Bitcoin Money Laundering Detection via Subgraph Contrastive Learning

The rapid development of cryptocurrencies has led to an increasing severity of money laundering activities. In recent years, leveraging graph neural networks for cryptocurrency fraud detection has yielded promising results. However, many existing methods predominantly focus on node classification, i.e., detecting individual illicit transactions, rather than uncovering behavioral pattern differences among money laundering groups. In this paper, we tackle the challenges presented by the organized, heterogeneous, and noisy nature of Bitcoin money laundering. We propose a novel subgraph-based contrastive learning algorithm for heterogeneous graphs, named Bit-CHetG, to perform money laundering group detection. Specifically, we employ predefined metapaths to construct the homogeneous subgraphs of wallet addresses and transaction records from the address–transaction heterogeneous graph, enhancing our ability to capture heterogeneity. Subsequently, we utilize graph neural networks to separately extract the topological embedding representations of transaction subgraphs and associated address representations of transaction nodes. Lastly, supervised contrastive learning is introduced to reduce the effect of noise, which pulls together the transaction subgraphs with the same class while pushing apart the subgraphs with different classes. By conducting experiments on two real-world datasets with homogeneous and heterogeneous graphs, the Micro F1 Score of our proposed Bit-CHetG is improved by at least 5% compared to others

Experiments and Numerical Simulation of Performances and Internal Flow for High-Speed Rescue Pump with Variable Speeds

The model pump is a high-speed, high-power pump designed to achieve rapid mine flooding rescue. This study conducted experiments to investigate pump performance curves, including head, efficiency, and power for the following six different rotation speeds: 3000, 3600, 4200, 4800, 5400, and 6000 rpm. Then, the numerical simulation method based on computational fluid dynamics commercial code Ansys was used to present the internal flow of the pump for the six different rotation speeds through steady and unsteady analyses. Results show that the numerical results agree well with experimental data. The designs of outlet and inlet angles of the impeller match each other well at high rotation speeds. The pressure pulsation coefficient Cp in the impeller and the diffuser channel remain constant at the same monitor point under different rotation speed conditions. The varying trend of the pressure-augmented coefficient ΔP indicates that, with the increase in rotation speed, the effect on pressure rise induced by the back part of the impeller channel is more evident than that by the front part. Also, the main frequency components of ΔP are concentrated on the region with low frequency. Moreover, the rotation speed has no significant effect on ΔP in the diffuser region. This study provides effective guidance and valuable reference for the design of high-speed, high-power pumps

High-Performance CVD Bernal-Stacked Bilayer Graphene Transistors for Amplifying and Mixing Signals at High Frequencies

Tunable bandgap can be induced in Bernal-stacked bilayer graphene by a perpendicularly electric displacement field. Here, we carry out a comprehensive study on the material synthesis of CVD Bernal-stacked bilayer graphene and devices for amplifying and mixing at high frequencies. The transistors show large output current density with excellent current saturation with high intrinsic voltage gain up to 77. Positive extrinsic forward power gain |<i>S</i>₂₁|² has been obtained up to 5.6 GHz as well as high conversion gain of −7 dB for the mixers. The conversion gain dependence on tunable on/off ratio of the transistors has also been discussed