List of Articles Knowledge Transfer

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  1 - A review of university-industry interaction patterns and designs
     
  University and industry are two key institutions in any society and since the comprehensive development of communities and countries, largely due to Capabilities of the application of either of these two institutions in order to meet needs is, The policy makers, planner More

  University and industry are two key institutions in any society and since the comprehensive development of communities and countries, largely due to Capabilities of the application of either of these two institutions in order to meet needs is, The policy makers, planners and executors of them has attracted And the concept of continuous improvement of university-industry relationship is considered. This paper aimed at explaining the relationship between universities and industry, the first world record in Iran And then this challenge as a society with higher education, has studied and the introduction of the mechanisms of interaction with universities and industry adoption of methods to transfer knowledge and technology, Emanating from university-industry interaction patterns with patterns of innovation and technology development, has been described. Used when reviewing proposals for university-industry interaction, Mutual benefits of establishing a relationship with these two institutions that together have given the data and outputs, is described Manuscript profile
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  2 - Knowledge Translation Model From Research to Industry Case Study: Defense Industries Research Center
  Yousef Naeimaei Hossein Ali  Hasan pour Mahdi Moghan Jahanbakhsh Mambini Soheyl Emamian
  Knowledge Translation (KT) is the meeting ground between two fundamentally different processes: research and action. It knits them with communicative relationships. Knowledge translation is a dynamic and interactive process that includes synthesis, dissemination, exchan More

  Knowledge Translation (KT) is the meeting ground between two fundamentally different processes: research and action. It knits them with communicative relationships. Knowledge translation is a dynamic and interactive process that includes synthesis, dissemination, exchange and ethically sound application of knowledge to provide more effective services and products and strengthen system. This is done through a complex system of researchers and knowledge users. Knowledge translation is catalyst for knowledge cycle in knowledge to action process. Utilization of observation and decision-making appropriate with field conditions that is proposed with knowledge translation is very useful in industrial researches. This research proposes a model for knowledge translation from research to industry (case study: A Defense Industry Research Center). By extending the concept of knowledge translation several models and structures are proposed that each one has a strategy to overcome the gap between what we know and what actually happens in practice. In this research we studied some of them and try to find an appropriate model. The proposed model has seven processes: research needs assessment, input to research, research processes, primary outputs from research, making a sample for taking customers endorsement, using product and final outcome. For data collection, interviews and questionnaires were used. The population is consisting of experts, managers and Defensive Research Centers researchers who are familiar with knowledge translation issues. Manuscript profile
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  3 - Designing a Model for Transfer of Nanotechnology and the Relevant Knowledge Using Fuzzy Delphi Method
  Saeed Shokohi
  10.7508/jstpi.2015.01.001
  Nanotechnology plays an important role in the scientific, economic, and political development of countries, so that adapting to today's dynamic world and becoming the leading power in the Middle East and a pioneer in this field is subject to the acquisition of nanotechn More

  Nanotechnology plays an important role in the scientific, economic, and political development of countries, so that adapting to today's dynamic world and becoming the leading power in the Middle East and a pioneer in this field is subject to the acquisition of nanotechnology and the related knowledge. One of the short-term, urgent and effective methods for obtaining knowledge and technology concerned with nanotechnology is technology transfer but despite its outstanding importance, little attention has been paid. On the other hand, in extraordinary circumstances imposed by the sanctions against Iran, the urgency of this issue increases, since the technical and knowledge interactions under sanctions are harder and more complex. Also, the possibility of complete transfer of knowledge and technology, especially in the field of high technologies such as nanotechnology decreases. Therefore, this study has aimed to design a customized model for Iran to facilitate making accurate and wise decisions about transfer of nanotechnology and the knowledge associated with that technology. To achieve this goal, three basic steps have been taken. First, eighteen major factors influencing the transfer of knowledge and technology of nano has been identified. Then, these factors have been weighed. Finally, they have been categorized into five clusters that are: 1. Technology source, 2. Technology receiver, 3. Technology process, 4. Technology nature, 5. Technology environment. It is notable that this classification has been based on open systems approach. Manuscript profile
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  4 - Identifying Factors that Influence the Process of Knowledge Transfer in Offshore Organizations
  وجیهه  هوشیار Mohammad لگزیان مهسا  بذرگری
  Offshoring is a trend during which organizatios directly replaced Internal labor with foreign labor and the activities are transferred from the source. At this time organization’s need to transfer knowledge of the local unit of offshoring jobs has become evident. In th More

  Offshoring is a trend during which organizatios directly replaced Internal labor with foreign labor and the activities are transferred from the source. At this time organization’s need to transfer knowledge of the local unit of offshoring jobs has become evident. In the process of transferring, knowledge from the source to the recipient or user, is transmitted via factors that affect the process.The aim of the present study was to identify factors influencing knowledge transfer in offshoring organizations. Content analysis is a research method used in this study. After the content analysis of more than 60 studies, a model in four levels of factors impacting knowledge transfer were identified. At the first level, three dimensions of content, the individual and the context were identifid as the effective factors in knowledge transfer process. At the second level, the following components were extracted from each dimension which affected knowledge transfer, especially in offshoring firms. Knowledge is a vital component in the content level. Confidence,‌ motivation and capacity are effective in individual level and culture was identified to influence the context level. At the third and fourth level of the model, elements of these components as well as methods of knowledge transfer were presented. To assess the validity of this model, all aspects, components, parts and materials were confirmed by a number of experts who were aware of the model Manuscript profile
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  5 - A Transfer Learning Algorithm to Improve the Convergence Rate and Accuracy in Cellular Learning Automata
  Seyyed Amir Hadi Minoofam Azam Bastanfard M. R.  Keyvanpour
  Cellular learning automaton is an intelligent model as a composition of cellular automaton and learning automaton. In this study, an extended algorithm of cellular learning automata is proposed based on transfer learning as the TL-CLA algorithm. In this algorithm, trans More

  Cellular learning automaton is an intelligent model as a composition of cellular automaton and learning automaton. In this study, an extended algorithm of cellular learning automata is proposed based on transfer learning as the TL-CLA algorithm. In this algorithm, transfer learning is used as an approach for computation deduction and minimizing the learning cycle. The proposed algorithm is an extended model based on merit function and attitude vector for transfer learning. In the TL-CLA algorithm, the value of the merit function is computed based on the local environment, and the value of the attitude vector is calculated based on the global environment. When these two measures get the threshold values, the transfer of action probabilities causes the transfer learning from the source CLA to the destination CLA. The experimental results show that the proposed TL-CLA model leads to increment the convergence accuracy as 2.7% and 2.2% in two actions and multi-action standard environments, respectively. The improvements in convergence rate are also 8% and 2% in these two environments. The TL-CLA could be applied in knowledge transfer from learning one task to learning another similar task Manuscript profile