejandra Lopez-Gil, Xavier Ma, Zilu MacNicol, Eilidh Madularu, Dan Mandino, Francesca Marciano, Sabina McAuslan, Matthew J. McCunn, Patrick McIntosh, Alison Meng, Xianzong Meyer-Baese, Lisa Missault, Stephan Moro, Federico Naessens, Daphne M. P. Nava-Gomez, Laura J. Nonaka, Hiroi Ortiz, Juan J. Paasonen, Jaakko Peeters, Lore M. Pereira, Mickael Perez, Pablo D. Pompilus, Marjory Prior, Malcolm Rakhmatullin, Rustam Reimann, Henning M. Reinwald, Jonathan Del Rio, Rodrigo Triana Rivera-Olvera, Alejandro Ruiz-Perez, Daniel Russo, Gabriele Rutten, Tobias J. Ryoke, Rie Sack, Markus Salvan, Piergiorgio Sanganahalli, Basavaraju G. Schroeter, Aileen Seewoo, Bhedita J. Selingue, Erwan Seuwen, Aline Shi, Bowen Sirmpilatze, Nikoloz Smith, Joanna A. B. Smith, Corrie Sobczak, Filip Stenroos, Petteri J. Straathof, Milou Strobelt, Sandra Sumiyoshi, Akira Takahashi, Kengo Torres-Garcia, Maria E. Tudela, Raul van den Berg, Monica van der Marel, Kajo van Hout, Aran T. B. Vertullo, Roberta Vidal, Benjamin Vrooman, Roel M. Wang, Victora X. Wank, Isabel Watson, David J. G. Yin, Ting Zhang, Yongzhi Zurbruegg, Stefan Achard, Sophie Alcauter, Sarael Auer, Dorothee P. Barbier, Emmanuel L. Baudewig, Juergen Beckmann, Chri Donders Institute for Brain Behaviour and Cognition Radboud University Nijmegen The Netherlands Department for Medical Imaging Radboud University Medical Center Nijmegen The Netherlands Cerebral Imaging Centre Douglas Mental Health University Institute Verdun QC Canada Integrated Program in Neuroscience McGill University Montreal QC Canada Bio-imaging Lab University of Antwerp Antwerp Belgium µNEURO Research Centre of Excellence University of Antwerp Antwerp Belgium Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro Mexico Biological and Biomedical Engineering McGill University Montreal QC Canada McConnell Brain Imaging Centre McGill University Montreal QC Canada Montreal Neurological Institute McGill University Montreal QC Canada UMR INRAE/CNRS 7247 Physiologie des Comportements et de la Reproduction Physiologie de la reproduction et des comportements Centre de recherche INRAE de Nouzilly Tours France Department of Neurology and Neurosurgery McGill University Montreal QC Canada Center for Animal MRI University of North Carolina at Chapel Hill Chapel Hill NC USA Department of Neurology University of North Carolina at Chapel Hill Chapel Hill NC USA Biomedical Research Imaging Center University of North Carolina at Chapel Hill Chapel Hill NC USA Preclinical MRI Champalimaud Research Champalimaud Centre for the Unknown Lisbon Portugal Laboratoire des Maladies Neurodégénératives Molecular Imaging Research Center (MIRCen) Université Paris-Saclay Commissariat à l’Énergie Atomique et aux Énergies Alternatives (CEA) CNRS Fontenay-aux-Roses France Departamento de Neurobiología Conductual y Cognitiva Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro México Translational Neuroimaging and Neural Control Group High-Field Magnetic Resonance Max Planck Institute for Biological Cybernetics Tuebingen Germany Graduate Training Centre of Neuroscience International Max

Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows for comparisons with data modalities collected under invasive or terminal procedures. Currently, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. Here we introduce StandardRat, a consensus rat functional magnetic resonance imaging acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired from rats across 46 centers. We developed a reproducible pipeline for analyzing rat data acquired with diverse protocols and determined experimental and processing parameters associated with the robust detection of functional connectivity across centers. We show that the standardized protocol enhances biologically plausible functional connectivity patterns relative to previous acquisitions. The protocol and processing pipeline described here is openly shared with the neuroimaging community to promote interoperability and cooperation toward tackling the most important challenges in neuroscience.

