The East Asian miracle: Economic growth and public policy: By the World Bank. (New York: Oxford University Press for the World Bank, 1993. 389 pp. $19.95, paper.)

* Starting from Need* Evolution of the Toyota Production System* Further Development* Genealogy of the Toyota Production System* The True Intention of the Ford System* Surviving the Low-Growth Period

Toyota production system : beyond large-scale production

本文对国际科学计量学杂志《Ｓｃｉｅｎｔｏｍｅｔｒｉｃｓ》１９７９－１９９１年的研究论文内容、栏目、作者及国别和编委及国别作了计量分析，揭示出科学计量学研究的重点、活动的中心及发展趋势，说明了学科带头人在发展科学计量学这门新兴学科中的作用。

国际刊物《Scientometrics》文献计量研究

Graph is an important data representation which appears in a wide diversity of real-world scenarios. Effective graph analytics provides users a deeper understanding of what is behind the data, and thus can benefit a lot of useful applications such as node classification, node recommendation, link prediction, etc. However, most graph analytics methods suffer the high computation and space cost. Graph embedding is an effective yet efficient way to solve the graph analytics problem. It converts the graph data into a low dimensional space in which the graph structural information and graph properties are maximumly preserved. In this survey, we conduct a comprehensive review of the literature in graph embedding. We first introduce the formal definition of graph embedding as well as the related concepts. After that, we propose two taxonomies of graph embedding which correspond to what challenges exist in different graph embedding problem settings and how the existing work addresses these challenges in their solutions. Finally, we summarize the applications that graph embedding enables and suggest four promising future research directions in terms of computation efficiency, problem settings, techniques, and application scenarios.

A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications

Introduction: Regenerative medicine involves research in a number of fields and disciplines such as stem cell research, tissue engineering and biological therapy in general. As research in these ar...

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Emerging trends in regenerative medicine: a scientometric analysis in CiteSpace

Extracting the commercialization gap between science and technology — Case study of a solar cell

Detecting emerging research fronts in regenerative medicine by the citation network analysis of scientific publications

On 11 March 2011, the day of the unforgettable disaster of the 9 magnitude Tohoku earthquake and quickly followed by the devastating Tsunami, a damageable amount of radionuclides had dispersed from the Fukushima Daiichi’s damaged nuclear reactors. Decontamination of the dispersed radionuclides from seawater and soil, due to the huge amounts of coexisting ions with competitive functionalities, has been the topmost difficulty. Ferric hexacyanoferrate, also known as Prussian blue (PB), has been the most powerful material for selectively trapping the radioactive cesium ions; its high tendency to form stable colloids in water, however, has made PB to be impossible for the open-field radioactive cesium decontamination applications. A nano/nano combinatorial approach, as is described in this study, has provided an ultimate solution to this intrinsic colloid formation difficulty of PB. Cellulose nanofibers (CNF) were used to immobilize PB via the creation of CNF-backboned PB. The CNF-backboned PB (CNF/PB) was found to be highly tolerant to water and moreover, it gave a 139 mg/g capability and a million (106) order of magnitude distribution coefficient (Kd) for absorbing of the radioactive cesium ion. Field studies on soil and seawater decontaminations in Fukushima gave satisfactory results, demonstrating high capabilities of CNF/PB for practical applications.

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Cellulose nanofiber backboned Prussian blue nanoparticles as powerful adsorbents for the selective elimination of radioactive cesium.

In this article, we build models to predict the existence of citations among papers by formulating link prediction for 5 large-scale datasets of citation networks. The supervised machine-learning model is applied with 11 features. As a result, our learner performs very well, with the F1 values of between 0.74 and 0.82. Three features in particular, link-based Jaccard coefficient, difference in betweenness centrality, and cosine similarity of term frequency-inverse document frequency vectors, largely affect the predictions of citations. The results also indicate that different models are required for different types of research areas--research fields with a single issue or research fields with multiple issues. In the case of research fields with multiple issues, there are barriers among research fields because our results indicate that papers tend to be cited in each research field locally. Therefore, one must consider the typology of targeted research areas when building models for link prediction in citation networks.

Link prediction in citation networks

The need for automatic document summarization that can be used for practical applications is increasing rapidly In this paper, we propose a general framework for summarization that extracts sentences from a document using externally related information Our work is aimed at single document summarization using small amounts of reference summaries In particular, we address document summarization in the framework of multi-task learning using curriculum learning for sentence extraction and document classification The proposed framework enables us to obtain better feature representations to extract sentences from documents We evaluate our proposed summarization method on two datasets: financial report and news corpus Experimental results demonstrate that our summarizers achieve performance that is comparable to state-of-the-art systems

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Ichiro Sakata

Papers

Extracting the commercialization gap between science and technology — Case study of a solar cell

Detecting emerging research fronts in regenerative medicine by the citation network analysis of scientific publications

Cellulose nanofiber backboned Prussian blue nanoparticles as powerful adsorbents for the selective elimination of radioactive cesium.

Link prediction in citation networks

Extractive Summarization Using Multi-Task Learning with Document Classification