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Готові списки джерел за темами / Knowledge identification / Статті в журналах

Щоб переглянути інші типи публікацій з цієї теми, перейдіть за посиланням: Knowledge identification.

Статті в журналах з теми "Knowledge identification"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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1

Farwig, H. Meier zu, and H. Unbehauen. "Knowledge-Based System Identification." IFAC Proceedings Volumes 25, no. 15 (July 1992): 21–29. http://dx.doi.org/10.1016/s1474-6670(17)50606-0.

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2

Grantham, Ross. "Corporate Knowledge: Identification or Attribution?" Modern Law Review 59, no. 5 (September 1996): 732–37. http://dx.doi.org/10.1111/j.1468-2230.1996.tb02689.x.

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3

Lim, Hyojeong, and Yongtae Park. "Identification of technological knowledge intermediaries." Scientometrics 84, no. 3 (December 25, 2009): 543–61. http://dx.doi.org/10.1007/s11192-009-0133-8.

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4

Arentz, Jason, Frederic Sautet, and Virgil Storr. "Prior-knowledge and opportunity identification." Small Business Economics 41, no. 2 (June 19, 2012): 461–78. http://dx.doi.org/10.1007/s11187-012-9437-9.

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5

Pathirage, Chaminda, Krisanthi Seneviratne, Dilanthi Amaratunga, and Richard Haigh. "Managing disaster knowledge: identification of knowledge factors and challenges." International Journal of Disaster Resilience in the Built Environment 3, no. 3 (September 28, 2012): 237–52. http://dx.doi.org/10.1108/17595901211263620.

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6

Alshawi, S., and W. Al‐Karaghouli. "Managing knowledge in business requirements identification." Logistics Information Management 16, no. 5 (October 2003): 341–49. http://dx.doi.org/10.1108/09576050310499336.

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7

Meehan, Bridget, and Ita Richardson. "Identification of Software Process Knowledge Management." Software Process: Improvement and Practice 7, no. 2 (June 2002): 47–55. http://dx.doi.org/10.1002/spip.154.

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8

Barcelo Valenzuela, Mario, Gerardo Sanchez Schmitz, Alonso Perez Soltero, Luis Felipe Romero Dessens, Fernando Martin Rubio, and Jose Palma. "Knowledge management opportunity space identification strategy." Knowledge and Process Management 17, no. 3 (July 2010): 111–17. http://dx.doi.org/10.1002/kpm.348.

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9

Becker, Karen, Frances Jørgensen, and Adelle Bish. "Knowledge Identification and Acquisition in SMEs." International Journal of Knowledge Management 11, no. 3 (July 2015): 1–16. http://dx.doi.org/10.4018/ijkm.2015070101.

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Анотація:

Researchers and practitioners have been preoccupied with identifying ways for larger organizations to acquire and manage knowledge, however far less research attention has been directed towards these same pursuits in small and medium-sized enterprises (SMEs). This paper examines how SMEs engage in knowledge identification and acquisition; in particular how they identify knowledge needs and source this knowledge to enhance their business. The research studied six SMEs in Australia and Denmark. Contrary to prevailing assumptions, the findings suggest that SMEs engage in identification and sourcing of critical knowledge, albeit often with less than formal processes. These organizations relied on business plans to direct knowledge activities and ensure balance between long-range planning and flexibility. The results address a lack of empirical evidence about SME approaches to knowledge identification and acquisition, and demonstrate that although SMEs may approach such activities in an informal way, they are nonetheless deliberate and strategic in their knowledge activities.

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10

Cayrel, Pierre-Louis, Richard Lindner, Markus Rückert, and Rosemberg Silva. "Improved Zero-Knowledge Identification with Lattices." Tatra Mountains Mathematical Publications 53, no. 1 (December 1, 2012): 33–63. http://dx.doi.org/10.2478/v10127-012-0038-4.

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Анотація:

ABSTRACT Zero-knowledge identification schemes solve the problem of authenticating one party to another via an insecure channel without disclosing any additional information that might be used by an impersonator. In this paper we propose a scheme whose security relies on the existence of a commitment scheme and on the hardness of worst-case lattice problems. We adapt a code- -based identification scheme devised by Cayrel, V´eron and El Yousfi, which constitutes an improvement of Stern’s construction. Our solution sports analogous improvements over the lattice adaption of Stern’s scheme which Kawachi et al. presented at ASIACRYPT ’08. Specifically, due to a smaller cheating probability close to 1/2 and a similar communication cost, any desired level of security will be achieved in fewer rounds. Compared to Lyubashevsky’s scheme presented at ASIACRYPT ’09, our proposal, like Kawachi’s, offers a much milder security assumption: namely, the hardness of SIS for trinary solutions. The same assumption was used for the SWIFFT hash function, which is secure for much smaller parameters than those proposed by Lyubashevsky.

