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Physical illness is an important risk factor for suicide in rural residents of China. Efforts for suicide prevention are needed and should be integrated with national strategies of health care in rural China.

The Barratt Impulsiveness Scale-11 (BIS-11) is an important tool for measuring impulsivity in suicide research. This study aimed to assess psychometric characteristics of Chinese version of BIS-11 in suicides and controls of rural China. Data of 200 pairs of suicide cases and living controls were collected by psychological autopsy method. The Cronbach's alpha coefficients of BIS-11 were 0.936 for suicides, and 0.892 for living controls. Convergent validity analysis demonstrated a significantly positive correlation between the scores of BIS-11 with the scores of Beck Hopelessness Scale and Trait Anxiety Inventory. Confirmatory factor analyses indicated that the BIS-11 structure was basically suitable in rural China. With its high reliability, few items in BIS-11 may need a modification in order to further improve the construct validity of this instrument for suicide research in rural China.

PurposeThe purpose of this paper is to use artificial intelligence to evaluate the risks of urban power network planning.Design/methodology/approachA fuzzy Bayesian least squares support vector machine (LS_SVM) model is established in this paper, which can learn the risk information of urban power network planning through artificial intelligence and acquire expert knowledge for its risk evaluation. With the advantage of possessing learning analog simulation precision and speed, the proposed model can be effectively applied in conducting a risk evaluation of an urban network planning system. First, fuzzy theory is applied to quantify qualitative risk factors of the planning to determine the fuzzy comprehensive evaluation value of the risk factors. Then, Bayesian evidence framework is utilized in LS_SVM model parameter optimization to automatically adjust the LS_SVM regularization parameters and nuclear parameters to obtain the best parameter values. Based on this, a risk comprehensive evaluation of urban network planning based on artificial intelligence is established.FindingsThe fuzzy Bayesian LS_SVM model established in this paper is an effective artificial intelligence method for risk comprehensive evaluation in urban network planning through empirical study.Originality/valueThe paper breaks new ground in using artificial intelligence to evaluate urban power network planning risks.

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With the scale extending and the structure complex of urban power network gradually, establishing a rational network structure is significant to reliable and sufficient power supply. This paper collected and sorted out network structure of typical cities and analyzed the characteristics, functions and grid structure used mainly. According to hierarchical thought and the coordinated principle of transmission and distribution, the conception that urban network structure system can be constructed based on reliability of different layers can be described the overall pattern systematically. Then the merits, drawbacks and main applicable scope of typical grid structure were analyzed and evaluated on the condition of saturated load density. Finally, typical grid structures applied in different cities were recommended based on the saturated load density in future.