关键词： Databases Neural circuits

来源：评论

学校读者我要写书评

暂无评论

Constraints on Cosmic Strings Using Data from the Third Advanced LIGO–Virgo Observing Run

引用

Physical Review Letters 2021年第24期126卷 241102-241102页

作者： R. Abbott T. D. Abbott S. Abraham F. Acernese K. Ackley A. Adams C. Adams R. X. Adhikari V. B. Adya C. Affeldt D. Agarwal M. Agathos K. Agatsuma N. Aggarwal O. D. Aguiar L. Aiello A. Ain P. Ajith T. Akutsu K. M. Aleman G. Allen A. Allocca P. A. Altin A. Amato S. Anand A. Ananyeva S. B. Anderson W. G. Anderson M. Ando S. V. Angelova S. Ansoldi J. M. Antelis S. Antier S. Appert Koya Arai Koji Arai Y. Arai S. Araki A. Araya M. C. Araya J. S. Areeda M. Arène N. Aritomi N. Arnaud S. M. Aronson H. Asada Y. Asali G. Ashton Y. Aso S. M. Aston P. Astone F. Aubin P. Auclair P. Aufmuth K. AultONeal C. Austin S. Babak F. Badaracco M. K. M. Bader S. Bae Y. Bae A. M. Baer S. Bagnasco Y. Bai L. Baiotti J. Baird R. Bajpai M. Ball G. Ballardin S. W. Ballmer M. Bals A. Balsamo G. Baltus S. Banagiri D. Bankar R. S. Bankar J. C. Barayoga C. Barbieri B. C. Barish D. Barker P. Barneo S. Barnum F. Barone B. Barr L. Barsotti M. Barsuglia D. Barta J. Bartlett M. A. Barton I. Bartos R. Bassiri A. Basti M. Bawaj J. C. Bayley A. C. Baylor M. Bazzan B. Bécsy V. M. Bedakihale M. Bejger I. Belahcene V. Benedetto D. Beniwal M. G. Benjamin T. F. Bennett J. D. Bentley M. BenYaala F. Bergamin B. K. Berger S. Bernuzzi D. Bersanetti A. Bertolini J. Betzwieser R. Bhandare A. V. Bhandari D. Bhattacharjee S. Bhaumik J. Bidler I. A. Bilenko G. Billingsley R. Birney O. Birnholtz S. Biscans M. Bischi S. Biscoveanu A. Bisht B. Biswas M. Bitossi M.-A. Bizouard J. K. Blackburn J. Blackman C. D. Blair D. G. Blair R. M. Blair F. Bobba N. Bode M. Boer G. Bogaert M. Boldrini F. Bondu E. Bonilla R. Bonnand P. Booker B. A. Boom R. Bork V. Boschi N. Bose S. Bose V. Bossilkov V. Boudart Y. Bouffanais A. Bozzi C. Bradaschia P. R. Brady A. Bramley A. Branch M. Branchesi M. Breschi T. Briant J. H. Briggs A. Brillet M. Brinkmann P. Brockill A. F. Brooks J. Brooks D. D. Brown S. Brunett G. Bruno R. Bruntz J. Bryant A. Buikema T. Bulik H. J. Bulten A. Buonanno R. Buscicchio D. Buskulic L. Cadonati M. Caesar G. Cagnoli C. Cahillane H. W. Cain, III J. Calderón Bustillo J. D LIGO Laboratory California Institute of Technology Pasadena California 91125 USA Louisiana State University Baton Rouge Louisiana 70803 USA Inter-University Centre for Astronomy and Astrophysics Pune 411007 India Dipartimento di Farmacia Università di Salerno I-84084 Fisciano Salerno Italy INFN Sezione di Napoli Complesso Universitario di Monte S.Angelo I-80126 Napoli Italy OzGrav School of Physics and Astronomy Monash University Clayton 3800 Victoria Australia Christopher Newport University Newport News Virginia 23606 USA LIGO Livingston Observatory Livingston Louisiana 70754 USA OzGrav Australian National University Canberra Australian Capital Territory 0200 Australia Max Planck Institute for Gravitational Physics (Albert Einstein Institute) D-30167 Hannover Germany Leibniz Universität Hannover D-30167 Hannover Germany University of Cambridge Cambridge CB2 1TN United Kingdom Theoretisch-Physikalisches Institut Friedrich-Schiller-Universität Jena D-07743 Jena Germany University of Birmingham Birmingham B15 2TT United Kingdom Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA) Northwestern University Evanston Illinois 60208 USA Instituto Nacional de Pesquisas Espaciais 12227-010 São José dos Campos São Paulo Brazil Gravity Exploration Institute Cardiff University Cardiff CF24 3AA United Kingdom Gran Sasso Science Institute (GSSI) I-67100 L’Aquila Italy INFN Laboratori Nazionali del Gran Sasso I-67100 Assergi Italy INFN Sezione di Pisa I-56127 Pisa Italy Università di Pisa I-56127 Pisa Italy International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bengaluru 560089 India Gravitational Wave Science Project National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan California State University Fullerton Fullerton California 92831 USA NCSA University of Illi

