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Objective
This study assesses and quantifies impairment of postoperative magnetic resonance imaging (MRI) at 7 Tesla (T) after implantation of titanium cranial fixation plates (CFPs) for neurosurgical bone flap fixation.
Materials and methods
The study group comprised five patients who were intra-individually examined with 3 and 7 T MRI preoperatively and postoperatively (within 72 h/3 months) after implantation of CFPs. Acquired sequences included T₁-weighted magnetization-prepared rapid-acquisition gradient-echo (MPRAGE), T₂-weighted turbo-spin-echo (TSE) imaging, and susceptibility-weighted imaging (SWI). Two experienced neurosurgeons and a neuroradiologist rated image quality and the presence of artifacts in consensus reading.
Results
Minor artifacts occurred around the CFPs in MPRAGE and T2 TSE at both field strengths, with no significant differences between 3 and 7 T. In SWI, artifacts were accentuated in the early postoperative scans at both field strengths due to intracranial air and hemorrhagic remnants. After resorption, the brain tissue directly adjacent to skull bone could still be assessed. Image quality after 3 months was equal to the preoperative examinations at 3 and 7 T.
Conclusion
Image quality after CFP implantation was not significantly impaired in 7 T MRI, and artifacts were comparable to those in 3 T MRI.
Cyber-physical systems are ever more common in manufacturing industries. Increasing their autonomy has been declared an explicit goal, for example, as part of the Industry 4.0 vision. To achieve this system intelligence, principled and software-driven methods are required to analyze sensing data, make goal-directed decisions, and eventually execute and monitor chosen tasks. In this chapter, we present a number of knowledge-based approaches to these problems and case studies with in-depth evaluation results of several different implementations for groups of autonomous mobile robots performing in-house logistics in a smart factory. We focus on knowledge-based systems because besides providing expressive languages and capable reasoning techniques, they also allow for explaining how a particular sequence of actions came about, for example, in the case of a failure.
This paper introduces a Competence Developing Game (CDG) for the purpose of a cybersecurity awareness training for businesses. The target audience will be discussed in detail to understand their requirements. It will be explained why and how a mix of business simulation and serious game meets these stakeholder requirements. It will be shown that a tablet and touchscreen based approach is the most suitable solution. In addition, an empirical study will be briefly presented. The study was carried out to examine how an interaction system for a 3D-tablet based CDG has to be designed, to be manageable for non-game experienced employees. Furthermore, it will be explained which serious content is necessary for a Cybersecurity awareness training CDG and how this content is wrapped in the game