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Durgun M.Y.,Technical University of Istanbul | Durgun M.Y.,Bartn University | Atahan H.N.,Technical University of Istanbul
Construction and Building Materials | Year: 2017

In this study, colloidal nano SiO2 (CNS), which can be assumed as a multi-functional nano-technological material in cement-based systems, was utilized in an effort to reduce the total fine material content in self-compacting concrete (SCC) designs. The influence of CNS addition on flow properties and rheological parameters has been investigated. Three different average particle diameter of CNS which was used to modify the SCC mixtures are 35 nm, 17 nm and 5 nm. CNSs were used at different amounts and total fly ash amount in the mix design was decreased step by step from 160 kg/m3 down to 0 kg/m3. The volumetric emptiness occurred by the reduction of fine materials was filled by aggregates. On fresh state, T500 slump flow time, slump flow diameter, V-funnel time, static penetration resistance, sieve segregation and visual stability index tests were performed. Moreover, rheological parameters such as static yield stress, dynamic yield stress and plastic viscosity were obtained by using a concrete rheometer. In this way, it was aimed to design SCC mixtures with reduced fine material content by benefiting of the viscosity modifying effect of CNSs with various sizes. Results have shown that the effectiveness of CNS, in an effort to reduce the fine material content in SCC design is very significant. © 2017 Elsevier Ltd

Sarac U.,Bartn University | Baykul M.C.,Eskiehir Osmangazi University | Uguz Y.,Eskiehir Osmangazi University
Journal of Superconductivity and Novel Magnetism | Year: 2015

Electrodeposited Ni–Co nanocrystalline thin films were grown onto indium tin oxide (ITO)-coated glass substrates from an electrolyte consisting of their sulfate salts and boric acid without stirring at ambient temperature. The effect of applied current density on the microstructural, compositional, magnetic, and morphological properties was investigated using different characterization techniques such as X-ray diffraction (XRD), energy dispersive X-ray (EDX) spectroscopy, vibrating sample magnetometer (VSM), and scanning electron microscopy (SEM). It was observed that the Ni content within the films increases as the applied current density increases. X-ray diffraction (XRD) analyses of Ni–Co films showed the formation of single phase face-centered cubic (FCC) structure and <111>crystallographic orientation. Morphological characterizations revealed that the applied current density affects the surface morphology of the films. The film electrodeposited at high current density has smaller grains than those prepared at lower current densities. Magnetic measurements showed that the coercivity field and remanence ratio of the films decrease as the applied current density increases. Consequently, Ni–Co thin films exhibited different microstructural, compositional, magnetic, and morphological properties according to current density applied during electroplating process. © 2014, Springer Science+Business Media New York.

Yilmaz R.,Bartn University | Kilic-Cakmak E.,Gazi University
Computers and Education | Year: 2012

This study examined the impacts of educational interface agents with different attributes on achievement, attitude and retention of elementary school students in their science and technology courses. The study was implemented in four different eighth- grade classes (aged 13-14) of an elementary school. Four different types of educational software, covering living things and life unit of 8th graders, were developed to analyze the impacts of educational software agents with different attributes on the results of learning. The study was conducted according to experimental model with pretest and posttest control group, which is one of the quasi-experimental designs. For five consecutive weeks experimental and control group students used the software designed for this research. The students were asked to fill an attitude scale on science and technology both before and after the implementation. And following completion, an achievement test was applied. The results of the study revealed that students who used a human-like educational interface agent were more successful than both the rest of the experimental groups and the control group in terms of achievement, attitude and retention of learning in science and technology classes. It is also revealed that the software to be developed for elementary school 8th graders (aged 13-14) should be supported with human-like educational interface agents. © 2012 Elsevier Ltd. All rights reserved.