We search for gravitational-wave signals produced by cosmic strings in the Advanced LIGO and Virgo full O3 dataset. Search results are presented for gravitational waves produced by cosmic string loop features such as cusps, kinks, and, for the first time, kink-kink collisions. A template-based search for short-duration transient signals does not yield a detection. We also use the stochastic gravitational-wave background energy density upper limits derived from the O3 data to constrain the cosmic string tension Gμ as a function of the number of kinks, or the number of cusps, for two cosmic string loop distribution models. Additionally, we develop and test a third model that interpolates between these two models. Our results improve upon the previous LIGO–Virgo constraints on Gμ by 1 to 2 orders of magnitude depending on the model that is tested. In particular, for the one-loop distribution model, we set the most competitive constraints to date: Gμ≲4×10−15. In the case of cosmic strings formed at the end of inflation in the context of grand unified theories, these results challenge simple inflationary models.

关键词： Cosmic strings & domain walls Gravitational waves

来源：评论

学校读者我要写书评

暂无评论

Open Data from the Third Observing Run of LIGO, Virgo, KAGRA, and GEO

引用

Astrophysical Journal, Supplement Series 2023年第2期267卷 29-29页

作者： Abbott, R. Abe, H. Acernese, F. Ackley, K. Adhicary, S. Adhikari, N. Adhikari, R.X. Adkins, V.K. Adya, V.B. Affeldt, C. Agarwal, D. Agathos, M. Aguiar, O.D. Aiello, L. Ain, A. Ajith, P. Akutsu, T. Albanesi, S. Alfaidi, R.A. Al-Jodah, A. Alléné, C. Allocca, A. Almualla, M. Altin, P.A. Amato, A. Amez-Droz, L. Amorosi, A. Anand, S. Ananyeva, A. Andersen, R. Anderson, S.B. Anderson, W.G. Andia, M. Ando, M. Andrade, T. Andres, N. Andrés-Carcasona, M. Andrić, T. Ansoldi, S. Antelis, J.M. Antier, S. Aoumi, M. Apostolatos, T. Appavuravther, E.Z. Appert, S. Apple, S.K. Arai, K. Araya, A. Araya, M.C. Areeda, J.S. Arène, M. Aritomi, N. Arnaud, N. Arogeti, M. Aronson, S.M. Arun, K.G. Asada, H. Ashton, G. Aso, Y. Assiduo, M. Assis de Souza Melo, S. Aston, S.M. Astone, P. Aubin, F. AultONeal, K. Babak, S. Badalyan, A. Badaracco, F. Badger, C. Bae, S. Bagnasco, S. Bai, Y. Baier, J.G. Baiotti, L. Baird, J. Bajpai, R. Baka, T. Ball, M. Ballardin, G. Ballmer, S.W. Baltus, G. Banagiri, S. Banerjee, B. Bankar, D. Baral, P. Barayoga, J.C. Barber, J. Barish, B.C. Barker, D. Barneo, P. Barone, F. Barr, B. Barsotti, L. Barsuglia, M. Barta, D. Barthelmy, S.D. Barton, M.A. Bartos, I. Basak, S. Basalaev, A. Bassiri, R. Basti, A. Bawaj, M. Bayley, J.C. Baylor, A.C. Bazzan, M. Bécsy, B. Bedakihale, V.M. Beirnaert, F. Bejger, M. Bell, A.S. Benedetto, V. Beniwal, D. Benoit, W. Bentley, J.D. Ben