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    Investigation of the dielectric and optic properties of rosehip seed extract loaded hydrogels
    (Elsevier, 2023) Okutan, M.; Coskun, R.; Yalcin, O.; Babucoglu, A. C.; Demir, A.
    Rosehip seed (RS) extract-loaded hydrogel samples RS1 (poly(AMPS-co-MBAAm), RS2 (poly(AMPS-co - CrA-co -MBAAm)), RS3 (poly(AMPS -co -MAA -co -MBAAm)) and RS4 (poly(AMPS-co -IA - co-MBAAm)) were prepared to examine their structural and optical properties. Furthermore, frequency and bias voltage evolution of dielectric and electrical parameters were analyzed using impedance spec-troscopy (IS) for all samples at room temperature (RT). Fluctuating and self-consistent results were ob-tained in the Ultraviolet-visible (UV) spectrum of the samples. The conductivity mechanisms were de-termined by calculating the s parameters of all samples for different regions. The complex impedance, capacitance, phase angles, dielectric constant, tangent factor, electric modulus, and frequency variation of ionic conductivity for the rosehip seed loaded hydrogel were successfully analyzed in detail using IS in the broadband frequency range. The increase in ionic conductivity of the RS-doped hydrogel with in-creasing frequency was attributed to its ability to exhibit the Langevin equivalent of Brownian motion in viscoelastic fluids, surface/bulk Maxwellian stress relaxation behavior, and the validity of the Stokes -Einstein relationship in biological fluids. In particular, the capacitive effect observed as a result of the Cole-Cole diagrams of the dielectric constant adapting to Smith Chat and the equivalent RC circuit al-lowed the material to be used as a capacitor. (c) 2022 Elsevier B.V. All rights reserved.
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    Modeling of the angle of shearing resistance of soils using soft computing systems
    (PERGAMON-ELSEVIER SCIENCE LTD, 2009) Kayadelen, C.; Gunaydin, O.; Fener, M.; Demir, A.; Ozvan, A.
    Precise determination of the effective angle of shearing resistance (phi') value is a major concern and an essential criterion in the design process of the geotechnical structures, such as foundations, embankments, roads, slopes, excavation and liner systems for the solid waste. The experimental determination of phi' is often very difficult, expensive and requires extreme cautions and labor. Therefore many statistical and numerical modeling techniques have been suggested for the phi' value. However they can only consider no more than one parameter, in a simplified manner and do not provide consistent accurate prediction of the phi' value. This study explores the potential of Genetic Expression Programming, Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy (ANFIS) computing paradigm in the prediction of phi' value of soils. The data from consolidated-drained triaxial tests (CID) conducted in this study and the different project in Turkey and literature were used for training and testing of the models. Four basic physical properties of soils that cover the percentage of fine grained (FG), the percentage of coarse grained (CG), liquid limit (LL) and bulk density (BD) were presented to the models as input parameters. The performance of models was comprehensively evaluated some statistical criteria. The results revealed that GEP model is fairly promising approach for the prediction of angle of shearing resistance of soils. The statistical performance evaluations showed that the GEP model significantly outperforms the ANN and ANFIS models in the sense of training performances and prediction accuracies. (C) 2009 Elsevier Ltd. All rights reserved.