Entstehung und Flora des Trasses im nördlichen Laachersee-Gebiet

Die Lagerungsverhältnisse des Trasses sowie der Erhaltungszustand und die Lage der in ihm eingeschlossenen pflanzlichen Fossilien lassen auf eine komplexe Entstehung des Trasses schließen. Die unterste Lage ist als vulkanischer Staub aus der Luft abgesetzt worden, die Hauptmenge dagegen als Ablagerung „glutwolkenähnlicher" vulkanischer Erscheinungen zu betrachten. Die allerödzeitliche Flora des Trasses gleicht der eines borealen Birken-Kiefern-Waldes mit reichlich Traubenkirsche und Zitterpappel, während Stieleiche und Bergahorn entgegen früheren Angaben bis jetzt nicht nachgewiesen sind. Der kontinentale Charakter wird besonders durch Korbweide und Kreuzdorn unterstrichen.researc

Årbok 1964

To date, only one study by Strick and Volbeda (2018), titled ‘When the valence of unconditioned stimuli evolves over time: Evaluative conditioning with good-ending and bad-ending stories’, investigated stories in the context of evaluative conditioning to change brand attitudes. To find additional support for stories as unconditioned stimuli, we performed a partial replication of this study. As an extension, we also investigated the role of the need for affect as a mediator in this conditioning process. Our study had a within-subject design, in which MTurk workers (N = 66) participated in both our good- and bad-ending story conditions. In line with the original study and our hypothesis, our results suggest that the valence of the story ending determines the direction of the conditioning effect. Brands presented after good-ending stories have a stronger brand liking than brands presented after bad-ending stories. In practice, this would imply that advertisements should always end positively to induce a positive brand evaluation. Furthermore, as we hypothesized, our results indicate that the need for affect mediates this conditioning effect as people with a high need for affect rate brands more emotionally and strongly according to the story-ending valence than people with a low need for affect. Future research may distinguish other characteristics that mediate this effect to identify separate groups for targeted advertisements. To conclude, the ending of dramatic stories is determinative in brand evaluation when the brand is presented directly after, and the effect of these story endings is mediated by the need for affect

Parallel Algebraic Multigrid

The efficient utilization of parallel computational capabilities of modern hardware architecture is a must in large scale industrial applications. In this paper we focus on the parallelization of algebraic multigrid (AMG) in general and identify the respective challenges imposed on any hierarchical iterative linear solver. Moreover, we summarize the strategies employed in the parallel implementation of our SAMG library to cope with these issues and present some performance indicators of SAMG in real-world industrial applications

Scalable Linear Solvers for Computational Material Design of Filled Rubbers

Material design of enhanced tire using nano-fillers requires multi-objective design optimization and data mining, where the Multi-Objective Design Exploration (MODE) method [Koishi et al. 2014] is introduced to enrich the design knowledge for decision making throughout the product design process. One very important procedure of MODE is to predict the mechanical properties of rubbers using nonlinear implicit analysis which involves multiple numerical issues including large model size (tens of millions d.o.f.), periodic constraints, large material deformation and material nonlinearity. To overcome these challenges, we utilize a convex generalized meshfree approximation (GMF) [Wu et al. 2009] for large material deformation analysis. This ensures the positive approximation in the discrete system and is less sensitive to the meshfree nodal support size and integration order effects. Moreover, we use a nearly-incompressible hyperelastic material model with linear viscoelasticity for the rubber matrix and interfacial bound material in nonlinear analysis. Thus, the GMF method is coupled with the pressure smoothing scheme [Hu et al. 2010] to relieve numerical volumetric locking at the incompressible limit of the rubber material. The large-scale discrete system is solved in implicit analysis with a cyclic loading path, where the numerical solution of the resulting very ill-conditioned linear systems of equations needs most of the computational time in the whole simulation. Classical direct solvers are in general too expensive for the required model size due to their nonlinear memory demands and operation counts. Thus, we apply an iterative SAMG solver [SAMG] to overcome these limitations. SAMG is a highly robust and efficient solver that typically shows optimal linear scaling with respect to memory and operations. We present numerical results to demonstrate the effectiveness of the computational framework proposed in this work. They clearly show that with the help of SAMG the numerical treatment of this extremely challenging application becomes feasible