Celal Bayar University

About: Celal Bayar University is a education organization based out in Magnesia ad Sipylum, Turkey. It is known for research contribution in the topics: Population & Heat transfer. The organization has 2960 authors who have published 6024 publications receiving 100646 citations.

An evaluation of quality of life of mothers of children with enuresis nocturna

Abstract: The aim of this study was to evaluate the impact of enuresis nocturna on quality of life of the mothers. Mothers who have a child with monosymptomatic nocturnal enuresis (n=28) and mothers who have a child without any health problems (n=38) were enrolled in the study. Groups were in balance for background variables (child's age, gender, and number of siblings; mother's age, marital status, highest year of education completed, and occupation; presence of health insurance; and type of residence). Short-Form Health Survey (SF-36) Questionnaire, the Beck Depression Inventory (BDI), and Spielberg's State-Trait Anxiety Inventory (STAI) were applied to all mothers. The mothers of children with enuresis had significantly lower quality-of-life scores in the SF-36 for the bodily pain (p=0.015) and role emotional (p=0.014) subscales. We observed significant difference between groups according to BDI; mean score was higher in mothers who have a child with enuresis nocturna (p=0.017). There was no significant difference between groups according to the STAI. Significant differences according to bodily pain and role emotional subscales of SF-36, and the BDI scores, show that the mothers were negatively affected by having a child with monosymptomatic nocturnal enuresis.

Production of waste tyre oil and experimental investigation on combustion, engine performance and exhaust emissions

Abstract: In this study, waste tyre was pyrolyzed at different conditions such as temperature, heating rate and inert purging gas (N2) flow rate. Pyrolysis parameters were optimized. Optimum parameters were determined. The main objective of this study was to investigate combustion, performance and emissions of diesel and waste tyre oil fuel blend. Experimental investigation was performed in a single cylinder, direct injection, air cooled diesel engine at maximum engine torque speed of 2200 rpm and four different engine load including 3.75, 7.5, 11.25 and 15 Nm. The effects of waste tyre oil on combustion characteristics such as cylinder pressure, heat release rate, ignition delay (ID), combustion duration, engine performance were investigated. In-cylinder pressure and heat release rate increased with waste tyre oil fuel blend (W10) with the increase of engine load. In addition, ID was shortened with the increase of engine load for test fuels but it increased with the addition of waste tyre oil. Lower imep values were obtained because of the lower calorific value of waste tyre oil fuels. Maximum thermal efficiencies were determined as 28.27% and %25.12 with diesel and W10 respectively at 11.25 Nm engine load. When test results were examined, it was seen that waste tyre oil highly affected combustion characteristics, performance and emissions.

Ttc-3600

Abstract: Owing to the rapid growth of the World Wide Web, the number of documents that can be accessed via the Internet explosively increases with each passing day. Considering news portals in particular, sometimes documents related to categories such as technology, sports and politics seem to be in the wrong category or documents are located in a generic category called others. At this point, text categorization TC, which is generally addressed as a supervised learning task is needed. Although there are substantial number of studies conducted on TC in other languages, the number of studies conducted in Turkish is very limited owing to the lack of accessibility and usability of datasets created. In this paper, a new dataset named TTC-3600, which can be widely used in studies of TC of Turkish news and articles, is created. TTC-3600 is a well-documented dataset and its file formats are compatible with well-known text mining tools. Five widely used classifiers within the field of TC and two feature selection methods are evaluated on TTC-3600. The experimental results indicate that the best accuracy criterion value 91.03% is obtained with the combination of Random Forest classifier and attribute ranking-based feature selection method in all comparisons performed after pre-processing and feature selection steps. The publicly available TTC-3600 dataset and the experimental results of this study can be utilized in comparative experiments by other researchers.