Trelleborg (Germany)

The rapid development in sensor technology as well as in communication mechanisms exponentially grows the amount of data that is permanently gathered via products, machines, or assets within Cyber-Physical Systems. While this data offers enormous potential for creating additional value by generating valuable insights or creating innovative services, these opportunities are often times not exploited: Especially in complex value creation networks, only individual actors tend to have access to the collected data and they hardly share it with other network members. In our conceptual work, we target a three-fold contribution: First, we develop a framework for system-wide learning that structures data-based collaboration with other actors in a complex value creation network based on IoT, a Cyber-Physical Service System. Second, we systematically develop five individual research questions that need to be addressed to realize viable solutions. And thirdly, we conceptualize a research design to address this research agenda in future steps.

Abstract In order to simulate the Payne effect of filler reinforced elastomers under cyclic loading a one‐dimensional material model for uniaxial tension and compression has been developed. It is able to cover the decrease of the dynamic stiffness with increasing loading amplitude (Payne effect) as well as the dependency of the stiffness on the static preload. The model has been extended by the description of the Mullins effect using modified strains for the computation of the uniaxial stresses. The subsequent generalization to a fully three‐dimensional constitutive model for the finite element method is achieved by using the concept of representative directions in form of a numerical integration of uniaxial stresses over a unit sphere. Finally, the generalized model is validated by means of experimental data of different rubber materials and testing procedures. (© 2014 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

When the same dose of iodine is given to all patients when performing abdominal computed tomography (CT) there may be a wide inter-individual variation in contrast medium (CM) enhancement of the liver. To evaluate if any of the measures body height (BH), body mass index (BMI), lean body mass (LBM), ideal body weight (IBW), and body surface area (BSA) correlated better than body weight (BW) with hepatic enhancement, and to compare the enhancement when using iodixanol and iomeprol. One hundred patients referred for standard three-phase CT examination of abdomen were enrolled. Body weight and height were measured at the time of the CT examination. Forty grams of iodine (iodixanol 320 mg I/mL or iomeprol 400 mg I/mL) was injected at a rate of 1.6 g-I/s, followed by a 50 mL saline flush. The late arterial phase was determined by using a semi-automatic smart prep technique with a scan delay of 20 s. The hepatic parenchymal phase started automatically 25 s after the late arterial phase. CM concentration was estimated by placement of regions of interest in aorta (native and late arterial phase) and in liver (native and parenchymal phase). BW (r = -0.51 and -0.64), LBM (r = -0.54 and -0.59), and BSA (r = -0.54 and -0.65) showed the best correlation coefficients with aortic and hepatic parenchymal enhancement, respectively, without any significant differences between the measures. Comparing iodixanol and iomeprol there was no significant difference in aortic enhancement. The liver enhancement was significantly higher (P < 0.05) using iodixanol than iomeprol. To achieve a consistent hepatic enhancement, CM dose may simply be adjusted to body weight instead of using more complicated calculated parameters based on both weight and height.