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    A strain energy function for rubber-like materials

    , Article Constitutive Models for Rubber VIII - Proceedings of the 8th European Conference on Constitutive Models for Rubbers, ECCMR 2013 ; 2013 , Pages 205-210 ; 9781138000728 (ISBN) Khajehsaeid, H ; Naghdabadi, R ; Arghavani, J ; Sharif University of Technology
    Hyperelastic behavior of isotropic incompressible rubbers are studied to develop a strain energy function. The proposed function includes only three material parameters which are related to physical properties of the material molecular network. Furthermore, the model benefits from well suitting in all ranges of stretch as well as possessing the property of deformation mode independency. This reduces the required number of experimental tests for parameter calibration. Results of the model are compared with results of Mooney-Rivlin, Arruda-Boyce, Gent and Gao models as well as the experimental data  

    Physical layer network coding in molecular two-way relay networks

    , Article IWCIT 2016 - Iran Workshop on Communication and Information Theory, 3 May 2016 through 4 May 2016 ; 2016 ; 9781509019229 (ISBN) Farahnak Ghazani, M ; Aminian, G ; Mirmohseni, M ; Gohari, A ; Nasiri Kenari, M ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2016
    In traditional wireless networks, physical layer network coding (PNC) exploits the inverse additivity property of broadcast signals in the medium, an idea which is not directly applicable to molecular networks. In this paper, to make the natural XOR operation possible at the physical layer, we exploit the reaction of different molecule types and we propose a PNC scheme in diffusion-based molecular relay networks. For comparison, we consider the straightforward network coding (SNC) scheme in molecular networks. Assuming the ligand-receptor binding process at the receivers, we investigate the error performance of both schemes. It is observed that in addition to the simplicity of the proposed... 

    Influences of polymer-surfactant interaction on the drop formation process: an experimental study

    , Article Langmuir ; Volume 37, Issue 3 , 2021 , Pages 1025-1036 ; 07437463 (ISSN) Dastyar, P ; Salehi, M. S ; Firoozabadi, B ; Afshin, H ; Sharif University of Technology
    American Chemical Society  2021
    The interaction between polymer and surfactant molecules affects the physical properties of liquids, which could be of great importance in an abundance of processes related to drop formation. Polymer and surfactant concentration is a factor that dramatically impacts the shape of molecular networks formed in the fluid bulk and the characteristics of a forming drop. In this study, the deformation and detachment of aqueous carboxymethyl cellulose (CMC) solutions' drops containing different concentrations of sodium dodecyl sulfate (SDS) are studied experimentally. Our purpose is to determine the effects of CMC and SDS concentrations on the parameters related to the formation process, including... 

    Inferring causal molecular networks: Empirical assessment through a community-based effort

