Uguz F.,University of Konya
Journal of Maternal-Fetal and Neonatal Medicine | Year: 2013
Data on the use of a combination of antidepressants during pregnancy are inadequate. This report presents the beneficial effect of low-dose mirtazapine added onto selective serotonin reuptake inhibitors in the treatment of the symptoms of severe nausea, insomnia and loss of appetite accompanying psychiatric disorders during pregnancy, which is an important problem in clinical practice. The psychiatric diagnoses were determined with the Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. Assessments were performed with the Clinical Global Impression-Improvement Scale and the 17-item Hamilton Rating Scale for Depression. Further studies should be carried out to confirm the positive effects and safety of an additional low-dose mirtazapine in these cases. © 2013 Informa UK Ltd.
Karabulut I.,University of Konya
Applied Surface Science | Year: 2010
In this paper the effect of the laser field on the nonlinear optical properties of a square quantum well under the applied electric field is investigated theoretically. The calculations are performed in saturation limit using the density matrix formalism and the effective mass approach. Our results show that the laser field considerably effects the confining potential of the quantum well and thus the nonlinear optical properties. © 2010 Elsevier B.V.
Torlak E.,University of Konya
International Journal of Food Microbiology | Year: 2014
Alicyclobacillus acidoterrestris survives during the typical pasteurization process and can cause the spoilage of fruit juices thanks to its spore forming and thermo-acidophilic nature. In recent years, A. acidoterrestris has become a major concern to the fruit juices industry worldwide. This study was undertaken to evaluate ozone for the reducing number of A. acidoterrestris spores in apple juice. Apple juice inoculated with A. acidoterrestris spores was bubbled with continuous stream of two different constant concentrations (2.8 and 5.3. mg/L) of ozone at 4 and 22. °C up to 40. min. Level of A. acidoterrestris spores in juice decreased by 2.2 and 2.8 log after 40. min of ozonation at 4. °C with concentrations of 2.8 and 5.3. mg/L, respectively. Treatments at 22. °C for 40. min with 2.8 and 5.3. mg/L ozone resulted in 1.8 and 2.4 log reductions of spore viability, respectively. At the ozone concentration of 5.3. mg/L, significant (P<. 0.05) reductions were observed in total phenolic content of juice at both temperature levels. However, treatments performed at 2.8. mg/L were observed to have no significant (P>. 0.05) effect on total phenolic content. The results presented in this study indicate that over the 2 log reduction in the count of A. acidoterrestris spores in apple juice can be achieved by bubbling ozonation at 4. °C without causing a significant decrease in total phenolic content of product. Therefore, it can be suggested that bubbling ozonation is a promising method for the control of A. acidoterrestris in fruit juices. © 2013 Elsevier B.V.
Uguz H.,University of Konya
Knowledge-Based Systems | Year: 2011
Text categorization is widely used when organizing documents in a digital form. Due to the increasing number of documents in digital form, automated text categorization has become more promising in the last ten years. A major problem of text categorization is its large number of features. Most of those are irrelevant noise that can mislead the classifier. Therefore, feature selection is often used in text categorization to reduce the dimensionality of the feature space and to improve performance. In this study, two-stage feature selection and feature extraction is used to improve the performance of text categorization. In the first stage, each term within the document is ranked depending on their importance for classification using the information gain (IG) method. In the second stage, genetic algorithm (GA) and principal component analysis (PCA) feature selection and feature extraction methods are applied separately to the terms which are ranked in decreasing order of importance, and a dimension reduction is carried out. Thereby, during text categorization, terms of less importance are ignored, and feature selection and extraction methods are applied to the terms of highest importance; thus, the computational time and complexity of categorization is reduced. To evaluate the effectiveness of dimension reduction methods on our purposed model, experiments are conducted using the k-nearest neighbour (KNN) and C4.5 decision tree algorithm on Reuters-21,578 and Classic3 datasets collection for text categorization. The experimental results show that the proposed model is able to achieve high categorization effectiveness as measured by precision, recall and F-measure. © 2011 Elsevier B.V. All rights reserved.
