Keyphrases are widely used as a brief summary of documents. Since manual assignment is time-consuming, various unsupervised ranking methods based on importance scores are proposed for keyphrase extraction. In practice, the keyphrases of a document should not only be statistically important in the document, but also have a good coverage of the document. Based on this observation, we propose an unsupervised method for keyphrase extraction. Firstly, the method finds exemplar terms by leveraging clustering techniques, which guarantees the document to be semantically covered by these exemplar terms. Then the keyphrases are extracted from the document using the exemplar terms. Our method outperforms sate-of-the-art graphbased ranking methods (TextRank) by 9.5% in F1-measure.
Thermoelectric technology can be another direct way to convert solar radiation into electricity, using the Seebeck effect. Herein, a prototype concentration solar thermoelectric generator (CTG) and a discrete numerical model for the evaluation of the whole system are presented. The model takes into account the temperature dependence of the thermoelectric material properties by dividing the thermoelectric leg into finite elements and is proved to be more accurate for calculation of the conversion efficiency of the thermoelectric modules when large temperature gradients occur in the CTG system. Based on the best available properties of various bulk thermoelectric materials reported in the literature, the best possible performance of the CTG system is predicted, and the CTG system design, including the selection of the concentration ratio and the cooling method for different thermoelectric materials, are discussed in detail.
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