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The application of single measures to complex networks has revealed important insights in many cases.
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The precautionary principle is increasingly discussed in debates over appropriate measures to address complex and uncertain risks.
These measures vary from simple travel time or congestion level measures to more complex network connectivity/centrality measures based on graph theory.
We think that our results can help to apply the measures to more complex network classes and to interpret the results more adequately than before.
Historically seen, Trucco [12] and Rashevsky [19] were the first who developed information measures to analyze complex biological and chemical systems.
Her group has developed various mass spectrometry (ICP-MS, LC-MS and GC-MS) to measure exposure to complex mixtures of trace elements and organic compounds.
Naturalistic stimuli, such as movies, closely mimic the real world and provide a window onto the brain's ability to respond in a timely and measured fashion to complex, everyday events.
Moreover, the corresponding mitigations measures to cope with complex grounds in mines are provided.
Some use complex measures to determine item price while others use an auction system to allocate goods via bidding.
They could also keep each other up-to-date and exchange views on the most effective measures to tackle this often complex task.
This model substitutes simpler, systems measures to replace the more complex, step-wise, mechanistic measures used elsewhere, [e.g. [ 13, 14]].
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