Agency: Cordis | Branch: FP7 | Program: MC-IRSES | Phase: FP7-PEOPLE-2011-IRSES | Award Amount: 172.20K | Year: 2012
The topics of this joint exchange programme are all related to a specific plant genus of commercial interest and its taxonomy, the propagation and investigation of endangered species of this plant genus, the natural products used in medicine occurring in Digitalis plants, and the isolation, structure elucidation and biological testing of some of the rare but pharmacological relevant cardenolides. Objectives are (1) Taxonomically critical Digitalis species and subspecies will be (re)-examined by molecular taxonomy, using the progesterone 5-reductase gene as a molecular marker, and chemotaxonomy by cardenolide profiling, using HPLC, (2) plant tissue cultures of endangered Digitalis species will be established and used in plant propagation. Hairy root cultures of selected species will be established for fundamental studies on cardenolide formation, (3) the structures of important cardenolides will be elucidated by NMR and bulk quantities of selected cardenolides will be produced, (4) antiviral and antiproliferative effects of individual cardenolides will be studied. Cardenolides will be screened for their pharmacological profiles (potential and selectivity). The objectives aim at intensifying circumstantial ongoing co-operations and exchange of materials and people in a more reliable and coordinated way. The profiles of expertise of the various partners are complementary and researchers from Turkey, Brazil, Germany and Portugal are involved.
Keskin A.,Abant Izzet Baysal University |
Emiroglu A.O.,Abant Izzet Baysal University
Energy Education Science and Technology Part A: Energy Science and Research | Year: 2010
Strict exhaust emission regulations set for limiting the air pollution caused by motor vehicles have oriented the producers and researchers to investigate new techniques to reduce exhaust emissions. The main pollutants caused by diesel engines are particle matters (PM), nitrogen oxides (NO x), hydrocarbons (HC), and carbon monoxides (CO). Among the preventive actions to keep the emissions caused by motor vehicles at a certain level are enhancing the fuel quality, preventing the pollutant formation in the engine, and developing the post- combustion emission control systems. There are many different technologies used for reducing the amount of pollutants in diesel engine exhausts. In this study, the main post-combustion techniques applied for reducing these pollutants catalytically are being examined. © Sila Science.
Camdali U.,Abant Izzet Baysal University
Energy Education Science and Technology Part A: Energy Science and Research | Year: 2010
As a result of increasing demands from users, photovoltaic (PV) applications are currently in use in some practical applications such as solar watches and for lighting purposes, and since 1981 it has been used in buildings. While PV arrays are used to produce electricity by the solar power plants which are uncovered, they are first used on the roof of the buildings and then produced as roof plates for modern buildings. Research studies and findings on the effect of sun light amounts, shadow, the value of the thermal transitivity and damp-proofing on the PV arrays show that they are used effectively on the vertical covering of the buildings and pilot applications have been increasing since 1992. Most of the researches in nowadays are focusing on finding out economic PV arrays production methods. In this study, the electricity consumption of a house in Turkey is calculated in order to determine cost of electricity consumption depending on using machines and instruments. Then, the life cycle analysis of PV system is investigated. At the end of the study, a comparative analysis of electrical cost by PV arrays and operating costs are presented with the other energy sources referenced in literature. Turkish market values are used in calculations of the PV system. © Sila Science.
Celik A.N.,Abant Izzet Baysal University
Renewable and Sustainable Energy Reviews | Year: 2011
Turkey is a free market economy that is oriented towards Western markets. It also has strong ambitions to join the European Union and this factor has been beneficial but also taxing with respect to its changing economic situation. Turkey imports nearly 70% of its energy requirements. The country spends 40-50% of its total export income to import fuel, mainly crude oil and natural gas. On the other hand, Turkey has significant wind energy potential because of its geographical characteristics, such as its shoreline and mountain-valley structures. The sea fronts of the Agean, Marmara, Mediterranean, and Black Seas, and some places of the Southeast Anatolian belt have a high wind potential, with an average speed of 4.5-10 m/s. Studies put wind-energy potential in terms of the technical aspects in the region of 80 GW. anakkale province that has more than 10% of the country's total installed wind power has been presently chosen for the case study. In the present study, hourly time-series wind data recorded from the year 2000 to 2005 at a height of 10 m in anakkale city centre and Bozcaada, an island in the Aegean Sea belonging to the anakkale province, has been statistically analysed. Overall, Bozcaada, with an annual mean density value higher than 350 W/m2, offers a much higher wind potential than the former location, indicating sufficient wind potential for large scale electricity generation. The mean power density value in the northeastern direction is highest for the typical year in Bozcaada with a value of 901.6 W/m2, while the directional power density distribution shows that over 60% of the wind energy comes from the band between northern and northeastern directions. © 2011 Elsevier Ltd. All rights reserved.
