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arXiv 2024年

作者： Kim, Dasom Hou, Jin Lee, Geon Agrawal, Ayush Kim, Sunghwan Zhang, Hao Bao, Di Baydin, Andrey Wu, Wenjing Tay, Fuyang Huang, Shengxi Chia, Elbert E.M. Kim, Dai-Sik Seo, Minah Mohite, Aditya D. Hagenmüller, David Kono, Junichiro Applied Physics Graduate Program Smalley–Curl Institute Rice University HoustonTX77005 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States Sensor System Research Center Korea Institute of Science and Technology Seoul02792 Korea Republic of Department of Physics and Astronomy Seoul National University Seoul08826 Korea Republic of Department of Chemical and Biomolecular Engineering Rice University Houston77005 United States Ulsan44919 Korea Republic of Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Singapore Smalley–Curl Institute Rice University HoustonTX77005 United States Rice Advanced Materials Institute Rice University HoustonTX77005 United States KU–KIST Graduate School of Converging Science and Technology Korea University Seoul02841 Korea Republic of Université de Strasbourg CNRS Strasbourg67200 France Department of Physics and Astronomy Rice University HoustonTX77005 United States

Phonons, or vibrational quanta, are behind some of the most fundamental physical phenomena in solids, including superconductivity, Raman processes, and broken-symmetry phases. It is therefore of fundamental importance to find ways to harness phonons for controlling these phenomena and developing novel quantum technologies. However, the majority of current phonon control techniques rely on the use of intense external driving fields or strong anharmonicities, which restricts their range of applications. Here, we present a scheme for controlling the intensity fluctuations in phonon emission at room temperature based on multimode ultrastrong light–matter coupling. The multimode ultrastrong coupling regime is achieved by coupling two optical phonon modes in lead halide perovskites to an array of nanoslots, which operates as a single-mode cavity. The extremely small mode volume of the nanoslots enables unprecedented coupling strengths in a cavity phonon-polariton system. In the far-detuned, low-cavity-frequency regime, we demonstrate that the nanoslot resonator mediates an effective coupling between the phonon modes, resulting in superthermal phonon bunching in thermal equilibrium, both within the same mode and between different modes. Experimental results are in good agreement with a multimode Hopfield model. Our work paves the way for the tailoring of phonons to modify charge and energy transport in perovskite materials, with potential applications in light-collecting or emitting devices. Copyright © 2024, The Authors. All rights reserved.

关键词： Perovskite

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Scientific discovery in the age of artificial intelligence

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NATURE 2023年第7978期621卷 E33-E33页

作者： Wang, Hanchen Fu, Tianfan Du, Yuanqi Gao, Wenhao Huang, Kexin Liu, Ziming Chandak, Payal Liu, Shengchao Van Katwyk, Peter Deac, Andreea Anandkumar, Anima Bergen, Karianne Gomes, Carla P. Ho, Shirley Kohli, Pushmeet Lasenby, Joan Leskovec, Jure Liu, Tie-Yan Manrai, Arjun Marks, Debora Ramsundar, Bharath Song, Le Sun, Jimeng Tang, Jian Velickovic, Petar Welling, Max Zhang, Linfeng Coley, Connor W. Bengio, Yoshua Zitnik, Marinka Department of Engineering University of Cambridge Cambridge UK Department of Computing and Mathematical Sciences California Institute of Technology Pasadena CA USA NVIDIA Santa Clara CA USA Department of Computational Science and Engineering Georgia Institute of Technology Atlanta GA USA Department of Computer Science Cornell University Ithaca NY USA Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Computer Science Stanford University Stanford CA USA Department of Physics Massachusetts Institute of Technology Cambridge MA USA Harvard-MIT Program in Health Sciences and Technology Cambridge MA USA Mila – Quebec AI Institute Montreal Quebec Canada Université de Montréal Montreal Quebec Canada HEC Montréal Montreal Quebec Canada CIFAR AI Chair Toronto Ontario Canada Department of Earth Environmental and Planetary Sciences Brown University Providence RI USA Data Science Institute Brown University Providence RI USA Center for Computational Astrophysics Flatiron Institute New York NY USA Department of Astrophysical Sciences Princeton University Princeton NJ USA Department of Physics Carnegie Mellon University Pittsburgh PA USA Department of Physics and Center for Data Science New York University New York NY USA Google DeepMind London UK Department of Computer Science and Technology University of Cambridge Cambridge UK Microsoft Research Beijing China Department of Biomedical Informatics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Harvard Data Science Initiative Cambridge MA USA Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Cambridge MA USA Department of Systems Biology Harvard Medical School Boston MA USA Deep Forest Sciences Palo Alto CA USA BioMap Beijing China Mo

