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Massive stars usually form groups such as OB associations. Their fast stellar winds sweep up collectively the surrounding insterstellar medium (ISM) to generate superbubbles. Observations suggest that superbubble evolution on the surrounding ISM can be very irregular. Numerical simulations considering these conditions could help to understand the evolution of these superbubbles and to clarify the dynamics of these objects as well as the difference between observed X-ray luminosities and the predicted ones by the standard model (Weaver et al. 1977).
We propose a paraconsistent declarative semantics of possibly inconsistent generalized logic programs which allows for arbitrary formulas in the body and in the head of a rule (i.e. does not depend on the presence of any specific connective, such as negation(-as-failure), nor on any specific syntax of rules). For consistent generalized logic programs this semantics coincides with the stable generated models introduced in [HW97], and for normal logic programs it yields the stable models in the sense of [GL88].
A deterministic cycle scheduling of partitions at the operating system level is supposed for a multiprocessor system. In this paper, we propose a tool for generating such schedules. We use constraint based programming and develop methods and concepts for a combined interactive and automatic partition scheduling system. This paper is also devoted to basic methods and techniques for modeling and solving this partition scheduling problem. Initial application of our partition scheduling tool has proved successful and demonstrated the suitability of the methods used.
Accelerating knowledge
(2019)
As knowledge-intensive processes are often carried out in teams and demand for knowledge transfers among various knowledge carriers, any optimization in regard to the acceleration of knowledge transfers obtains a great economic potential. Exemplified with product development projects, knowledge transfers focus on knowledge acquired in former situations and product generations. An adjustment in the manifestation of knowledge transfers in its concrete situation, here called intervention, therefore can directly be connected to the adequate speed optimization of knowledge-intensive process steps. This contribution presents the specification of seven concrete interventions following an intervention template. Further, it describes the design and results of a workshop with experts as a descriptive study. The workshop was used to assess the practical relevance of interventions designed as well as the identification of practical success factors and barriers of their implementation.
This paper aims to contribute to exploring the design possibilities of robots for use in human-robot interaction. In an experiment, we investigate the influence of the human's personality and the robot's design, especially its humanization, on its acceptance. We use the Almere model, the Big 5 personality traits, and the anthropomorphic gestalt variants to build the foundation for our investigation. The assumption that an anthropomorphized robot variant would, in principle, be preferred to the standard variant when a natural choice is enforced could not be evidenced in our experiment. This allows for the interpretation that anthropomorphism does not necessarily lead to intentional perception and, consequently, does not guarantee that it can automatically generate acceptance.
How does a shared lexicon arise in population of agents with differing lexicons, and how can this shared lexicon be maintained over multiple generations? In order to get some insight into these questions we present an ALife model in which the lexicon dynamics of populations that possess and lack metacommunicative interaction (MCI) capabilities are compared. We ran a series of experiments on multi-generational populations whose initial state involved agents possessing distinct lexicons. These experiments reveal some clear differences in the lexicon dynamics of populations that acquire words solely by introspection contrasted with populations that learn using MCI or using a mixed strategy of introspection and MCI. The lexicon diverges at a faster rate for an introspective population, eventually collapsing to one single form which is associated with all meanings. This contrasts sharply with MCI capable populations in which a lexicon is maintained, where every meaning is associated with a unique word. We also investigated the effect of increasing the meaning space and showed that it speeds up the lexicon divergence for all populations irrespective of their acquisition method.