The Gaziantep University is a state research university located in Gaziantep, Southeastern Anatolia, Turkey. The university of Gaziantep has 10 faculties, containing a total of 22 academic departments, with a strong emphasis on scientific and technological research.Gaziantep is the largest trade and industrial center in the west of Southeastern Turkey. The University of Gaziantep was founded as a state university on 27 June 1987, but higher education on campus began in 1973 when the institute was an extension campus of the Middle East Technical University. The main campus is located at Gaziantep, which is close to the city centre, with its extension campuses situated in the neighbouring cities.The objectives of the university are:Cultural, scientific, technical, medical and vocational education and training,Fundamental and applied research,Technical, scientific and cultural exchanges with similar institutions at national and international levels,The University of Gaziantep enrolled 24,406 undergraduates, 482 postgraduate students, and employed 1,048 faculty members in the 2008/09 school year. The language of instruction at the Gaziantep University is English. Wikipedia.
Yilmaz C.,University of Gaziantep
Geothermics | Year: 2017
Thermoeconomic optimization procedure is applied using genetic algorithm method to an integrated system composed of an alkaline water electrolysis unit for hydrogen production and a combined flash-binary geothermal power plant for providing power input to the electrolysis unit. The objective is to minimize the unit costs of the products (electricity and hydrogen production) of the composed system. The optimization approach is developed based on the cost optimal exergetic efficiency that is obtained for a component isolated from the remaining of the system components. Objectives to be optimized given certain constraints and variables are developed for each subcomponent of the system. Using genetic algorithm method of optimization, the variables, relative cost differences, and exergetic efficiencies with the corresponding optimal values are obtained. Thermoeconomic optimal values for product cost flow rate, fuel cost flow rate, unit cost of electricity, and unit cost of hydrogen production are calculated to be 2412 $/h, 289.4 $/h, 0.01066 $/kWh, and 1.088 $/kg, respectively, whereas the corresponding actual base case values are 2607 $/h, 295.9 $/h, 0.01105 $/kWh, and 1.149 $/kg, respectively. © 2016 Elsevier Ltd
Agency: European Commission | Branch: FP7 | Program: MC-IAPP | Phase: FP7-PEOPLE-IAPP-2008 | Award Amount: 1.43M | Year: 2009
Currently most research into efficient algal-oil production is being carried out by the private sector, but if predictions from small scale production experiments are realised then using algae to produce biodiesel may be the only viable method by which to produce enough bio-fuel to replace current world petrol/diesel usage. Micro-algae in particular have much faster growth-rates than terrestrial crops. The yield of oil from algae is estimated to be from between 19,000 to 75,000 litres per acre, per year; this is 7 to 31 times greater than the next best crop, oil of palm. As terrestrial contributions are greatly limited by the finite area of land available under any culture method, it is essential that the potential of the marine environment as a source of biomass for bio-fuel production is realised. The group intends to facilitate a multi-disciplinary research programme through the recruitment of experienced researchers aimed at the acquisition of new knowledge and skills in the production of biofuels from native seaweed and cultured micro-algae. The project will identify the native seaweed and cultured micro-algal processes with the most potential for fuel production, the best time and technique to harvest seaweed and the culture methodologies for micro-algae along with an economic and environmental appraisal which will identify the size of the farm required and the feasibility of a commercial size operation. This will provide the physical (biomass product) and the intellectual (methodology for production and extraction) tools to enable the bio-fuel sector to base its business on the most suitable and profitable process.
Gullu H.,University of Gaziantep
Soils and Foundations | Year: 2014
In developing designs for engineered mixtures to improve soil with stabilizers, it is always required that the effective rate of the stabilizer be determined in the stabilization. However, there seems to be an insufficient amount of effort in the decision-making related to the accuracy and the reliability of the effective rate. This paper presents an application of a factorial experimental analysis, together with an effect size estimation, to investigate the effective dosage rates of bottom ash to improve a fine-grained soil. Unconfined compression tests have been conducted to measure the strength parameters considered in the mixtures of soil +bottom ash for which the bottom ash dosages were 0, 5, 10, 15, 20, 25, and 30% by dry weight of the mixture. The effective dosage rates, based on the strength parameters, have been evaluated primarily by multiple comparisons and Cohen's d. The results indicate that while there is a significant strength development with "moderate" to "strong" sizes on the untreated soil, due to the 30% bottom ash (po0.05, Cohen's d41.15), the strength is insignificantly changed (increased or decreased) below the 30% dosage (p40.05, Cohen's dr0.41). It is found from the experimental analysis that a factorial approach and an effect size estimation compare well in the decision-making. It is suggested from the results that bottom ash can be adequately added to soil in the stabilization for both improvement (i.e., at 30% dosage) and replacement (i.e., below 30% dosage). The proposed use of bottom ash would also be beneficial for recycling and the sustainable development of the environment. © 2014 The Japanese Geotechnical Society. Production and hosting by Elsevier B.V. All rights reserved.
