It is well known that the long-term evolution of the photospheric magnetic field plays an important role in building up free energy to power solar eruptions. Observations, despite being controversial, have also revealed a rapid and permanent variation of the photospheric magnetic field in response to the coronal magnetic field restructuring during the eruption. The Helioseismic and Magnetic Imager instrument (HMI) on board the newly launched Solar Dynamics Observatory produces seeing-free full-disk vector magnetograms at consistently high resolution and high cadence, which finally makes possible an unambiguous and comprehensive study of this important back-reaction process. In this study, we present a near disk-center, GOES-class X2.2 flare, which occurred in NOAA AR 11158 on 2011 February 15. Using the magnetic field measurements made by HMI, we obtained the first solid evidence of a rapid (in about 30 minutes) and irreversible enhancement in the horizontal magnetic field at the flaring magnetic polarity inversion line (PIL) by a magnitude of ∼30%. It is also shown that the photospheric field becomes more sheared and more inclined. This field evolution is unequivocally associated with the flare occurrence in this sigmoidal active region, with the enhancement area located in between the two chromospheric flare ribbons and the initial conjugate hard X-ray footpoints. These results strongly corroborate our previous conjecture that the photospheric magnetic field near the PIL must become more horizontal after eruptions, which could be related to the newly formed low-lying fields resulting from the tether-cutting reconnection.

Background During the COVID-19 pandemic, special education schools for children in most areas of China were closed between the end of January and the beginning of June in 2020. The sudden interruption in schooling and the pandemic itself caused parents to be anxious and even to panic. Mobile-based parenting skills education has been demonstrated to be an effective method for improving the psychological well-being of mothers with children with autism. However, whether it can improve the psychological states of mothers in the context of the COVID-19 pandemic is a subject that should be urgently investigated. Objective The aim of this study is to evaluate the efficacy of WeChat-based parenting training on anxiety, depression, parenting stress, and hope in mothers with children with autism, as well as the feasibility of the program during the COVID-19 pandemic. Methods This was a quasi-experimental trial. A total of 125 mothers with preschool children with autism were recruited in January 2020. The participants were assigned to the control group (n=60), in which they received routine care, or the intervention group (n=65), in which they received the 12-week WeChat-based parenting training plus routine care, according to their preferences. Anxiety, depression, parenting stress, hope, satisfaction, and adherence to the intervention were measured at three timepoints: baseline (T0), postintervention (T1), and a 20-week follow-up (T2). Results In total, 109 mothers completed the T1 assessment and 104 mothers completed the T2 assessment. The results of the linear mixed model analysis showed statistically significant group × time interaction effects for the intervention on anxiety (F=14.219, P<.001), depression (F=26.563, P<.001), parenting stress (F=68.572, P<.001), and hope (F=197.608, P<.001). Of all mothers in the intervention group, 90.4% (48.8/54) reported that they were extremely satisfied with the WeChat-based parenting training. In total, 40.0% (26/65) logged their progress in home training each week and 61.5% (40/65) logged their progress more than 80% of the time for all 20 weeks. Conclusions The WeChat-based parenting training is acceptable and appears to be an effective approach for reducing anxiety, depression, and parenting stress, as well as increasing hope in mothers with children with autism during the global COVID-19 pandemic. Future studies with rigorous designs and longer follow-up periods are needed to further detect the effectiveness of the WeChat-based parenting training. Trial Registration Chinese Clinical Trial Registry ChiCTR2000031772; http://www.chictr.org.cn/showproj.aspx?proj=52165

In recent years, research on brain-computer interfaces has been increasing in the field of education, and mobile learning has become a very important way of learning. In this study, EEG experiment of a group of iPad-based mobile learners was conducted through algorithm optimization on the TGAM chip. Under the three learning media (text, text + graphic, and video), the researchers analyzed the difference in learners’ attention. The study found no significant difference in attention in different media, but learners using text media had the highest attention value. Later, the researchers studied the attention of learners with different learning styles and found that active and reflective learners’ attention exhibited significant differences when using video media to learn.

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A robust, integrated and flexible charging network is essential for the growth and deployment of electric vehicles (EVs). The State Grid of China has developed a Smart Internet of Electric Vehicle Charging Network (SIEN). At present, there are three main ways to attack SIEN maliciously: distributed data tampering; distributed denial of service (DDoS); and forged command attacks. Network attacks are random and continuous, closely related to time. By contrast, when analyzing the alarm in malicious attacks, the traditional Markov chain based model ignores the association relationship in the time series between states of alarm, so that the analysis and prediction of alarms are not suitable for real situations. This paper analyzes the characteristics of the three types of attack and proposes an association state analysis method on the time series. This method firstly analyzes alarm logs at different locations, different levels, and different types, and then establishes the temporal association of scattered and isolated alarm information. Secondly, it tracks the transition trend of abnormal events in the SIEN’s main station layer, the channel layer, and the sub-station layer. It also identifies the real attack behavior. This method not only provides a prediction of security risks, but, more importantly, it can also accurately analyze the trend of SIEN security risks. Compared with the ordinary Markov chain model, this method can better smooth the fluctuation of processing values, with higher real-time performance, stronger robustness, and higher precision. This method has been applied to the State Grid of China.