Yaala, M. Bera, S. Berbel, M. Bergamin, F. Berger, B.K. Bernuzzi, S. Beroiz, M. Berry, C.P.L. Bersanetti, D. Bertolini, A. Betzwieser, J. Beveridge, D. Bevins, N. Bhandare, R. Bhandari, A.V. Bhardwaj, U. Bhatt, R. Bhattacharjee, D. Bhaumik, S. Bianchi, A. Bilenko, I.A. Bilicki, M. Billingsley, G. Bini, S. Birnholtz, O. Biscans, S. Bischi, M. Biscoveanu, S. Bisht, A. Biswas, B. Bitossi, M. Bizouard, M.-A. Blackburn, J.K. Blair, C.D. Blair, D.G. Blair, R.M. Bobba, F. Bode, N. Boër, M. Bogaert, G. Boileau, G. Boldrini, M. Bolingbroke, G.N. Bonavena, L.D. Bondarescu, R. Bondu, F. Bonilla, E. Bonilla, G.S. Bonnand, R. Booker, P. Bork, R. Boschi, V. Bose, N. LIGO Laboratory California Institute of Technology Pasadena 91125 CA United States Graduate School of Science Tokyo Institute of Technology 2-12-1 Ookayama Meguro-ku Tokyo 152-8551 Japan Dipartimento di Farmacia Università di Salerno Salerno Fisciano I-84084 Italy INFN Sezione di Napoli Napoli I-80126 Italy OzGrav School of Physics & Astronomy Monash University Clayton 3800 VIC Australia The Pennsylvania State University University Park 16802 PA United States University of Wisconsin-Milwaukee Milwaukee 53201 WI United States Louisiana State University Baton Rouge 70803 LA United States OzGrav Australian National University Canberra 0200 ACT Australia Max Planck Institute for Gravitational Physics (Albert Einstein Institute) Hannover D-30167 Germany Leibniz Universität Hannover Hannover D-30167 Germany Inter-University Centre for Astronomy and Astrophysics Pune 411007 India University of Cambridge Cambridge CB2 1TN United Kingdom Instituto Nacional de Pesquisas Espaciais São Paulo São José dos Campos 12227-010 Brazil Cardiff University Cardiff CF24 3AA United Kingdom INFN Sezione di Pisa Pisa I-56127 Italy International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bengaluru 560089 India Gravitational Wave Science Project National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Dipartimento di Fisica Università degli Studi di Torino Torino I-10125 Italy INFN Sezione di Torino Torino I-10125 Italy SUPA University of Glasgow Glasgow G12 8QQ United Kingdom OzGrav University of Western Australia Crawley 6009 WA Australia Univ. Savoie Mont Blanc CNRS Laboratoire d'Annecy de Physique des Particules - IN2P3 Annecy F-74000 France Università di Napoli “Federico II” Napoli I-80126 Italy University of Minnesota Minneapolis 55455 MN United States M

