The increasing digitalization of business and society has led to drastic changes within companies.Nearly all enterprises are facing enormous challenges dealing with topics such as Industry 4.0/Industrial Internet.With the goal of supporting companies to handle these challenges and "move" in an Industry 4.0 environment, several frameworks or reference models already exist.Here, we share the results of a detailed analysis of selected Industry 4.0 models.In particular, we foster in our analysis Lean Production aspects since the basic principles of Lean Management/Lean Production in existence since the 1980s have yielded appropriate measures to optimize production.These principles can and should be addressed and included by Industry 4.0 models as well.Our study provides a classification of 31 Industry 4.0 models/frameworks as well as the identification of needs for further research to enhance existing Industry 4.0 models more holistically.

A new type of divergence measure for the registration of medical images is introduced that exploits the properties of the modified Bessel functions of the second kind. The properties of the proposed divergence coefficient are analysed and compared with those of the classic measures, including Kullback-Leibler, Renyi, and Iinfinity, divergences. To ensure its effectiveness and widespread applicability to any arbitrary set of data types, the performance of the new measure is analysed for Gaussian, exponential, and other advanced probability density functions. The results verify its robustness. Finally, the new divergence measure is used in the registration of CT to MR medical images to validate the improvement in registration accuracy.