The association between neighborhood-level social fragmentation and distressing psychotic-like experiences in early adolescence: the moderating role of close friends.

BACKGROUND: Early exposure to neighborhood social fragmentation has been shown to be associated with schizophrenia. The impact of social fragmentation and friendships on distressing psychotic-like experiences (PLE) remains unknown. We investigate the relationships between neighborhood social fragmentation, number of friends, and distressing PLE among early adolescents. METHODS: Data were collected from the Adolescent Brain Cognitive Development Study. Generalized linear mixed models tested associations between social fragmentation and distressing PLE, as well as the moderating role of the number of total and close friends. RESULTS: Participants included 11 133 adolescents aged 9 to 10, with 52.3% being males. Greater neighborhood social fragmentation was associated with higher levels of distressing PLE (adjusted ß = 0.05; 95% CI: 0.01-0.09). The number of close but not total friends significantly interacted with social fragmentation to predict distressing PLE (adjusted ß = -0.02; 95% CI: -0.04 to <-0.01). Among those with fewer close friends, the association between neighborhood social fragmentation and distressing PLE was significant (adjusted ß = 0.07; 95% CI: 0.03-0.11). However, among those with more close friends, the association was non-significant (adjusted ß = 0.03; 95% CI: -0.01 to 0.07). CONCLUSIONS: Greater neighborhood social fragmentation is associated with higher levels of distressing PLE, particularly among those with fewer close friends. Further research is needed to disentangle aspects of the interaction between neighborhood characteristics and the quality of social interactions that may contribute to psychosis, which would have implications for developing effective interventions at the individual and community levels.

Artificial intelligence-based network traffic analysis and automatic optimization technology.

Network operation and maintenance (O & M) activities of data centers focus mainly on checking the operating states of devices. O & M engineers determine how services are running and the bearing capacity of a data center by checking the operating states of devices. However, this method cannot reflect the real transmission status of business data; therefore, engineers cannot fully comprehensively perceive the overall running conditions of businesses. In this paper, ERSPAN (Encapsulated Remote Switch Port Analyzer) technology is applied to deliver stream matching rules in the forwarding path of TCP packets and mirror the TCP packets into the network O & M AI collector, which is used to conduct an in-depth analysis on the TCP packets, collect traffic statistics, recapture the forwarding path, carry out delayed computing, and identify applications. This enables O & M engineers to comprehensively perceive the service bearing status in a data center, and form a tightly coupled correlation model between networks and services through end-to-end visualized modeling, providing comprehensive technical support for data center optimization and early warning of network risks.