As a typical representative of a permissioned blockchain system, Hyperledger Fabric has garnered substantial attention in recent years. Despite the application and promotion of Hyperledger Fabric in fields such as smart grid and smart healthcare, challenges persist with respect to its performance, especially its transaction latency. High latency always discourages Hyperledger Fabric from latency-sensitive applications. In this paper, we focus on the execution phase of the unique Execute-Order-Validate architecture of Hyperledger Fabric and conduct a comprehensive analysis of its endorsement policies. We summarize three theorems and give corresponding mathematical proofs based on the definition of the endorsement policy. To better analyze the generation of latency during the endorsement process, we further developed a theoretical model using queuing theory. Subsequently, we conducted multiple experiments on Hyperledger Fabric v2.0 for performance evaluation. With the experiment results, we discuss the reasons for the performance differences between different endorsement strategies and between equivalent logical expressions. Eventually, this paper offers some suggestions for endorsement policy selection which can provide a reference for the application of Hyperledger Fabric in the actual production environment.

In recent years, the diabetes population has grown younger. Therefore, it has become a key problem to make a timely and effective prediction of diabetes, especially given a single data source. Meanwhile, there are many data sources of diabetes patients collected around the world, and it is extremely important to integrate these heterogeneous data sources to accurately predict diabetes. For the different data sources used to predict diabetes, the predictors may be different. In other words, some special features exist only in certain data sources, which leads to the problem of missing values. Considering the uncertainty of the missing values within the fused dataset, multiple imputation and a method based on graph representation is used to impute the missing values within the fused dataset. The logistic regression model and stacking strategy are applied for diabetes training and prediction on the fused dataset. It is proved that the idea of combining heterogeneous datasets and imputing the missing values produced in the fusion process can effectively improve the performance of diabetes prediction. In addition, the proposed diabetes prediction method can be further extended to any scenarios where heterogeneous datasets with the same label types and different feature attributes exist.

As the integration of distributed energy into the power grid continues to rise, the significance of electricity transactions in promoting renewable energy consumption grows substantially. This necessitates the establishment of a robust electricity trading framework to facilitate reliable and trustworthy transactions for prosumers. This paper introduces an innovative blockchain‐based electricity trading framework. Within this framework, we present a decentralized collaborative model training approach aimed at predicting the power generation of distributed photovoltaic power stations. Simultaneously, we propose a graph‐based algorithm for efficiently matching producers and consumers. Our experiments, conducted on the Hyperledger Fabric blockchain platform using real‐world datasets, demonstrate enhanced prediction accuracy compared to existing models. The framework effectively handles transactions within one second, sustaining a sending rate of up to 200 transactions per second. The outcomes of this study not only surpass the performance of existing prediction models but also provide valuable insights for the development of trading frameworks in actual production environments. The proposed framework stands as a reference point for constructing reliable trading infrastructures, thereby contributing to the acceleration of green electricity trading.

This brief addresses phase locking and frequency synchronization in undirected networks of second-order Kuramoto oscillators with bounded intrinsic-frequency heterogeneity. Existing fixed-time designs for oscillator networks often rely on nonsmooth feedback and rarely quantify how regulation effort is distributed across nodes. We propose a smooth fully distributed controller that reinforces coupling and applies a two-regime fixed-time injection with hyperbolic-tangent smoothing, enabling practical fixed-time convergence without discontinuous switching. A sharp pseudoinverse-based tuning condition is derived to guarantee entry into and invariance of a phase-cohesive region, which yields uniform sector bounds for the sinusoidal coupling along the closed-loop trajectory. Within this cohesive regime, we establish a uniform practical fixed-time settling-time bound independent of initial conditions and characterize the tunable residual accuracy induced by smoothing and heterogeneity. We further derive an explicit tunable upper bound on the disparity of accumulated control energy across nodes, linking fast synchronization to balanced actuation burden. Numerical examples demonstrate multi-trajectory synchronization, cohesiveness preservation, and the resulting energy-disparity behavior.