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The network approach towards the analysis of the dynamics of complex systems has been successfully applied in a multitude of studies in the neurosciences and has yielded fascinating insights. With this approach, a complex system is considered to be composed of different constituents which interact with each other. Interaction structures can be compactly represented in interaction networks. In this contribution, we present a brief overview about how interaction networks are derived from multivariate time series, about basic network characteristics, and about challenges associated with this analysis approach.
Learning- and memory-related processes are thought to result from dynamic interactions in large-scale brain networks that include lateral and mesial structures of the temporal lobes. We investigate the impact of incidental and intentional learning of verbal episodic material on functional brain networks that we derive from scalp-EEG recorded continuously from 33 subjects during a neuropsychological test schedule. Analyzing the networks' global statistical properties we observe that intentional but not incidental learning leads to a significantly increased clustering coefficient, and the average shortest path length remains unaffected. Moreover, network modifications correlate with subsequent recall performance: the more pronounced the modifications of the clustering coefficient, the higher the recall performance. Our findings provide novel insights into the relationship between topological aspects of functional brain networks and higher cognitive functions.
Seismic excited liquid filled tanks are subjected to extreme loading due to hydrodynamic pressures, which can lead to nonlinear stability failure of the thinwalled cylindrical tanks, as it is known from past earthquakes. A significant reduction of the seismically induced loads can be obtained by the application of base isolation systems, which have to be designed carefully with respect to the modified hydrodynamic behaviour of the tank in interaction with the liquid. For this reason a highly sophisticated fluid-structure interaction model has to be applied for a realistic simulation of the overall dynamic system. In the following, such a model is presented and compared with the results of simplified mathematical models for rigidly supported tanks. Finally, it is examined to what extent a simple mechanical model can represent the behaviour of a base isolated tank in case of seismic excitation
A methodology for assessment, seismic verification and strengthening of existing masonry buildings is presented in this paper. The verification is performed using a calculation model calibrated with the results from ambient vibration measurements. The calibrated model serves as an input for a deformation-based verification procedure based on the Capacity Spectrum Method (CSM). The bearing capacity of the building is calculated from experimental capacity curves of the individual walls idealized with bilinear elastic-perfectly plastic curves. The experimental capacity curves were obtained from in-plane cyclic loading tests on unreinforced and strengthened masonry walls with reinforced concrete jackets. The seismic action is compared with the load-bearing capacity of the building considering non-linear material behavior with its post-peak capacity. The application of the CSM to masonry buildings and the influence of a traditional strengthening method are demonstrated on the example of a public school building in Skopje, Macedonia.