@article{KleefeldZimmermann2022,
  author    = {Kleefeld, Andreas and Zimmermann, M.},
  title     = {Computing Elastic Interior Transmission Eigenvalues},
  series = {Integral Methods in Science and Engineering},
  journal   = {Integral Methods in Science and Engineering},
  editor    = {Constanda, Christian and Bodmann, Bardo E.J. and Harris, Paul J.},
  publisher = {Birkh{\"a}user},
  address   = {Cham},
  isbn      = {978-3-031-07171-3},
  doi       = {10.1007/978-3-031-07171-3_10},
  pages     = {139 -- 155},
  year      = {2022},
  abstract  = {An alternative method is presented to numerically compute interior elastic transmission eigenvalues for various domains in two dimensions. This is achieved by discretizing the resulting system of boundary integral equations in combination with a nonlinear eigenvalue solver. Numerical results are given to show that this new approach can provide better results than the finite element method when dealing with general domains.},
  language  = {en}
}
@article{HerssensCowburnAlbrachtetal.2022,
  author    = {Herssens, Nolan and Cowburn, James and Albracht, Kirsten and Braunstein, Bjoern and Cazzola, Dario and Colyer, Steffi and Minetti, Alberto E. and Pavei, Gaspare and Rittweger, J{\"o}rn and Weber, Tobias and Green, David A.},
  title     = {Movement in low gravity environments (MoLo) programme - the MoLo-L.O.O.P. study protocol},
  series = {PLOS ONE / Public Library of Science},
  volume    = {17},
  journal   = {PLOS ONE / Public Library of Science},
  number    = {11},
  editor    = {Cattaneo, Luigi},
  publisher = {Plos},
  address   = {San Francisco},
  issn      = {1932-6203},
  doi       = {10.1371/journal.pone.0278051},
  pages     = {e0278051},
  year      = {2022},
  abstract  = {Exposure to prolonged periods in microgravity is associated with deconditioning of the musculoskeletal system due to chronic changes in mechanical stimulation. Given astronauts will operate on the Lunar surface for extended periods of time, it is critical to quantify both external (e.g., ground reaction forces) and internal (e.g., joint reaction forces) loads of relevant movements performed during Lunar missions. Such knowledge is key to predict musculoskeletal deconditioning and determine appropriate exercise countermeasures associated with extended exposure to hypogravity.},
  language  = {en}
}
@article{UlmerBraunChengetal.2022,
  author    = {Ulmer, Jessica and Braun, Sebastian and Cheng, Chi-Tsun and Dowey, Steve and Wollert, J{\"o}rg F.},
  title     = {Gamification of virtual reality assembly training: Effects of a combined point and level system on motivation and training results},
  series = {International Journal of Human-Computer Studies},
  volume    = {165},
  journal   = {International Journal of Human-Computer Studies},
  number    = {Art. No. 102854},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {1071-5819},
  doi       = {10.1016/j.ijhcs.2022.102854},
  year      = {2022},
  abstract  = {Virtual Reality (VR) offers novel possibilities for remote training regardless of the availability of the actual equipment, the presence of specialists, and the training locations. Research shows that training environments that adapt to users' preferences and performance can promote more effective learning. However, the observed results can hardly be traced back to specific adaptive measures but the whole new training approach. This study analyzes the effects of a combined point and leveling VR-based gamification system on assembly training targeting specific training outcomes and users' motivations. The Gamified-VR-Group with 26 subjects received the gamified training, and the Non-Gamified-VR-Group with 27 subjects received the alternative without gamified elements. Both groups conducted their VR training at least three times before assembling the actual structure. The study found that a level system that gradually increases the difficulty and error probability in VR can significantly lower real-world error rates, self-corrections, and support usages. According to our study, a high error occurrence at the highest training level reduced the Gamified-VR-Group's feeling of competence compared to the Non-Gamified-VR-Group, but at the same time also led to lower error probabilities in real-life. It is concluded that a level system with a variable task difficulty should be combined with carefully balanced positive and negative feedback messages. This way, better learning results, and an improved self-evaluation can be achieved while not causing significant impacts on the participants' feeling of competence.},
  language  = {en}
}