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The complex financial networks, with their nonlinear nature, often exhibit considerable noises, inhibiting the analysis of the market dynamics and portfolio optimization. Existing studies mainly focus on the application of the global motion filtering on the linear matrix to reduce the noise interference. To minimize the noise in complex financial networks and enhance timing strategies, we introduce an advanced methodology employing global motion filtering on nonlinear dynamic networks derived from mutual information. Subsequently, we construct investment portfolios, focusing on peripheral stocks in both the Chinese and American markets. We utilize the growth and decline patterns of the eigenvalue associated with the global motion to identify trends in collective market movement, revealing the distinctive portfolio performance during periods of reinforced and weakened collective movements and further enhancing the strategy performance. Notably, this is the first instance of applying global motion filtering to mutual information networks to construct an investment portfolio focused on peripheral stocks. The comparative analysis demonstrates that portfolios comprising peripheral stocks within global-motion-filtered mutual information networks exhibit higher Sharpe and Sortino ratios compared to those derived from global-motion-filtered Pearson correlation networks, as well as from full mutual information and Pearson correlation matrices. Moreover, the performance of our strategies proves robust across bearish markets, bullish markets, and turbulent market conditions. Beyond enhancing the portfolio optimization, our results provide significant potential implications for diverse research fields such as biological, atmospheric, and neural sciences. [ABSTRACT FROM AUTHOR]

The meshing impact of gear is the main vibration source and noise source of automobile transmission and the meshing impact of gears can be effectively reduced through modification.In order to explore the influence of different parameters on the vibration and noise of gear,the contrast experimental study on the dynamic characteristic of the modification gear is carried out,and the suitable modification parameters are obtained.The experimental results provide basic data for determining the modification parameters of gear.

In view of bearing compound fault of the low signal-to-noise ratio of the gear transmission system,firstly,the fault signal is decomposed to the order of IMFs and residue. Taking the kurtosis as characteristic index,the kurtosis value of the IMF components is calculated and the IMF component which has the maximum kurtosis is demodulated by Hilbert envelope. The final result shows that the method can effectively improve the signal-to-noise ratio,the bearing fault characteristic frequency can be extracted from strong background noise of gear transmission system.

The Locati method is used to explore the influence of modification parameters on the fatigue property of gears. The corresponding bending stress of meshing gear with staggering tooth is obtained by using the Romax software,then the bending fatigue strength for gears with different modification parameters are obtained according to experimental data by using Matlab. The results show that the appropriate addendum modification can improve the bending fatigue life of gear with the given conditions,which provides the basic reference data for modification in the application of gear.

This paper introduces policy incentive factors to explore their impact on innovation diffusion in enterprise clusters, and reveals the relationship between policy incentives and innovation adoption ratio through dynamic correlation simulation of policy incentive intensity and the type and scale of enterprise clusters. The research results show that policy incentives have short-term timeliness, and the impact of incentives over time will submerge the impact of interconnected enterprises, and its effect is related to the incentive evaluation value of related node enterprises. In the initial enterprise innovation is more, policy incentives can further enhance the overall innovation adoption ratio, while in the initial enterprise innovation is less, it can only decelerate recession. Besides, the effect of policy incentive is closely related to the type of enterprise cluster. The key factor of that is the choice of incentive intensity should match the type and scale of enterprise cluster. Moreover, the incentive intensity of traditional enterprise cluster is positively correlated with the scale of cluster, while the incentive intensity of innovative enterprise cluster is negatively correlated with the scale of cluster. In addition, the local incentive effect is better than that of overall incentives. Targeted incentives for limited target groups are more conducive to innovation diffusion. [ABSTRACT FROM AUTHOR]