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Lithium (Li) is one of the most important light elements that was primordially synthesized in the Big Bang Nucleosynthesis (BBN). It is also an element that confused astrophysicists for decades, as its observed abundance often contradicts with the theoretical prediction in many different types of celestial objects. Li-rich giant stars are such objects. Their atmospheres contain anomaly high Li abundance than that expected by the standard stellar evolution model. Although the first Li-rich giant star was discovered almost 40 years ago, their origin is still being debated. With the launch of massive spectroscopic survey program such as the Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST) survey, the extending of available asteroseismology data from space satellites (such as Kepler), and the developments of data-driven techniques, breakthroughs have been archived in the field of Li-rich studies. In this paper, we review the progress that was made during the past four decades, and present our up-to-date understanding to Li-rich giant stars. [ABSTRACT FROM AUTHOR]

The years-long high-precision photometric data observed by Kepler satellite combining with huge amount of spectra observed by LAMOST provide a great opportunity to study the relations between surface rotation and lithium abundance of li-rich giants. In this study, we cross match the Kepler data with li-rich giants catalog from LAMOST, and obtain 619 common sources. Then we measure 36 rotation periods from the full set of 295 li-rich giants with good data quality which consists of two sub-samples. The rotation periods of 14 stars was extracted from 205 stars with evolutionary stages determined using asteroseismology, including 11 core helium-burning stars (HeBs), 2 red giant branch stars (RGBs), and 1 unclassified star. All the super lithium-rich giant stars (A (Li) > 3.3 dex) are HeBs in our sample. The remaining 90 giants do not have evolutionary stages confirmed, and in this sub-sample, 22 giants have their rotation period measured. The rotation detection rate of the former sub-sample is 6.8%, which is significantly higher than the detection rate of a large giant sample in previous studies (2.08%). With the surface rotation period measured, we confirm the relation between stellar rotation and lithium enrichment of giants. Meanwhile, we find that the less Li-enriched stars have a relatively dispersed distribution of rotation periods, and the giants with a high Li enrichment is concentrated on the rapidly rotating area, which is consistent with earlier studies. The present work also shows a jump at A (Li) ≈ 3.3 dex in the relation between rotation period and Li abundance that coincidently is the boundary between Li-rich giants and super Li-rich giants which may indicate the different mechanisms. The rotation periods of super Li-rich giants become shorter as the lithium enrichment increases. This correlation provides an evidence for the rotational induced extra-mixing mechanism responsible for Li enrichment of giants. [ABSTRACT FROM AUTHOR]

Solar flares are a kind of violent solar eruptive activity phenomenon and an important warning device of space weather disturbance. In space weather forecasting, flare forecasting is an important forecast content. This paper proposes a flare prediction model based on long and short-term memory neural network, which uses the time sequence of magnetic field changes in the solar active region in the past 24 h to construct samples, and analyzes the time series evolution of magnetic field characteristics through the long and short-term memory neural network to predict whether ≥ M-level flares will occur in the next 48 h. This paper uses a data set for all active region samples from May 2010 to May 2017, and selects 10 magnetic field characteristic parameters of SDO/HMI SHARP. In the modeling process, six feature parameters with high weight, gain rate, and coverage rate were selected as input parameters through XGBoost method. Through test comparison, the false report rate and accuracy rate of the model are similar to the traditional machine learning model, and the accuracy rate and critical success index are better than the traditional machine learning model, which are 0.7483 and 0.7402, respectively. The overall effect of the model is better than that of the traditional machine learning model. [ABSTRACT FROM AUTHOR]