An experimental investigation has been carried out to determine the thermal behavior of cooling fluid as it passes through a vortex tube and the effects of the orifice nozzle number and the inlet pressure on the heating and cooling performance of the counterflow type vortex tube (RHVT). Experiments have been performed using oxygen (O2), nitrogen (N2), and argon (Ar). Five orifices have been fabricated and used during the experimental study with different nozzle numbers of 2, 3, 4, 5, and 6. The orifices used at these experiments are made of the polyamide plastic material. The thermal conductivity of polyamide plastic material is 0.25 W/m K. To determine the energy separation, the inlet pressure values were adjusted from 150 kPa to 700 kPa with 50 kPa increments for each one of the orifices and each one of the studied fluids. The vortex tube that was used during the experiments has L/D ratio of 15 and the cold mass fraction was held constant at 0.5. As a result of the experimental study, it is determined that the temperature gradient between the cold and hot exits is decreased depending on the orifice nozzle number increase. Exergy analyses have been realized for each one of the studied fluids under the same inlet pressures with the experiments (Pi =150-700). The exergy efficiency of the vortex tube is more affected by inlet pressure than nozzle number. © 2010 American Society of Mechanical Engineers.

Kocabas F.,Bartn University | Korkmaz M.,Hacettepe University | Sorgucu U.,Bartn University | Donmez S.,Çankiri Karatekin University
International Journal of Refrigeration | Year: 2010

In this study, the effect of the nozzle number and the inlet pressures, which vary from 150 to 700 kPa with 50 kPa increments, on the heating and cooling performance of the counter flow type vortex tube has been modeled with an artificial neural network (ANN) and multi-linear regression (MLR) models by using the experimentally obtained data. In the developed system output parameter temperature gradiant between the cold and hot outlets (ΔT) has been determined using inlet parameters such as the inlet pressure (P inlet), nozzle number (N), cold mass fraction (μc) and inlet mass flow rate (ṁinlet). The back-propagation learning algorithm with variant which is Levenberg-Marquardt (LM) and Sigmoid transfer function have been used in the network. In addition, the statistical validity of the developed model has been determined by using the coefficient of determination (R 2), the root means square error (RMSE), and the relative absolute errors (RAE). R2, RMSE and RAE have been determined for ΔT as 0.9989, 0.5016, 0.0540 respectively. © 2010 Elsevier Ltd and IIR. All rights reserved.

Polat K.,Bartn University | Durduran S.S.,Selcuk University
Neural Computing and Applications | Year: 2012

In this study, the traffic accidents recognizing risk factors related to the environmental (climatological) conditions that are associated with motor vehicles accidents on the Konya-Afyonkarahisar highway with the aid of Geographical Information Systems (GIS) have been determined using the combination of K-means clustering (KMC)-based attribute weighting (KMCAW) and classifier algorithms including artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS). The dynamic segmentation process in ArcGIS9. 0 from the traffic accident reports recorded by District Traffic Agency has identified the locations of the motor vehicle accidents. The attributes obtained from this system are day, temperature, humidity, weather conditions, and month of occurred traffic accidents. The traffic accident dataset comprises five attributes (day, temperature, humidity, weather conditions, and month of occurred traffic accidents) and 358 observations including 179 without accident and 179 with accident. The proposed comprises two stages. In the first stage, the all attributes of dataset have been weighted using KMCAW method. The aims of this weighting method are both to increase the classification performance of used classifier algorithm and to transform from linearly non-separable traffic accidents dataset to a linearly separable dataset. In the second stage, after weighting process, ANN and ANFIS classifier algorithms have been separately used to determine the case of traffic accidents as with accident or without accident. In order to evaluate the performance of proposed method, the classification accuracy, sensitivity, specificity and area under the ROC (Receiver Operating Characteristic) curves (AUC) values have been used. While ANN and ANFIS classifiers obtained the overall prediction accuracies of 53. 93 and 38. 76%, respectively, the combination of KMCAW and ANN and the combination of KMCAW and ANFIS achieved the overall prediction accuracies of 74. 15 and 55. 06% on the prediction of traffic accidents. The experimental results have demonstrated that the proposed attribute weighting method called KMCAW is a robust and effective data pre-processing method in the prediction of traffic accidents on Konya-Afyonkarahisar highway in Turkey. © 2011 Springer-Verlag London Limited.

