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Institution

Nagoya Institute of Technology

EducationNagoya, Japan
About: Nagoya Institute of Technology is a education organization based out in Nagoya, Japan. It is known for research contribution in the topics: Thin film & Turbulence. The organization has 10766 authors who have published 19140 publications receiving 255696 citations. The organization is also known as: Nagoya Kōgyō Daigaku & Nitech.


Papers
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Journal ArticleDOI
TL;DR: In this paper, pure and Pb-doped (5 and 10mol %) titanium dioxide (TiO2) thin films have been deposited on single-crystal Si (100) and vitreous silica substrates by the sol-gel dip-coating method.

205 citations

Journal ArticleDOI
TL;DR: In this paper, a toolbox of analytical relations is proposed to describe the effective thermal conductivity as a function of solid phase thermal conductivities, pore thermal properties, and pore volume fraction.
Abstract: Incorporation of porosity into a monolithic material decreases the effective thermal conductivity. Porous ceramics were prepared by different methods to achieve pore volume fractions from 4 to 95%. A toolbox of analytical relations is proposed to describe the effective thermal conductivity as a function of solid phase thermal conductivity, pore thermal conductivity, and pore volume fraction (νp). For νp 0.65, the thermal conductivity of kaolin-based foams and calcium aluminate foams was well described by the Hashin Shtrikman upper bound and Russell’s relation. Finally, numerical simulation on artificially generated microstructures yields accurate predictions of thermal conductivity when fine detail of the spatial distribution of the phases needs to be accounted for, as demonstrated with a bio-aggregate material.

205 citations

Journal ArticleDOI
TL;DR: Subtype-specific genetic lesions can be used to stratify patients within each LGG subtype, enabling better prognostication and management.
Abstract: Background Diffuse lower-grade gliomas (LGGs) are genetically classified into 3 distinct subtypes based on isocitrate dehydrogenase (IDH) mutation status and codeletion of chromosome 1p and 19q (1p/19q). However, the subtype-specific effects of additional genetic lesions on survival are largely unknown. Methods Using Cox proportional hazards regression modeling, we investigated the subtype-specific effects of genetic alterations and clinicopathological factors on survival in each LGG subtype, in a Japanese cohort of LGG cases fully genotyped for driver mutations and copy number variations associated with LGGs (n = 308). The results were validated using a dataset from 414 LGG cases available from The Cancer Genome Atlas (TCGA). Results In Oligodendroglioma, IDH-mutant and 1p/19q codeleted, NOTCH1 mutations (P = 0.0041) and incomplete resection (P = 0.0019) were significantly associated with shorter survival. In Astrocytoma, IDH-mutant, PIK3R1 mutations (P = 0.0014) and altered retinoblastoma pathway genes (RB1, CDKN2A, and CDK4) (P = 0.013) were independent predictors of poor survival. In IDH-wildtype LGGs, co-occurrence of 7p gain, 10q loss, mutation in the TERT promoter (P = 0.024), and grade III histology (P < 0.0001) independently predicted poor survival. IDH-wildtype LGGs without any of these factors were diagnosed at a younger age (P = 0.042), and were less likely to have genetic lesions characteristic of glioblastoma, in comparison with other IDH-wildtype LGGs, suggesting that they likely represented biologically different subtypes. These results were largely confirmed in the cohort of TCGA. Conclusions Subtype-specific genetic lesions can be used to stratify patients within each LGG subtype. enabling better prognostication and management.

203 citations

Journal ArticleDOI
TL;DR: A speaker-adaptive HMM-based speech synthesis system that employs speaker adaptation, feature-space adaptive training, mixed-gender modeling, and full-covariance modeling using CSMAPLR transforms, in addition to several other techniques that have proved effective in previous systems are described.
Abstract: This paper describes a speaker-adaptive HMM-based speech synthesis system. The new system, called ldquoHTS-2007,rdquo employs speaker adaptation (CSMAPLR+MAP), feature-space adaptive training, mixed-gender modeling, and full-covariance modeling using CSMAPLR transforms, in addition to several other techniques that have proved effective in our previous systems. Subjective evaluation results show that the new system generates significantly better quality synthetic speech than speaker-dependent approaches with realistic amounts of speech data, and that it bears comparison with speaker-dependent approaches even when large amounts of speech data are available. In addition, a comparison study with several speech synthesis techniques shows the new system is very robust: It is able to build voices from less-than-ideal speech data and synthesize good-quality speech even for out-of-domain sentences.

203 citations

Journal ArticleDOI
TL;DR: In this article, the authors achieved a 9?m-thick AlGaN/GaN high-electron mobility transistor (HEMT) epilayer on silicon using thick buffer layers with reduced dislocation density (DD).
Abstract: We have achieved a 9 ?m-thick AlGaN/GaN high-electron mobility transistor (HEMT) epilayer on silicon using thick buffer layers with reduced dislocation density (DD). The crack-free 9 ?m-thick epilayer included 2 ?m i-GaN and 7 ?m buffer. The HEMTs fabricated on these devices showed a maximum drain-current density of 625 mA/mm, transconductance of 190 mS/mm, and a high three-terminal OFF breakdown of 403 V for device dimensions of LgWgLgd=1.5/15/3 ?m . Without using a gate field plate, this is the highest BV reported on an AlGaN/GaN HEMT on silicon for a short Lgd of 3 ?m. A very high BV of 1813 V across 10 ?m ohmic gap was achieved for i-GaN grown on thick buffers. As the thickness of buffer layers increased, the decreased DD of GaN and increased resistance between surface electrode and substrate yielded a high breakdown.

203 citations


Authors

Showing all 10804 results

NameH-indexPapersCitations
Luis M. Liz-Marzán13261661684
Hideo Hosono1281549100279
Shunichi Fukuzumi111125652764
Andrzej Cichocki9795241471
Kwok-Hung Chan9140644315
Kimoon Kim9041235394
Alex Martin8840636063
Manijeh Razeghi82104025574
Yuichi Ikuhara7597424224
Richard J. Cogdell7348023866
Masaaki Tanaka7186022443
Kiyotomi Kaneda6537813337
Yulin Deng6464116148
Motoo Shiro6472017786
Norio Shibata6357414469
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202316
202272
2021631
2020718
2019701
2018764