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Institution

Tohoku University

EducationSendai, Japan
About: Tohoku University is a education organization based out in Sendai, Japan. It is known for research contribution in the topics: Magnetization & Population. The organization has 72116 authors who have published 170791 publications receiving 3941714 citations. The organization is also known as: Tōhoku daigaku.


Papers
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Journal ArticleDOI
TL;DR: In this article, the authors reviewed the currents in the South China Sea (SCS) and the interaction between the SCS and its adjacent seas and reviewed the seasonal circulation characteristics of the SW currents.
Abstract: Researches on the currents in the South China Sea (SCS) and the interaction between the SCS and its adjacent seas are reviewed Overall seasonal circulation in the SCS is cyclonic in winter and anticyclonic in summer with a few stable eddies The seasonal circulation is mostly driven by monsoon winds, and is related to water exchange between the SCS and the East China Sea through the Taiwan Strait, and between the SCS and the Kuroshio through the Luzon Strait Seasonal characteristics of the South China Sea Warm Current in the northern SCS and the Kuroshio intrusion to the SCS are summarized in terms of the interaction between the SCS and its adjacent seas

647 citations

Journal ArticleDOI
TL;DR: In this paper, three empirical rules for the achievement of high amorphous-forming ability (AFA) were calculated with thermodynamical functions for the gross number of 6450 alloys in 351 ternary ammorphous systems.
Abstract: Chemical mixing enthalpy (ΔH chem ) and mismatch entropy normalized by Boltzmann constant (S σ /k B ) corresponding to the three empirical rules for the achievement of high amorphous-forming ability (AFA) were calculated with thermodynamical functions for the gross number of 6450 alloys in 351 ternary amorphous systems. The temary amorphous alloys have ΔH chem of -86 to 25 kJ/mol and S σ /k B of 1.0 × 10 -3 to 5.7. The average values of ΔH chem and S σ /k B are calculated to be -33 kJ/mol and 0.33, respectively. The 30 alloys in 9 ternary amorphous systems including 10 alloys in Ag-Cu-Fe system have positive values of ΔH chem . Most of the ternary amorphous alloys have the values of ΔH chem and S σ /k B inside a trapezoid regicn in ΔH chem - log(S σ /k B ) chart except mainly for the H- and the C-containing alloys. Si-W-Zr system and the 32 alloys having positive values of ΔH chem . The analysis of AFA was carried out for typical five ternary amorphous systems. The following four results are derived. 1) Al-La-Ni and B-Fe-Zr alloys have high AFA in accordance with the concept of the three empirical rules. 2) The further multiplication of alloy components causes an increase in the AFA of Al-B-Fe alloys. 3) Thermodynamical factors represented by melting temperature and viscosity at the melting temperature are required for evaluation of AFA for Mg- and Pd-based amorphous alloys. 4) A tendency for log(S σ /k B ) to increase with decreasing ΔH chem is recognized in each alloys system, implying the stabilization of an amorphous phase against solid solution and intermediate phase.

644 citations

Journal ArticleDOI
TL;DR: An overview of the state-of-the-art deep learning architectures and algorithms relevant to the network traffic control systems, and a new use case, i.e., deep learning based intelligent routing, which is demonstrated to be effective in contrast with the conventional routing strategy.
Abstract: Currently, the network traffic control systems are mainly composed of the Internet core and wired/wireless heterogeneous backbone networks. Recently, these packet-switched systems are experiencing an explosive network traffic growth due to the rapid development of communication technologies. The existing network policies are not sophisticated enough to cope with the continually varying network conditions arising from the tremendous traffic growth. Deep learning, with the recent breakthrough in the machine learning/intelligence area, appears to be a viable approach for the network operators to configure and manage their networks in a more intelligent and autonomous fashion. While deep learning has received a significant research attention in a number of other domains such as computer vision, speech recognition, robotics, and so forth, its applications in network traffic control systems are relatively recent and garnered rather little attention. In this paper, we address this point and indicate the necessity of surveying the scattered works on deep learning applications for various network traffic control aspects. In this vein, we provide an overview of the state-of-the-art deep learning architectures and algorithms relevant to the network traffic control systems. Also, we discuss the deep learning enablers for network systems. In addition, we discuss, in detail, a new use case, i.e., deep learning based intelligent routing. We demonstrate the effectiveness of the deep learning-based routing approach in contrast with the conventional routing strategy. Furthermore, we discuss a number of open research issues, which researchers may find useful in the future.

643 citations

Journal ArticleDOI
TL;DR: The morphology and crystallography of lath martensite in two Mn-containing interstitial free steels and a maraging steel were examined in detail by a combination of transmission electron microscopy, electron backscatter diffraction in a scanning electron microscope and optical microscopy.

641 citations

Journal ArticleDOI
01 Apr 2004-Nature
TL;DR: It is demonstrated that, in a ferromagnetic semiconductor structure, magnetization reversal through domain-wall switching can be induced in the absence of a magnetic field using current pulses with densities below 105 A cm-2.
Abstract: Magnetic information storage relies on external magnetic fields to encode logical bits through magnetization reversal. But because the magnetic fields needed to operate ultradense storage devices are too high to generate, magnetization reversal by electrical currents is attracting much interest as a promising alternative encoding method. Indeed, spin-polarized currents can reverse the magnetization direction of nanometre-sized metallic structures through torque; however, the high current densities of 10(7)-10(8) A cm(-2) that are at present required exceed the threshold values tolerated by the metal interconnects of integrated circuits. Encoding magnetic information in metallic systems has also been achieved by manipulating the domain walls at the boundary between regions with different magnetization directions, but the approach again requires high current densities of about 10(7) A cm(-2). Here we demonstrate that, in a ferromagnetic semiconductor structure, magnetization reversal through domain-wall switching can be induced in the absence of a magnetic field using current pulses with densities below 10(5) A cm(-2). The slow switching speed and low ferromagnetic transition temperature of our current system are impractical. But provided these problems can be addressed, magnetic reversal through electric pulses with reduced current densities could provide a route to magnetic information storage applications.

639 citations


Authors

Showing all 72477 results

NameH-indexPapersCitations
John Q. Trojanowski2261467213948
Aaron R. Folsom1811118134044
Marc G. Caron17367499802
Masayuki Yamamoto1711576123028
Kenji Watanabe1672359129337
Rodney S. Ruoff164666194902
Frederik Barkhof1541449104982
Takashi Taniguchi1522141110658
Yoshio Bando147123480883
Thomas P. Russell141101280055
Ali Khademhosseini14088776430
Marco Colonna13951271166
David H. Barlow13378672730
Lin Gu13086856157
Yoichiro Iwakura12970564041
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023162
2022754
20216,412
20206,426
20196,076
20185,898