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Automatic keyphrase extraction techniques aim to extract quality keyphrases for higher level summarization of a document. Majority of the existing techniques are mainly domain-specific, which require application domain knowledge and employ higher order statistical methods, and computationally expensive and require large train data, which is rare for many applications. Overcoming these issues, this paper proposes a new unsupervised keyphrase extraction technique. The proposed unsupervised keyphrase extraction technique, named TeKET or Tree-based Keyphrase Extraction Technique, is a domain-independent technique that employs limited statistical knowledge and requires no train data. This technique also introduces a new variant of a binary tree, called KeyPhrase Extraction (KePhEx) tree, to extract final keyphrases from candidate keyphrases. In addition, a measure, called Cohesiveness Index or CI, is derived which denotes a given node's degree of cohesiveness with respect to the root. The CI is used in flexibly extracting final keyphrases from the KePhEx tree and is co-utilized in the ranking process. The effectiveness of the proposed technique and its domain and language independence are experimentally evaluated using available benchmark corpora, namely SemEval-2010 (a scientific articles dataset), Theses100 (a thesis dataset), and a German Research Article dataset, respectively. The acquired results are compared with other relevant unsupervised techniques belonging to both statistical and graph-based techniques. The obtained results demonstrate the improved performance of the proposed technique over other compared techniques in terms of precision, recall, and F1 scores.

The investigation of iron-deficiency anaemia in older patients is important but in order to detect 26 patients with colorectal cancer a year earlier, the investigation of approximately 5000 patients would be required--a detection rate of less than 1%.

We report on an exhaustive and systematic study about the photoluminescent properties of nanoporous anodic alumina membranes fabricated by the one-step anodization process under hard conditions in oxalic and malonic acids. This optical property is analysed as a function of several parameters (i.e. hard anodization voltage, pore diameter, membrane thickness, annealing temperature and acid electrolyte). This analysis makes it possible to tune the photoluminescent behaviour at will simply by modifying the structural characteristics of these membranes. This structural tuning ability is of special interest in such fields as optoelectronics, in which an accurate design of the basic nanostructures (e.g. microcavities, resonators, filters, supports, etc.) yields the control over their optical properties and, thus, upon the performance of the nanodevices derived from them (biosensors, interferometers, selective filters, etc.)

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A high-performance electrochemical sensing platform inspired by a functional ‘green’ electrochemical reduction pathway was developed to identify and detect circulating tumor DNA (ctDNA) of gastric carcinoma in peripheral blood.

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