Yong Chen

The recent convergence of blockchain-based federated learning (BCFL) offers enhanced data privacy, traceability, and ownership protection for decentralized data sharing. However, current research lacks consensus on accurate and fair data valuation methods for decentralized environments—a critical yet unresolved challenge. Existing Shapley value (SV)-based data valuation methods, while promising, rely on centralized frameworks ill-suited for decentralized environments and incur high computational costs. To address this, we propose a scalable dual-chain architecture: the main chain handles primary BCFL data-sharing operations, while a dedicated side-chain offloads data valuation tasks. For the side chain, we introduce the proof-of-lightweight-Shapley (PoLS) protocol, enabling decentralized SV computation with theoretical convergence guarantees. PoLS leverages a proof-of-work (PoW)-inspired mechanism to facilitate parallel and lightweight computation of SVs using Monte Carlo sampling. Furthermore, we develop an SV-based data aggregation protocol to validate PoLS effectiveness and enhance resilience against low-quality data attacks. Extensive experiments confirm that PoLS significantly reduces computational overhead while maintaining an average deviation as low as 2.3% from the true SV. Moreover, in adversarial scenarios, the PoLS-driven aggregation strategy improves model accuracy by 25.7% over baselines, thereby strengthening the robustness and fairness of the decentralized valuable data sharing.

Distributed flexible job shops are increasingly becoming the predominant production method in manufacturing due to their advantages in low-cost production and high customization. In practical production environments, jobs arrive randomly but follow a regular pattern. This paper addresses the scheduling problem of the Distributed Flexible Job shop Scheduling Problem (DFJSP) with random job arrivals. The DFJSP consists of three sub-problems: factory selection, job assignment, and operation sequencing. To tackle this issue, the DFJSP is modeled as a Markov Decision Process (MDP), and a multi-agent approach based on deep reinforcement learning (DRL) is proposed. This approach includes a Distribute Agent (DA) and a Sequence Agent (SA). For the MDP of the DA, we designed 12 state features, 5 candidate actions, and a reward based on the current state of production tardiness. The SA is configured with 7 state features, 6 candidate actions, and rewards that reflect delay conditions. A deep Q-network (DQN) framework that incorporates a linearly decreasing threshold probability was designed to effectively balance exploration and exploitation during the training phase. Comparative experiments conducted on randomly generated instances demonstrate the effectiveness of the DA when used both independently and in conjunction with the SA.

Control room operators encounter a substantial risk of mental fatigue, which can reduce their human reliability by diminishing concentration and responsiveness, leading to unsafe operations. There is value in detection of individuals’ mental fatigue status in the workplace. This study introduces a new method for mental fatigue detection (MFD) that combines computer vision and machine learning. Traditional methods for MFD typically rely on multi-dimensional data for fatigue analysis and detection, which can be challenging to apply in a real situation. The traditional methods such as the use of biological data, e.g., electrocardiograms, require operators to be in constant contact with sensors, while this study utilizes computer vision to collect facial data, and a machine learning model to assess fatigue states. The developed machine learning method consists both Deep Residual Network and Random Forest (DRN-RF). A comparison with existing MFD methods, including K Nearest Neighbors and Gradient Boosting Machine, has been carried out. The results show that the accuracy of the DRN-RF model reaches 94.2% and the deviation is 0.004. Evidently, the DRN-RF model demonstrates high accuracy and stability. Overall, the proposed method has the potential to contribute to improving the safety of process system operations, particularly in the aspect of human factor management.

The traditional agent-based model requires high computing power of the central processing unit. Thus, an improved agent-based model combined with the discrete event simulation method is proposed. The result of the equation-based Susceptible-Exposed-Infective-Asymptomatic-Recovered (SEIAR) model with the same parameter combination, which has been demonstrated to be effective, is used to verify the validity of this improved agent-based model. Additionally, an analysis based on simulation results of the Contact Tracing Measure (CTM), Location-Based Checking-Testing Measure (LCTM), Lockdown Measure (LM), Mobile Cabin Isolation and Hospital Measure (MCHM) is presented. The simulation results show that implementing long-term lockdown measures has the best effect on epidemic control. Moreover, according to the simulation results, we inferred that using only nonpharmaceutical epidemic prevention measures may result in a second outbreak of COVID-19 owing to the risk of asymptomatic transmission.

As the complexity of the real-world engineering problems increases, numerous efficient constraint-handling methods and optimization algorithms have emerged recently. However, the majority of the research consider the constraint-handling method and the optimization algorithm independently. In this paper, we propose a constraint-based mutation operator, in which the constraint violation and objective function are considered simultaneously. We define the pbest individuals as the best in top 5% constraint violators if all the individuals are infeasible. In this way, we could guide the population move towards the feasible region. Two real-world engineering applications are used to test the performance of the IεJADE. Compared with the state-of-the-art algorithms, the experimental results illustrate the effectiveness of the IεJADE algorithm, which also exhibits a fast convergence rate in terms of computation efficiency.

The K₃PO₄‐catalyzed cascade Michael addition/alkyne carbocyclization of acetylenedicarboxylate or propargylic ester and propargylamines is developed to synthesize tri‐, tetra‐, and fully substituted pyrroles.