Akselrod, M. ., Fidler, M. ., & Lübben, R. . (2018). Stochastic Guarantees for Rate-Adaptive Streaming. In NetCal. http://doi.org/10.1109/ITC30.2018.10056
eri, J. S. M., Bin Ali, N. ., Petersen, K. ., Minhas, N. M., & Chatzipetrou, P. . (2018). Teaching students critical appraisal of scientific literature using checklists. In Proceedings of the 3rd European Conference of Software Engineering Education (S. 8–17).
Wetzel, S. ". (2018). "A Comparison of Mental and Physical Rotation Using Gaze-Based Measures". In S. " Creem-Regehr, J. . Schöning, & A. . Klippel (Hrsg.), "Spatial Cognition XI" (S. 167–179). "Cham": "Springer International Publishing".
Abstract
"Over the past few years, a number of studies have reported on procedural similarities and differences between mental rotation and physical (i.e., manual) rotation of Shepard and Metzler-type stimuli. These similarities include comparable angular disparity effects and comparable final angular offsets in problem solving. This paper presents results from further comparisons based on gazed-derived measures obtained across the course of trials. In a within-subject design, participants solved the same tasks as mental and as physical rotation problems. We compare time courses of mean fixation duration and of saccade amplitude, and interpret these with respect to underlying mental processes and loads. The results point to additional specific procedural similarities and differences, which nicely complement the previous findings. The results are of additional, practical use for establishing how and when physical rotation can provide a useful proxy for mental rotation for purposes of process analysis, of ability assessment, and of training."
Hahn, G. J., & Brandenburg, M. . (2018). A sustainable aggregate production planning model for the chemical process industry. Computers & Operations Research, 94, 154–168. http://doi.org/10.1016/j.cor.2017.12.011
Abstract
Process industries typically involve complex manufacturing operations and thus require adequate decision support for aggregate production planning (APP). In this paper, we focus on two relevant features of APP in process industry operations: (i) sustainable operations planning involving multiple alternative production modes/routings with specific production-related carbon emission and the social dimension of varying operating rates, (ii) integrated campaign planning with the operational level in order to anticipate production mix/volume/routing decisions on campaign lead times and WIP inventories as well as the impact of variability originating from a stochastic manufacturing environment. We focus on the issue of multi-level chemical production processes and highlight the mutual trade-offs along the triple bottom line concerning economic, environmental and social factors. To this end, production-related carbon emission and overtime working hours are considered as externalized factors as well as internalized ones in terms of resulting costs. A hierarchical decision support tool is presented that combines a deterministic linear programming model and an aggregate stochastic queuing network model. The approach is exemplified at a case example from the chemical industry to illustrate managerial insights and methodological benefits of our approach.
Thomsen, F. ., Hofmann, G. ., Ebel, T. ., & Willumeit-Römer, R. . (2018). An elementary simulation model for neck growth and shrinkage during solid phase sintering. Materialia, 3, 9. http://doi.org/https://doi.org/10.1016/j.mtla.2018.08.031
Ghazi, A. N., Petersen, K. ., Reddy, S. S. V. R., & Nekkanti, H. . (2018). Survey research in software engineering: Problems and mitigation strategies. IEEE Access, 7, 24703–24718.
Byl, B. ., Süncksen, M. ., & Teistler, M. . (2018). A serious virtual reality game to train spatial cognition for medical ultrasound imaging. In 2018 IEEE 6th International Conference on Serious Games and Applications for Health (SeGAH) (S. 1–4). http://doi.org/10.1109/SeGAH.2018.8401365
Fuchkina, E. ., Schneider, S. ., Bertel, S. ., & Osintseva, I. . (2018). Design Space Exploration Framework - A modular approach to flexibly explore large sets of design variants of parametric models within a single environment. Computing for a better tomorrow - the 36th eCAADe Conference. Lodz, Poland: Lodz University of Technology.
Abstract
Parametric modelling allows to relatively easily generate large sets of design variants (so called design space). Typically, a designer intuitively moves through this design space, resulting in one or several satisfying solutions. Due to the theoretically large number of variants that can be created with parametric models, obviously, there is a high probability that potentially good solutions could be missed, which is not at least because of human cognitive limitations. Consequently, it is necessary to develop a certain strategy to support designers in order to search for design solutions. Even though, various methods to systematically approach large data sets exist, the application of them in the design process is a special case, firstly, due to the existence of many non-specifiable and subjective dimensions (e.g. aesthetics) and secondly because of the multiple ways how designers actually search for solutions. This demands for a more flexible approach to design space exploration. This paper investigates how different methods can be combined to support the exploration of design spaces. Therefore, a conceptual framework with a modular architecture is proposed and its prototypical implementation is demonstrated.
Wallbaum, T. ., Matviienko, A. ., Ananthanarayan, S. ., Olsson, T. ., Heuten, W. ., & Boll, S. C. (2018). Supporting communication between grandparents and grandchildren through tangible storytelling systems. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (S. 1–12).
Jabbari, R. ., Bin Ali, N. ., Petersen, K. ., & Tanveer, B. . (2018). Towards a benefits dependency network for DevOps based on a systematic literature review. Journal of Software: Evolution and Process, 30, e1957.