Decentralised control of material or traffic flows in networks using phase-synchronisation

We present a self-organising, decentralised control method for material flows in networks. The concept applies to networks where time sharing mechanisms between conflicting flows in nodes are required and where a coordination of these local switches on a system-wide level can improve the performance. We show that, under certain assumptions, the control of nodes can be mapped to a network of phase-oscillators. By synchronising these oscillators, the desired global coordination is achieved. We illustrate the method in the example of traffic signal control for road networks. The proposed concept is flexible, adaptive, robust and decentralised. It can be transferred to other queuing networks such as production systems. Our control approach makes use of simple synchronisation principles found in various biological systems in order to obtain collective behaviour from local interactions

Dynamic Effects Increasing Network Vulnerability to Cascading Failures

We study cascading failures in networks using a dynamical flow model based on simple conservation and distribution laws to investigate the impact of transient dynamics caused by the rebalancing of loads after an initial network failure (triggering event). It is found that considering the flow dynamics may imply reduced network robustness compared to previous static overload failure models. This is due to the transient oscillations or overshooting in the loads, when the flow dynamics adjusts to the new (remaining) network structure. We obtain {\em upper} and {\em lower} limits to network robustness, and it is shown that {\it two} time scales $\tau$ and $\tau_0$, defined by the network dynamics, are important to consider prior to accurately addressing network robustness or vulnerability. The robustness of networks showing cascading failures is generally determined by a complex interplay between the network topology and flow dynamics, where the ratio $\chi=\tau/\tau_0$ determines the relative role of the two of them.Comment: 4 pages Latex, 4 figure

Intelligent network-based early warning systems

Abstract. In this paper we present an approach for an agent-based early warning system (A-EWS) for critical infrastructures. In our approach we combine existing security infrastructures, e.g. firewalls or intrusion detection systems, with new detection approaches to create a global view and to determine the current threat state

A search for large-scale effects of ship emissions on clouds and radiation in satellite data

Ship tracks are regarded as the most obvious manifestations of the effect of anthropogenic aerosol particles on clouds (indirect effect). However, it is not yet fully quantified whether there are climatically relevant effects on large scales beyond the narrow ship tracks visible in selected satellite images. A combination of satellite and reanalysis data is used here to analyze regions in which major shipping lanes cut through otherwise pristine marine environments in subtropical and tropical oceans. We expect the region downwind of a shipping lane is affected by the aerosol produced by shipping emissions but not the one upwind. Thus, differences in microphysical and macrophysical cloud properties are analyzed statistically. We investigate microphysical and macrophysical cloud properties as well as the aerosol optical depth and its fine-mode fraction for the years 2005–2007 as provided for by retrievals of the two Moderate Resolution Imaging Spectroradiometer instruments. Water-cloud properties include cloud optical depth, cloud droplet effective radius, cloud top temperature, and cloud top pressure. Large-scale meteorological parameters are taken from ERA-Interim reanalysis data and microwave remote sensing (sea surface temperature). We analyze the regions of interest in a Eulerian and Lagrangian sense, i.e., sampling along shipping lanes and sampling along wind trajectories, respectively. No statistically significant impacts of shipping emissions on large-scale cloud fields could be found in any of the selected regions close to major shipping lanes. In conclusion, the net indirect effects of aerosols from ship emissions are not large enough to be distinguishable from the natural dynamics controlling cloud presence and formation

Recommendations for Discipline-Specific FAIRness Evaluation Derived from Applying an Ensemble of Evaluation Tools

From a research data repositories’ perspective, offering research data management services in line with the FAIR principles is becoming increasingly important. However, there exists no globally established and trusted approach to evaluate FAIRness to date. Here, we apply five different available FAIRness evaluation approaches to selected data archived in the World Data Center for Climate (WDCC). Two approaches are purely automatic, two approaches are purely manual and one approach applies a hybrid method (manual and automatic combined). The results of our evaluation show an overall mean FAIR score of WDCC-archived (meta) data of 0.67 of 1, with a range of 0.5 to 0.88. Manual approaches show higher scores than automated ones and the hybrid approach shows the highest score. Computed statistics indicate that the test approaches show an overall good agreement at the data collection level. We find that while neither one of the five valuation approaches is fully fit-forpurpose to evaluate (discipline-specific) FAIRness, all have their individual strengths. Specifically, manual approaches capture contextual aspects of FAIRness relevant for reuse, whereas automated approaches focus on the strictly standardised aspects of machine actionability. Correspondingly, the hybrid method combines the advantages and eliminates the deficiencies of manual and automatic evaluation approaches. Based on our results, we recommend future FAIRness evaluation tools to be based on a mature hybrid approach. Especially the design and adoption of the discipline-specific aspects of FAIRness will have to be conducted in concerted community efforts

Recommendations for Discipline-Specific FAIRness Evaluation Derived from Applying an Ensemble of Evaluation Tools

From a research data repositories’ perspective, offering research data management services in line with the FAIR principles is becoming increasingly important. However, there exists no globally established and trusted approach to evaluate FAIRness to date. Here, we apply five different available FAIRness evaluation approaches to selected data archived in the World Data Center for Climate (WDCC). Two approaches are purely automatic, two approaches are purely manual and one approach applies a hybrid method (manual and automatic combined). The results of our evaluation show an overall mean FAIR score of WDCC-archived (meta) data of 0.67 of 1, with a range of 0.5 to 0.88. Manual approaches show higher scores than automated ones and the hybrid approach shows the highest score. Computed statistics indicate that the test approaches show an overall good agreement at the data collection level. We find that while neither one of the five valuation approaches is fully fit-forpurpose to evaluate (discipline-specific) FAIRness, all have their individual strengths. Specifically, manual approaches capture contextual aspects of FAIRness relevant for reuse, whereas automated approaches focus on the strictly standardised aspects of machine actionability. Correspondingly, the hybrid method combines the advantages and eliminates the deficiencies of manual and automatic evaluation approaches. Based on our results, we recommend future FAIRness evaluation tools to be based on a mature hybrid approach. Especially the design and adoption of the discipline-specific aspects of FAIRness will have to be conducted in concerted community efforts