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.

Aiming at real-time detection of UAVs, small UAV targets are easily missed and difficult to detect in complex backgrounds. To maintain high detection performance while reducing memory and computational costs, this paper proposes the SEB-YOLOv8s detection method. Firstly, the YOLOv8 network structure is reconstructed using SPD-Conv to reduce the computational burden and accelerate the processing speed while retaining more shallow features of small targets. Secondly, we design the AttC2f module and replace the C2f module in the backbone of YOLOv8s with it, enhancing the model’s ability to obtain accurate information and enriching the extracted relevant information. Finally, Bi-Level Routing Attention is introduced to optimize the Neck part of the network, reducing the model’s attention to interfering information and filtering it out. The experimental results show that the mAP50 of the proposed method reaches $90.5\%$ and the accuracy reaches $95.9\%$, which are improvements of $2.2\%$ and $1.9\%$, respectively, compared with the original model. The mAP50-95 is improved by $2.7\%$, and the model’s occupied memory size only increases by 2.5 MB, effectively achieving high-accuracy real-time detection with low memory consumption.

Quasi-periodic pulsation (QPP) is a common phenomenon in solar flares. Studying QPP is important to further our understanding of the physical processes operating in flares. However, detection of QPP is complicated by the presence of noise in flaring lightcurves. In this study, we apply the Bayesian-based Markov-Chain-Monte-Carlo (MCMC) technique to the QPP detection. We use MCMC to fit the Fourier power spectral density (PSD) profiles of flaring lightcurves, aiming to determine a quasi-periodic component by model comparison and test statistics. Two models fitting the PSD were compared: the first model consists of colored and white noise only, and the second model adds a spectral peak of a Gaussian shape representing a short-living oscillatory signal. To evaluate MCMC of the QPP detection, we test it on 100 synthetic signals with spectral properties similar to those observed in flares. Subsequently, we analyzed QPP events in 699 flare signals in the 1–8 Å channel recorded by the Geostationary Operational Environmental Satellite from 2010 to 2017, including 250 B-class, 250 C-class, 150 M-class, and 49 X-class flares. Approximately 57% X-class, 39% M-class, 20% C-class, and 16% B-class flares are found to show a strong evidence of QPP, whose periods range mainly from 6.2 to 75.3 s. The results demonstrate that QPP events are easier to detect in more powerful flares. The distribution of the detected QPP periods is found to follow a logarithmic normal distribution. The distributions in the four flare classes are similar. This suggests that the established distribution is a common feature for flares of different classes.

The widespread application of drone technology has raised security concerns, as unauthorized drones can lead to illegal intrusions and privacy breaches. Traditional detection methods often fall short in balancing performance and lightweight design, making them unsuitable for resource-constrained scenarios. To address this, we propose the IASL-YOLO algorithm, which optimizes the YOLOv8s model to enhance detection accuracy and lightweight efficiency. First, we design the CFE-AFPN network to streamline the architecture while boosting feature fusion capabilities across non-adjacent layers. Second, we introduce the SIoU loss function to address the orientation mismatch issue between predicted and ground truth bounding boxes. Finally, we employ the LAMP pruning algorithm to compress the model. Experimental results on the Anti-UAV dataset show that the improved model achieves a 2.9% increase in Precision, a 6.8% increase in Recall, and 3.9% and 3.8% improvements in mAP50 and mAP50-95, respectively. Additionally, the model size is reduced by 75%, the parameter count by 78%, and computational workload by 30%. Compared to mainstream algorithms, IASL-YOLO demonstrates significant advantages in both performance and lightweight design, offering an efficient solution for drone detection tasks.

Photospheric magnetic fields are manifested as sunspots, which cover various sizes over high-resolution, full-disk, solar continuum images. This paper proposes a novel deep learning method named SIPNet, which is designed to extract and segment multiscale sunspots. It presents a new Switchable Atrous Spatial Pyramid Pooling (SASPP) module based on ASPP, employs an IoU-aware dense object detector, and incorporates a prototype mask generation technique. Furthermore, an open-source framework known as Slicing Aided Hyper Inference (SAHI) is integrated on top of the trained SIPNet model. A comprehensive sunspot dataset is built, containing more than 27,000 sunspots. The precision, recall, and average precision metrics of the SIPNet & SAHI method were measured as 95.7%, 90.2%, and 96.1%, respectively. The results indicate that the SIPNet & SAHI method has good performance in detecting and segmenting large-scale sunspots, particularly in small and ultra-small sunspots. The method also provides a new solution for solving similar problems.

Solar flares are one of the most intense solar activities, the result of a sudden large-scale release of magnetic energy in the form of electromagnetic radiation and energetic particles. Intense solar flares can severely threaten communication and navigation systems, oil pipelines, and power grids on Earth. Therefore, it is crucial to establish highly accurate solar flare prediction models to enable humans to anticipate solar flare eruptions in advance, thereby reducing human and economic losses. In this paper, we utilized the solar active region (AR) magnetogram provided by the Solar Dynamics Observatory’s Helioseismic and Magnetic Imager and the associated feature parameters of the magnetic field; specifically, the feature vectors of the magnetic field’s spatial structure characteristics and the magnetic field feature parameters are fused to predict solar flares. We built two solar flare prediction models based on a combination of convolutional neural networks (CNN) and a temporal convolutional network (TCN), called CNN-TCN, and predicted whether a ≥C- or ≥M-class flare event would erupt in ARs in the next 24 hr, respectively. Then, after training and testing our model, we focused on the true skill statistic (TSS). Through the model superiority discussion, the model obtained high average TSS values, with the ≥C and ≥M models achieving TSS scores of 0.798 ± 0.032 and 0.850 ± 0.074, respectively, suggesting that our models have good forecasting performance. We speculate that some key features automatically extracted by our model may not have been previously identified, and these features could provide important clues for studying the mechanisms of flares.