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The aim is to apply pseudo‐random correlation method to detect very weak electrical signals because of various natural and artificial electron‐magnetic interferences in electrical prospecting.

Electrical prospecting is an important method of geophysical exploration and the electrical prospecting instruments are required to detect very weak electrical signals against strong interferences. Recently, pseudo‐random correlation coding has been widely applied in telecommunications and measurement and test systems to improve the signal noise ratio with great success, but has not been used in electric prospecting. This paper theoretically investigated the application model of pseudo‐random correlation techniques in electrical prospecting.

The model of pseudo‐random correlation techniques in electrical prospecting, including its principle, detailed protocol and parameter selection, is established.

With the continuing improvement in the capacity of electrical prospecting transmitters, the pseudo‐random correlation method will be widely used in electrical prospecting.

The pseudo‐random correlation techniques is originally investigated for its application in electrical prospecting. This paper is aimed at researchers and engineers in geophysical exploration.

This paper examines diverging views on the Chongqing model, the policy experiment led by Bo Xilai from 2007 to 2012 that was famous for its “red songs” and the campaign against organized crime. It has impressed both the supporters of socialist identity of China and the supporters of liberal identity and led to an intense debate concerning China’s path of development. This paper attempts to discuss and clarify to what extent the Chongqing model represented a genuine socialist experiment and the implications of the model for China’s future.

This study aims to clarify influencing factors of overcapacity of new energy enterprises in China and accurately predict whether these enterprises have overcapacity.

Based on relevant data including the experience and evidence from the capital market in China, the research establishes a generic univariate selection-comparative machine learning model to study relevant factors that affect overcapacity of new energy enterprises from five dimensions. These include the governmental intervention, market demand, corporate finance, corporate governance and corporate decision. Moreover, the bridging approach is used to strengthen findings from quantitative studies via the results from qualitative studies.

The authors' results show that the overcapacity of new energy enterprises in China is brought out by the combined effect of governmental intervention corporate governance and corporate decision. Governmental interventions increase the overcapacity risk of new energy enterprises mainly by distorting investment behaviors of enterprises. Corporate decision and corporate governance factors affect the overcapacity mainly by regulating the degree of overconfidence of the management team and the agency cost. Among the eight comparable integrated models, generic univariate selection-bagging exhibits the optimal comprehensive generalization performance and its area under the receiver operating characteristic curve Area under curve (AUC) accuracy precision and recall are 0.719, 0.960, 0.975 and 0.983, respectively.

The proposed integrated model analyzes causes and predicts presence of overcapacity of new energy enterprises to help governments to formulate appropriate strategies to deal with overcapacity and new energy enterprises to optimize resource allocation. Ten main features which affect the overcapacity of new energy enterprises in China are identified through generic univariate selection model. Through the bridging approach, the impact of the main features on the overcapacity of new energy enterprises and the mechanism of the influence are analyzed.