Celik A.N.,Abant Izzet Baysal University
Solar Energy | Year: 2011
This article presents the artificial neural network modelling of the operating current of a 120. Wp of mono-crystalline photovoltaic module. As an alternative method to analytical modelling approaches, this study uses the advantages of neural networks such as no required knowledge of internal system parameters, less computational effort and a compact solution for multivariable problems. Generalised regression neural network model is used in the present article to predict the operating current of the photovoltaic module. To show its merit, the current predicted from the artificial neural network modelling is compared to that from the analytical model. The five-parameter analytical model is drawn from the equivalent electrical circuit that includes light-generated current, diode reverse saturation current, and series and shunt resistances. The operating current predicted from both the neural and analytical models are compared to the measured current. Results have shown that the artificial neural network modelling provides a better prediction of the current than the five-parameter analytical model. © 2011 Elsevier Ltd.
Terzioglu C.,Abant Izzet Baysal University
Journal of Alloys and Compounds | Year: 2011
X-ray powder diffraction (XRD), scanning electron microscopy (SEM), dc electrical resistivity, critical current density and static microindentation measurements are performed to investigate some physical properties of Bi 1.8Pb0.35Sr1.9Ca2.1Cu 3GdxOy superconducting samples with x = 0.0, 0.1, 0.3 and 0.5. We observe from the transport measurements that, for the Gd added sample, the critical transition temperature (Tc) and the critical current density (Jc) are decreased in comparison with that of undoped sample. In addition, surface morphology and grain connectivity of the samples are degraded and the high-Tc phase of the samples decreases with increasing Gd addition. The indentation load versus diagonal length of the samples under different indentation loads in the range of 0.245-2.940 N are measured. The microindentation measurements showed that, for the Gd added sample, the load dependent (apparent) microhardness value (Hv) is lower in comparison with that of the pure sample (x = 0). The values of H v are found to be load dependent. In addition, we extract the load independent (true) microhardness using the Kick's law, proportional specimen resistance (PSR), modified proportional specimen resistance (MPSR) and the Hays-Kendall (HK) approach and compare the true hardness with the apparent hardness. The possible reasons for the observed degradation in microstructure, superconducting and mechanical properties due to Gd addition are discussed. © 2010 Elsevier B.V. All rights reserved.
Altintas F.,Abant Izzet Baysal University
Optics Communications | Year: 2010
We study the dynamics of geometric measure of quantum discord between two non-interacting qubits each immersed in its own non-Markovian environment with a spectal distribution representing the electromagnetic field inside off-resonant high-Q cavity. We compared the dynamics of geometric measure of quantum discord with quantum discord for an initial Werner-like state and conclude three important findings. First, when there is an instantaneous disappearance in the dynamics of quantum discord at some timepoints, there is a disappearance in geometric measure of quantum discord, but not instantly. Second, the sudden change in the decay rate of geometric measure of quantum discord might not imply the sudden change in the decay rate of the dynamics of quantum discord. Third, there is a preservation for a long time in both quantum discord and geometric measure of quantum discord when the detuning and non-Markovian conditions are simultaneously satisfied. © 2010 Elsevier B.V. All rights reserved.