The global network of gravitational-wave observatories now includes five detectors, namely LIGO Hanford, LIGO Livingston, Virgo, KAGRA, and GEO 600. These detectors collected data during their third observing run, O3, composed of three phases: O3a starting in 2019 April and lasting six months, O3b starting in 2019 November and lasting five months, and O3GK starting in 2020 April and lasting two weeks. In this paper we describe these data and various other science products that can be freely accessed through the Gravitational Wave Open Science Center at https://***. The main data set, consisting of the gravitational-wave strain time series that contains the astrophysical signals, is released together with supporting data useful for their analysis and documentation, tutorials, as well as analysis software packages. © 2023. The Author(s). Published by the American Astronomical Society.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Genomic basis for RNA alterations in cancer (vol 578, pg 129, 2020)

引用

NATURE 2023年第7948期614卷 E37-E37页

作者： Calabrese, Claudia Davidson, Natalie R. Demircioglu, Deniz Fonseca, Nuno A. He, Yao Lehmann, Kjong-Van Liu, Fenglin Shiraishi, Yuichi Soulette, Cameron M. Urban, Lara Greger, Liliana Li, Siliang Liu, Dongbing Perry, Marc D. Xiang, Qian Zhang, Fan Zhang, Junjun Bailey, Peter Erkek, Serap Hoadley, Katherine A. Hou, Yong Huska, Matthew R. Kilpinen, Helena Korbel, Jan O. Marin, Maximillian G. Markowski, Julia Nandi, Tannistha Pan-Hammarstrom, Qiang Pedamallu, Chandra Sekhar Siebert, Reiner Stark, Stefan G. Su, Hong Tan, Patrick Waszak, Sebastian M. Yung, Christina Zhu, Shida Awadalla, Philip Creighton, Chad J. Meyerson, Matthew Ouellette, B. F. Francis Wu, Kui Yang, Huanming Brazma, Alvis Brooks, Angela N. Goke, Jonathan Ratsch, Gunnar Schwarz, Roland F. Stegle, Oliver Zhang, Zemin European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources Universidade do Porto Vairão Portugal Berlin Institute for Medical Systems Biology Max Delbruck Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) partner site Berlin Germany German Cancer Research Center (DKFZ) Heidelberg Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) Partner site Berlin Berlin Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Division of Computational Genomics and Systems Genetics German Cancer Research Center (DKFZ) Heidelberg Germany ETH Zurich Zurich Switzerland Memorial Sloan Kettering Cancer Center New York NY USA Weill Cornell Medical College New York NY USA SIB Swiss Institute of Bioinformatics Lausanne Switzerland University Hospital Zurich Zurich Switzerland Computational Biology Center Memorial Sloan Kettering Cancer Center New York NY USA Department of Biology ETH Zurich Zürich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Korea University Seoul South Korea Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA National University of Singapore Singapore Singapore Genome Institute of Singapore Singapore Singapore Computational and Systems Biology Genome Institute of Singap

来源：评论

学校读者我要写书评

暂无评论

The evolutionary history of 2,658 cancers (vol 578, pg 122, 2020)

引用

NATURE 2023年第7948期614卷 E42-E42页

作者： Gerstung, Moritz Jolly, Clemency Leshchiner, Ignaty Dentro, Stefan C. Gonzalez, Santiago Rosebrock, Daniel Mitchell, Thomas J. Rubanova, Yulia Anur, Pavana Yu, Kaixian Tarabichi, Maxime Deshwar, Amit Wintersinger, Jeff Kleinheinz, Kortine Vazquez-Garcia, Ignacio Haase, Kerstin Jerman, Lara Sengupta, Subhajit Macintyre, Geoff Malikic, Salem Donmez, Nilgun Livitz, Dimitri G. Cmero, Marek Demeulemeester, Jonas Schumacher, Steven Fan, Yu Yao, Xiaotong Lee, Juhee Schlesner, Matthias Boutros, Paul C. Bowtell, David D. Zhu, Hongtu Getz, Gad Imielinski, Marcin Beroukhim, Rameen Sahinalp, S. Cenk Ji, Yuan Peifer, Martin Markowetz, Florian Mustonen, Ville Yuan, Ke Wang, Wenyi Morris, Quaid D. Spellman, Paul T. Wedge, David C. Van Loo, Peter European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Wellcome Sanger Institute Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany University of Ljubljana Ljubljana Slovenia The Francis Crick Institute London UK Big Data Institute University of Oxford Oxford UK Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Big Data Institute Li Ka Shing Centre University of Oxford Oxford UK University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge UK Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA Department of Statistics Columbia University New York NY USA Department of Haematology University of Cambridge Cambridge UK University of Leuven Leuven Belgium Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Charlestown MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Dana-Farber Cancer Institute Boston MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Oxford NIHR Biomedical Research Centre Oxford UK Oxford NIHR Biomedical Research Centre University of Oxford Oxford UK University of Toronto Toronto Ontario Canada Vector Institute Toronto Ontario Canada Vector Institute Toronto ON Canada Department of Computer Science University of Toronto Toronto ON Canada Ontario Institute for Cancer Research Toronto Ontario Canada University of California Los Angeles

