@article{Tran2014,
  author    = {Tran, Duc Hung},
  title     = {Multiple corporate governance attributes and the cost of capital - Evidence from Germany},
  series = {The British Accounting Review},
  volume    = {46},
  journal   = {The British Accounting Review},
  number    = {2},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0890-8389},
  doi       = {https://doi.org/10.1016/j.bar.2014.02.003},
  pages     = {179 -- 197},
  year      = {2014},
  abstract  = {This paper investigates the extent to which corporate governance affects the cost of debt and equity capital of German exchange-listed companies. I examine corporate governance along three dimensions: financial information quality, ownership structure and board structure. The results suggest that firms with high levels of financial transparency and bonus compensations face lower cost of equity. In addition, block ownership is negatively related to firms' cost of equity when the blockholders are other firms, managers or founding-family members. Consistent with the conjecture that agency costs increase with firm size, I find significant cost of debt effects only in the largest German companies. Here, the creditors demand lower cost of debt from firms with block ownerships held by corporations or banks. My findings demonstrate that a uniform set of governance attributes is unlikely to satisfy suppliers of debt and equity capital equally.},
  language  = {en}
}
@article{YangKriechbaumerAlbrachtetal.2014,
  author    = {Yang, Peng-Fei and Kriechbaumer, Andreas and Albracht, Kirsten and Sanno, Maximilian and Ganse, Bergita and Koy, Timmo and Shang, Peng and br{\"u}ggemann, Gert-Peter and M{\"u}ller, Lars Peter and Rittweger, J{\"o}rn},
  title     = {A novel optical approach for assessing in vivo bone segment deformation and its application in muscle-bone relationship studies in humans},
  series = {Journal of Orthopaedic Translation},
  volume    = {2},
  journal   = {Journal of Orthopaedic Translation},
  number    = {4},
  publisher = {Elsevier},
  address   = {Singapore},
  issn      = {2214-0328},
  doi       = {10.1016/j.jot.2014.07.078},
  pages     = {238 -- 238},
  year      = {2014},
  language  = {en}
}
@article{ArinkinDigelPorstetal.2014,
  author    = {Arinkin, Vladimir and Digel, Ilya and Porst, Dariusz and Temiz Artmann, Ayseg{\"u}l and Artmann, Gerhard},
  title     = {Phenotyping date palm varieties via leaflet cross-sectional imaging and artificial neural network application},
  series = {BMC bioinformatics},
  volume    = {15},
  journal   = {BMC bioinformatics},
  number    = {55},
  issn      = {1471-2105},
  doi       = {10.1186/1471-2105-15-55},
  pages     = {1 -- 8},
  year      = {2014},
  abstract  = {Background True date palms (Phoenix dactylifera L.) are impressive trees and have served as an indispensable source of food for mankind in tropical and subtropical countries for centuries. The aim of this study is to differentiate date palm tree varieties by analysing leaflet cross sections with technical/optical methods and artificial neural networks (ANN). Results Fluorescence microscopy images of leaflet cross sections have been taken from a set of five date palm tree cultivars (Hewlat al Jouf, Khlas, Nabot Soltan, Shishi, Um Raheem). After features extraction from images, the obtained data have been fed in a multilayer perceptron ANN with backpropagation learning algorithm. Conclusions Overall, an accurate result in prediction and differentiation of date palm tree cultivars was achieved with average prediction in tenfold cross-validation is 89.1\% and reached 100\% in one of the best ANN.},
  language  = {en}
}