Matematik Bölümü Yayın Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/413

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Author: search.filters.author.Memic, AdnanPublisher: search.filters.publisher.Hindawi Ltd

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  • Article
    Citation - WoS: 6
    Citation - Scopus: 7
    Raman Spectra of Nanodiamonds: New Treatment Procedure Directed for Improved Raman Signal Marker Detection
    (Hindawi Ltd, 2013) Baleanu, Dumitru; Povarova, Diana; Salah, Numan; Habib, Sami S.; Memic, Adnan; Nigmatullin, Raoul R.
    Detonation nanodiamonds (NDs) have shown to be promising agents in several industries, ranging from electronic to biomedical applications. These NDs are characterized by small particle size ranging from 3 to 6 nm, while having a reactive surface and a stable inert core. Nanodiamonds can exhibit novel intrinsic properties such as fluorescence, high refractive index, and unique Raman signal making them very attractive imaging agents. In this work, we used several nanodiamond preparations for Raman spectroscopic studies. We exposed these nanodiamonds to increasing temperature treatments at constant heating rates (425-575 degrees C) aiding graphite release. We wanted to correlate changes in the nanodiamond surface and properties with Raman signal which could be used as a detection marker. These observations would hold potential utility in biomedical imaging applications. First, the procedure of optimal linear smoothing was applied successfully to eliminate the high-frequency fluctuations and to extract the smoothed Raman spectra. After that we applied the secondary Fourier transform as the fitting function based on some significant set of frequencies. The remnant noise was described in terms of the beta-distribution function. We expect this data treatment to provide better results in biomolecule tracking using nanodiamond base Raman labeling.
  • Article
    Citation - WoS: 2
    Citation - Scopus: 2
    On Classification of Pdz Domains: a Computational Study
    (Hindawi Ltd, 2013) Memic, Adnan; Baleanu, Dumitru; Aftab, Wasim
    Our goal in this present study is to introduce new wavelet based methods for differentiating and classifying Class I and Class II PDZ domains and compare the resulting signals. PDZ domains represent one of the most common protein homology regions playing key roles in several diseases. To perform the classification, we developed two methods. The first of our methods was comparable to the standard wavelet approaches while the second one surpasses it in recognition accuracy. Our models exhibited interesting results, and we anticipate that it can be used as a computational technique to screen out the misfit candidates and to reduce the search space, while achieving high classification and accuracy.