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Detecting fraudulent users in social networks could reduce online fraud and telecommunication fraud cases, which is essential to protect the lives and properties of internet users and maintain social harmony and stability. We study how to detect fraudulent users by using heterogeneous graph representation learning and propose a heterogeneous graph representation learning algorithm to learn user node embeddings to reduce human intervention. The experimental results show promising results. This article investigates how to use better heterogeneous graph representation learning to detect fraudulent users in social networks and improve detection accuracy. [ABSTRACT FROM AUTHOR]

The model errors are often inevitably encountered when a practical structure is analyzed by a mathematical baseline model, and such model errors would have undesired effects on damage identification. Aiming to alleviate the effect of the model errors, a new Measurement-Changes-Correction strategy that simply uses measurement changes to correct the measured data is developed in this article. To this end, the performance of the Measurement-Changes-Correction strategy under the aleatoric and epistemic model errors is systematically analyzed. The analysis is proceeded along with comparison to the conventional two-step strategy where the intact damage parameters are first updated from the measured data and then, damage identification is conducted upon the updated parameters. Thereafter, it is found that (1) the Measurement-Changes-Correction strategy performs as well as the two-step strategy under small aleatoric and epistemic model errors, (2) the Measurement-Changes-Correction strategy can still work under large scaling model parameter errors, but the two-step strategy may not, and (3) the Measurement-Changes-Correction strategy only requires correction of the measured data by the measurement changes so that the computation cost by the Measurement-Changes-Correction strategy is almost half of that by the two-step strategy. Numerical examples and International Association for Structural Control–American Society of Civil Engineers benchmark problems are studied to verify the performance of the proposed Measurement-Changes-Correction strategy. [ABSTRACT FROM AUTHOR]

Cardiometabolic disease is a common clinical syndrome with exact causal relationship between the aberrant of glucose/lipid metabolism and cardiovascular disfunction, but its pathogenesis is unclear. Cyclic guanosine monophosphate-adenosine monophosphate synthase (cGAS)-stimulator of interferon gene (STING) signaling pathway regulates the activation of innate immunity by sensing intracellular double stranded DNA. Metabolic risk factors drive the activation of cGAS-STING pathway through mitochondrial DNA, nuclear DNA and endoplasmic reticulum stress. In addition, the activation of the cGAS-STING pathway triggers chronic sterile inflammation, excessive activation of autophagy, senescence and apoptosis in related cells of cardiovascular system. These changes induced by cGAS-STING pathway might be implicated in the onset and deterioration of cardiometabolic disease. Therefore, the targeting intervention of cGAS-STING signaling pathway may emerge as a novel treatment for cardiometabolic disease.