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Background: Physical illness is linked with an increased risk of suicide; however, evidence from China is limited. Aims: To assess the influence of physical illness on risk of suicide among rural residents of China, and to examine the differences in the characteristics of people completing suicide with physical illness from those without physical illness. Method: In all, 200 suicide cases and 200 control subjects, 1:1 pair-matched on sex and age, were included from 25 townships of three randomly selected counties in Shandong Province, China. One informant for each suicide or control subject was interviewed to collect data on the physical health condition and psychological and sociodemographic status. Results: The prevalence of physical illness in suicide cases (63.0%) was significantly higher than that in paired controls (41.0%; χ2 = 19.39, p < .001). Compared with suicide cases without physical illness, people who were physically ill and completed suicide were generally older, less educated, had lower family income, and reported a mental disorder less often. Physical illness denoted a significant risk factor for suicide with an associated odds ratio of 3.23 (95% CI: 1.85–5.62) after adjusted for important covariates. The elevated risk of suicide increased progressively with the number of comorbid illnesses. Cancer, stroke, and a group of illnesses comprising dementia, hemiplegia, and encephalatrophy had a particularly strong effect among the commonly reported diagnoses in this study population. Conclusion: 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.

The practice of urban power network planning in China is facing more and more complex risks. The nature of the risks themselves is unclear and uncertain. In order to ensure that urban power networks are planned in a scientific manner, a reasonable risk assessment needs to be performed. Based on extension analysis and the matter-element extension theory, a model of risk evaluation designed to be applied to urban power network planning is put forward in this paper. This model has the advantage that it conveniently quantifies the qualitative indices. At the same time, it also overcomes the main limitation of the matter-element method, namely, that if the risk index exceeds the risk grading standard, it can not be calculated using the correlation function. Finally, taking a specific urban power network as an example, the empirical analysis results show that this model is credible and practical.

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.

With the development of the urban social economy in China, the uncertain factors faced by urban network planners and their complexity are continuously increasing. In order to promote the scientific nature of urban network planning in China, the risks of urban network planning need to be identified and analysed systemically. Questionnaires about the risks of urban network planning in China were designed from the perspectives of policy, technology, economics and management. The membership degree of possibility, loss value and expected loss which affect the risks of the urban network planning in China were determined, based on the theory of fuzzy membership through statistical investigation and analysis. Key factors that should be considered in the future were determined. Those factors mainly include whether the government supports the urban power network plan, increases in the cost of levy land and immigration, grid project quality risk management, the deviation of load forecasting and so on. The method improves the comprehensiveness and objectivity of risk identification and provides a decision foundation for the risk aversion of urban power network planning in the developing countries.