Baysal E.,University of Gaziantep
European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery | Year: 2013
Chronic otitis media usually presents with a benign tumor-like lesion of the temporal bone known as a cholesteatoma. The role of oxidative stress in the pathogenesis of chronic otitis media and cholesteatoma has not yet been fully explored. Therefore, the aim of this study was to investigate the oxidative stress markers and antioxidant enzymes in patients with cholesteatomatous and noncholesteatomatous chronic otitis media and in healthy subjects. A prospective controlled trial was performed on cholesteatomatous and noncholesteatomatous chronic otitis media patients in a tertiary referral center in a university hospital. A total of 75 subjects, including 25 cholesteatomatous and 25 noncholesteatomatous chronic otitis media patients and 25 healthy subjects participated in this study. Serum total oxidant status (TOS) and oxidative stress index (OSI) levels were significantly increased in the patient groups with or without cholesteatoma compared with the control group. Serum total antioxidant status (TAS) levels and Paraoxonase and arylesterase activity were significantly lower in the patient groups with or without cholesteatoma compared with the control group. Serum TOS and OSI levels were lower in the noncholesteatomatous group, whereas serum TAS levels were higher compared with the cholesteatomatous group. Serum arylesterase activity was significantly lower in the noncholesteatomatous group compared with the control group. The results of this study reveal that in cholesteatoma cases, the oxidative stress and antioxidant enzyme imbalance were more significant than in cases of chronic otitis media without cholesteatoma.
Gullu H.,University of Gaziantep
Engineering Applications of Artificial Intelligence | Year: 2014
In order to understand the treatment of a marginal soil well, the underlying input-output relationship on the strength and elastic responses due to nonlinearity has always been a great importance in the stabilized mixtures for an optimal design. This paper employs a relatively new soft computing approach, genetic expression programming (GEP), to formulations for unconfined compressive strength (UCS) and elasticity modulus (Es) of clay stabilized with bottom ash, using a database obtained from the laboratory tests conducted in the study. The predictor variables included in the formulations are bottom ash dosage, dry unit weight, relative compaction, brittleness index and energy absorption capacity. The results demonstrate that the GEP-based formulas of UCS and Es are significantly able to predict the measured values to high degree of accuracy against the nonlinear behavior of soil (p<0.05, R>0.847). The GEP approach is found to have a better correlation performance as compared with the nonlinear regression as well. The sensitivity analysis for the parameter importance shows that the dominant influence on the predictions of UCS and Es is exerted by the variables of bottom ash dosage and energy absorption capacity. This study reveals that the GEP is a potential tool for establishing the functions and identifying the key variables for predicting the strength and elastic responses of the clay treated with bottom ash. Including a waste material in the proposed formulas can also serve to the environment for the development of recycling and sustainability. © 2014 Elsevier Ltd.
Guneyisi E.M.,University of Gaziantep
Earthquake Engineering and Structural Dynamics | Year: 2012
The present paper investigates the seismic reliability of the application of buckling restrained braces (BRBs) for seismic retrofitting of steel moment resisting framed buildings through fragility analysis. Samples of regular three-storey and eight-storey steel moment resisting frames were designed with lateral stiffness insufficient to comply with the code drift limitations imposed for steel moment resisting frame systems in earthquake-prone regions. The frames were then retrofitted with concentrically chevron conventional braces and BRBs. To obtain robust estimators of the seismic reliability, a database including a wide range of natural earthquake ground motion records with markedly different characteristics was used in the fragility analysis. Nonlinear time history analyses were utilized to analyze the structures subjected to these earthquake records. The improvement of seismic reliability achieved through the use of conventional braces and BRBs was evaluated by comparing the fragility curves of the three-storey and eight-storey model frames before and after retrofits, considering the probabilities of four distinct damage states. Moreover, the feasibility of mitigating the seismic response of moment resisting steel structures by using conventional braces and BRBs was determined through seismic risk analysis. The results obtained indicate that both conventional braces and especially BRBs improve significantly the seismic behavior of the original building by increasing the median values of the structural fragility curves and reducing the probabilities of exceedance of each damage state. © 2011 John Wiley & Sons, Ltd.
