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Institution

Otsuma Women's University

EducationTokyo, Japan
About: Otsuma Women's University is a education organization based out in Tokyo, Japan. It is known for research contribution in the topics: Differential scanning calorimetry & Population. The organization has 422 authors who have published 913 publications receiving 12796 citations. The organization is also known as: Otsuma-Joshi-Daigaku.


Papers
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Journal ArticleDOI
TL;DR: Principal component analysis showed that general body thickness and back depth formed opposite clusters on PC I, dorsal length contrasts with dorsal angle on PC II, and general body length formed a cluster on PC III.
Abstract: We studied the dorsal moire topographies of a group of 142 girls with an average age of 13.7 and another group of 113 girls with an average age of 16.4. Of the respective groups, 60 and 68 subjects were measured anthropometrically. They were photographed in a natural standing position, and 15% of the former group and 26% of the latter were excluded from the study beforehand because they had more than 7.5mm left-right dorsal depth difference. The Type II high-thoracic moire pattern was most frequent (85% and 86%) in each group. Neither left-right differences or age group differences were detected in the length, depth and angle measurements on the dorsal moire photographs. The angle between the posterior median line and the left/right prominent point on the back fit the Gaussian distribution. The girls with Type I or II thoracic moire pattern were obese as indicated by their skinfold thickness, Rohrer index, the body mass index, and circumferential measurements. Principal component analysis showed that general body thickness and back depth formed opposite clusters on PC I, dorsal length contrasts with dorsal angle on PC II, and general body length formed a cluster on PC III.
Journal ArticleDOI
TL;DR: In this article, the authors applied dendrogram analysis to the CARMA-NRO C$18$O ($J$=1--0) data and identified 692 dense cores in the Orion Nebula Cluster (ONC) region.
Abstract: Applying dendrogram analysis to the CARMA-NRO C$^{18}$O ($J$=1--0) data having an angular resolution of $\sim$ 8", we identified 692 dense cores in the Orion Nebula Cluster (ONC) region. Using this core sample, we compare the core and initial stellar mass functions in the same area to quantify the step from cores to stars. About 22 \% of the identified cores are gravitationally bound. The derived core mass function (CMF) for starless cores has a slope similar to Salpeter's stellar initial mass function (IMF) for the mass range above 1 $M_\odot$, consistent with previous studies. Our CMF has a peak at a subsolar mass of $\sim$ 0.1 $M_\odot$, which is comparable to the peak mass of the IMF derived in the same area. We also find that the current star formation rate is consistent with the picture in which stars are born only from self-gravitating starless cores. However, the cores must gain additional gas from the surroundings to reproduce the current IMF (e.g., its slope and peak mass), because the core mass cannot be accreted onto the star with a 100\% efficiency. Thus, the mass accretion from the surroundings may play a crucial role in determining the final stellar masses of stars.
Journal ArticleDOI
01 Jan 2019
TL;DR: 1. はじめに 近年,講義形態におけるアクティブラーニング の重要性が増している.
Abstract: 1. はじめに 近年,講義形態におけるアクティブラーニング の重要性が増している.これは教員から学生への 一方的な講義ではなく教員と学生との双方向性や 学生らの能動的な学習を促進するというスタイル である.このようなスタイルの講義では,学生ら の状態を教員や TA が把握し,状況に応じて学生 とのやり取りや,講義の進行速度や内容を調整す ることが望ましい. しかしながら,1 名の教員と 1 名程度の TA に対 して履修生が多数という形態の講義では,講義中 に,個々の学生の状態を常時把握することは難し い. 一方,カメラの高解像度化やコンピュータの高 性能化に伴い,コンピュータがリアルタイムに外 界の状況を認識することができるようになってき ている.これらを講義で用いれば,教員や TA に 替わって学生らの状態を把握し,それを元に教員 や TA の行動を支援することで質の高い教育効果 を上げられることが期待できる. そこで本研究では,学生らの状況をリアルタイ ムに把握するため,着席した学生らの机上におけ る動作に着目し,資料閲覧,スマートフォン操作, 居眠りといった状態を認識するために,深層学習 の手法により状態を推定する基盤システムを構築, 評価した.本稿では,関連研究,想定環境,シス テムの概要,精度向上の工夫,まとめと今後の課 題について述べる.

Authors

Showing all 423 results

NameH-indexPapersCitations
Tatsuko Hatakeyama371744301
Sakae Inouye371304270
Shigeko Hara331224300
Minatsu Kobayashi31613797
Seiichiro Aoe291633615
Motoo Arai291542669
Akira Mochizuki28802525
Tomomi Shimoikura25881903
Akira Shimatsu24652406
Shuhachi Kiriyama241082099
Yoshiyuki Koyama21661381
Ko Fujimura20401449
Masakazu Horie201011434
Shinji Sakamoto19791000
Yusuke Kanke1735779
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20231
20223
202145
202054
201954
201829