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Publisher’s Note: “Guided-mode-resonance-coupled plasmonic-active SiO2 nanotubes for surface enhanced Raman spectroscopy” [Appl. Phys. Lett. 100, 191114 (2012)]

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Applied Physics Letters 2012年第5期101卷

作者： Xiaobin Xu Dihan Hasan Lei Wang Swapnajit Chakravarty Ray T. Chen D. L. Fan Alan X. Wang 1Materials Science and Engineering Program Texas Materials Institute University of Texas at Austin Austin Texas 78712 USA 2School of Electrical Engineering and Computer Science Oregon State University Corvallis Oregon 97331 USA 3Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology 430074 Wuhan China 4Microelectronic Research Center University of Texas at Austin Austin Texas 78758 USA 5Omega Optics Inc. Austin Texas 78759 USA 6Department of Mechanical Engineering University of Texas at Austin Austin Texas 78712 USA

This article was originally published online on 10 May 2012 with an incorrect affiliation for co-author D. L. Fan and an incorrect footnote designation for co-a

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engineering chirality at wafer scale with ordered carbon nanotube architectures

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Nature communications 2023年第1期14卷 7380页

作者： Jacques Doumani Minhan Lou Oliver Dewey Nina Hong Jichao Fan Andrey Baydin Keshav Zahn Yohei Yomogida Kazuhiro Yanagi Matteo Pasquali Riichiro Saito Junichiro Kono Weilu Gao Department of Electrical and Computer Engineering Rice University Houston TX USA. Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston TX USA. Department of Electrical and Computer Engineering The University of Utah Salt Lake City UT USA. Carbon Hub Rice University Houston TX USA. Department of Chemical and Biomolecular Engineering Rice University Houston TX USA. J.A. Woollam Co. Inc. Lincoln NE USA. Smalley-Curl Institute Rice University Houston TX USA. Department of Physics Tokyo Metropolitan University Tokyo Japan. Department of Chemistry Rice University Houston TX USA. Department of Materials Science and NanoEngineering Rice University Houston TX USA. Department of Physics Tohoku University Sendai Japan. Department of Physics National Taiwan Normal University Taipei Taiwan. Department of Physics and Astronomy Rice University Houston TX USA. Department of Electrical and Computer Engineering The University of Utah Salt Lake City UT USA. weilu.gao@utah.edu. Carbon Hub Rice University Houston TX USA. weilu.gao@utah.edu.

Creating artificial matter with controllable chirality in a simple and scalable manner brings new opportunities to diverse areas. Here we show two such methods based on controlled vacuum filtration - twist stacking and mechanical rotation - for fabricating wafer-scale chiral architectures of ordered carbon nanotubes (CNTs) with tunable and large circular dichroism (CD). By controlling the stacking angle and handedness in the twist-stacking approach, we maximize the CD response and achieve a high deep-ultraviolet ellipticity of 40 ± 1 mdeg nm. Our theoretical simulations using the transfer matrix method reproduce the experimentally observed CD spectra and further predict that an optimized film of twist-stacked CNTs can exhibit an ellipticity as high as 150 mdeg nm, corresponding to a g factor of 0.22. Furthermore, the mechanical rotation method not only accelerates the fabrication of twisted structures but also produces both chiralities simultaneously in a single sample, in a single run, and in a controllable manner. The created wafer-scale objects represent an alternative type of synthetic chiral matter consisting of ordered quantum wires whose macroscopic properties are governed by nanoscopic electronic signatures and can be used to explore chiral phenomena and develop chiral photonic and optoelectronic devices.

