Chaos in two black holes with next-to-leading order spin-spin interactions

We take into account the dynamics of a complete third post-Newtonian conservative Hamiltonian of two spinning black holes, where the orbital part arrives at the third post-Newtonian precision level and the spin-spin part with the spin-orbit part includes the leading-order and next-to-leading-order contributions. It is shown through numerical simulations that the next-to-leading order spin-spin couplings play an important role in chaos. A dynamical sensitivity to the variation of single parameter is also investigated. In particular, there are a number of \textit{observable} orbits whose initial radii are large enough and which become chaotic before coalescence

Detecting Variation Trends of Temperature and Precipitation for the Dadu River Basin, China

This study analyzes the variation trends of temperature and precipitation in the Dadu River Basin of China based on observed records from fourteen meteorological stations. The magnitude of trends was estimated using Sen’s linear method while its statistical significance was evaluated using Mann-Kendall’s test. The results of analysis depict increase change from northwest to southeast of annual temperature and precipitation in space. In temporal scale, the annual temperature showed significant increase trend and the annual precipitation showed increase trend. For extreme indices, the trends for temperature are more consistent in the region compared to precipitation. This paper has practical meanings for an effective management of climate risk and provides a foundation for further study of hydrological situation in this river basin

Deep Multibranch Fusion Residual Network for Insect Pest Recognition

Earlier insect pest recognition is one of the critical factors for agricultural yield. Thus, an effective method to recognize the category of insect pests has become significant issues in the agricultural field. In this paper, we proposed a new residual block to learn multi-scale representation. In each block, it contains three branches: one is parameter-free, and the others contain several successive convolution layers. Moreover, we proposed a module and embedded it into the new residual block to recalibrate the channel-wise feature response and to model the relationship of the three branches. By stacking this kind of block, we constructed the Deep Multi-branch Fusion Residual Network (DMF-ResNet). For evaluating the model performance, we first test our model on CIFAR-10 and CIFAR-100 benchmark datasets. The experimental results show that DMF-ResNet outperforms the baseline models significantly. Then, we construct DMF-ResNet with different depths for high-resolution image classification tasks and apply it to recognize insect pests. We evaluate the model performance on the IP102 dataset, and the experimental results show that DMF-ResNet could achieve the best accuracy performance than the baseline models and other state-of-art methods. Based on these empirical experiments, we demonstrate the effectiveness of our approach