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Background:In the past few years, computer-based analysis of atomic-force microscopic images has acquired increasing importance for studying biomolecules such as DNA. On the one hand, fully automated methods do not allow analysis of complex shapes; on the other hand, manual methods are usually time consuming and inaccurate. The semiautomated approach presented in this report overcomes the drawbacks of both methods. Methods: Two kinds of images were analyzed: computergenerated filaments that modeled circular DNA molecules on a surface and real atomic-force microscopic images of DNA molecules adsorbed on an appropriate substrate surface.Results: The algorithm was tested on a group of 140 simulated and 189 real plasmids with a nominal length of 913 nm. The accuracy of the length measurement was statistically evaluated on the ensemble of molecules, with particular attention to the influence of the noise. Mean contour lengths of 912 Ϯ 5 nm and 910 Ϯ 47 nm were

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Working together is important aspect of future industry. Therefore, technologies like Internet of Things (IoT), cloud computing, SOA give rise to another industrial revolution. We propose here a concept definition, which focuses on data acquisition, integration and predictive control in the industry. The concept consists of industrial IoT gateway, cloud services and machine learning services. We used machine learning to verify our data acquisition solution and we implemented prediction control as a cloud service. Finally, proposed solution will exceed boundaries inside ICS (Information and Control System), improve flexibility, interoperability and test plant prediction control in smart manufacturing.

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