Pristine and transition-metal-doped Mn3O4 nanocrystals shaped in octahedrons have been synthesized by hydrothermal reduction of potassium permanganate and characterized by SEM/TEM, X-ray diffraction, X-ray photoelectron spectroscopy, and electrochemical experiments. The results reveal that a multistep reduction process is taking place, and the introduction of doping ions causes a direct synthesis of single-phase Mn3O4 nanocrystals. To assess the properties of Mn3O4 nanocrystals for their use in supercapacitors, cyclic voltammetry and galvanostatic charging-discharging measurements are performed. The phase stability during cycling and charge-transfer behavior are greatly improved by doping with transition metal, and Cr-doped Mn3O4 nanocrystals exhibit a maximum specific capacitance of 272 F g(-1) at a current density of 0.5 A g(-1). These doped Mn3O4 nanocrystals could be a promising candidate material for high-capacity, low-cost, and environmentally friendly electrodes for supercapacitors. In addition, these results have verified the ability of doping to improve capacitive performances of spinel-structured transition-metal oxides.

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In recent years, with the rapid development of Internet technology, the number of credit card users has increased significantly. Subsequently, credit card fraud has caused a large amount of economic losses to individual users and related financial enterprises. At present, traditional machine learning methods (such as SVM, random forest, Markov model, etc.) have been widely studied in credit card fraud detection, but these methods are often have difficulty in demonstrating their effectiveness when faced with unknown attack patterns. In this paper, a new Unsupervised Attentional Anomaly Detection Network-based Credit Card Fraud Detection framework (UAAD-FDNet) is proposed. Among them, fraudulent transactions are regarded as abnormal samples, and autoencoders with Feature Attention and GANs are used to effectively separate them from massive transaction data. Extensive experimental results on Kaggle Credit Card Fraud Detection Dataset and IEEE-CIS Fraud Detection Dataset demonstrate that the proposed method outperforms existing fraud detection methods.

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