Cevik A.,University of Gaziantep
Expert Systems with Applications | Year: 2011
This study presents the application of soft computing techniques namely as genetic programming (GP) and stepwise regression (SR), neuro-fuzzy (NF) and neural networks (NN) for modeling of strength enhancement of FRP (fiber-reinforced polymer) confined concrete cylinders. The proposed soft computing models are based on experimental results collected from literature. The accuracy of the proposed soft computing models are quite satisfactory as compared to experimental results. Moreover the results of proposed soft computing formulations are compared with 10 models existing in the literature proposed by various researchers so far and are found to be by far more accurate. © 2010 Elsevier Ltd. All rights reserved.
Ozcift A.,University of Gaziantep
Computers in Biology and Medicine | Year: 2011
Supervised classification algorithms are commonly used in the designing of computer-aided diagnosis systems. In this study, we present a resampling strategy based Random Forests (RF) ensemble classifier to improve diagnosis of cardiac arrhythmia. Random forests is an ensemble classifier that consists of many decision trees and outputs the class that is the mode of the classs output by individual trees. In this way, an RF ensemble classifier performs better than a single tree from classification performance point of view. In general, multiclass datasets having unbalanced distribution of sample sizes are difficult to analyze in terms of class discrimination. Cardiac arrhythmia is such a dataset that has multiple classes with small sample sizes and it is therefore adequate to test our resampling based training strategy. The dataset contains 452 samples in fourteen types of arrhythmias and eleven of these classes have sample sizes less than 15. Our diagnosis strategy consists of two parts: (i) a correlation based feature selection algorithm is used to select relevant features from cardiac arrhythmia dataset. (ii) RF machine learning algorithm is used to evaluate the performance of selected features with and without simple random sampling to evaluate the efficiency of proposed training strategy. The resultant accuracy of the classifier is found to be 90.0% and this is a quite high diagnosis performance for cardiac arrhythmia. Furthermore, three case studies, i.e., thyroid, cardiotocography and audiology, are used to benchmark the effectiveness of the proposed method. The results of experiments demonstrated the efficiency of random sampling strategy in training RF ensemble classification algorithm. © 2011 Elsevier Ltd.
Ozcift A.,University of Gaziantep
Journal of Medical Systems | Year: 2012
Parkinson disease (PD) is an age-related deterioration of certain nerve systems, which affects movement, balance, and muscle control of clients. PD is one of the common diseases which affect 1% of people older than 60 years. A new classification scheme based on support vector machine (SVM) selected features to train rotation forest (RF) ensemble classifiers is presented for improving diagnosis of PD. The dataset contains records of voice measurements from 31 people, 23 with PD and each record in the dataset is defined with 22 features. The diagnosis model first makes use of a linear SVM to select ten most relevant features from 22. As a second step of the classification model, six different classifiers are trained with the subset of features. Subsequently, at the third step, the accuracies of classifiers are improved by the utilization of RF ensemble classification strategy. The results of the experiments are evaluated using three metrics; classification accuracy (ACC), Kappa Error (KE) and Area under the Receiver Operating Characteristic (ROC) Curve (AUC). Performance measures of two base classifiers, i.e. KStar and IBk, demonstrated an apparent increase in PD diagnosis accuracy compared to similar studies in literature. After all, application of RF ensemble classification scheme improved PD diagnosis in 5 of 6 classifiers significantly. We, numerically, obtained about 97% accuracy in RF ensemble of IBk (a K-Nearest Neighbor variant) algorithm, which is a quite high performance for Parkinson disease diagnosis. © 2011 Springer Science+Business Media, LLC.
Kaplanoglu V.,University of Gaziantep
Applied Soft Computing Journal | Year: 2014
Scheduling of single machine in manufacturing systems is especially complex when the order arrivals are dynamic. The complexity of the problem increases by considering the sequence-dependent setup times and machine maintenance in dynamic manufacturing environment. Computational experiments in literature showed that even solving the static single machine scheduling problem without considering regular maintenance activities is NP-hard. Multi-agent systems, a branch of artificial intelligence provide a new alternative way for solving dynamic and complex problems. In this paper a collaborative multi-agent based optimization method is proposed for single machine scheduling problem with sequence-dependent setup times and maintenance constraints. The problem is solved under the condition of both regular and irregular maintenance activities. The solutions of multi-agent based approach are compared with some static single machine scheduling problem sets which are available in the literature. The method is also tested under real-time manufacturing environment where computational time plays a critical role during decision making process. © 2014 Elsevier B.V.