Cascading failure is a potentially devastating process that spreads on real-world complex networks and can impact the integrity of wide-ranging infrastructures, natural systems and societal cohesiveness. One of the essential features that create complex network vulnerability to failure propagation is the dependency among their components, exposing entire systems to significant risks from destabilizing hazards such as human attacks, natural disasters or internal breakdowns. Developing realistic models for cascading failures as well as strategies to halt and mitigate the failure propagation can point to new approaches to restoring and strengthening real-world networks. In this review, we summarize recent progress on models developed based on physics and complex network science to understand the mechanisms, dynamics and overall impact of cascading failures. We present models for cascading failures in single networks and interdependent networks and explain how different dynamic propagation mechanisms can lead to an abrupt collapse and a rich dynamic behaviour. Finally, we close the review with novel emerging strategies for containing cascades of failures and discuss open questions that remain to be addressed.
Based on the study on Jerk chaotic system, a multiscroll hyperchaotic system with hidden attractors is proposed in this paper, which has infinite number of equilibriums. The chaotic system can generate [Formula: see text] scroll hyperchaotic hidden attractors. The dynamic characteristics of the multiscroll hyperchaotic system with hidden attractors are analyzed by means of dynamic analysis methods such as Lyapunov exponents and bifurcation diagram. In addition, we have studied the synchronization of the system by applying an adaptive control method. The hardware experiment of the proposed multiscroll hyperchaotic system with hidden attractors is carried out using discrete components. The hardware experimental results are consistent with the numerical simulation results of MATLAB and the theoretical analysis results.
The shipping market, a major component of the global economy, is characterized by high risk and volatility. The Baltic dry index is an influential indicator in the world shipping market and international trade. Several studies have used a variety of techniques to generate Baltic dry index predictions. The most prominent techniques utilize either econometric or artificial intelligence computing. We compare the forecasting accuracy of two typical univariant econometric models and three artificial neural networks (ANNs)-based algorithms. We find that when using daily data, econometric forecasting models produce better one-step-ahead predictions than ANN-based algorithms. When forecasting weekly and monthly data, ANN-based algorithms produce fewer errors and a higher direction matching rate than econometric models. We also compare the predictive power of a number of different models when applied to the 2008 financial crisis and find that the generalized autoregressive conditional heteroskedasticity model and the back propagation neural network algorithm produce the best one-step-ahead and seven-steps ahead predictions, respectively. INDEX TERMS Baltic dry index prediction, ARIMA,GARCH, artificial neural networks(ANN), BP neural network, RBFNN, ELM.
PurposeTo predict precision and other performance characteristics of chromatographic purity methods, which represent the most widely used form of analysis in the biopharmaceutical industry.MethodsWe have conducted a comprehensive survey of purity methods, and show that all performance characteristics fall within narrow measurement ranges. This observation was used to develop a model called Uncertainty Based on Current Information (UBCI), which expresses these performance characteristics as a function of the signal and noise levels, hardware specifications, and software settings.ResultsWe applied the UCBI model to assess the uncertainty of purity measurements, and compared the results to those from conventional qualification. We demonstrated that the UBCI model is suitable to dynamically assess method performance characteristics, based on information extracted from individual chromatograms.ConclusionsThe model provides an opportunity for streamlining qualification and validation studies by implementing a “live validation” of test results utilizing UBCI as a concurrent assessment of measurement uncertainty. Therefore, UBCI can potentially mitigate the challenges associated with laborious conventional method validation and facilitates the introduction of more advanced analytical technologies during the method lifecycle.Electronic supplementary materialThe online version of this article (doi:10.1007/s11095-012-0836-z) contains supplementary material, which is available to authorized users.
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