Solar flares are immense energy explosions in the solar atmosphere and severely influence space weather. So, forecasting solar flare eruptions is extremely important. Spatial distribution and evolution of active region (AR) magnetic fields are closely related to flare eruptions. In this paper, we simultaneously utilized the two characteristics to build two flare-forecast models using three-dimensional convolutional neural networks (3D CNNs). The two models forecast whether an AR would erupt a ≥C- or ≥M-class flare within the next 24 hr, respectively. The magnetogram sequences provided by the Space-weather Helioseismic and Magnetic Imager Active Region Patches are selected to train our models. We used several performance metrics, such as true skill statistics (TSS), to evaluate our models. The TSS scores of the ≥C and ≥M models reach 0.756 and 0.826, respectively, indicating that our models have superior forecast performance. We used the the gradient-weighted class activation mapping (Grad-CAM) method to visually explain our flare-forecast models. The Grad-CAM illustrates that the 3D CNNs may extract the spatial distribution and evolution of AR magnetic fields simultaneously for flare forecasts. So, the 3D CNN method is valid for flare forecasts, and it utilizes the characteristics related to flare eruptions.

Essential genes play an indispensable role in supporting the life of an organism. Identification of essential genes helps us to understand the underlying mechanism of cell life. The essential genes of bacteria are potential drug targets of some diseases genes. Recently, several computational methods have been proposed to detect essential genes based on the static protein–protein interactive (PPI) networks. However, these methods have ignored the fact that essential genes play essential roles under certain conditions. In this work, a novel method was proposed for the identification of essential proteins by fusing the dynamic PPI networks of different time points (called by FDP). Firstly, the active PPI networks of each time point were constructed and then they were fused into a final network according to the networks’ similarities. Finally, a novel centrality method was designed to assign each gene in the final network a ranking score, whilst considering its orthologous property and its global and local topological properties in the network. This model was applied on two different yeast data sets. The results showed that the FDP achieved a better performance in essential gene prediction as compared to other existing methods that are based on the static PPI network or that are based on dynamic networks.

Solar activities lead to Sun variation with an 11 yr periodicity. The periodic variation affects space weather and heliophysics research. So it is important to accurately predict solar cycle variations. In this paper, we predicted the ongoing Solar Cycle 25 using neural basis expansion analysis for the interpretable time series deep learning method. 13 months of smoothed monthly total sunspot numbers taken by sunspot Index and Long-term Solar Observations are selected to train and evaluate our model. We used root mean square error (RMSE) and mean absolute time lag (MATL) to evaluate our model performance. RMSE and MATL measure the difference between our predicted values and the actual values along the Y- and X-axis, respectively. The RMSE value is 26.62 ± 1.56 and the MATL value is 1.34 ± 0.35, demonstrating that our model is able to better predict sunspot number variation. Finally, we predicted the variation of the sunspot numbers for Solar Cycle 25 using the model. The sunspot number of Solar Cycle 25 will peak around 2024 February with an amplitude of 133.9 ± 7.2. This means that Solar Cycle 25 will be slightly more intense than Solar Cycle 24.