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11

Rajkovič, V., M. Bohanec, and V. Batagelj. "Knowledge engineering techniques for utility identification." Acta Psychologica 68, no. 1-3 (September 1988): 271–86. http://dx.doi.org/10.1016/0001-6918(88)90060-1.

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12

Königová, M., and J. Hron. " Methodology for the identification of managerial competencies in knowledge-based organizations." Agricultural Economics (Zemědělská ekonomika) 58, No. 8 (August 22, 2012): 347–53. http://dx.doi.org/10.17221/206/2011-agricecon.

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Анотація:

In the current entrepreneurial environment characterised by the dynamic development and changes, organizations have to promote the development of their employees in order to achieve a competitive advantage. Therefore, the personality of a manager who is a holder of specific knowledge, abilities, skills, traits, motives, attitudes and values becomes increasingly important as it is the main source for gaining a competitive advantage. Managerial competencies, i.e. the individual characteristic necessary to reach the required level of a manager’s performance, together with an efficient management thus become the key factor of success. The article focuses on the identification of managerial competencies in knowledge-based organizations in the Czech Republic. The first part of the article focuses on the theoretical approaches to managerial competencies, competency models and knowledge-based organizations. The second part of the article deals with the proposed methodology for the identification of managerial competencies. The final part summarizes the benefits and difficulties of applying the competency-based approach as a tool of the efficient management of organizations for the purpose of achieving a competitive advantage. The utilization of managerial competencies to achieve competitive advantage is important in all fields including the economics and management of agriculture and other similar fields.  

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13

Hongyun Luo, Qingpu Zhang, and Xiangyi Lin. "Identification and Evaluation on Tacit Knowledge Sharing Risks in Knowledge Alliances." International Journal of Information Processing and Management 4, no. 3 (May 31, 2013): 1–11. http://dx.doi.org/10.4156/ijipm.vol4.issue3.1.

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14

Hasiewicz, Zygmunt, Przemys law Śliwiński, and Grzegorz Mzyk. "Nonlinear system identification under various prior knowledge." IFAC Proceedings Volumes 41, no. 2 (2008): 7849–58. http://dx.doi.org/10.3182/20080706-5-kr-1001.01327.

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15

ITO, Haruka, Masanori HIROTOMO, Youji FUKUTA, Masami MOHRI, and Yoshiaki SHIRAISHI. "Zero-Knowledge Identification Scheme Using LDPC Codes." IEICE Transactions on Information and Systems E101.D, no. 11 (November 1, 2018): 2688–97. http://dx.doi.org/10.1587/transinf.2017icp0020.

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16

Kozjek Varl, Katja, and Jerneja Herzog. "KNOWLEDGE AND IDENTIFICATION OF DIFFERENT ARTISTIC PRACTICES." Research in Pedagogy 8, no. 2 (December 31, 2018): 145–61. http://dx.doi.org/10.17810/2015.80.

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17

KAMEJIMA, Kohji, Toshiyuki AOKI, and Yuriko C. WATANABE. "Knowledge Completion Mechanism for Interactive Object Identification." Transactions of the Society of Instrument and Control Engineers 30, no. 11 (1994): 1351–59. http://dx.doi.org/10.9746/sicetr1965.30.1351.

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18

Kusumawati, Dara, and Dini Faktasari. "Drop Out Students Identification Using Knowledge Base." bit-Tech 2, no. 1 (October 30, 2019): 20–27. http://dx.doi.org/10.32877/bt.v2i1.102.

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Анотація:

The purpose of this study is modeling for the initial identification of college dropout students. Samples were taken from drop out student data for the past 4 years. Information from this sample will be acquired as a knowledge base in system modeling. The research aims to provide a knowledge-based system approach using Dempster Shafer for the management of student drop outs at universities especially in Yogyakarta. The symptoms of DO students are obtained from knowledge about DO that appears on campus in Yogyakarta. The system output is in the form of 3 groups of classification namely initial potential DO, enough potential and once potential. The results of the study produced a system that could help university managers deal with drop-out problems early.