OZTuRK M.,Bartn University
Plant Biosystems | Year: 2015

Leaf area index (LAI) analysis of deciduous forest trees is usually restricted to seasonal monitoring involving the assessment of distinct leaf phenological stages within definite time intervals of the year. However, continuous LAI monitoring that includes entire leaf periods is necessary to define the ecophysiological characteristics of deciduous trees. Therefore, this study investigated the intra-annual cycle of the LAI for a Platanus orientalis L. stand in the Bartın watershed of Turkey. A complete cycle involves three periods: foliation, stable, and defoliation. The foliation period comprises budburst, leaf emergence and flushing sessions, whereas the defoliation period consists of leaf senescence and leaf fall sessions. The stable period is in between these two periods when LAI values are at a climax around maximum. Eight points were determined in the field for the analysis of LAI by a hemispherical photography technique. Over a relatively frequent schedule, photographs were taken almost weekly during the foliation period. Both weekly and approximate monthly photographs were applied during the stable period. Finally, near-monthly photographs were taken for the defoliation period. The foliation period lasted for about 1.5 months from mid-April to May with the mean LAI reaching from 0.16 up to 2.38. Mean LAI was between 2.38 and 2.47 for a stable period over 2 months (June and July). For the defoliation period, mean LAI dropped from 2.42 down to 0.35 over 5 months from August to December. The total foliated period was more than 8 months, which is relatively long for a temperate forest. In addition, correlations between mean LAI and maximum, mean and minimum temperatures were highly significant (P < 0.01) with coefficients (r) of 0.79, 0.90 and 0.93, respectively. By describing the intra-annual LAI pattern, this study fills a gap in the literature on the phenology of Platanus orientalis L. © 2015 Società Botanica Italiana

Polat K.,Bartn University
International Journal of Systems Science | Year: 2012

This study presents the application of fuzzy c-means (FCM) clustering-based feature weighting (FCMFW) for the detection of Parkinson's disease (PD). In the classification of PD dataset taken from University of California - Irvine machine learning database, practical values of the existing traditional and non-standard measures for distinguishing healthy people from people with PD by detecting dysphonia were applied to the input of FCMFW. The main aims of FCM clustering algorithm are both to transform from a linearly non-separable dataset to a linearly separable one and to increase the distinguishing performance between classes. The weighted PD dataset is presented to k-nearest neighbour (k-NN) classifier system. In the classification of PD, the various k-values in k-NN classifier were used and compared with each other. Also, the effects of k-values in k-NN classifier on the classification of Parkinson disease datasets have been investigated and the best k-value found. The experimental results have demonstrated that the combination of the proposed weighting method called FCMFW and k-NN classifier has obtained very promising results on the classification of PD. © 2012 Copyright Taylor and Francis Group, LLC.

Polat K.,AbantzzetBaysal University | Krmac V.,Bartn University
International Journal of Refrigeration | Year: 2011

This paper focused on the determining of gas types in counter flow type vortex tubes. In the present study, four different gas types including air, oxygen, nitrogen, and argon in the vortex tube with different inlet pressures and nozzle numbers have been used. The main aims of this paper are to investigate the correlations between gas types and input parameters comprising nozzle numbers, inlet pressures, inlet mass flow rate, temperature of cold outlet, temperature of hot outlet, and cold mass fraction and to select the most important attributes using correlation based attribute reduction and pairwise fisher score attribute reduction (PFSAR). After attribute reduction methods applied to dataset, k-nearest neighbor and C4.5 decision tree classifiers have been used to determine the gas type in the RHVT. The results have demonstrated that the PFSAR is a robust and efficient method in the reduction of attributes belonging to vortex tube. © 2011 Elsevier Ltd and IIR. All rights reserved.

In this study, the performance of the counter flow type vortex tube with the input parameters including the nozzle number (N), the densities of inlet gases (air, oxygen, nitrogen, and argon) and the inlet pressure (P inlet) has been modeled with the proposed hybrid method combining a novel data preprocessing called output dependent feature scaling (ODFS) and adaptive network based fuzzy inference system (ANFIS) by using the experimentally obtained data. In the developed system, output parameter temperature gradient between the cold and hot outlets has been determined using input parameters comprising (P inlet), (N), and the density of gases. In order to evaluate the performance of hybrid method, the mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), determination coefficient (R 2), and Index of Agreement (IA) values have been used. The obtained results are 9.0670e-004 (MAE), 5.8563e-006 (MSE), 0.0024 (RMSE), 1.00 (R 2), and 1.00 (IA) using the hybrid method. © 2011 Elsevier Ltd and IIR. All rights reserved.

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