Yildirim G.,Abant Izzet Baysal University
Journal of Alloys and Compounds | Year: 2013
This comprehensive study examines the change of the microstructural, electrical and superconducting properties of the Eu doped Bi 1.8Pb0.4EuxSr2Ca2. 2Cu3.0Oy ceramic cuprates (with ×≤0.7) produced by the conventional solid state reaction method at the constant annealing temperature of 840 °C for 24 h with the aid of the standard characterization measurements such as bulk density, dc resistivity (ρ-T), transport critical current density (Jc), X-ray diffraction (XRD), scanning electron microscopy (SEM) and electron dispersive X-ray (EDX) examinations. For the full characterization of the pure and Eu doped Bi-2223 samples, the degree of granularity (from the bulk density and porosity measurements); the room temperature resistivity, onset-offset critical transition temperature, variation of transition temperature, hole carrier concentration, spin-gap opening temperature and thermodynamic fluctuations (from the dc resistivity experiments); the texturing, crystal structure, crystallite size, phase purity and cell parameters (from the XRD investigations); the variation of the flux pinning centers and the boundary weak-links between the superconducting grains (from the critical current density values); the crystallinity, specimen surface morphology, grain connectivity between the superconducting grains and grain size distribution (from the SEM examinations), the elemental compositions and distributions (from EDX measurements) of the samples are determined and discussed clearly. The results obtained confirm that all the properties degrade with the enhancement of the Eu concentration in the Bi-2223 superconducting matrix up to x = 0.5 beyond which they are destroyed surprisingly due to not only the distortion between the Bi-2223 slabs but also the increase in the porosities and grain boundary weak-links. For example; the onset (offset) critical temperature decreases from 117.6 K (109.9 K) to 68.1 K (14.8 K) with the impurity content. The critical temperatures (Tonset c and Toffset c ) are not measurable for the sample doped with the Eu content level of x = 0.7 as a consequence of the metal to insulator transition (MIT). On the other hand, the critical current density (J ct) is measured in the range from 3201 A/cm2 (for the pure sample) to 29 A/cm2 (for the sample doped with x = 0.5). For the sample doped with x = 0.7, each value is not measurable due to the nonsuperconducting behavior. Besides, the SEM pictures display that the surface morphology and grain connectivity degrade considerably with the Eu concentration. Moreover, the XRD measurements show that the Eu inclusions enter into the crystal structure by reducing the formation velocity of the Bi-2223 phase. Similar to the XRD evidences, the EDX measurement results demonstrate that all the elements used in the samples successfully introduce into the Bi-2223 structure, and the observed peaks of Ca, Cu and especially Pb reduce gradually with the Eu individuals in the Bi-2223 matrix, meaning that the Eu nanoparticles may substitute for the elements given above. This is enough to explain why the superconducting properties retrograde rapidly with the Eu impurities. © 2013 Elsevier B.V. All rights reserved.
Top E.,Abant Izzet Baysal University
Internet and Higher Education | Year: 2012
The purpose of the study was to examine pre-service teachers' sense of community, perception of collaborative learning, and perceived learning. Fifty pre-service teachers from two undergraduate ICT courses which incorporated blogs participated in this study. The data were obtained via three online questionnaires (Collaborative Learning scale, Sense of Community scale, and Perceived Learning scale) administered throughout Fall 2009-2010. The research questions were answered by using Pearson Product-Moment Correlation and multiple linear regressions. Results indicated that the pre-service teachers had positive feelings about the collaborative learning and perceived learning; also, they had moderate feelings related to sense of community in the classes which incorporated blogs. Additionally, to a great extent sense of community and to a much lesser extent of computer knowledge level were the predictors of explaining their learning perceptions. © 2011 Elsevier Inc.
Celik A.N.,Abant Izzet Baysal University |
Kolhe M.,University of Agder
Applied Energy | Year: 2013
Even though a number of new mathematical functions have been proposed for modeling wind speed probability density distributions, still the Weibull function continues to be the most commonly used model in the literature. Therefore, the parameters of this function are still widely used to obtain typical wind probability density distributions for finding the wind energy potential by researchers, engineers and designers. Once long-term average of Weibull function's parameters are known, then the probability density distributions can easily be obtained. Artificial neural network (ANN) can be used as alternative to analytical approach as ANN offers advantages such as no required knowledge of internal system parameters, compact solution for multi-variable problems. In this work, a generalized feed-forward type of neural network is used to predict an annual wind speed probability density distribution by using the Weibull function's parameters as inputs. For verifying its validity and merits, the annual wind speed probability density distribution is also predicted by using the Weibull function. The wind speed distribution predicted from the ANN modeling is compared with the analytical model's results. Total 9. year long hourly wind speed data, belonging to one of the windiest locations in Turkey with mean wind speed of over 6. m/s, are used in this study. It is observed that ANN based wind speed distribution estimation gives better results for calculating the energy output from some commercial wind turbine generators. © 2012 Elsevier Ltd.