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19

Niederlinski, A., J. Kasprzyk, and J. Figwer. "Intelligent Identification: Decision Trees or Knowledge Bases?" IFAC Proceedings Volumes 27, no. 8 (July 1994): 767–72. http://dx.doi.org/10.1016/s1474-6670(17)47802-5.

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20

Abonyi, J., R. Babuska, H. B. Verbruggen, and F. Szeifert. "Incorporating prior knowledge in fuzzy model identification." International Journal of Systems Science 31, no. 5 (January 2000): 657–67. http://dx.doi.org/10.1080/002077200290966.

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21

Martin, Joyce. "Multiple intelligence theory, knowledge identification and trust." Knowledge Management Research & Practice 4, no. 3 (August 2006): 207–15. http://dx.doi.org/10.1057/palgrave.kmrp.8500101.

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22

Zoysa, Sanjaya De, and Alan D. Russell. "Knowledge-based risk identification in infrastructure projects." Canadian Journal of Civil Engineering 30, no. 3 (June 1, 2003): 511–22. http://dx.doi.org/10.1139/l03-001.

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Анотація:

Effective risk management is a central function in the successful planning and execution of large infrastructure projects. This paper explores how current knowledge-based approaches for risk management can be improved upon so that they are more responsive to the attributes of a project and the needs of system users. A review of existing knowledge-based systems for risk management provides a backdrop for a discussion on desirable characteristics of such an approach. The proposed methodology adopts a model-based technique in that explicit abstractions of project components and processes, and the physical, regulatory, political, social, financial, economic, contractual, and organizational environments in which they are located, are created to assist in the reasoning about possible risks. This contrasts with several current systems that use only implicit representations. The reasoning schema and models of the physical project and environment that are used for the reasoning process are described in the paper.Key words: risk identification, project modeling, knowledge management, infrastructure projects.

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23

Yuan Fu, Qiu, Yoon Ping Chui, and Martin G. Helander. "Knowledge identification and management in product design." Journal of Knowledge Management 10, no. 6 (November 2006): 50–63. http://dx.doi.org/10.1108/13673270610709215.

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24

Lee, T. H., Q. G. Wang, and K. K. Tan. "Knowledge-based process identification from relay feedback." Journal of Process Control 5, no. 6 (December 1995): 387–97. http://dx.doi.org/10.1016/0959-1524(95)00023-j.

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25

Chernyshov, K. R. "Knowledge Based Identification of Non-Linear Systems." IFAC-PapersOnLine 51, no. 6 (2018): 30–35. http://dx.doi.org/10.1016/j.ifacol.2018.07.125.

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26

Kobashi, Masaharu, and Linda G. Shapiro. "Knowledge-based organ identification from CT images." Pattern Recognition 28, no. 4 (April 1995): 475–91. http://dx.doi.org/10.1016/0031-3203(94)00124-5.

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27

Ortiz, Beatriz, Mario J. Donate, and Fátima Guadamillas. "Relationships between structural social capital, knowledge identification capability and external knowledge acquisition." European Journal of Management and Business Economics 26, no. 1 (July 3, 2017): 48–66. http://dx.doi.org/10.1108/ejmbe-07-2017-004.

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Анотація:

Purpose The purpose of this paper is to analyze the mediating effect of the identification of valuable external knowledge on the relationship between the development of inter-organizational ties (structural social capital) and the acquisition of external knowledge. Design/methodology/approach Using a sample of 87 firms from Spanish biotechnology and pharmaceutics industries, the authors have tested the proposed mediation hypothesis by applying the partial least squares technique to a structural equations model. Findings The study results show that those firms with stronger, more frequent and closer inter-relationships are able to increase the amount of intentionally acquired knowledge, partly due to the greater level of development of their knowledge identification capability. Thus, firms with a higher capability to recognize the value of the knowledge embedded in their inter-organizational networks will be more likely to design better strategies to acquire and integrate such knowledge into their current knowledge bases for either present or future use. Originality/value This research contributes to knowledge management and social capital literature by means of the study of two key determinants of knowledge acquisition – structural social capital and knowledge identification capability – and the explanation of their relationships of mutual influence. The paper thus tries to fill this literature gap and connects the relational perspective of social capital with the knowledge-based view from a strategic point of view.