来源：评论

学校读者我要写书评

暂无评论

Application of Interpolation Image by using Bi-Cubic Algorithm

Application of Interpolation Image by using Bi-Cubic Algorit...

引用

2018 Workshop on Multidisciplinary and Its Applications: Applied Mathematics, Computer Science, information Systems, and information technology, WMA-Mathcomtech 2018

作者： Abdullah, Dahlan Fajriana, F. Maryana, M. Rosnita, Lidya Utama Siahaan, Andysah Putera Rahim, Robbi Harliana, P. Harmayani, H. Ginting, Zainuddin Erliana, Cut Ita Irwansyah, Defi Zulmiardi, Z. Khaddafi, Muammar Milanie, Feby Aspan, Henry Huda, Ismul Saddhono, Kundharu Mulyaningsih, Indrya Moonti, Roy Marthen Parwito, P. Djanggih, Hardianto Amalia, Ainna Winarno, Edy Hadikurniawati, Wiwien Department of Informatics Universitas Malikussaleh Aceh Indonesia School of Computer and Communication Engineering Universiti Malaysia Perlis Malaysia Department of Informatics Engineering Sekolah Tinggi Teknik Harapan Medan Indonesia Department of Informatics AMIK Intel Com Global Indo Indonesia Department of Chemical Engineering Universitas Malikussaleh Aceh Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Department of Mechanical Engineering Universitas Malikussaleh Aceh Indonesia Department of Accounting Universitas Malikussaleh Aceh Indonesia Universitas Pembangunan Panca Budi Medan Indonesia Universitas Syiah Kuala Aceh Indonesia Graduate Program Universitas Sebelas Maret Surakarta Indonesia Indonesian Language Teaching Department Institut Agama Islam Negeri Syekh Nurjati Cirebon Indonesia Department of Law Universitas Gorontalo Gorontalo Indonesia Department of Agrotechnology Universitas Ratu Samban Bengkulu Indonesia Department of Law Universitas Tompotika Luwuk Banggai Luwuk Indonesia Islamic Institut of Miftahul Ula Kertosono Nganjuk-East-Java Indonesia Faculty of Information Technology Universitas Stikubank Semarang Indonesia

Image magnification is one of the branches in digital image processing that is often required in various applications such as in the field of medicine, multimedia, and in satellite imagery. As technology grows, more and more methods are used for image enlargement. In this study, the image enlargement process performs by using Bi-Cubic spline interpolation method, and the result of image try to compare between the original one and picture after enlargement. © Published under licence by IOP Publishing Ltd.

