Carbon fiber reinforced polymer (CFRP) plays an important role in many fields, especially in aviation and civil industries. The electrical conductivity of CFRP is critical for its electrical behavior, such as its lightning strike vulnerability, electromagnetic shielding ability, and potential uses for self-sensing. In addition, the electrical conductivity is related to the mechanical integrity. Therefore, electrical properties can be measured as an indication when detecting delamination and other defects in CFRP. This review provides a comprehensive basis for readers to grasp recent research progresses on electrical behaviors of CFRP.

With the growing integration of artificial intelligence (AI) in education, understanding its cognitive implications has become increasingly important. This study examines how university students' AI dependence influences their critical thinking, exploring cognitive fatigue as a mediating mechanism and information literacy as a moderating factor. Data were collected from 580 Chinese university students, and a moderated mediation model (PROCESS Model 8) was tested. Results indicated that greater AI dependence was associated with lower levels of critical thinking, with cognitive fatigue partially mediating this relationship. Information literacy buffered the negative impact of AI dependence on critical thinking but also amplified cognitive fatigue when AI reliance was high. These findings extend theories of cognitive offloading and automation bias by revealing the dual role of information literacy in AI-supported learning. Practical implications include the need for digital literacy programs and instructional designs that manage cognitive fatigue in AI-integrated education.

Medical staff are direct providers of medical services and a key element in the development of health services, and their life satisfaction is important to both their work satisfaction and their patients’ satisfaction, subsequently influencing the quality of medical care in general. This cross-sectional study aimed to explore the mechanisms underlying the influence of perceived social support on medical staff’s life satisfaction. Convenience sampling was used to recruit participants from two non-tertiary hospitals in Shaoguan City, Guandong Province, China. A total of 533 medical staff completed the Multidimensional Scale of Perceived Social Support, the Satisfaction with Life Scale, the Connor and Davidson Resilience Scale, and the depression subscale of the Depression, Anxiety, and Stress Scales (DASS-21). The results showed that perceived social support could influence medical staff’s life satisfaction not only through the separate effects of resilience and depression, but also through the chain mediation effect of resilience and depression. This study suggests that reducing the depressive symptoms of medical staff and improving their perceived social support as well as resilience could help to enhance their life satisfaction.

To solve the problems of high latency, high system overhead, and small supported scale in the current application of pharmaceutical traceability combined with blockchain technology, an algorithm called Pharmaceutical-Practical Byzantine Fault Tolerance (P-PBFT) based on PBFT, grouping, and credit voting is proposed. The algorithm combines the characteristics of a pharmaceutical supply chain, optimizes the consistency protocol in the original algorithm, divides large-scale network nodes into different consensus sets by response speed, and performs grouping consensus. The algorithm's credit model and voting mechanism dynamically updates user status according to the behavior of nodes in consensus, evaluates the reliability of users, and also serves as a basis for electing management nodes. Experimental results show that the improved P-PBFT consensus algorithm provides smaller latency and higher throughput for pharmaceutical traceability systems, supports larger-scale traceability, effectively alleviates the dramatic increase in communication among network nodes, and reduces the influence of malicious nodes.

In the digital age, social media has not only transformed the way individuals interact but has also become a significant platform for self-presentation, especially among young people. Social comparison regarding appearance has become more prevalent in this environment, raising concerns about its impact on mental health. This study specifically examines the effects of upward social comparison (USC) on appearance anxiety, providing empirical support for the relationship between USC and appearance anxiety. Based on the Stress-Vulnerability Model, the Stress and Coping Model, Objectification Theory, and Self-Compassion Theory, the study constructs a moderated mediation model. An anonymous survey was conducted of 397 young adults (mean age = 21.6 years, SD = 2.12 years). The results showed that: (1) USC significantly predicted appearance anxiety (β = 0.546, p < 0.001); (2) self-objectification partially mediated the relationship between USC and appearance anxiety, with the mediation effect accounting for 21% of the total effect; (3) self-compassion moderated the relationship between USC and self-objectification, such that higher levels of self-compassion weakened the effect of USC on self-objectification.

To improve the blockchain consensus algorithm practical Byzantine fault tolerance (PBFT) with random master node selection, which has high communication overhead and a small supported network size, this paper proposes a Byzantine fault tolerant consensus algorithm based on credit (CBFT) enhanced with a grouping and credit model. The CBFT algorithm divides the network nodes according to the speed of their response to the management nodes, resulting in different consensus sets, and achieves consensus within and outside the group separately to reduce communication overhead and increase system security. Second, the nodes are divided into different types according to the credit model, each with different responsibilities to reduce the probability that the master node is a malicious node. Experimental results show that the throughput of the CBFT algorithm is 3.1 times that of PBFT and 1.5 times that of GPBFT when the number of nodes is 52. Our scheme has latency that is 7.4% that of PBFT and 38.8% that of GPBFT; CBFT has communication overhead that is 6.4% that of PBFT and 87.3% that of GPBFT. The number of nodes is 300, and the Byzantine fault tolerance is improved by 59.3%. These improvements are clearer with the increase in the number of nodes.