Akiko Nakashima

435 total citations
18 papers, 386 citations indexed

About

Akiko Nakashima is a scholar working on Electrical and Electronic Engineering, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Akiko Nakashima has authored 18 papers receiving a total of 386 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Electrical and Electronic Engineering, 4 papers in Computer Vision and Pattern Recognition and 4 papers in Artificial Intelligence. Recurrent topics in Akiko Nakashima's work include Advancements in Battery Materials (8 papers), Advanced Battery Materials and Technologies (6 papers) and Neural Networks and Applications (4 papers). Akiko Nakashima is often cited by papers focused on Advancements in Battery Materials (8 papers), Advanced Battery Materials and Technologies (6 papers) and Neural Networks and Applications (4 papers). Akiko Nakashima collaborates with scholars based in Japan, United Kingdom and Switzerland. Akiko Nakashima's co-authors include Mitsuharu Tabuchi, Hiroyuki Kageyama, Kuniaki Tatsumi, Kazuaki Ado, Hikarí Sakaebe, H. Kobayashi, Tatsuya Nakamura, Ryoji Kanno, Hikari Shigemura and Atsushi Hirano and has published in prestigious journals such as Chemistry of Materials, Journal of The Electrochemical Society and Journal of Power Sources.

In The Last Decade

Akiko Nakashima

17 papers receiving 379 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Akiko Nakashima Japan 10 323 147 87 87 53 18 386
Yu Lei China 8 280 0.9× 47 0.3× 84 1.0× 66 0.8× 50 0.9× 36 345
Jiangtao Ma China 6 441 1.4× 94 0.6× 108 1.2× 41 0.5× 113 2.1× 12 500
Shiwei Li China 8 287 0.9× 96 0.7× 45 0.5× 43 0.5× 30 0.6× 32 331
Xinguang Yuan China 8 463 1.4× 118 0.8× 145 1.7× 89 1.0× 55 1.0× 13 490
Haoran Zhan China 9 248 0.8× 78 0.5× 29 0.3× 26 0.3× 120 2.3× 31 362
Abrar Ahmad India 5 431 1.3× 115 0.8× 109 1.3× 68 0.8× 105 2.0× 10 458
Chi Chen China 10 322 1.0× 123 0.8× 62 0.7× 53 0.6× 63 1.2× 23 352
Jingyuan Chen China 5 296 0.9× 213 1.4× 33 0.4× 46 0.5× 113 2.1× 15 383
Alice V. Llewellyn United Kingdom 10 493 1.5× 48 0.3× 338 3.9× 102 1.2× 57 1.1× 12 571
Jing-Zhou Chen China 15 365 1.1× 177 1.2× 67 0.8× 44 0.5× 119 2.2× 38 447

Countries citing papers authored by Akiko Nakashima

Since Specialization
Citations

This map shows the geographic impact of Akiko Nakashima's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Akiko Nakashima with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Akiko Nakashima more than expected).

Fields of papers citing papers by Akiko Nakashima

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Akiko Nakashima. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Akiko Nakashima. The network helps show where Akiko Nakashima may publish in the future.

Co-authorship network of co-authors of Akiko Nakashima

This figure shows the co-authorship network connecting the top 25 collaborators of Akiko Nakashima. A scholar is included among the top collaborators of Akiko Nakashima based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Akiko Nakashima. Akiko Nakashima is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

