Phosphonate ligands as structure-directing components have been employed to construct four new high-nuclearity silver(I) sulfide-ethynide-thiolate clusters, in which silver(I) aggregates BuC≡C⊃Ag, BuC≡C⊃Ag, and 2BuC≡C⊃Ag are bridged by BuS ligands to engender respective silver(I) ethynide-thiolate clusters functioning as integral shell components, which are supported by phosphonate ligands. In each silver(I) sulfide-ethynide-thiolate cluster, a different encapsulated silver sulfide cluster serves as a core template.
Frequency prediction after a disturbance has received increasing research attention given its substantial value in providing a decision-making foundation in power system emergency control. With the advancing development of machine learning, analysis power systems with machine-learning methods has become completely different from traditional approaches. In this paper, an ensemble algorithm using cross-entropy as a combination strategy is presented to address the trade-off between prediction accuracy and calculation speed. The prediction difficulty caused by inadequate numbers of severe disturbance samples is also overcome by the ensemble model. In the proposed ensemble algorithm, base learners are selected following the principle of diversity, which guarantees the ensemble algorithm's accuracy. Cross-entropy is applied to evaluate the fitting performance of the base learners and to set the weight coefficient in the ensemble algorithm. Subsequently, an online prediction model based on the algorithm is established that integrates training, prediction and updating. In the Western System Coordinating Council 9-bus (WSCC 9) system and the Institute of Electrical and Electronics Engineers 39-bus (IEEE 39) system, the algorithm is shown to significantly improve the prediction accuracy in both sample-rich and sample-poor situations, verifying the effectiveness and superiority of the proposed ensemble algorithm.
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