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The emergence of numerous consensus algorithms for distributed systems has resulted from the swift advancement of blockchain and its related technologies. Consensus algorithms play a key role in decentralized distributed systems, because all nodes in the system need to reach a consensus on requests or commands through consensus algorithms. In a distributed system where nodes work together to reach consensus, there may be Byzantine nodes present. The emergence of Byzantine nodes will affect the consensus of nodes in the distributed system. Therefore, tolerating Byzantine nodes in a distributed system and then reaching a consensus is an essential function of a consensus algorithm. So far, many Byzantine fault-tolerant (BFT) consensus algorithms have emerged, and there are correspondingly many methods to improve the performance of these algorithms. In order to allow researchers to have a clearer understanding of the existing methods, this paper systematically investigated and studied the research progress of the current Byzantine fault-tolerant consensus algorithm. The scope of the research ranged from the classic Byzantine consensus algorithm to some of the latest Byzantine consensus algorithms. The articles were classified according to the methods used to improve the Byzantine consensus algorithm. Through classification and centralized analysis and discussion, we achieved a clearer understanding of the development of Byzantine consensus algorithms and, at the same time, clarified the advantages and disadvantages of this type of method and the latest research progress using this method. At the end of this article, an in-depth discussion and analysis is also presented. By analyzing the impact of the use of these methods on the performance of the BFT consensus algorithm, it is proposed that future research can be improved. [ABSTRACT FROM AUTHOR]

Introduction: The triglyceride glucose index (TyG) is associated with cardiovascular diseases; however, its association with stroke remains unclear. This study aimed to elucidate this relationship by examining two extensive cohort studies using two-sample Mendelian randomization (MR)., Methods: Using data from the 1999-2018 National Health and Nutrition Examination Survey (NHANES) and the Medical Information Mart for Intensive Care (MIMIC)-IV, the correlation between TyG (continuous and quartile) and stroke was examined using multivariate Cox regression models and sensitivity analyses. Two-sample MR was employed to establish causality between TyG and stroke using the inverse variance weighting method. Genome-wide association study catalog queries were performed for single nucleotide polymorphism-mapped genes, and the STRING platform used to assess protein interactions. Functional annotation and enrichment analyses were also conducted., Results: From the NHANES and MIMIC-IV cohorts, we included 740 and 589 participants with stroke, respectively. After adjusting for covariates, TyG was linearly associated with the risk of stroke death (NHANES: hazard ratio [HR] 0.64, 95% confidence interval [CI]: 0.41-0.99, P=0.047; Q3 vs. Q1, HR 0.62, 95%CI: 0.40-0.96, P=0.033; MIMIC-IV: HR 0.46, 95%CI: 0.27-0.80, P=0.006; Q3 vs. Q1, HR 0.32, 95%CI: 0.12-0.86; Q4 vs. Q1, HR 0.30, 95%CI: 0.10-0.89, P=0.030, P for trend=0.017). Two-sample MR analysis showed genetic prediction supported a causal association between a higher TyG and a reduced risk of stroke (odds ratio 0.711, 95%CI: 0.641-0.788, P=7.64e-11)., Conclusions: TyG was causally associated with a reduced risk of stroke. TyG is a critical factor for stroke risk management., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)