18 of 18 papers shown
1.
Nakashima, Akiko, Tomoko Watanabe, Katsuhisa Kurogi, et al.. (2016). Studies on the Anti-oxidative Stress Effect of Shiitake Mushroom. Nippon Shokuhin Kagaku Kogaku Kaishi. 63(5). 199–208. 1 indexed citations
2.
Okumura, Kazu, et al.. (2009). Enhancement of the Catalytic Activity of a Dawson-type Heteropoly Acid Induced by the Loading on a Silica Support. Topics in Catalysis. 52(6-7). 649–656. 9 indexed citations
3.
Yamaguchi, Osamu, et al.. (2005). Head Pose Estimation using Adaptively Scaled Template Matching.. 285–289. 3 indexed citations
4.
Tabuchi, Mitsuharu, Akiko Nakashima, Kazuaki Ado, Hiroyuki Kageyama, & Kuniaki Tatsumi. (2005). Heat-Treatment Effect on Phase Stability, Cation Distribution, Chemical Composition, and Electrochemical Behavior for Fe-Substituted Li2MnO3. Chemistry of Materials. 17(18). 4668–4677. 38 indexed citations
5.
Takeuchi, Tomonari, Mitsuharu Tabuchi, Akiko Nakashima, Hiroyuki Kageyama, & Kuniaki Tatsumi. (2005). Preparation of Dense Li[sub 1.05]Mn[sub 1.95]O[sub 4]∕C Composite Positive Electrodes Using Spark-Plasma-Sintering-Process. Electrochemical and Solid-State Letters. 8(4). A195–A195. 9 indexed citations
6.
Takeuchi, Tomonari, Mitsuharu Tabuchi, Akiko Nakashima, et al.. (2005). Preparation of dense LiFePO4/C composite positive electrodes using spark-plasma-sintering process. Journal of Power Sources. 146(1-2). 575–579. 34 indexed citations
7.
Tabuchi, Mitsuharu, Akiko Nakashima, Kazuaki Ado, et al.. (2005). The effects of preparation condition and dopant on the electrochemical property for Fe-substituted Li2MnO3. Journal of Power Sources. 146(1-2). 287–293. 41 indexed citations
8.
Akimoto, Junji, Junji Awaka, Yasuhiko Takahashi, et al.. (2005). Synthesis and Electrochemical Properties of Li[sub 0.44]MnO[sub 2] as a Novel 4 V Cathode Material. Electrochemical and Solid-State Letters. 8(10). A554–A554. 20 indexed citations
9.
West, Anthony R., Mitsuharu Tabuchi, Akiko Nakashima, et al.. (2004). Study of the Capacity Fading Mechanism for Fe-Substituted LiCoO[sub 2] Positive Electrode. Journal of The Electrochemical Society. 151(5). A672–A672. 35 indexed citations
10.
Maki, Atsuto, et al.. (2003). Mimicking video. 132–139. 7 indexed citations
11.
Tabuchi, Mitsuharu, Akiko Nakashima, Hikari Shigemura, et al.. (2003). Fine Li(4 ? x)/3Ti(2 ? 2x)/3FexO2 (0.18 ? x ? 0.67) powder with cubic rock-salt structure as a positive electrode material for rechargeable lithium batteries. Journal of Materials Chemistry. 13(7). 1747–1747. 75 indexed citations
12.
Nakashima, Akiko & Haruo Ogawa. (2003). How to design a regularization term for improving generalization. 1. 222–227. 3 indexed citations
13.
Tabuchi, Mitsuharu, Akiko Nakashima, Hikari Shigemura, et al.. (2002). Synthesis, Cation Distribution, and Electrochemical Properties of Fe-Substituted Li[sub 2]MnO[sub 3] as a Novel 4 V Positive Electrode Material. Journal of The Electrochemical Society. 149(5). A509–A509. 86 indexed citations
14.
Nakashima, Akiko, Akira Hirabayashi, & Haruo Ogawa. (2002). Noise suppression in training data for improving generalization. 3. 2236–2241.
15.
Nakashima, Akiko, Akira Hirabayashi, & Haruo Ogawa. (2001). Error correcting memorization learning for noisy training examples. Neural Networks. 14(1). 79–92. 9 indexed citations
16.
Nakashima, Akiko & Hidemitsu Ogawa. (2001). Noise suppression in training examples for improving generalization capability. Neural Networks. 14(4-5). 459–469. 2 indexed citations
17.
Nakashima, Akiko, et al.. (1997). Correlation between tensile properties and network draw ratio for poly(ethylene terephthalate) fibers with wide range of molecular orientation and crystallinity. Journal of Applied Polymer Science. 64(13). 2631–2646. 10 indexed citations
18.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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