Prina, M. G., Casalicchio, V. ., Kaldemeyer, C. ., Manzolini, G. ., Moser, D. ., Wanitschke, A. ., & Sparber, W. . (2020). Multi-objective investment optimization for energy system models in high temporal and spatial resolution. Applied Energy, 264, 114728. http://doi.org/10.1016/j.apenergy.2020.114728 (Original work published 2024)
Wetzel, S. ., Bertel, S. ., Montag, M. ., & Zander, S. . (2020). Spatial task solving on tablets: analysing mental and physical rotation processes of 12–13-year olds. Educational Technology Research and Development, 68(1), 363–381. http://doi.org/10.1007/s11423-019-09699-8
Abstract
Spatial skill assessment and training are promising fields of application for tablets, as touch-based interaction can prime and support mental transformations of spatial knowledge. We report on a study with 49 secondary school students who used our iPad app to solve mental and physical rotation tasks. During physical rotation, students were able to rotate 3D stimuli using touch interaction. Results show specific similarities (e.g., regarding angular disparity effects) as well as differences between mental and physical conditions, such as for task success, mental effort, efficiency; all to the advantage of the physical condition. 12–13-year olds can benefit from these advantages without previous task training, whereas previous research showed this to be different for younger students. In a second step, our analysis compares low and high achievers regarding physical rotation behaviour and motivational variables, including expected success. The results lay grounds for constructing individualized, tablet-based training apps for spatial skills.
Teistler, M. ., Süncksen, M. ., & Reinhold, S. . (2020). Utilizing Game Engine Technology to Develop Medical Visualization Software. In 106th Scientific Assembly and Annual Meeting (RSNA 2020). Chicago, IL, USA: Radiological Society of North America. Abgerufen von https://archive.rsna.org/2020/20000761.html (Original work published Dezember 2020)
Teistler, M. ., Süncksen, M. ., Reinhold, S. ., Bott, O. ., & Dresing, K. . (2020). Simulation of Intraoperative Fluoroscopy in an Immersive Virtual Operating Room to Enhance Training Opportunities and Increase Radiation Awareness. In 106th Scientific Assembly and Annual Meeting (RSNA 2020). Chicago, IL, USA: Radiological Society of North America. Abgerufen von https://archive.rsna.org/2020/20015344.html (Original work published Dezember 2020)
Schmitt, F. ., Sundermeier, J. ., Bohn, N. ., & Sasso, A. M. (2020). Spotlight on Women in Tech: Fostering an Inclusive Workforce when Exploring and Exploiting Digital Innovation Potentials. In .
Weber, S. ., & Volta, D. . (2020). Das Physikalische Optimum als Bewertungsmethode für Sauerstoffherstellungsverfahren. Web: VDI-Expertenforum - Effizienzsteigerungen durch grenzwertorientierte Kennzahlen in der Praxis. (Original work published September 2020)
Weber, S. ., & Volta, D. . (2020). Bewertung der Drucklufterzeugung durch das Physikalische Optimum. Web-Konferenz: VDI-Expertenforum - Effizienzsteigerungen durch grenzwertorientierte Kennzahlen in der Praxis. (Original work published September 2020)
Slavich, P. ., Heinemeyer, S. ., Bagnaschi, E. ., Bahl, H. ., Goodsell, M. ., Haber, H. E., … Staub, F. . (2020). Higgs-mass predictions in the MSSM and beyond. The European Physical Journal C, 81, 71. http://doi.org/10.1140/epjc/s10052-021-09198-2 (Original work published Mai 2022)
Abstract
Predictions for the Higgs masses are a distinctive feature of supersymmetric extensions of the Standard Model, where they play a crucial role in constraining the parameter space. The discovery of a Higgs boson and the remarkably precise measurement of its mass at the LHC have spurred new efforts aimed at improving the accuracy of the theoretical predictions for the Higgs masses in supersymmetric models. The "Precision SUSY Higgs Mass Calculation Initiative" (KUTS) was launched in 2014 to provide a forum for discussions between the different groups involved in these efforts. This report aims to present a comprehensive overview of the current status of Higgs-mass calculations in supersymmetric models, to document the many advances that were achieved in recent years and were discussed during the KUTS meetings, and to outline the prospects for future improvements in these calculations.
Süncksen, M. ., Bott, O. ., Dresing, K. ., & Teistler, M. . (2020). Simulation of scattered radiation during intraoperative imaging in a virtual reality learning environment. International Journal of Computer Assisted Radiology and Surgery , 15, 691–701. http://doi.org/10.1007/s11548-020-02126-x (Original work published April 2020)
Abstract
Purpose
Scattered radiation, which occurs when using a C-arm for intraoperative radiography, can be better understood through interactive visualization. We developed a virtual reality (VR) approach for the simulation of scattered radiation (SSR) as part of a C-arm training system. In VR, it is important to avoid cyber sickness, which is often caused by increased latency between head motion and image presentation inside the head-mounted display. As the latency requirement interferes with the computational complexity of the SSR, the goal has been to maintain a low latency during the simultaneous computation of the SSR on moderate-cost consumer hardware.
Methods
For use with a VR C-arm simulator, a CUDA-based Monte Carlo SSR has been improved to utilize GPU resources unused by the VR image generation. Resulting SSR data are visualized through volume rendering with pseudo-colored scattered radiation superimposed onto the virtual operating room. The resulting interactive VR–SSR environment was evaluated with operating room personnel (ORP) and surgeons using questionnaires.
Results
Depending on the imaged body part and computation parameters, the required computation time to complete one SSR run was between 1.6 and 4.2 s (ankle) and between 7.9 and 14.9 s (thigh), and VR frame times from 11 to 12 ms (95th percentile). The system was evaluated with ORP (n = 46) and surgeons (n = 25). The median of professional C-arm experience was 5 (range 1 to 34) years (ORP) and 12.5 (range 2 to 48) years (surgeons), respectively. The demonstrated prototype was found useful by 78% of ORP and 88% of the surgeons. On a Likert scale, more than 90% of both groups “agreed fully” that the presented way of visualizing SSR in VR helps understanding intraoperative exposure to scattered radiation.
Conclusions
Leveraging off-the-shelf computer equipment, the feasibility of SSR and VR for interactive training has been demonstrated. Evaluation participants showed a high interest for the presented approach. Feedback suggests that the visualization experienced by the users helps understanding radiation hazards in the operating room.