Multiscale fluctuation dispersion entropy (MFDE) has been proposed to measure the dynamic features of complex signals recently. Compared with multiscale sample entropy (MSE) and multiscale fuzzy entropy (MFE), MFDE has higher calculation efficiency and better performance to extract fault features. However, when conducting multiscale analysis, as the scale factor increases, MFDE will become unstable. To solve this problem, refined composite multiscale fluctuation dispersion entropy (RCMFDE) is proposed and used to improve the stability of MFDE. And a new fault diagnosis method for hydraulic pumps using particle swarm optimization variational mode decomposition (PSO-VMD) and RCMFDE is proposed in this paper. Firstly, PSO-VMD is adopted to process the original vibration signals of hydraulic pumps, and the appropriate components are selected and reconstructed to get the denoised vibration signals. Then, RCMFDE is adopted to extract fault information. Finally, particle swarm optimization support vector machine (PSO-SVM) is adopted to distinguish different work states of hydraulic pumps. The experiments prove that the proposed method has higher fault recognition accuracy in comparison with MSE, MFE, and MFDE.

The theory of privacy calculus in terms of the trade-offs between benefits and risks is believed to explain people’s willingness to disclose private information online. However, the phenomenon of privacy paradox, referring to the preference-behavior inconsistency, misfits the risk–benefit analysis. The phenomenon of privacy paradox matters because it reflects an illusion of personal control over privacy choices. The anomaly of privacy paradox is perhaps attributed to cognitive heuristics and biases in making privacy decisions. We consider the stability-instability of privacy choices is better used to explain the underlying mechanisms of paradoxical relationship. A rebalanced trade-off, referring to the embeddedness of “bridging” and “bonding” social support in privacy calculus, is derived to develop the risk–benefit paradigms to explain the underlying mechanisms. In this study we address the underlying mechanisms of privacy choices in terms of self-disclosure and user resistance. To test the hypotheses (or mechanisms) of the research model, we developed the instrument by modifying previous scales. A general sample of 311 experienced Facebook users was collected via online questionnaire survey. From the empirical results, perceived benefits based on information support rather than emotion support can motivate self-disclosure willingness. In contrast, privacy risks rather than privacy concerns inhibit the willingness to disclose private information. The risk–benefit paradigms instead of the imbalanced trade-offs help to explain the instability of privacy choices where privacy calculus sticks with the stability view. Implications for the theory and practice of privacy choices are discussed accordingly.

The support vector machine (SVM) does not have a fixed parameter selection method and the manual selection of parameters is difficult to determine the validity, which affects the accuracy of recognition. simultaneously, The existing coarse-grained approach cannot effectively analyze the high-frequency components of time series. In view of the shortcomings of the above method, we put forward a new technique of rolling bearings for fault detection, which combines wavelet packet dispersion entropy (WPDE) and artificial fish swarm algorithm (AFSA) optimize support vector machines (AFSA-SVM). First of all, wavelet packet is devoted to decompose the original vibration signal into components of different frequency bands. Secondly, the dispersion entropy (DE) are calculated for each of the obtained frequency band components to acquire more comprehensive and complete fault information. Afterward, Input feature samples into the SVM model for training, and AFSA is used to optimize the parameters of SVM to obtain the optimal value so as to establish the best classification model. Finally, the prepared test set is input into AFSA-SVM for fault classification. The achievement of bearing detection experiments show that this approach can accurately and quickly identify fault types.

PurposeIn the context of multi-sided platforms (MSPs), the authors address the evaluation of search- and experience-based information and the effect on different components of user satisfaction.Design/methodology/approachThe instrument was developed by either modifying previous measures or developing new scales. The authors collected the sample of experienced 300 TripAdvisor users via online questionnaire survey of a customer panel. The structural equation modeling (SEM) package (AMOS) with the maximum likelihood estimation method was used to test the sample data.FindingsAttitudes toward search-based information can foster user satisfaction with information interaction rather than user satisfaction with social interaction. Attitudes toward experience-based information can foster user satisfaction with information interaction and user satisfaction with social interaction. The motivation for information interaction is stronger than the motivation for social interaction to enhance user satisfaction with information quality.Research limitations/implicationsThe distinction between search- and experience-based information provides different route messages to develop the attitude-driven framework of platform-enabled interactions.Practical implicationsThe support for platform-enabled interactions to enhance the motivation for information and social interactions should be aligned with the evaluation of information quality.Originality/valueThe satisfaction-driven framework has been widely used to examine the post-adoption of information technologies (IT). In contrast, the attitude-driven framework was less examined in the literature. The authors develop a research model based on the attitude-driven framework to examine the platform-enabled interactions that can foster repeated intention.

The rotating machinery possesses complicated structures and various fault types, whose health state monitoring is essential for the normal production and operation of the equipment. To distinguish different working states of rotating machinery efficiently and accurately, this paper presents a novel approach for extracting fault features of vibration signals called modified hierarchical multiscale dispersion entropy (MHMDE). And on this basis, an innovative approach for fault diagnosis of rotating machinery based on MHMDE, multi-cluster feature selection (MCFS) and particle swarm optimization kernel extreme learning machine (PSO-KELM) is developed. Firstly, MHMDE is employed to extract the high-dimensional fault features of rotating machinery. This approach can effectively overcome the shortcomings that multi-scale entropy only focuses on the information in the low-frequency components but discards the high-frequency information, as well as the significant dropping of efficiency if the number of hierarchical layers of hierarchical entropy is large. Then MCFS is employed to screen the sensitive features from the high-dimensional fault features. Finally, the sensitive feature vectors are input into the PSO-KELM-based fault classifier to complete the rotating machinery fault diagnosis. It is proved that the presented approach can effectively identify different fault states of rotating machinery through three typical examples. Meanwhile, the presented approach is compared with multi-scale dispersion entropy (MDE) and hierarchical dispersion entropy (HDE), etc. The results show that the presented approach possesses more superior performance.