Institution
Tokyo Institute of Technology
Education•Tokyo, Tôkyô, Japan•
About: Tokyo Institute of Technology is a education organization based out in Tokyo, Tôkyô, Japan. It is known for research contribution in the topics: Catalysis & Thin film. The organization has 46775 authors who have published 101656 publications receiving 2357893 citations. The organization is also known as: Tokyo Tech & Tokodai.
Topics: Catalysis, Thin film, Laser, Phase (matter), Polymerization
Papers published on a yearly basis
Papers
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TL;DR: In this article, a novel feed structure was proposed to excite a plane TEM wave in a parallel-plate waveguide, which is composed of densely arrayed posts on the same layer as the parallel plate.
Abstract: The authors propose a novel feed structure to excite a plane TEM wave in a parallel-plate waveguide. The feed waveguide is composed of densely arrayed posts on the same layer as the parallel plate. The posts can be easily fabricated at low cost by making metalized via holes in a grounded dielectric substrate. Such a procedure results in a quite simple fabrication of the antenna. The feed waveguide is designed to obtain a uniform division, which is confirmed by measurements on a 40-GHz band model.
568 citations
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01 Jun 1989TL;DR: The authors develop a control method for space manipulators based on the resolved motion control concept that is widely applicable in solving not only free-flying manipulation problems but also attitude-control problems.
Abstract: The authors establish a control method for space manipulators taking dynamical interaction between the manipulator arm and the base satellite into account. The kinematics of free-flying multibody systems is investigated by introducing the momentum conservation law into the formulation and a novel Jacobian matrix in generalized form for space robotic arms is derived. The authors develop a control method for space manipulators based on the resolved motion control concept. The proposed method is widely applicable in solving not only free-flying manipulation problems but also attitude-control problems. The validity of the method is demonstrated by computer simulations with a realistic model of a robot satellite. >
568 citations
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University of Tokyo1, Boston University2, University of Minnesota3, Brookhaven National Laboratory4, University of California, Irvine5, California State University, Dominguez Hills6, George Mason University7, Gifu University8, University of Hawaii at Manoa9, Kobe University10, Kyoto University11, Los Alamos National Laboratory12, Louisiana State University13, University of Maryland, College Park14, Stony Brook University15, Niigata University16, Osaka University17, Seoul National University18, Shizuoka University19, Tohoku University20, Tokai University21, Tokyo Institute of Technology22, University of Warsaw23, University of Washington24
TL;DR: Using data recorded in 1100 live days of the Super-Kamiokande detector, three complementary data samples are used to study the difference in zenith angle distribution due to neutral currents and matter effects and find no evidence favoring sterile neutrinos, and reject the hypothesis at the 99% confidence level.
Abstract: The previously published atmospheric neutrino data did not distinguish whether muon neutrinos were oscillating into tau neutrinos or sterile neutrinos, as both hypotheses fit the data. Using data recorded in 1100 live days of the Super-Kamiokande detector, we use three complementary data samples to study the difference in zenith angle distribution due to neutral currents and matter effects. We find no evidence favoring sterile neutrinos, and reject the hypothesis at the $99%$ confidence level. On the other hand, we find that oscillation between muon and tau neutrinos suffices to explain all the results in hand.
568 citations
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TL;DR: The Database of Disordered Protein Prediction (D2P2) will increase the understanding of the interplay between disorder and structure, the genomic distribution of disorder, and its evolutionary history.
Abstract: We present the Database of Disordered Protein Prediction (D2P2), available at http://d2p2.pro (including website source code). A battery of disorder predictors and their variants, VL-XT, VSL2b, PrDOS, PV2, Espritz and IUPred, were run on all protein sequences from 1765 complete proteomes (to be updated as more genomes are completed). Integrated with these results are all of the predicted (mostly structured) SCOP domains using the SUPERFAMILY predictor. These disorder/structure annotations together enable comparison of the disorder predictors with each other and examination of the overlap between disordered predictions and SCOP domains on a large scale. D2P2 will increase our understanding of the interplay between disorder and structure, the genomic distribution of disorder, and its evolutionary history. The parsed data are made available in a unified format for download as flat files or SQL tables either by genome, by predictor, or for the complete set. An interactive website provides a graphical view of each protein annotated with the SCOP domains and disordered regions from all predictors overlaid (or shown as a consensus). There are statistics and tools for browsing and comparing genomes and their disorder within the context of their position on the tree of life.
© The Author(s) 2012. Published by Oxford University Press.
567 citations
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Cornell University1, Tokyo Institute of Technology2, Yonsei University3, Linköping University4, Uppsala University5, Memorial Sloan Kettering Cancer Center6, National Institutes of Health7, University of Porto8, Rockefeller University9, École Polytechnique Fédérale de Lausanne10, Carlos III Health Institute11, University of Tokyo12, Ohio State University13, Princeton University14, University of Oviedo15, Icahn School of Medicine at Mount Sinai16, Weizmann Institute of Science17, Lucile Packard Children's Hospital18, New York University19, Champalimaud Foundation20, University of Nebraska Medical Center21, Fred Hutchinson Cancer Research Center22, University of Pennsylvania23, University of California, San Diego24, University of Vermont25, University of Southern California26, City of Hope National Medical Center27, Lawrence Berkeley National Laboratory28
TL;DR: EVP proteins can serve as reliable biomarkers for cancer detection and determining cancer type, and a panel of tumor-type-specific EVP proteins in TEs and plasma are defined, which can classify tumors of unknown primary origin.
565 citations
Authors
Showing all 46967 results
Name | H-index | Papers | Citations |
---|---|---|---|
Matthew Meyerson | 194 | 553 | 243726 |
Yury Gogotsi | 171 | 956 | 144520 |
Masayuki Yamamoto | 171 | 1576 | 123028 |
H. Eugene Stanley | 154 | 1190 | 122321 |
Takashi Taniguchi | 152 | 2141 | 110658 |
Shu-Hong Yu | 144 | 799 | 70853 |
Kazunori Kataoka | 138 | 908 | 70412 |
Osamu Jinnouchi | 135 | 885 | 86104 |
Hector F. DeLuca | 133 | 1303 | 69395 |
Shlomo Havlin | 131 | 1013 | 83347 |
Hiroyuki Iwasaki | 131 | 1009 | 82739 |
Kazunari Domen | 130 | 908 | 77964 |
Hideo Hosono | 128 | 1549 | 100279 |
Hideyuki Okano | 128 | 1169 | 67148 |
Andreas Strasser | 128 | 509 | 66903 |