Reentrant phase transitions and triple points of topological AdS black holes in Born-Infeld-massive gravity

Motivated by recent developments of black hole thermodynamics in de Rham, Gabadadze and Tolley(dRGT) massive gravity, we study the critical behaviors of four-dimensional topological Anti-de Sitter(AdS) black holes in the presence of Born-Infeld nonlinear electrodynamics by treating the cosmological constant as pressure and the corresponding conjugate quantity is interpreted as thermodynamic volume. It shows that besides the Van der Waals-like SBH/LBH phase transitions appears, the so-called reentrant phase transitions (RPTs) are also observed when the coupling coefficients $c_i m^2$ of massive potential and Born-Infeld parameter $b$ satisfy some certain conditions.Comment: arXiv admin note: text overlap with arXiv:1612.08056; text overlap with arXiv:1402.2837, arXiv:1306.5756 by other autho

The Selection Model for Compound or Portfolio Relationships Oriented in Supply Chain

As the environment changed, the inter-organizat ional in Supply chain has been transferred fro m simple relations to complex relations “Comp ound or Portfolio Relationships”. The main pur pose of this research is to integrate external/int ernal resource and maintain flexible volatility o f inter-organization for helping organizations/fir ms could increase the competitive advantage fo r them. To survey the current researches which discuss inter-organization in supply chain; it c ould be found that most literatures are focused on each simple relationship or portfolio relatio nship about their types and features. Our resear ch uses multiple relative theory and interviews to perform the research. To develop theory mo del and analyse the nature of relations about c ompound relationships oriented and portfolio rel ationships oriented. The theoretical framework model concerns the influence of the difference selection factors between inter-organizational in supply chain. We hope this research will cont ribute to further studies and provide some sugg estions for implementing management of the rel ationship between supply chains

CpGPAP: CpG island predictor analysis platform

<p>Abstract</p> <p>Background</p> <p>Genomic islands play an important role in medical, methylation and biological studies. To explore the region, we propose a CpG islands prediction analysis platform for genome sequence exploration (CpGPAP).</p> <p>Results</p> <p>CpGPAP is a web-based application that provides a user-friendly interface for predicting CpG islands in genome sequences or in user input sequences. The prediction algorithms supported in CpGPAP include complementary particle swarm optimization (CPSO), a complementary genetic algorithm (CGA) and other methods (CpGPlot, CpGProD and CpGIS) found in the literature. The CpGPAP platform is easy to use and has three main features (1) selection of the prediction algorithm; (2) graphic visualization of results; and (3) application of related tools and dataset downloads. These features allow the user to easily view CpG island results and download the relevant island data. CpGPAP is freely available at <url>http://bio.kuas.edu.tw/CpGPAP/</url>.</p> <p>Conclusions</p> <p>The platform's supported algorithms (CPSO and CGA) provide a higher sensitivity and a higher correlation coefficient when compared to CpGPlot, CpGProD, CpGIS, and CpGcluster over an entire chromosome.</p