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28

Reysen, Stephen, Iva Katzarska-Miller, and Shonda A. Gibson. "World Knowledge and Global Citizenship: Factual and Perceived World Knowledge as Predictors of Global Citizenship Identification." International Journal of Development Education and Global Learning 5, no. 1 (January 1, 2013): 49–68. http://dx.doi.org/10.18546/ijdegl.05.1.04.

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Анотація:

We examine the influence of factual and perceived world knowledge on global citizenship identification. Perceived world knowledge directly predicted global citizenship identification, while factual world knowledge did not (Study 1). Students' factual (Study 1) and perceived (Study 2) world knowledge predicted students' normative environment (degree that valued others prescribe being a global citizen) and global awareness (perceived knowledge of the world and one's connection to the world), which then predicted global citizenship identification, and identification with global citizens predicted endorsement of pro-social values and behaviours (e.g., intergroup empathy, valuing diversity). Overall, the results highlight the indirect influence of factual and direct influence of perceived world knowledge on students' felt connection with global citizens.

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29

Visser, Marjolein, Yung Hung, and Wim Verbeke. "Protein Knowledge of Older Adults and Identification of Subgroups with Poor Knowledge." Nutrients 13, no. 3 (March 20, 2021): 1006. http://dx.doi.org/10.3390/nu13031006.

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Анотація:

The aim was to investigate the protein knowledge of community-dwelling older adults. A survey was conducted among 1825 adults aged ≥65 years and living in Finland, Netherlands, Poland, Spain and United Kingdom in 2017. Protein knowledge was measured with nine objective knowledge statements provided only to participants who indicated to know what the nutrient "protein" is (64.7% of sample). Demographic, socioeconomic and health determinants of poor protein knowledge were investigated using multiple logistic regression analyses. The sample was 49.6% female and 87.0% reported no walking difficulties. Participants scored best on the true statement "You need protein in the diet for repairing bones and muscles" (89.3% correct), and worst on the false statement "One meal per day with a good protein source is sufficient" (25.4% correct). Median knowledge score was 5.0 (scale 0–9) and poor knowledge was present in 49.4% of the sample. Males (Odds Ratio 1.57), those unable to walk for 5 min (2.66), not always making their own food decision (1.36) and having lower income (1.44) were more likely to have poor knowledge. Large differences were observed across countries. In conclusion, poor protein knowledge is present in about half of community-dwelling older adults. Communication strategies should be tailored to target the identified risk groups with poor knowledge.

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30

Bailey, Penny. "Collection Development for Knowledge Management." Legal Information Management 7, no. 4 (December 2007): 247–51. http://dx.doi.org/10.1017/s1472669607002058.

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Анотація:

AbstractThis paper was presented by Penny Bailey at the 38th BIALL Annual Study Conference held in Sheffield in June 2007 and covers the challenges involved in introducing a knowledge management strategy to a law firm. It suggests methods to be adopted in trying to implement such a service including identification with the firm's business objectives; persuasive communication; identification of the benefits accruing and strategies for collecting knowledge.

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31

Gibb, Forbes, and Blaise Cronin. "Prototype identification using a knowledge-base management system." Education for Information 3, no. 4 (October 1, 1985): 307–12. http://dx.doi.org/10.3233/efi-1985-3405.

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32

OKADA, Masafumi, and Toshiharu SUGIE. "Subspace State Space System Identification using Prior Knowledge." Transactions of the Institute of Systems, Control and Information Engineers 10, no. 2 (1997): 47–52. http://dx.doi.org/10.5687/iscie.10.47.

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33

Konigova, Martina, Hana Urbancova, and Jiri Fejfar. "Identification of Managerial Competencies in Knowledge-based Organizations." Journal of Competitiveness 4, no. 1 (March 30, 2012): 129–42. http://dx.doi.org/10.7441/joc.2012.01.10.

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34

Gautler, M., C. Janin, and C. Presse. "A Priori Knowledge for Dynamic Identification of Robots." IFAC Proceedings Volumes 26, no. 2 (July 1993): 431–36. http://dx.doi.org/10.1016/s1474-6670(17)48764-7.

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35

Louage, F., T. P. Dobrowiecki, and B. Pataki. "Knowledge-Based System Identification - Components of the Expertise." IFAC Proceedings Volumes 27, no. 3 (June 1994): 307–12. http://dx.doi.org/10.1016/s1474-6670(17)46127-1.

