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A series of single-phase white-light-emitting phosphors, Eu 2+ -activated Ba 3 GdNa(PO 4 ) 3 F phosphors were synthesized by solid-state reactions. The crystal structure of Ba 3 GdNa(PO 4 ) 3 F was been identified by Rietveld refinement of X-ray diffraction pattern. The Eu 2+ -activated Ba 3 GdNa(PO 4 ) 3 F phosphors exhibit broad excitation spectra from 250 to 420 nm, which matched well with the n-UV LED chips. Under the excitation of 365 nm, the emission spectrum almost covered the entire visible region including two emission bands peaked at 472 nm and 640 nm. Three different Eu 2+ emission centers in Ba 3 GdNa(PO 4 ) 3 F:Eu 2+ phosphor were confirmed by their fluorescence decay lifetimes. The optimal concentration of Eu 2+ in Ba 3 GdNa(PO 4 ) 3 F: x Eu 2+ was 3 mol% and the corresponding concentration quenching mechanism was verified to be exchange coupling interaction. Furthermore, the white light-emitting diode fabricated with Ba 3 GdNa(PO 4 ) 3 F:0.05Eu 2+ phosphor and a 370 nm UV chip has a CIE of (0.3267, 0.2976) with a color-rendering index of 78.4 at the CCT of 5287 K.

With the integration of cloud computing and big data, it is difficult for the masses to discover valuable service from big data. The understanding of historical data and streaming data is fundamental to the value discovery, and the construction of topic knowledge is essential to the understanding of big data. This paper proposes an approach for the construction of topic knowledge based on ontology meta-modeling, and the approach follows three stages: classification, clustering and integration. Furthermore, the realization of the three stages is based on support vector machine, probability computing, and ontology meta-modeling. Finally, experiments on scientific papers of service computing were conducted in order to get the recommended reviewers. The results of the experiments demonstrate the effectiveness of the approach. In conclusion, the approach provides a solution for the value discovery from big data.

The deepening adoption of Web2.0 technology causes more and more users to publish reviews on the web about products, services, brands or business, and the online customer reviews (OCR) greatly influence customers' purchasing decisions and corporate reputations. Therefore, it has significant value for enterprises to identify the opinion leaders of OCR. This study proposes a RFMS model to measure the influential power of OCR publisher combining RFM model and automatically measuring method of sentiment words, then identify opinion leaders by applying artificial neural network, finally assess the validity of identifying results based on the degree of centrality. This research analysis the online reviews of dianping.com, and make a data verification of the proposed method. Results show that the proposed method in this paper can accurately identify opinion leaders.

The development of internet and information technology provides customers a new opportunity to share their shopping experience online. Online customer reviews (OCR) have a significant value on purchasing decisions and corporate reputation management. However, how to aggregate and utilize OCR is a big challenge. This paper explores the OCR processing and proposes an OCR processing model by introducing relative theories from three-dimensional perspectives of demanding, ordering and centralization. Moreover, we study on the formalization of the three-dimensional OCR processing model. Case analysis shows that the processing model proposed in this paper can provide practical ideas and methods for further improvement of OCR processing.

Software has become a complex piece of work by the collective efforts of many. And it is often hard to predict what the final outcome will be. This transition poses new challenge to the Software Engineering (SE) community. By employing methods from the study of complex network, we investigate the Object Oriented (OO) software metrics from a different perspective. We incorporate the Weighted Methods per Class (WMC) metric into our definition of the Weighted OO Software Coupling Network as the node weight. Empirical results from four open source OO software demonstrate power law distribution of weight and a clear correlation between the weight and the out degree. According to its definition, it suggests uneven distribution of function among classes and a close correlation between the functionality of a class and the number of classes it depending on. Further experiment shows similar distribution also exists between average LCOM and WMC as well as out degree. These discoveries will help uncover the underlying mechanisms of software evolution and will be useful for SE to cope with the emerged complexity in software as well as efficient test cases design.

This research examines the mechanism of consumer responses to CSR under multiindustry context in China. Data is collected by a three-industry comparative survey. The empirical results show that: 1) Two positive influences of consumers’ perceiving CSR performance on their corporate associations and product associations are respectively partially and fully mediated by consumers’ CSR trust; 2) Product category is a moderator of the relationship between consumers’ CSR trust and product association, and the positive path from product association to purchase intention. This study could help managers to understand how consumers think about CSR, and provides them strategic guidance to implement CSR programme in China.