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Compared with the point prediction of the wind power, the interval prediction provides more information to assist power dispatching and optimize quotation strategies in power trading. To improve the accuracy of the prediction intervals, this paper proposes a novel model based on the lower upper bound evaluation (LUBE) by the gate recurrent unit (GRU) network. First, the Pearson correlation coefficient is selected to screen out the variables related to wind power, which can simplify the input variables of the prediction model while preserving the feature information as much as possible. Afterwards, with the consideration of the prediction interval relative deviation (PIRD), the improved objective function is introduced. Based on the GRU network, the prediction intervals of the wind power can be obtained to minimize the new objective function. Finally, by choosing several mainstream neural networks, experiments are conducted towards a certain wind farm in China. The results show that the proposed model has a significant improvement in both prediction interval width and PIRD under the given prediction interval coverage probability (PICP). [ABSTRACT FROM AUTHOR]

Based on the relevant data of construction waste (CW) in the People's Republic of China (PRC) from 2010 to 2018, this study applied K-means clustering algorithm and grey prediction methods to systematically investigate the spatiotemporal characteristic distribution and provincial clustering of CW in the PRC, and predicted the annual output of CW in the next five years from the scientific perspective. Results showed that the annual output of CW in the PRC displayed an overall trend of "rising first and then falling" and "being high in the middle east and low in the northwest," and the areas with obvious agglomeration gradually spread from the west to the middle and eastern regions. The law of development was consistent with the goals of the Chinese government to promulgate urban agglomeration development policies, prefabricated building encouragement policies, and CW management regulations. In the next five years, the annual output of CW in the PRC will increase by a small margin. Thus, all aspects of CW resource management should be conducted in a planned and step-by-step manner. [ABSTRACT FROM AUTHOR]

Why would dictators purge members of their ruling coalition? Some argue that doing so can minimize the risk to dictators' political survival, while others contend that dictators who mount purges are motivated by the desire to share resources with fewer allies. In this study, we analyze an original dataset, compiled from biographical data on the subordinates of the founding emperors of seven ancient Chinese imperial dynasties. Analyzing the data with competing risks models, we find that military experience is a strong predictor of political purges. Emperors were less likely to execute officers who had fought in more battles, but more likely to execute commanders, especially those who had established military credentials prior to the founding of an empire. In addition, the incidence of political purges heightened toward the end of an emperor's life, which implies that the founding emperors were concerned about the security threats against their designated successors. Potential challengers came not only from the military, but also from the aristocracy. Indeed, we find that the blood relatives of the emperors were more likely to experience a mild form of purges: deprivation of titles. These findings suggest that dictators are more likely to use purges to reduce existential threats. [ABSTRACT FROM AUTHOR]