关键词： Satellite imagery

来源：评论

学校读者我要写书评

暂无评论

Nitrogen and phosphorus constrain the CO2 fertilization of global plant biomass (vol 21, pg 561, 2020)

引用

NATURE CLIMATE CHANGE 2020年第7期10卷 696-697页

作者： Terrer, Cesar Jackson, Robert B. Prentice, I. Colin Keenan, Trevor F. Kaiser, Christina Vicca, Sara Fisher, Joshua B. Reich, Peter B. Stocker, Benjamin D. Hungate, Bruce A. Penuelas, Josep McCallum, Ian Soudzilovskaia, Nadejda A. Cernusak, Lucas A. Talhelm, Alan F. Van Sundert, Kevin Piao, Shilong Newton, Paul C. D. Hovenden, Mark J. Blumenthal, Dana M. Liu, Yi Y. Mueller, Christoph Winter, Klaus Field, Christopher B. Viechtbauer, Wolfgang Van Lissa, Caspar J. Hoosbeek, Marcel R. Watanabe, Makoto Koike, Takayoshi Leshyk, Victor O. Polley, H. Wayne Franklin, Oskar Department of Earth System Science Stanford University Stanford USA Institut de Ciència i Tecnologia Ambientals Universitat Autònoma de Barcelona Barcelona Spain Ecosystems Services and Management Program International Institute for Applied Systems Analysis Laxenburg Austria Woods Institute for the Environment and Precourt Institute for Energy Stanford University Stanford USA AXA Chair Programme in Biosphere and Climate Impacts Department of Life Sciences Imperial College London Silwood Park Campus Ascot UK Department of Biological Sciences Macquarie University North Ryde Australia Department of Earth System Science Tsinghua University Beijing China Department of Environmental Science Policy and Management UC Berkeley Berkeley USA Climate and Ecosystem Sciences Division Lawrence Berkeley National Laboratory Berkeley USA Department of Microbiology and Ecosystem Science Division of Terrestrial Ecosystem Research Faculty of Life Sciences University of Vienna Vienna Austria Evolution and Ecology Program International Institute for Applied Systems Analysis Laxenburg Austria Centre of Excellence PLECO (Plants and Ecosystems) Biology Department University of Antwerp Wilrijk Belgium Jet Propulsion Laboratory California Institute of Technology Pasadena USA Joint Institute for Regional Earth System Science and Engineering University of California at Los Angeles Los Angeles USA Department of Forest Resources University of Minnesota St. Paul USA Hawkesbury Institute for the Environment Western Sydney University Penrith Australia CREAF Cerdanyola del Vallès Spain Center for Ecosystem Science and Society Northern Arizona University Flagstaff USA Department of Biological Sciences Northern Arizona University Flagstaff USA CSIC Global Ecology Unit CREAF-CEAB-UAB Bellaterra Spain Environmental Biology Department Institute of Environmental Sciences Leiden University Leiden the Netherlands College of Marine and Environmental Sciences James Cook University Cairns

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Feature extraction of character image using shape energy

Feature extraction of character image using shape energy

引用

International Electronics Symposium (IES)

作者： Galih Hendra Wibowo Riyanto Sigit Aliridho Barakbah Department of Information and Computer Engineering Graduate Program of Engineering Technology Surabaya Indonesia

ISBN: (纸本)9781509016419

One effort to maintain documents or records is to make changes in the form of digital images. The drawings further processing needs to be done so that the text or sentences therein can be operated as do the search, analysis, or manipulation of the contents of the text. The treatment process is known as optical character recognition (OCR) and continues to develop. OCR is generally divided into three main stages, namely preprocessing, feature extraction and classification. Feature extraction is one of the essential or fundamental processes in character recognition. The purpose of feature extraction is to obtain the characteristics of each character. The results at this stage can affect the quality of character recognition. Generally, feature extraction on character is done by a complex calculation so as to cause the necessary time computing is not a little, especially in real time recognition case. In this paper, feature extraction can be done simply proposed as an alternative, called Shape Energy. This method uses the approach of how humans are able to distinguish between characters or numbers in a simple. It results in three elements which are elasticity, curvature, and texture. The elasticity is first derivative and the curvature is second derivative of each pixel in the frame of the character, which is obtained from thinning. While the texture value is 4-direction chain-codes. This method testing has been done on some type of character by using back propagation neural network as a method on classification stage. This testing resulted in average value accuracy rate of success in identifying these characters by 90.3%.

关键词： Feature extraction Optical character recognition software Character recognition Shape Elasticity Histograms Filtering

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：