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Titlebook: Innovation Networks in the German Laser Industry; Evolutionary Change, Muhamed Kudic Book 2015 Springer International Publishing Switzerlan

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发表于 2025-3-21 16:51:50 | 显示全部楼层 |阅读模式
书目名称Innovation Networks in the German Laser Industry
副标题Evolutionary Change,
编辑Muhamed Kudic
视频video
概述Longitudinal empirical analysis of innovation networks.Provides a unique overview of R&D cooperation activities in the German laser industry.Evolutionary perspective on complex economic systems
丛书名称Economic Complexity and Evolution
图书封面Titlebook: Innovation Networks in the German Laser Industry; Evolutionary Change, Muhamed Kudic Book 2015 Springer International Publishing Switzerlan
描述.Technological innovation is fundamental to firm performance and economic prosperity. The aim of this book is to contribute to an in-depth understanding of collective innovation processes by analyzing publicly funded R&D cooperation and innovation networks in the German laser industry. Standing in a neo-Schumpeterian tradition, it employs interdisciplinary analytical concepts and draws upon a unique longitudinal dataset from the laser industry that covers more than two decades of observations. In brief, the book makes a valuable contribution by exploring how and why firm-specific R&D cooperation activities and network positions, large-scale network patterns, and evolutionary network change processes affect the innovative performance of laser source manufacturers in Germany..
出版日期Book 2015
关键词Evolutionary Charge; Firm Innovatieness; Innovation Network; Laser Industry; Network Positioning; R&D Coo
版次1
doihttps://doi.org/10.1007/978-3-319-07935-6
isbn_softcover978-3-319-35573-3
isbn_ebook978-3-319-07935-6Series ISSN 2199-3173 Series E-ISSN 2199-3181
issn_series 2199-3173
copyrightSpringer International Publishing Switzerland 2015
The information of publication is updating

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al information including the microscopic proximity structure and mesoscopic community structure. In particular, both the microscopic proximity structure and node attributes are factorized by Nonnegative Matrix Factorization (NMF), from which the low-dimensional node representations can be obtained.
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Muhamed Kudics problem. We first get the fuzzy emotion intensity of every sentiment word by solving a fuzzy relation equation, and then utilize an improved fuzzy-rough set method to predict emotion intensity for sentences, paragraphs, and documents. Compared with previous work, our proposed algorithm can simulta
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Muhamed KudicQs accurately, we propose the Pattern-Concept-Time-Based (PCTB) method that utilizes query reformulation patterns, concepts behind the queries and the user’s continuous searching behavior to compute the similarity between two queries. To evaluate the effectiveness of our approach, we employ the AOL
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