AbstractCurrently we are saying that we are at the dawn of the fourth revolution, which is marked by using cyberphysical systems and the Internet of Things. This is marked as Industry 4.0 (I4.0). With Industry 4.0 is also closely linked concept Logistics 4.0. The highly dynamic and uncertain logistic markets and huge logistic networks require new methods, products and services. The concept of the Internet of Things and Services (IoT&S), Big Data/ Data Mining (DM), cloud computing, 3D printing, Blockchain and cyber physical system (CPS) etc. seem to be the probable technical solution for that. However, associated risks hamper its implementation and lack a comprehensive overview. In response, the paper proposes a framework of risks in the context of Logistics 4.0. They are here economic risks, that are associated e.g. with high or false investments. From a social perspective, risks the job losses, are considered too. Additionally, risks can be associated with technical risks, e.g. technical integration, information technology (IT)-related risks such as data security, and legal and political risks, such as for instance unsolved legal clarity in terms of data possession. It is therefore necessary to know the potential risks in the implementation process.

Abstract. 3D printing technology has emerged as one of the most disruptive innovations to impact the logistics industry and the global supply chain. Some claim that the technology merely enhances some aspects of production process, while others argue that technology will revolutionize and replace existing manufacturing technologies. Whether revolutionary or evolutionary, 3D printing technology is recognized as an important trend that will significantly impact supply chains. The objective of this article is to explore basic issues related to 3D printing technology and possibilities for altering manufacturing and supply chain.

This paper deals with segmentation of organs at risk (OAR) in head and neck area in CT images which is a crucial step for reliable intensity modulated radiotherapy treatment. We introduce a convolution neural network with encoder-decoder architecture and a new loss function, the batch soft Dice loss function, used to train the network. The resulting model produces segmentations of every OAR in the public MICCAI 2015 Head And Neck Auto-Segmentation Challenge dataset. Despite the heavy class imbalance in the data, we improve accuracy of current state-of-the-art methods by 0.33 mm in terms of average surface distance and by 0.11 in terms of Dice overlap coefficient on average.

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