With the rapid development of the capital market, financial fraud cases are becoming increasingly common. The evolving fraud strategies pose significant threats to financial regulation, market order, and the interests of ordinary investors. In order to combine the generalization performance of different machine learning methods and improve the effectiveness of financial fraud prediction, this paper proposes a novel financial fraud prediction framework based on stacking ensemble learning. This framework, based on data from listed companies, comprehensively considers financial ratio indicators and non-financial indicators. It uses the stacking ensemble technique to integrate numerous base models of machine learning algorithms for predicting financial fraud. Furthermore, the proposed framework has high versatility and is suitable for various tasks related to financial fraud prediction, addressing the problem of model selection difficulties in previous research due to different scenarios and data. We also conducted case studies on specific companies and industries, confirming the significant interpretability and practical applicability of the proposed framework. The results show that the recall rate and Area Under Curve (AUC) of our framework reached 0.8246 and 0.8146, respectively, surpassing mainstream machine learning models such as XGBoost and LightGBM in existing studies. This research study is of great significance for predicting the increasing number of financial fraud cases, providing a reliable tool for financial regulatory institutions and investors.

The relationships within a supply chain are crucial for analyzing business transactions and can reveal significant patterns in disclosed financial data. These relationships also aid in the assessment and detection of financial fraud. Recent studies employing graph neural networks (GNNs) have demonstrated enhanced detection capabilities by integrating corporate financial features with supply chain relationships, surpassing traditional methods that rely solely on financial features. However, these studies face notable limitations: (1) they do not model enterprise associations across consecutive years, hindering the detection of long-term financial fraud, and (2) they lack efficacy in interpretive analyses of supply chain relationships to uncover patterns of fraud or risk transfer. To address these gaps, this paper introduces an interpretable and efficient Heterogeneous Graph Convolutional Network (ieHGCN) designed to analyze supply chain knowledge graphs. It also extends the model’s learning scope to multi-year financial data for detecting fraud. The experimental results indicate that our method, offering both extensibility and interpretability, significantly outperforms existing machine learning and GNN approaches in continuous multi-year fraud detection, achieving the highest AUC of 0.7498, a 3.8% improvement over the leading method. Furthermore, meta-path analysis reveals that companies sharing the same supplier exhibit correlated financial fraud risks and that fraud can propagate through the supply chain, providing novel insights into anti-fraud and risk management strategies through enhanced interpretability.

Space applications face severe challenges from soft errors caused by cosmic rays. Soft errors can change the storage state of memories used in electronic system, leading to system failure. To avoid the system corruption, error correction codes (ECCs) as the general mitigation strategy in system level are utilized to eliminate the soft error influence. As the feature size goes down, more memory cells are integrated in the energy deposited range of radiation particles and MBU becomes the main error patterns. In this paper, we propose a new method to design 5-bit burst error correction code against more complex burst error. To achieve that, a technique to design a code with unequal correction ability and a customized interleaving plan combined with the proposed code is presented. The experiment result implies that this method is efficient for MBUs mitigation and a potential option for system designers.