Literature data suggests that Dipeptidyl peptidase-4 (DPP-4) is a potential target for type 2 Diabetes Mellitus. Therefore, it is of interest to identify new DPP-4 inhibitors using molecular docking analysis. We document compounds such as STOCK1N-98884, STOCK1N-98881, and STOCK1N-98866 with optimal binding features with DPP-4 from the ligand database at https://www.ibscreen.com/ for further consideration.
Currently, the industrial and economic environment is highly competitive, forcing companies to keep up with technological progress and to be efficient in terms of quality and responsiveness, not only to survive, but also to dominate the market. So, to achieve this goal, companies are always looking to master their production processes, as well as to enlarge their range of products, either by developing new products or by improving old ones. This confronts companies to many problems, including the identification of adequate and optimal production parameters for the development of their products. In this context, a decision making system based on digital twins (DT), case-based reasoning (CBR) and Ontologies is proposed. The originality of this work lies in the fact that it combines three emerging artificial intelligence tools for modeling, reasoning and decision making. Thus, this work proposes a new flexible and automated system that ensures an optimal selection of production parameters for a given complex product. An industrial case of study is developed to illustrate the effectiveness of the proposed approach.
Now-a-days manufacturing processes are becoming more and more complex which constantly complicate the management of their life cycle. Although, in order to survive and maintain a good position in the competitive industrial context, industrials have understood that they must optimize the whole life cycle of their manufacturing processes. The maintenance constitutes one of the key processes indispensable to ensure the proper functioning and to optimize the lifetime of machines and production lines, and thus to optimize quality and production costs. Therefore, its automation and optimization represent until now a center of interest for researches and manufacturers, especially those related to the integration of artificial intelligence tools in the industry. In this context, several new concepts and technologies have emerged, particularly in the context of industry 4.0. One of these new concepts is digital twins, which has become a promising direction to optimize manufacturing processes lifecycle. However, the implementation of this technology faces several complex problems related to the interoperability between physical entities and their virtual counterparts, as well as to the logical reasoning between the different elements constituting the digital twin. It is in this context that an approach based on digital twins and ontologies is proposed. The originality of this paper lies in two important points: the first is the exploitation of the expressiveness and reasoning capabilities of ontologies to solve cyber-physical interoperability problems at the digital twin level, while the second is the automation of the whole maintenance process and its decision making key points using the inference potentialities of ontologies. The applicability and effectiveness of the proposed approach is validated through an industrial case of study.
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