Dincer F.,University of Konya
Renewable and Sustainable Energy Reviews | Year: 2011
Energy is an essential ingredient of socio-economic development and economic growth. Many countries frequently held meetings and discussions has energy agenda. These countries are working to balance energy demand and supply. Renewable energy technology is one of the solutions, which produces energy by transforming natural phenomena into useful energy forms. Wind energy is a reliable and promising renewable energy. Wind energy becomes more and more attractive as one of the clean renewable energy resources. The installed capacity of electricity generation from wind energy is rapidly increasing in many countries and these countries are implementing variety incentive policies. Therefore, the importance of wind energy is expected to increase much more in the coming decades. The presented study comprehensively reviews wind energy in terms of three aspects, namely status, potential and policies analyses and assessments, for the first time to the best of the authors knowledge. This review paper covers the status, potential and policies of the wind energy, the issues faced, and the latest research. Also, the current situation, potential and development of the increasing are discussed. This study is presented recommendations for increasing the installed capacity of wind power. © 2011 Elsevier Ltd. All rights reserved.
Kus R.,University of Konya
Energy Education Science and Technology Part A: Energy Science and Research | Year: 2011
The energy requirement in the world is provided from fossil energy resources. However, the decrease in fossil energy resources and their negative impacts on the environment has increased the attention towards alternative energy resources. One of the alternative energy resources is vegetable oils. In this study, the effect of 50% raw corn oil and 50% diesel fuel (CO50) mixtures on the engine performances and emissions is investigated on a single-cylinder direct injection engine. With the use of CO50 fuel, we observed 4.153% decrease in engine power and 10.53% increase in specific fuel consumption on average compared to diesel fuel. In NO x and CO emissions, relatively, the decrease rate is 23% and 35.2%. © Sila Science.
Salman M.S.,University of Konya
International Journal of Adaptive Control and Signal Processing | Year: 2014
In this paper, a novel adaptive filter for sparse systems is proposed. The proposed algorithm incorporates a log-sum penalty into the cost function of the standard leaky least mean square (LMS) algorithm, which results in a shrinkage in the update equation. This shrinkage, in turn, enhances the performance of the adaptive filter, especially, when the majority of unknown system coefficients are zero. Convergence analysis of the proposed algorithm is presented, and a stability criterion for the algorithm is derived. This algorithm is given a name of zero-attracting leaky-LMS (ZA-LLMS) algorithm. The performance of the proposed ZA-LLMS algorithm is compared to those of the standard leaky-LMS and ZA-LMS algorithms in sparse system identification settings, and it shows superior performance compared to the aforementioned algorithms. Copyright © 2013 John Wiley & Sons, Ltd.
Uguz F.,University of Konya
General Hospital Psychiatry | Year: 2014
This report presents the successful use of low-dose mirtazapine in the treatment of major depression that developed following severe nausea and vomiting symptoms during the early gestational weeks in two cases. The psychiatric diagnosis was determined with the Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. Assessments were performed with the Clinical Global Impression - Improvement Scale and the 17-item Hamilton Rating Scale for Depression. Further large-scale studies should be carried out to confirm the useful effects observed in these cases. © 2014 Elsevier Inc.
Korkmaz O.,University of Konya
Computers and Education | Year: 2012
Determination of students' attitudes towards online cooperative learning is an important issue, which has not been studied adequately. In the literature, there are few scales to measure the attitude towards online cooperative learning for which validity and reliability have been proven. The main purpose of this study is to develop an attitude scale in order to specify students' attitudes towards online cooperative learning. The sample group is composed of 599 students for the first application and 242 students for the second. In order to detect the validity of the scale, exploratory and confirmatory factor analyses, item factor total correlations, corrected correlations and item discriminations were conducted. In order to assess the reliability of the scale, the level of internal consistency and the stability levels were calculated. OCLAS is a five-point Likert-type scale and includes 17 items that can be gathered under 2 factors. The analyses provided evidence that the Online Cooperative Learning Attitude Scale (OCLAS) is a valid and reliable scale that can be used in order to determine students' attitudes towards cooperative learning in online environments. © 2012 Elsevier Ltd. All rights reserved.
Asilturk I.,University of Konya |
Cunkas M.,University of Konya
Expert Systems with Applications | Year: 2011
Machine parts during their useful life are significantly influenced by surface roughness quality. The machining process is more complex, and therefore, it is very hard to develop a comprehensive model involving all cutting parameters. In this study, the surface roughness is measured during turning at different cutting parameters such as speed, feed, and depth of cut. Full factorial experimental design is implemented to increase the confidence limit and reliability of the experimental data. Artificial neural networks (ANN) and multiple regression approaches are used to model the surface roughness of AISI 1040 steel. Multiple regression and neural network-based models are compared using statistical methods. It is clearly seen that the proposed models are capable of prediction of the surface roughness. The ANN model estimates the surface roughness with high accuracy compared to the multiple regression model. © 2010 Elsevier Ltd. All rights reserved.