Consideration on the Relationship Between Dielectric Breakdown Voltage and Water Content in Fatty Acid Esters

Mineral oil is commonly used as an electrical insulating oil in transformers because of its relatively high electrical insulating ability and fluidity. Considering the depletion of resources and environmental problems, however, fatty acid esters synthesized from natural plant oils are attracting attention as an environmentally friendly insulating oil. In addition, fatty acid esters such as methyl octanoate, methyl dodecanoate, 2-ethylhexyl octanoate, and 2-ethylhexyl dodecanoate have high fluidity, and also show excellent moisture tolerance against dielectric breakdown compared to mineral oil. In the present study, to clarify the reason for the superior moisture tolerance of fatty acid esters, the status of dissolved water in esters is investigated with IR spectroscopic measurements and density functional theory (DFT) calculations. It is revealed that water molecules in fatty acid esters are trapped by the ester moiety of fatty acid esters. As a result, fatty acid esters have a higher moisture tolerance against dielectric breakdown than mineral oil.     

Mass transfer in polycrystalline ytterbium monosilicate under oxygen potential gradients at high temperatures

Mass transfer in polycrystalline Yb₂SiO₅ wafers with precise composition control was evaluated and analyzed by oxygen permeation experiments at high temperatures using an oxygen tracer. Oxygen permeation proceeded due to mutual grain boundary diffusion of oxide ions and Yb ions without synergistic effects such as acceleration or suppression. The oxygen shielding properties of Yb₂SiO₅ were compared with those of the other line compounds such as Yb₂Si₂O₇ and Al₂O₃ based on the determined mass transfer parameters. It was found that the more preferentially an oxide ion diffuses in the grain boundary compared to the interior of the grain, the greater the effect of suppressing the movement of the oxide ion by applying an oxygen potential gradient becomes.     

Preparation of Pd-Ag thin films by the RF-sputtering method and its catalysis for oxidative dehydrogenation of allylic alcohols

Pd-Ag thin films were prepared by the RF sputtering method on a Pyrex glass substrate to give monophasic alloys. Oxidation of allylic alcohols using the films gave selectively , -unsaturated carbonyl compounds in the presence of O₂; highly dispersed Pd species on Ag, Pd₇Ag₉₃ showed 95% selectivity for acrolein from allyl alcohol at 473 K.     

DNA recognition by a {beta}-sheet

DNA recognition by a ß-sheet is discussed in the lightof crystal structures of the MetJ and Arc repressors. The DNAbinding geometry of a ß-sheet can be understood interms of (i) close fitting of the two surfaces and (ii) matchingof residue and base positions. A ß-sheet is not entirelyflat but has a curvature. A ß-sheet of the Met-Arcfamily faces the DNA major groove with its convex surface; thelocal DNA major groove is deepest at the centre. The ß-sheetfollows 6 bp; every two residues face the DNA and the firstand fifth residues, which are separated by 13.2 Å, bind,respectively, to the third and sixth bases, which are separatedby 13.5 Å, on the same DNA strand.     

Effect of Surface Treatment on the Selective Photocatalytic Oxidation of Benzyl Alcohol into Benzaldehyde by O2 on TiO2 Under Visible Light

The photocatalytic oxidation of benzyl alcohol into benzaldehyde proceeded with high conversion and selectivity on a TiO₂ photocatalyst by O₂ under visible light irradiation. Surface complex formed by the interaction of benzyl alcohol with the Ti sites and/or surface OH groups of TiO₂ play an important role in the absorption of visible light and unique selective photocatalytic reaction.     

Combination of cytomegalovirus enhancer with human cellular promoters for gene-induced chondrogenesis of human bone marrow mesenchymal stem cells

High-expression plasmid vectors for human mesenchymal stem cells (MSCs) were constructed by combination of cytomegalovirus immediate-early enhancer with cellular promoters. MSCs transfected with the vector showed higher transgene production of a cytokine, which increased the differentiation level to chondrocytes.     

Incremental learning of context free grammars based on bottom-up parsing and search

This paper describes approaches for machine learning of context free grammars (CFGs) from positive and negative sample strings, which are implemented in Synapse system. The grammatical inference consists of a rule generation by “inductive CYK algorithm,” mechanisms for incremental learning, and search. Inductive CYK algorithm generates minimum production rules required for parsing positive samples, when the bottom-up parsing by CYK algorithm does not succeed. The incremental learning is used not only for synthesizing grammars by giving the system positive strings in the order of their length but also for learning grammars from other similar grammars. Synapse can synthesize fundamental ambiguous and unambiguous CFGs including nontrivial grammars such as the set of strings not of the form ww with w∈{a,b}⁺.     

Isolation of dimethyl sulfone-degrading microorganisms and application to odorless degradation of dimethyl sulfoxide

With the objective of developing an odorless biodegradation process for dimethyl sulfoxide (DMSO), Hyphomicrobium sp. WU-OM3 was isolated. During the cultivation of strain WU-OM3 cells with 20 mM dimethyl sulfone (DMSO2) as the sole carbon source, DMSO2 was completely consumed within 48 h and sulfate ion accumulated in the culture broth. Methanesulfonate was also detected as an intermediate of DMSO2 degradation. By combining the DMSO-oxidizing microorganism and strain WU-OM3 cells, 0.64 mM (50 mg/l) DMSO was degraded to sulfate ion with 80% molar conversion ratio.     

Trading variance reduction with unbiasedness: the regularized subspace information criterion for robust model selection in kernel regression

A well-known result by Stein (1956) shows that in particular situations, biased estimators can yield better parameter estimates than their generally preferred unbiased counterparts. This letter follows the same spirit, as we will stabilize the unbiased generalization error estimates by regularization and finally obtain more robust model selection criteria for learning. We trade a small bias against a larger variance reduction, which has the beneficial effect of being more precise on a single training set. We focus on the subspace information criterion (SIC), which is an unbiased estimator of the expected generalization error measured by the reproducing kernel Hilbert space norm. SIC can be applied to the kernel regression, and it was shown in earlier experiments that a small regularization of SIC has a stabilization effect. However, it remained open how to appropriately determine the degree of regularization in SIC. In this article, we derive an unbiased estimator of the expected squared error, between SIC and the expected generalization error and propose determining the degree of regularization of SIC such that the estimator of the expected squared error is minimized. Computer simulations with artificial and real data sets illustrate that the proposed method works effectively for improving the precision of SIC, especially in the high-noise-level cases. We furthermore compare the proposed method to the original SIC, the cross-validation, and an empirical Bayesian method in ridge parameter selection, with good results.