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Lithium-Ion Batteries: Operando Magnetometry Probing the Charge Storage Mechanism of CoO Lithium-Ion Batteries (Adv. Mater. 12/2021)

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Advanced Materials 2021年第12期33卷

作者： Hongsen Li Zhengqiang Hu Qingtao Xia Hao Zhang Zhaohui Li Huaizhi Wang Xiangkun Li Fengkai Zuo Fengling Zhang Xiaoxiong Wang Wanneng Ye Qinghao Li Yunze Long Qiang Li Shishen Yan Xiaosong Liu Xiaogang Zhang Guihua Yu Guo-Xing Miao College of Physics Center for Marine Observation and Communications Qingdao University Qingdao 266071 China School of Physics State Key Laboratory of Crystal Materials Shandong University Jinan 250100 China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology (SIMIT) Chinese Academy of Sciences Shanghai 200050 China College of Material Science and Engineering Nanjing University of Aeronautics and Astronautics Nanjing 210016 China Materials Science and Engineering Program and Department of Mechanical Engineering The University of Texas at Austin Austin TX 78712 USA Department of Electrical and Computer Engineering & Institute for Quantum Computing University of Waterloo Waterloo ON N2L 3G1 Canada

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Corrigendum to “Cost-minimized combinations of wind power, solar power and electrochemical storage, powering the grid up to 99.9% of the time” [J. Power Sources 225 (2013) 60–74]

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Journal of Power Sources 2013年 232卷 402-402页

作者： Cory Budischak DeAnna Sewell Heather Thomson Leon Mach Dana E. Veron Willett Kempton Department of Electrical and Computer Engineering University of Delaware Newark DE 19716 USA Department of Energy Management Delaware Technical Community College 400 Stanton-Christiana Road Newark DE 19713 USA Center for Carbon-Free Power Integration School of Marine Science and Policy College of Earth Ocean and Environment University of Delaware Newark DE 19716 USA Energy and Environmental Policy Program College of Engineering University of Delaware Newark DE 19716 USA Center for Electric Technology DTU Elektro Danmarks Tekniske Universitet Kgs. Lungby Denmark

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Sensors: Stretchable, Multiplexed pH Sensors With Demonstrations on Rabbit and Human Hearts Undergoing Ischemia (Adv. Healthcare Mater. 1/2014)

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Advanced Healthcare Materials 2014年第1期3卷

作者： Hyun‐Joong Chung Matthew S. Sulkin Jong‐Seon Kim Camille Goudeseune Hsin‐Yun Chao Joseph W. Song Sang Yoon Yang Yung‐Yu Hsu Roozbeh Ghaffari Igor R. Efimov John A. Rogers Department of Chemical and Materials Engineering University of Alberta Edmonton AB T6G 2V4 Canada Department of Materials Science and Engineering University of Illinois at Urbana‐Champaign Urbana IL 61801 USA Department of Biomedical Engineering Washington University in Saint Louis Saint Louis MO USA Department of Chemical and Biomolecular Engineering (BK21 Program) Korea Advanced Institute of Science and Technology Daejeon Korea Illinois Simulator Laboratory Beckman Institute University of Illinois Urbana IL 61801 USA MC10 Inc. 36 Cameron Avenue Cambridge MA 02140 USA Department of Materials Science and Engineering Chemistry Mechanical Science and Engineering Electrical and Computer Engineering Beckman Institute for Advanced Science and Technology and Frederick Seitz Materials Research Laboratory University of Illinois at Urbana‐Champaign Urbana IL 61801 USA

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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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Expression of concern: Microchip-based structure determination of low-molecular weight proteins using cryo-electron microscopy

