Ultrasmall metal nanoclusters (NCs) have attracted increasing attention due to their unique characteristics, such as ultrasmall size, fascinating physical and chemical properties, and good biocompatibility, which are desirable for biological and other applications. In recent years, multiple types of NCs have been developed, such as gold NCs, silver NCs, platinum NCs, and copper NCs. These NCs not only play important roles in making chemicals and materials, but some NCs are also used in medical research, showing good prospects due to their advantages. This review summarizes the recent advances in NCs research in the medical field, with particular emphasis on tumor treatment, antimicrobial applications, and bioimaging. Finally, the challenges and outlook of metal NCs in nanomedicine are briefly discussed.

In recent years, deep‐learning‐based fault detection and diagnosis (FDD) methods have received extensive attention. As we all know, different input forms have a great impact on the final performance. In this paper, three categories and seven representation methods are discussed: numeric representations, image mapping representations (radar chart mapping and Gramian angular summation field (GASF) mapping), and signal transforming representations (fast Fourier transform (FFT) and wavelet). The tests on the Tennessee Eastman process (TEP) dataset prove that the FFT method has achieved the best performance on average. Based on this, a general FDD integration framework is proposed to integrate multiple base learners together to make decisions by weighted voting or maximum voting. Finally, the comparison between our proposed method and other five typical models (FFT, a GASF and a multi‐scale neural network (GASF–MSNN), convolutional neural network (CNN), Long Short‐Term Memory (LSTM), and Support Vector Machine (SVM)) illustrates the effectiveness of our method for FDD on the TEP. The proposed integrated method provides an effective platform for deep‐learning‐based FDD.