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Distributed manufacturing systems have become consensus, particularly networked manufacturing systems (NMS) owing to its flexible and adaptable nature in response to customized requirements. Highlighting of functions in NMS and their integration with recent key enabling technologies i.e., Artificial intelligence, Machine learning, Internet of Things, Block chain technology, and Agent-based techniques, etc. are of utmost importance to identify problems and increase its efficiency. Hence, besides the above-mentioned approaches, this paper surveyed and analysed various articles systematically related to networked manufacturing in the context of knowledge creation and information, security, interoperability, and reliability. To identify the most related papers, the search has been conducted with Web of Science and Scopus databases. Subsequently, after evaluation 30 most related papers were selected and analyzed that further extended by identifying the issues and gaps in the existing empirical knowledge. Finally, the paper presents a roadmap for future research directions and developments.

The way students learn changed significantly over the past two years, due to the current pandemic. However, this change was neither desired not planed beforehand. As a result, in many cases, it may have been undertaken without the appropriate care. In this paper we propose a framework for online education tailored for Computer Science degrees. Its goals are twofold: to avoid disruptive changes by providing a familiar and supportive structure for teaching/learning activities, and to motivate Students to learn autonomously, despite their reduced contact with their peers or the Teacher .

Industrial processes produce a considerable volume of data and thus information. Whether it is structured sensory data or semi- to unstructured textual data, the knowledge that can be derived from it is critical to the sustainable development of the industrial process. A key challenge of this sustainability is the intelligent management of the generated data, as well as the knowledge extracted from it, in order to utilize this knowledge for improving future procedures. This challenge is a result of the tailored documentation methods and domain-specific requirements, which include the need for quick visibility of the documented knowledge. In this paper, we utilize the expert knowledge documented in chip-design failure reports in supporting user access to information that is relevant to a current chip design. Unstructured, free, textual data in previous failure documentations provides a valuable source of lessons-learned, which expert design-engineers have experienced, solved and documented. To achieve a sustainable utilization of knowledge within the company, not only the inherent knowledge has to be mined from unstructured textual data, but also the relations between the lessons-learned, uncovering potentially unknown links. In this research, a knowledge graph is constructed, in order to represent and use the interconnections between reported design failures. A search engine is developed and applied onto the graph to answer queries. In contrast to mere keyword-based searching, the searchability of the knowledge graph offers enhanced search results beyond direct matches and acts as a mean for generating explainable results and result recommendations. Results are provided to the design engineer through an interactive search interface, in which, the feedback from the user is used to further optimize relations for future iterations of the knowledge graph.