Multimode is the characteristic of industrial manufacturing processes due to different production strategies and environments. For multimode process monitoring, it is a challenge to identify different steady modes and transition modes. In this paper, a k nearest neighbours (KNN)‐based density peaks clustering (DPC) method is applied to identify different modes. First, the local density of each sample, which is obtained with a KNN constraint and its minimum distance to the higher local density points are calculated as two indicators of the DPC algorithm to find the cluster centres of the training data. Then, the transition modes are identified by combining the moving window strategy and the DPC algorithm, where an index called the local density‐distance ratio (LDDR) is employed. Finally, the monitoring algorithm is used to detect the faults for each operation mode. The effectiveness and advantages of the proposed method are illustrated by a numerical example and a Tennessee Eastman (TE) benchmark process.

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The COVID-19 pandemic has led to an increase in users’ knowledge anxiety, which has been further intensified by the diversity of information platforms and the emphasis on digital personal branding. While previous research has examined the relationship between digital personal branding and negative emotions, little is known about the mechanisms behind negative reactions to digital personal branding from non-direct factors or users’ spontaneous negative emotions. To address this gap, this study draws on cognitive appraisal theory (CAT) and social identity theory (SIT) to explore the relationships between users’ knowledge anxiety, cognitive processing biases, and brand avoidance, and the impacts of herding behavior and attachment anxiety on these relationships. A sample of 530 consumers completed an online survey, and the data were analyzed using a partial least squares path model. The results revealed that user knowledge anxiety directly and indirectly influenced brand avoidance behavior through cognitive processing bias, and attachment anxiety moderated the path between cognitive processing bias and user knowledge anxiety. However, herding behavior was not found to be significant in online knowledge sharing communities.