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Nanoscale 2024年第9期16卷 4919页

作者： Michael A Casasanta G M Jonaid Liam Kaylor William Y Luqiu Maria J Solares Mariah L Schroen William J Dearnaley Jarad Wilson Madeline J Dukes Deborah F Kelly Department of Biomedical Engineering Pennsylvania State University University Park PA 16802 USA. Debkelly@psu.edu. Materials Research Institute Pennsylvania State University University Park PA 16802 USA. Bioinformatics and Genomics Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University Park PA 16802 USA. Molecular Cellular and Integrative Biosciences Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University Park PA 16802 USA. Department of Electrical and Computer Engineering Duke University Durham NC 27708 USA. RayBiotech Life Peachtree Corners GA 30092 USA. Applications Science Protochips Inc. Morrisville NC 27560 USA.

Expression of concern for 'Microchip-based structure determination of low-molecular weight proteins using cryo-electron microscopy' by Michael A. Casasanta , , 2021, , 7285-7293, https://***/10.1039/D1NR00388G.

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Manipulating multiple order parameters via oxygen vacancies: The case of Eu0.5Ba0.5TiO3−δ

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Physical Review B 2017年第11期96卷 115105-115105页

作者： Weiwei Li Qian He Le Wang Huizhong Zeng John Bowlan Langsheng Ling Dmitry A. Yarotski Wenrui Zhang Run Zhao Jiahong Dai Junxing Gu Shipeng Shen Haizhong Guo Li Pi Haiyan Wang Yongqiang Wang Ivan A. Velasco-Davalos Yangjiang Wu Zhijun Hu Bin Chen Run-Wei Li Young Sun Kuijuan Jin Yuheng Zhang Hou-Tong Chen Sheng Ju Andreas Ruediger Daning Shi Albina Y. Borisevich Hao Yang College of Science Nanjing University of Aeronautics and Astronautics Nanjing 211106 China Department of Materials Science and Metallurgy University of Cambridge Cambridge CB3 0FS United Kingdom Materials Science and Technology Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Beijing National Laboratory for Condensed Matter Physics and Institute of Physics Chinese Academy of Science Beijing 100190 China State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology Chengdu 610054 China Center for Integrated Nanotechnologies MS K771 Los Alamos National Laboratory Los Alamos New Mexico 87545 USA High Magnetic Field Laboratory Chinese Academy of Science Hefei 230031 China Materials Science and Engineering Program Department of Electrical and Computer Engineering Texas A&M University College Station Texas 77843-3128 USA College of Physics Optoelectronics and Energy Soochow University Suzhou 215006 China Materials Science and Technology Division Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Institut National de la Recherche Scientifique—Énergie Matériaux et Télécommunication (INRS-EMT) 1650 Boulevard Lionel Boulet Varennes J3 X 1S2 QC Canada Center for Soft Condensed Matter Physics and Interdisciplinary Research Soochow University Suzhou 215006 China Key Laboratory of Magnetic Materials and Devices Ningbo Institute of Materials Technology and Engineering Chinese Academy of Science Ningbo 315201 China

Controlling functionalities, such as magnetism or ferroelectricity, by means of oxygen vacancies (VO) is a key issue for the future development of transition-metal oxides. Progress in this field is currently addressed through VO variations and their impact on mainly one order parameter. Here we reveal a mechanism for tuning both magnetism and ferroelectricity simultaneously by using VO. Combining experimental and density-functional theory studies of Eu0.5Ba0.5TiO3−δ, we demonstrate that oxygen vacancies create Ti3+3d1 defect states, mediating the ferromagnetic coupling between the localized Eu 4f7 spins, and increase an off-center displacement of Ti ions, enhancing the ferroelectric Curie temperature. The dual function of Ti sites also promises a magnetoelectric coupling in the Eu0.5Ba0.5TiO3−δ.

关键词： Ferroelectricity Magnetoelectric effect Vacancies Multiferroics Oxides Thin films Transition metals Magnetization measurements Optical second-harmonic generation

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