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In real-world scenarios, individuals often cooperate for mutual benefit. However, differences in wealth can lead to varying outcomes for similar actions. In complex social networks, individuals' choices are also influenced by their neighbors. To explore the evolution of strategies in realistic settings, we conducted repeated asymmetric prisoners dilemma experiments on a weighted BA scale-free network. Our analysis highlighted how the four components of memory-one strategies affect win rates, found two special strategies in the evolutionary process, and increased the cooperation levels among individuals. These findings offer practical insights for addressing real-world problems., Comment: 25 pages, 17 figures, 35 references

We apply the four dimensional variational method to reconstruct the small scales of three-dimensional turbulent velocity fields with a moderate Reynolds number, given a time sequence of measurement data on a coarse set of grid points. The results show that, reconstruction is successful when the resolution of the measurement data, given in terms of the wavenumber, is at the order of the threshold value $k_c = 0.2\eta_K^{-1}$ where $\eta_K$ is the Kolmogorov length scale of the flow. When the data are available over a period of one large eddy turn-over time scale, the filtered enstrophy and other small scale quantities are reconstructed with a $30\%$ or smaller normalized point-wise error, and a $90\%$ point-wise correlation. The spectral correlation between the reconstructed and target fields is higher than $80\%$ for all wavenumbers. Minimum volume enclosing ellipsoids (MVEEs) and MVEE trees are introduced to quantitatively compare the geometry of non-local structures. Results show that, for the majority samples, errors in the locations and the sizes of the reconstructed structures are within $15\%$, and those in the orientations is within $15^\circ$. This investigation demonstrates that satisfactory reconstruction of the scales two or more octaves smaller is possible if data at large scales are available for at least one large eddy turn-over time. In comparison, a direct substitution scheme results in three times bigger point-wise discrepancy in enstrophy. The spectral difference between the reconstructed and target velocity fields is more than ten times higher than what is obtained with the four dimensional variational method., Comment: 37 pages, 36 figures

Data-driven approaches to automated machine condition monitoring are gaining popularity due to advancements made in sensing technologies and computing algorithms. This paper proposes the use of a deep learning model, based on Long Short-Term Memory (LSTM) architecture for a recurrent neural network (RNN) which captures long term dependencies for modeling sequential data. In the context of estimating cutting tool wear amounts, this LSTM based RNN approach utilizes a system transition and system observation function based on a minimally intrusive vibration sensor signal located near the workpiece fixtures. By applying an LSTM based RNN, the method helps to avoid building an analytic model for specific tool wear machine degradation, overcoming the assumptions made by Hidden Markov Models, Kalman filter, and Particle filter based approaches. The proposed approach is tested using experiments performed on a milling machine. We have demonstrated one-step and two-step look ahead cutting tool state prediction using online indirect measurements obtained from vibration signals. Additionally, the study also estimates remaining useful life (RUL) of a machine cutting tool insert through generative RNN. The experimental results show that our approach, applying the LSTM to model system observation and transition function is able to outperform the functions modeled with a simple RNN.