    , Article Nature Methods ; Volume 13, Issue 4 , 2016 , Pages 310-322 ; 15487091 (ISSN) Hill, S. M ; Heiser, L.M ; Cokelaer, T ; Linger, M ; Nesser, N. K ; Carlin, D. E ; Zhang, Y ; Sokolov, A ; Paull, E. O ; Wong, C. K ; Graim, K ; Bivol, A ; Wang, H ; Zhu, F ; Afsari, B ; Danilova, L. V ; Favorov, A. V ; Lee, W. S ; Taylor, D ; Hu, C. W ; Long, B. L ; Noren, D. P ; Bisberg, A. J ; Mills, G. B ; Gray, J. W ; Kellen, M ; Norman, T ; Friend, S ; Qutub, A. A ; Fertig, E. J ; Guan, Y ; Song, M ; Stuart, J. M ; Spellman, P. T ; Koeppl, H ; Stolovitzky, G ; Saez Rodriguez, J ; Mukherjee, S ; Afsari, B ; Al-Ouran, R ; Anton, B ; Arodz, T ; Askari Sichani, O ; Bagheri, N ; Berlow, N ; Bisberg, A. J ; Bivol, A ; Bohler, A ; Bonet, J ; Bonneau, R ; Budak, G ; Bunescu, R ; Caglar, M ; Cai, B ; Cai, C ; Carlin, D. E ; Carlon, A ; Chen, L ; Ciaccio, M. F ; Cokelaer, T ; Cooper, G ; Coort, S ; Creighton, C. J ; Daneshmand, S. M. H ; De La Fuente, A ; Di Camillo, B ; Danilova, L. V ; Dutta-Moscato, J ; Emmett, K ; Evelo, C ; Fassia, M. K. H ; Favorov, A. V ; Fertig, E. J ; Finkle, J. D ; Finotello, F ; Friend, S ; Gao, X ; Gao, J ; Garcia Garcia, J ; Ghosh, S ; Giaretta, A ; Graim, K ; Gray, J. W ; Großeholz, R ; Guan, Y ; Guinney, J ; Hafemeister, C ; Hahn, O ; Haider, S ; Hase, T ; Heiser, L. M ; Hill, S. M ; Hodgson, J ; Hoff, B ; Hsu, C. H ; Hu, C. W ; Hu, Y ; Huang, X ; Jalili, M ; Jiang, X ; Kacprowski, T ; Kaderali, L ; Kang, M ; Kannan, V ; Kellen, M ; Kikuchi, K ; Kim, D. C ; Kitano, H ; Knapp, B ; Komatsoulis, G ; Koeppl, H ; Krämer, A ; Kursa, M. B ; Kutmon, M ; Lee, W. S ; Li, Y ; Liang, X ; Liu, Z ; Liu, Y ; Long, B. L ; Lu, S ; Lu, X ; Manfrini, M ; Matos, M. R. A ; Meerzaman, D ; Mills, G. B ; Min, W ; Mukherjee, S ; Müller, C. L ; Neapolitan, R. E ; Nesser, N. K ; Noren, D. P ; Norman, T ; Oliva, B ; Opiyo, S. O ; Pal, R ; Palinkas, A ; Paull, E. O ; Planas Iglesias, J ; Poglayen, D ; Qutub, A. A ; Saez Rodriguez, J ; Sambo, F ; Sanavia, T ; Sharifi-Zarchi, A ; Slawek, J ; Sokolov, A ; Song, M ; Spellman, P. T ; Streck, A ; Stolovitzky, G ; Strunz, S ; Stuart, J. M ; Taylor, D ; Tegnér, J ; Thobe, K ; Toffolo, G. M ; Trifoglio, E ; Unger, M ; Wan, Q ; Wang, H ; Welch, L ; Wong, C. K ; Wu, J. J ; Xue, A. Y ; Yamanaka, R ; Yan, C ; Zairis, S ; Zengerling, M ; Zenil, H ; Zhang, S ; Zhang, Y ; Zhu, F ; Zi, Z ; Sharif University of Technology
    Nature Publishing Group  2016
    It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was... 

    Effects of rubber curing ingredients and PhenolicResin on mechanical, thermal, and morphological characteristics of rubber/phenolic-resin blends

    , Article Journal of Applied Polymer Science ; Volume 108, Issue 6 , 2008 , Pages 3808-3821 ; 00218995 (ISSN) Derakhshandeh, B ; Shojaei, A ; Faghihi, M ; Sharif University of Technology
    This article examines the physical and mechanical characteristics of mixtures of two different synthetic rubbers, namely styrene-butadiene rubber (SBR) and nitril-butadiene rubber (NBR), with novolac type phenolicresin (PH). According to Taguchi experimental design method, it is shown that the addition of PH increases the crosslinking density of rubber phase probably due to its curative effects. Thermal analysis of the blends indicates that, contrary to NBR/PH blend, thermal stability of SBR/PH blend is dependent on sulfur content due to predominant polysulfidic crosslinks formed in SBR. Slight shift in glass-transition temperature (Tg) of pure SBR and NBR vulcanizates by the addition of PH...