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The safe operation of the metro power transformer directly relates to the safety and efficiency of the entire metro system. Through voiceprint technology, the sounds emitted by the transformer can be monitored in real-time, thereby achieving real-time monitoring of the transformer’s operational status. However, the environment surrounding power transformers is filled with various interfering sounds that intertwine with both the normal operational voiceprints and faulty voiceprints of the transformer, severely impacting the accuracy and reliability of voiceprint identification. Therefore, effective preprocessing steps are required to identify and separate the sound signals of transformer operation, which is a prerequisite for subsequent analysis.

This paper proposes an Adaptive Threshold Repeating Pattern Extraction Technique (REPET) algorithm to separate and denoise the transformer operation sound signals. By analyzing the Short-Time Fourier Transform (STFT) amplitude spectrum, the algorithm identifies and utilizes the repeating periodic structures within the signal to automatically adjust the threshold, effectively distinguishing and extracting stable background signals from transient foreground events. The REPET algorithm first calculates the autocorrelation matrix of the signal to determine the repeating period, then constructs a repeating segment model. Through comparison with the amplitude spectrum of the original signal, repeating patterns are extracted and a soft time-frequency mask is generated.

After adaptive thresholding processing, the target signal is separated. Experiments conducted on mixed sounds to separate background sounds from foreground sounds using this algorithm and comparing the results with those obtained using the FastICA algorithm demonstrate that the Adaptive Threshold REPET method achieves good separation effects.

A REPET method with adaptive threshold is proposed, which adopts the dynamic threshold adjustment mechanism, adaptively calculates the threshold for blind source separation and improves the adaptability and robustness of the algorithm to the statistical characteristics of the signal. It also lays the foundation for transformer fault detection based on acoustic fingerprinting.

The purpose of this study is to examine the impact of managerial short-termism on green innovation of firms and the moderating role of digital transformation of enterprises in the association between managerial short-termism and green innovation.

This study uses data from Chinese A-share listed companies from 2001 to 2021 and employ panel fixed model and moderating effect model to examine the impact of managerial short-termism on green innovation of firms and the moderating role of digital transformation of enterprises in the association between managerial short-termism and green innovation.

The findings of this study reveal that managerial short-termism exerts negative influence on green innovation. Digital transformation enables firms to reduce the adverse effect of managerial short-termism on green innovation because digital transformation enhances information processing ability and then improves internal corporate governance and analyst coverage. Moreover, the moderating role of digital transformation is more prominent for firms with lower internal corporate governance, for firms with less analyst coverage and for non-state-owned enterprises.

This paper intends to address the following two questions: what is the impact of managerial short-termism on green innovation and what is the role of digital transformation in the two variables’ association? By using data of Chinese A-share listed companies from 2001 to 2021 and developing two individual indexes to measure managerial short-termism and digital transformation, the authors empirically test these above two questions. The results of this study indicate that: First, drawn on time-oriented theory and upper echelon theory, managerial short-termism has an adverse effect on firms’ green innovation. Second, digital transformation enables firms to reduce the negative effect of managerial short-termism on green innovation. Furthermore, the moderating mechanism tests show that the corporate governance effects of digital transformation play a supervisory role that impels managers to reduce short-term investments and promote firms’ green R&D investments, which helps to reduce the negative effect of managerial short-termism on green innovation. Additionally, the heterogeneity checks show that the moderating role of digital transformation in the relation between managerial short-termism and green innovation is more prominent for firms with lower internal corporate governance, with less analyst coverage and for non-state-owned enterprises.

The stability maintenance system has played an essential role in maintaining social stability although it also has brought about social problems worthy of attention. Admittedly compensation-based stability maintenance policy can address the appeals of citizens whose rights are infringed and the dissolving effect in the provision of compensation can save the cost of stability maintenance but such stability maintenance system lacks equilibrium.

The establishment of a strict assessment system for stability maintenance performance can encourage the stability maintenance authorities to eliminate the “fuse effect” as much as possible and ensure the effective implementation of the stability maintenance system. However, the rigorous stability maintenance performance assessment also provides the possibility for profit-driven petitions.

Due to the continuous accumulation of social dissatisfaction and the lack of stability maintenance equilibrium in the implementation of the compensation-based stability maintenance policy, public governance will fall into a stability maintenance paradox of “greater instability resulting from stability maintenance”.

The provision of sufficient means for the people to protect their interest by implementing measures such as strengthening the rule of law mechanisms is the key to achieve long-term social stability.

China's economic transition is essentially the process of China's institutional changes. During the changes, the appearance of institutional innovation is not regular; instead, it is intermittent and random. The purpose of this paper is to show that the fitful appearance of institutional innovation is the root of China's economic growth and fluctuations.

This paper constructs a real business cycle (RBC) model introducing the institutional factor expressed in the quantitative form under the dynamic stochastic general equilibrium (DSGE) framework by measuring China's institutional changes quantitatively.

By comparing the characteristics of the actual economic data with those of the simulated economic data, we find that this RBC model can explain 94.44%, 66.07%, 23.46%, 21.03% and 15.45% of the cyclical fluctuations in output, investment, labor, consumption and capital, respectively.

The impulse response analysis finds that the institutional shocks have a relatively long duration, lasting about 30 years, and decline slowly over time, while technological shocks decline relatively fast, lasting approximately ten years.