FeNiSiO/SiO2 multi-layer granular magnetic films with high-resistivity

ArticleTransactions of the Materials Research Society of Japan. 34(1):9-13 (2009)journal articl

Performance of nano-hydraulic turbine utilizing waterfalls

The aim of this investigation was to develop an environmentally friendly nano-hydraulic turbine utilizing waterfalls. A model of an impulse type hydraulic turbine constructed and tested with an indoor type waterfall to arrive at an optimum installation condition. Effects of an installation parameter, namely distance between the rotor and the waterfall on the power performance were studied. The flow field around the rotor was examined visually to clarify influences of installation conditions on the flow field. The flow visualization showed differences of flow pattern around the rotor by the change of flow rate and rotational speed of the rotor. From this study it was found that the power performances of the rotor were changed with the distance between the rotor and the waterfalls. The maximum power coefficient of this turbine is approximately 60%. Also, to respond to changes in the waterfall flow rate, we placed a flat plate on the upper side of the rotor to control the water flow direction. As a result, we found that the coefficient of this turbine is increased with the flow rate and power could be obtained even when the flow rate changed by 3.5 times if the plate was placed on the upper side of the rotor. Although the power coefficient decreased when the plate was installed, the power coefficient still is from 53 to 58%.ArticleRenewable Energy. 35(1):293-300 (2010)journal articl

CoFeSiO/SiO(2) Multilayer Granular Films With Very Narrow Ferromagnetic Resonant Linewidth

In order to fabricate GHz-band micro-magnetic devices, we have investigated the high-frequency permeability of CoFeSiO/SiO multilayer granular films comprising alternate stacks of granular and insulator layers, which can enable us to control the magnetic grain size and intergrain spacing along the film thickness direction independent of each other. A very narrow ferromagnetic resonance (FMR) linewidth was obtained under specific film conditions. A sharp FMR peak was obtained for granular layer and insulator layer thicknesses of 6 and 1 nm, respectively. The CoFe/SiO(2) volume ratio of the granular layer has a strong influence on the FMR linewidth. A high CoFe/SiO(2) volume ratio resulted in a very narrow FMR linewidth. HR-TEM observation of a film with a high CoFe/SiO(2) volume ratio revealed a well-defined multilayer granular structure and a homogeneous CoFe grain size, which seem to be necessary for obtaining a very narrow FMR linewidth. The narrowest FMR linewidth observed in this study is 420 MHz, for an FMR frequency of 2.4 GHz, a permeability of 380, and a damping factor alpha of 0.007.ArticleIEEE TRANSACTIONS ON MAGNETICS. 45(10):4290-4293 (2009)journal articl

Mastery-approach goals eliminate retrieval-induced forgetting: the role of achievement goals in memory inhibition

The present study examined how achievement goals affect retrieval-induced forgetting. Researchers have suggested that mastery-approach goals (i.e., developing one’s own competence) promote a relational encoding, whereas performance-approach goals (i.e., demonstrating one’s ability in comparison to others) promote item-specific encoding. These different encoding processes may affect the degree to which participants integrate the exemplars within a category and, as a result, we expected that retrieval-induced forgetting may be reduced or eliminated under mastery-approach goals. Three experiments were conducted using a retrieval-practice paradigm with different stimuli, where participants’ achievement goals were manipulated through brief written instructions. A meta-analysis that synthesized the results of the three experiments showed that retrieval-induced forgetting was not statistically significant in the mastery-approach goal condition, whereas it was statistically significant in the performance-approach goal condition. These results suggest that mastery-approach goals eliminate retrieval-induced forgetting, but performance-approach goals do not, demonstrating that motivation factors can influence inhibition and forgetting

Normal-state properties of the antiperovskite oxide Sr3−x_{3-x}SnO revealed by 119^{119}Sn-NMR

We have performed $^{119}$Sn-NMR measurements on the antiperovskite oxide superconductor Sr$_{3-x}$SnO to investigate how its normal state changes with the Sr deficiency. A two-peak structure was observed in the NMR spectra of all the measured samples. This suggests that the phase separation tends to occur between the nearly stoichiometric and heavily Sr-deficient Sr$_{3-x}$SnO phases. The measurement of the nuclear spin-lattice relaxation rate $1/T_1$ indicates that the Sr-deficient phase shows a conventional metallic behavior due to the heavy hole doping. In contrast, the nearly stoichiometric phase exhibits unusual temperature dependence of $1/T_1$, attributable to the presence of a Dirac-electron band.Comment: 5 pages, 4 figure

Coupled channel approach to strangeness S = -2 baryon-bayron interactions in Lattice QCD

The baryon-baryon interactions with strangeness S = -2 with the flavor SU(3) breaking are calculated for the first time by using the HAL QCD method extended to coupled channel system in lattice QCD. The potential matrices are extracted from the Nambu-Bethe-Salpeter wave functions obtained by the 2+1 flavor gauge configurations of CP-PACS/JLQCD Collaborations with a physical volume of 1.93 fm cubed and with m_pi/m_K = 0.96, 0.90, 0.86. The spatial structure and the quark mass dependence of the potential matrix in the baryon basis and in the SU(3) basis are investigated.Comment: 17 pages, 15 figure

Structured Hammerstein-Wiener Model Learning for Model Predictive Control

This paper aims to improve the reliability of optimal control using models constructed by machine learning methods. Optimal control problems based on such models are generally non-convex and difficult to solve online. In this paper, we propose a model that combines the Hammerstein-Wiener model with input convex neural networks, which have recently been proposed in the field of machine learning. An important feature of the proposed model is that resulting optimal control problems are effectively solvable exploiting their convexity and partial linearity while retaining flexible modeling ability. The practical usefulness of the method is examined through its application to the modeling and control of an engine airpath system.Comment: 6 pages, 3 figure