The need of Taxonomy is vital for knowledge sharing. This need has been portrayed by through-life engineering services/systems. This paper addresses this issue by repair process Taxonomy development. Framework for repair process Taxonomywas developed followed by its implementation. The importance of repair process Taxonomy has been highlighted.

The interest in the concept of "open innovation" has increased dramatically in the last decade. However, the widespread literature on this subject is scattered. It is because open innovation is a "fuzzy" concept, and there are no exact boundaries with closed innovation. In other words, any innovation process can be considered to be "open" and partly "closed". Therefore, innovation is a spectrum and not a precise boundary. One of the ways to bring an understanding of open innovation is to use Taxonomy. The purpose of this paper is a Taxonomy of open source using a literature review. This research uses a qualitative review method. To collect the data, the study uses the library method of data collection (secondary studies, meta-synthesis) and is based on an online study of information sources. By briefly reviewing the concept of open innovation and knowledge management, the paper tries to bring these texts together using the new literature in the field of open innovation array. The presented Taxonomy also helps to understand the terminology of this field, and comparing similarities and differences can be used as a decision tool.

The numerical Taxonomy technique is a multi - criteria decision making technique for evaluating and ranking alternatives, and is widely used in areas of planning and development. This Model like the other multi- criteria decision making models has some drawbacks, the most important criticism on this method is uniform presuming the indicators and being the indicators in same direction. Therefore, the objective of this paper is presenting appropriate methodology for improvement of this method and eliminating the limitations of this technique and introducing the modified Taxonomy model. In the following of introducing the modified model, for better representing the difference, the two models is used to assess the level of development in rural areas of Mazandaran province. The research method is descriptive and applied, and libraries based on data of the 1385 Census. Results showed the more accurate the modified model compared with the contemporary Taxonomy model. Development coefficient (Fi) and rank in these models have substantially different. Based on numerical Taxonomy rural areas of Ghaemshahr Township with a grade of 0.455 are in rating 1 and rural areas of Ghaloga Township with a grade of 0.455 are in rating 3, Whereas with the modified model that to weight important indicators and also the direction of indicators (being positive or negative) is significant, Levels of development in rural areas of Ghaloga Township with a grade of. 396 to rating 1 and Ghaemshahr Township to rating 2 change.

The number of reported vulnerabilities is dramatically rising every year. In addition, the combination of different kinds of network devices, services and applications in a complex manner lead to increase the complexity of vulnerabilities. Increasing the number of vulnerabilities and their complications show the importance of vulnerability taxonomies which could provide a common language for defining vulnerabilities and help analyze and assess them. Both the advantages of using vulnerability taxonomies and the features of the taxonomies that have ever been suggested encouraged us to offer the new network vulnerability Taxonomy. Our proposed Taxonomy is a multi-dimensional and hierarchical Taxonomy which classifies network vulnerabilities based on their location, cause and impact. These are three dimensions of our Taxonomy. We use ITU-T X-805 security architecture to provide a comprehensive layered classification for the location dimension and also use common weakness enumeration (CWE) project to provide a complete layered classification for the cause dimension of the proposed Taxonomy. Finally, we evaluate our Taxonomy based on Taxonomy requirements. In addition, to demonstrate the usefulness of our Taxonomy, a case study applies the Taxonomy to a number of network vulnerabilities. We also use this Taxonomy to analyze network vulnerabilities. The result of our analysis is a matrix that demonstrates the distribution of network vulnerabilities based on their causes, locations and impacts. In addition to offering a Taxonomy that is specific to network vulnerabilities and is beneficial for analyzing network vulnerabilities by covering almost all possible combinations of causes, locations, and impacts, we also introduce and consider network activities in the classification of location dimension for the first time.