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M. Hara

Publications and source records attributed to M. Hara.

7 recordsLinked to original sources

An electron linac with high beam intensity over 1-Ampere for disposal of radioactive waste

In order to dissipate long-lived radioactive waste, not only high ux proton accelerator but also electron linac have been proposed. A proton accelerator directly induces nuclear fission and mutation. On the other hand, electron beam has two processes: production of gamma rays through bremsstrahlung and produced gamma rays are available for the (gamma ; n) reaction. Protons dissipate nuclei and simultaneously newly produce radioisotopes of by-products. Since an electron linac produces less radioactive wastes, we focus only an electron linac with high beam intensity more than 1-Ampere. To reduce the yield of by-products for radioisotopes as less as possible, we accelerate electron beam with the energy less than 30 MeV. The accelerated beam intensity is designed to be more than 1-Ampere. To realize an electron linac with high intensity, the operation for the linac is not pulsed one, but continuous wave. To accelerate electron beam, we install higher-order-modes (HOMs) free normal conducting cavities to suppress beam instabilities.

physics.acc-ph

First elastic electron scattering from $^{132}$Xe at the SCRIT facility

The first elastic electron scattering has been successfully performed at the self-confining RI ion target (SCRIT) facility, the world's first electron scattering facility for exotic nuclei. The SCRIT technique achieved high luminosity (over 10$^{27}$~cm$^{-2}$s$^{-1}$, sufficient for determining the nuclear shape) with only 10$^8$ target ions. While $^{132}$Xe used in this time as a target is stable isotope, the charge density distribution was firstly extracted from the momentum transfer distributions of the scattered electrons by comparing the results with those calculated by a phase shift calculation.

nucl-ex

Composition profiling InAs quantum dots and wetting layers by atom probe tomography and cross-sectional scanning tunnelling microscopy

This study compares cross-sectional scanning tunnelling microscopy (XSTM) and atom probe tomography (APT). We use epitaxially grown self-assembled InAs quantum dots (QDs) in GaAs as an exemplary material with which to compare these two nanostructural analysis techniques. We studied the composition of the wetting layer and the QDs, and performed quantitative comparisons of the indium concentration profiles measured by each method. We show that computational models of the wetting layer and the QDs, based on experimental data, are consistent with both analytical approaches. This establishes a link between the two techniques and shows their complimentary behaviour, an advantage which we exploit in order to highlight unique features of the examined QD material.

cond-mat.mtrl-sci

Current-induced vortex-vortex switching in a nanopillar comprising two Co nano-rings

We fabricated a current-perpendicular-to-plane pseudo-spin-valve nanopillar comprising a thick and a thin Co rings with deep submicron lateral sizes. The dc current can effectively induce the flux-closure vortex states in the rings with desired chiralities. Abrupt transitions between the vortex states are also realized by the dc current and detected with the giant magnetoresistance effect. Both Oersted field and spin-transfer torque are found important to the magnetic transitions, but the former is dominant. They can be designed to cooperate with each other in the vortex-to-vortex transitions by carefully setting the chirality of the vortex state in the thick Co ring.

cond-mat.mes-hall

Magnetic characterization and switching of Co nano-rings in current-perpendicular-to-plane configuration

We fabricated Co nano-rings incorporated in the vertical pseudo-spin-valve nanopillar structures with deep submicron lateral sizes. It is shown that the current-perpendicular-to-plane giant magnetoresistance can be used to characterize a very small magnetic nano-ring effectively. Both the onion state and the flux-closure vortex state are observed. The Co nano-rings can be switched between the onion states as well as between onion and vortex states not only by the external field but also by the perpendicularly injected dc current.

cond-mat.other

Transport in two-dimensional electron gas narrow channel with a magnetic field gradient

We have investigated distinctive transport phenomena in two-dimensional electron gas (2DEG) narrow channel subjected to a large magnetic field gradient, a unique situation created in a ferromagnet/2DEG hybrid structure. The so-called snake orbits which propagate in the direction determined by the sign of field gradient, give rise to conductivity enhancement and rectification effect with respect to the direction of DC bias current.

cond-mat.mes-hall

Pattern Matching and Discourse Processing in Information Extraction from Japanese Text

Information extraction is the task of automatically picking up information of interest from an unconstrained text. Information of interest is usually extracted in two steps. First, sentence level processing locates relevant pieces of information scattered throughout the text; second, discourse processing merges coreferential information to generate the output. In the first step, pieces of information are locally identified without recognizing any relationships among them. A key word search or simple pattern search can achieve this purpose. The second step requires deeper knowledge in order to understand relationships among separately identified pieces of information. Previous information extraction systems focused on the first step, partly because they were not required to link up each piece of information with other pieces. To link the extracted pieces of information and map them onto a structured output format, complex discourse processing is essential. This paper reports on a Japanese information extraction system that merges information using a pattern matcher and discourse processor. Evaluation results show a high level of system performance which approaches human performance.

cs.AI