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Fast radio bursts (FRBs) are bright millisecond radio bursts at cosmological distances. Only three FRBs have exhibited extreme activities, such as achieving a peak event rate $\gtrsim 100$ hr$^{-1}$ or being persistently active. Only these three among $\sim 50$ known repeating FRBs have circular polarization. We observed the FRB 20220912A with the Robert C. Byrd Green Bank Telescope (GBT) at L-band on 24 October 2022 and detected 128 bursts in 1.4 hours, corresponding to a burst rate of about 90 hr$^{-1}$, which is the highest yet for FRBs observed by the GBT and makes it the fourth extremely active FRB. The median energy of the bursts is $4.0\times10^{37}$ erg, close to the characteristic energy of FRB 20121102A. The average rotation measure (RM) was $-$0.4 rad m$^{-2}$ with unnoticeable intraday RM change, indicating a likely clean environment, in contrast to the other three extremely active repeating FRBs. Most bursts have nearly 100% linear polarization. Approximately 56% of the bright bursts have circular polarization, the highest such fraction among all FRBs. A downward drift in frequency and polarization angle swings were found in our sample. The discovery and characterization of FRB 20220912A support the view that the downward drift in frequency, polarization angle swings, and circular polarization are intrinsic to radiation physics, which may be shared by active repeaters regardless of the environments.

Posttraumatic Growth (PTG) is a positive change that might occur after some serious traumatic events (Tedeschi & Calhoun, 2004). Compared to resilience (i.e., ability to recover when facing trauma), PTG is more like a transformation where people’s thoughts, behaviors, interests might all grow or change in a positive way (Tedeschi & Calhoun, 2004). These positive changes are typically classified into 5 domains: symptoms including appreciation of life, spiritual change, new possibilities, etc. (Tedeschi & Calhoun, 1996). Researchers have found that PTG is positively associated with ongoing distress, demonstrating that stressful life events could have potentially positive impacts (Loiselle, Devine, Reed-Knight, & Blount, 2011). Indeed, a handful of studies have found a positive association between social support and PTG. For example, Yongju and colleagues (2014) found that social support is strongly associated with PTG in a sample of Chinese women with infertility. Another study in Chinese rescue workers showed that social support is partially playing a mediating role between self-acceptance and PTG and it suggested that individuals with a good social support system can help provide more spiritual support for people to cope with trauma (Zhao, An, Sun, & Liu, 2020). A cross-sectional Cancer study also indicated that social support has a significant positive correlation with PTG which are consistent with the idea that the spiritual support provided by social support can help people better deal with the information from trauma (Roohi, Salehi, Mahmoodzadeh, & Morovati, 2020). However, these studies used underrepresented sample with restricted types of traumatic events, which limits the generalizability of the conclusion. Moreover, existing studies have largely overlooked the distinction between subjectively perceived social support and objectively accessible social resources. Theories from social cognitive support the idea that subjectively perceived social support has a greater impact on people’s mental health, as perceived support has consequences on how we think about, react to, and subsequently, utilize received support or accessible social resources (Lakey & Drew, 1997). In the present study, we operationalize subjectively perceived social support as self-report feelings of supportiveness, and objectively accessible social resources as socioeconomic status and social network diversity, respectively. We will use the Perceived Social Support Scale (PSSS; Zimet et al., 1988) to measure subjectively perceived social support, and objectively accessible social resources will be measured as socioeconomic status and social network diversity. In accordance with the social-cognitive model of social support (Lakey & Drew, 1997), We hypothesize that compared to objectively accessible social resources, perceived social support will have a stronger association with PTG.

In order to obtain the real-time traffic flows between on-ramps and off-ramps of the ring expressway, a model to estimate the OD(origin-destination) matrix with consideration of the structural characteristics of the ring expressway is proposed in this paper.The ring expressway is looked as a loop network, without considering traffic congestion,the distance fromthe on-ramps to off-ramps that drivers may choose is not exceeding 1/2 of the loopperimeter. According to this principle, we use on-ramps’ flows to express mainline sections’ and off-ramps’ flows. These expressions form the simultaneous equations which can be solved with Gaussian Elimination, and the flow proportions between on and off-ramps (OD matrix) can be achieved. The propose method is applied to Xi’an Ring Expressway, the OD matrix is extracted from the estimated flow proportions between on and off ramps, and is compared with the actual survey. The model is verified to be feasible.

Blockchain technology has the characteristics of decentralization, traceability and tamper proof, which creates a reliable decentralized transaction mode, further accelerating the development of the blockchain platforms. However, with the popularization of various financial applications, security problems caused by blockchain digital assets, such as money laundering, illegal fundraising and phishing fraud, are constantly on the rise. Therefore, financial security has become an important issue in the blockchain ecosystem, and identifying the types of accounts in blockchain (e.g. miners, phishing accounts, Ponzi contracts, etc.) is of great significance in risk assessment and market supervision. In this paper, we construct an account interaction graph using raw blockchain data in a graph perspective, and proposes a joint learning framework for account identity inference on blockchain with graph contrast. We first capture transaction feature and correlation feature from interaction graph, and then perform sampling and data augmentation to generate multiple views for account subgraphs, finally jointly train the subgraph contrast and account classification task. Extensive experiments on Ethereum datasets show that our method achieves significant advantages in account identity inference task in terms of classification performance, scalability and generalization.

With the development of science and technology, people’s awareness of health has increased, but in life, people’s health is mostly not controlled by themselves. There are many external constraints, such as how to save themselves when people are in danger. This bracelet is for this purpose. Users only need to wear a bracelet and touch the button to connect with emergency contacts and the 110 alarm center, providing users with professional personal security services. Bracelet holders need to apply for the real-name system, which can promptly sense when people encounter difficulties, dial 110, send the location, and record when the enemy can’t find it. Incoming mobile phone calls will be sensed and the mute answer button will not be selected. He was found and recorded and sent a text message to the emergency contact. In the event of a non-human hazard, such as a fire or an earthquake, he would promptly notify the relevant departments according to the people’s situation.