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36

Reuter, M. A., and J. S. J. van Deventer. "Knowledge Based Simulation and Identification of Metallurgical Reactors." IFAC Proceedings Volumes 25, no. 17 (August 1992): 125–30. http://dx.doi.org/10.1016/b978-0-08-041704-2.50028-5.

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37

Hou, Jie, Fengwei Chen, Penghua Li, and Zhiqin Zhu. "Prior-knowledge-based subspace identification for batch processes." Journal of Process Control 82 (October 2019): 22–30. http://dx.doi.org/10.1016/j.jprocont.2019.07.002.

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38

Algorta, Marcos, and Fernando Zeballos. "Human resource and knowledge management: best practices identification." Measuring Business Excellence 15, no. 4 (November 15, 2011): 71–80. http://dx.doi.org/10.1108/13683041111184125.

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39

Burmester, M., Y. Desmedt, and T. Beth. "Efficient Zero-Knowledge Identification Schemes for Smart Cards." Computer Journal 35, no. 1 (February 1, 1992): 21–29. http://dx.doi.org/10.1093/comjnl/35.1.21.

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40

Mehrez, Abraham, and Geoffrey Steinberg. "A Matching Identification Problem: A Knowledge Acquisition Approach." ORSA Journal on Computing 7, no. 2 (May 1995): 218–31. http://dx.doi.org/10.1287/ijoc.7.2.218.

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41

Bekey, G. A. "Knowledge Based Systems in Modeling, Simulation and Identification." IFAC Proceedings Volumes 21, no. 9 (August 1988): 73–78. http://dx.doi.org/10.1016/s1474-6670(17)54705-9.

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42

van de Sande, Wendy W. J., El Sheikh Maghoub, Ahmed H. Fahal, Michael Goodfellow, Oliverio Welsh, and Ed Zijlstra. "The Mycetoma Knowledge Gap: Identification of Research Priorities." PLoS Neglected Tropical Diseases 8, no. 3 (March 27, 2014): e2667. http://dx.doi.org/10.1371/journal.pntd.0002667.

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43

Chen, Deng Neng, and Po Chun Hsieh. "The expert identification in the cyber knowledge community." International Journal of Computational Science and Engineering 13, no. 2 (2016): 175. http://dx.doi.org/10.1504/ijcse.2016.078437.

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44

Hsieh, Po Chun, and Deng Neng Chen. "The expert identification in the cyber knowledge community." International Journal of Computational Science and Engineering 13, no. 2 (2016): 175. http://dx.doi.org/10.1504/ijcse.2016.10000103.

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45

Dehkordi, Massoud Hadian, and Reza Alimoradi. "Zero-knowledge identification scheme based on Weil pairing." Lobachevskii Journal of Mathematics 30, no. 3 (July 2009): 203–7. http://dx.doi.org/10.1134/s1995080209030020.

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46

Zhong, Bi-Juan. "Organizational Identification and Knowledge Creation in Multinational Teams." Academy of Management Proceedings 2014, no. 1 (January 2014): 16564. http://dx.doi.org/10.5465/ambpp.2014.16564abstract.

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47

Münz, Eberhard, and Volker Krebs. "IDENTIFICATION OF HYBRID SYSTEMS USING A PRIORI KNOWLEDGE." IFAC Proceedings Volumes 35, no. 1 (2002): 451–56. http://dx.doi.org/10.3182/20020721-6-es-1901.00563.

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48

Betta, A., and D. A. Linkens. "Intelligent knowledge-based system for dynamic system identification." IEE Proceedings D Control Theory and Applications 137, no. 1 (1990): 1. http://dx.doi.org/10.1049/ip-d.1990.0001.

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49

Rotshtein, A. P., and D. I. Katel’nikov. "Identification of nonlinear objects by fuzzy knowledge bases." Cybernetics and Systems Analysis 34, no. 5 (September 1998): 676–83. http://dx.doi.org/10.1007/bf02667040.

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50

Priebe, Sarah J., Janice M. Keenan, and Amanda C. Miller. "How prior knowledge affects word identification and comprehension." Reading and Writing 25, no. 1 (September 3, 2010): 131–49. http://dx.doi.org/10.1007/s11145-010-9260-0.

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