Ahi Evran University

About: Ahi Evran University is a education organization based out in Kırşehir, Turkey. It is known for research contribution in the topics: Density functional theory & Ab initio. The organization has 842 authors who have published 2270 publications receiving 21904 citations.

Study on physiochemical structure and in vitro release behaviors of doxycycline‐loaded PCL microspheres

Abstract: This study aimed to develop drug delivery system of doxycycline-loaded polycaprolactone (PCL) microspheres The investigated microsphere formulation can be considered for local application in bone infections and degenerative joint diseases, which generally require long-term treatments via systemic drugs PCL-14 kDa and 65 kDa were used in microsphere preparation Before release, the microspheres were characterized by scanning electron microscopy, differential scanning calorimetry, and X-ray photoelectron spectroscopy The mean particle size of microspheres was in the range of 74–122 µm and their drug loadings ranged between 10 and 30% In vitro release profiles were described using the Higuchi and the Korsmeyer–Peppas equations Diffusion model was applied to experimental data for estimating diffusion coefficients of microspheres; calculated as between 45 × 10−10 and 95 × 10−10 cm2/s Although long-term release from microspheres of PCL-14 kDa obeyed diffusion model, PCL-65 kDa microspheres showed this tendency only for some period Modeling studies showed that the drug release mechanism was mainly dependent on loading and molecular weight differences Release behavior of PCL-65 kDa microspheres, however, might be better represented by derivation of a different equation to model for the total release period © 2014 Wiley Periodicals, Inc J Appl Polym Sci 2015, 132, 41768

An Analysis of the Relationship between Internet Addiction and Depression Levels of High School Students.

Abstract: The concept of internet addiction refers to the excessive use of internet which in turn causes various problems in individual, social and professional aspects. The aim of this study is to determine the relationship between depression and internet addiction in terms of grades, sex, the existence of internet connection at home and time spent on internet. The study used survey model. The study group consisted of 369 adolescents from three high schools of different socio-economic background. Data was collected by means of “Personal Information Form”, “Internet Addiction Scale” and “Beck Depression Inventory”. Arithmetic mean, standard deviation, independent sampling t test, ANOVA and Scheffe tests were performed on collected data. The results of the study show that the internet addiction and depression scores of the adolescents are low and the internet addiction levels of ninth and tenth grade students with compared to the eleventh grade ones; of males with compared to females; of those having computer at home with compared to the ones having no internet at home and of the ones spending much more time on internet with compared to those spending less time have been found to be higher. No significant difference has been found between depression and internet addiction.

Photocatalytic activity of polyvinyl borate/titanium dioxide composites for UV light degradation of organic pollutants

Abstract: In this study, it is aimed to prepare polyvinyl borate (PVB)/titanium dioxide (TiO2) composites through the condensation reaction of polyvinyl alcohol and boric acid in the presence of TiO2...

Fiber optic tactile sensor for surface roughness recognition by machine learning algorithms

Abstract: In this study, a sensor tip with a metallic hemispherical nozzle tip (MHNT) design based on the Fabry-Perot interferometer was developed for surface roughness recognition (SRR). Sandpaper samples with ten different arithmetical mean deviations of the surface (Sa) values were used as surfaces to be recognized. The feature vectors were found by applying the discrete wavelet transform (DWT) to the analog signals obtained from the sandpaper samples. Machine learning (ML) algorithms K-nearest neighbor (KNN) and support vector machine (SVM) were used for classification. An in-depth recognition process was carried out using the classifiers’ different length criteria and kernel types. In the test process, each category consists of two sub-categories as testing within the training dataset (TWITD) and testing without the training dataset (TWOTD). The experiments were carried out in a controlled manner with the conveyor belt system (CBS) and manual. As a result of the experimental studies, the average recognition rates (Rave) for CBS were found as 84.2% and 81.6% for TWITD and TWOTD, while the Rave for the manual are found as 80% and 77.5% for TWITD and TWOTD, respectively.