@article{BassamArtmannHescheleretal.2011,
  author    = {Bassam, Rasha and Artmann, Gerhard and Hescheler, J{\"u}rgen and Graef, T. and Temiz Artmann, Ayseg{\"u}l and Porst, Dariusz and Linder, Peter and Kayser, Peter and Arinkin, Vladimir and Gossmann, Matthias and Digel, Ilya},
  title     = {Alterations in human hemoglobin structure related to red blood cell storage},
  year      = {2011},
  abstract  = {The importance of the availability of stored blood or blood cells, respectively, for urgent transfusion cannot be overestimated. Nowadays, blood storage becomes even more important since blood products are used for epidemiological studies, bio-technical research or banked for transfusion purposes. Thus blood samples must not only be processed, stored, and shipped to preserve their efficacy and safety, but also all parameters of storage must be recorded and reported for Quality Assurance. Therefore, blood banks and clinical research facilities are seeking more accurate, automated means for blood storage and blood processing.},
  subject      = {H{\"a}moglobin},
  language  = {en}
}
@inproceedings{ArinkinDigel2009,
  author    = {Arinkin, Vladimir and Digel, Ilya},
  title     = {Water bridge phenomenon : [abstract]},
  year      = {2009},
  abstract  = {One of interesting but not well known water properties is related to appearance of highly ordered structures in response to strong electrical field. In 1893 Sir William Armstrong placed a cotton thread between two wine glasses filled with chemically pure water. When high DC voltage was applied between the glasses, a connection consisting of water formed, producing a "water bridge"},
  subject      = {Hydrodynamik},
  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}
}