Meslek Yüksek Okulları / Vocational Schools
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Item Evaluation of Radioactivity Levels and Radiological Hazards of Some Endemic Plants Used as Medicine in Ankara, Turkey(2023) Guven, Aysel; Cengiz, Gulcin Bilgici; Caglar, Ilyas; Ates, Simge; 37536028In this study, natural radioactivity levels (Ra-226, Th-232, and (4) K) of some medicinal plant samples with known anti-oxidative properties, which are frequently consumed by animals and humans, were obtained from Ankara province and its surroundings (Mamak, Kizilcahamam, Beypazari, Kahramankazan, and Polatli districts) were determined using a thallium-doped sodium iodide NaI(Tl) gamma spectrometry. By using the determined natural radioactivity concentrations in the collected plant samples, the number of radiological doses that people could be exposed by consuming these plants was calculated. As a result of the study, Ra-226, Th-232, and (4) K radioactivity concentration ranges of the plant samples were found be 14.69 +/- 1.27-59.08 +/- 3.12 Bq kg(-1), 1.78 +/- 0.04-50.05 +/- 2.76 Bq kg(-1) and 207.24 +/- 34.09-826.13 +/- 25.40 Bq kg(-1), respectively. The highest Ra-226, Th-232, and (4) K activity concentrations were measured in Astragalus densifolius subsp. ayashensis (Kahramankazan), Astragalus kochakii (Kahramankazan) and Rumex patientia (Patience Dock) (Kahramankazan) plants, respectively. The lowest Ra-226, Th-232 and,(4) K activity concentration plants were determined respectively as Rumex patientia (Mamak), Lavandula angustifolia (Kizilcahamam), and Astragalus acikirensis (Polatli). The establishment and routine repetition of environmental radioactivity monitoring programs in each region are important for human and animal health, and the results of this study gain importance for Ankara and its surroundings in terms of environmental health.Item The Effects of Illumination on the Current Conduction Mechanisms of the Au/C20H12/N-Si Schottky Barrier Diode (SBD)(2023) Bengi, Seda; 0000-0002-3348-0712; HPH-9613-2023Using the I-V characteristics both in the dark and under varied illumination-intensities (50-250 mW.cm(-2)) by 50 mW.cm(-2) steps in the wide range bias-voltage (+/- 5 V), specific fundamental electrical and photo effects parameters of the Au/C20H12/n-Si SBD were examined. Due to the creation of electron-hole pairs and their drift in opposite directions under the influence of an electric field, the value of photocurrent in the reverse bias (I-ph) increases when illumination density is increased. The barrier height (Phi(B0)) tended to decrease due to the increase in photocurrent, while the diode's ideality factor (n) increased with increasing illumination intensity. Also, the Schottky structure's open-circuit voltage (V-oc), short circuit current (I-sc), filling factor (FF), and efficiency (eta) were determined to be 0.358 V, 95.5 mu A, 33%, and 0.028% under 50 mW.cm(-2), respectively. The R-s and R-sh values found using Ohm's law are a vital function of illumination and voltage which decrease with increasing illumination intensity. Additionally, using the forward bias I-V data and accounting for the bias dependent of the effective barrier height (Phi(e)) and ideality factor n(V) for different illumination intensities, the energy density distribution profile of surface states (N-ss) was obtained. According to these results, the organic interlayer consisting of C20H12 is light-sensitive and suitable for optoelectronic devices such as photodetectors and photodiodes.Item Rates of Weighted Statistical Convergence for A Generalization of Positive Linear Operators(2023) Ilbey, Reyhan Canatan; Dogru, OgunIn the present paper, some direct and inverse theorems relating to a generalization of positive linear operators are given. Also some rates of weighted statistical convergence are computed by means of a weighted modulus of continiuty.Item The Transnational Spread of Turkish Television Soap Operas(2014) Karlidag, Serpil; Bulut, Selda; J-2443-2015Turkish soap operas that are primarily popular in Turkic Republics, Middle East, and the Balkans have in recent years spread to different countries. With the entry into Ukrainian, Pakistani, Russian, and Chinese markets last year, tens of Turkish soap operas now reach their viewers in over 50 countries, and generate export revenues. Short-term return on investment, new communication technologies, Istanbul's ` magnetic nature attracting new talents' and different sociocultural forces and policies play key role in such extensive spread of Turkish TV series. As cultural commodities, TV series, while the images and identities they contain spread, reach at the same time new customers through geographical expansion, and increase the earnings of producers. Yet, the distribution of soap operas is important as much as their production. This requires focusing on the commodities with geo-linguistic and geo-cultural markets rather than companies producing these commodities. Therefore, demand for these non-Western commodities of different geographies points to a contra-flow. In a sense, while new media centers are emerging elsewhere other than the United States of America (USA)-Europe axis, it can be spoken of a regional opposition to the Western hegemony, though there is not an important historical leitmotive or transformation. Thus, in this study, relations between power, cultural commodity, and geography and the spread of Turkish soap operas in different geographies will be discussed with a political economic approach by also drawing attention to the historical commonality.Item A Novel Approach for Estimating Heat Transfer Coefficients of Ethylene Glycol-Water Mixtures(2014) Bulut, Murat; Ankishan, Haydar; Demircioglu, Erdem; Ari, Seckin; Sengul, Orhan; https://orcid.org/0000-0002-6240-2545; AAH-4421-2019Ethylene glycol-water mixtures (EGWM) are vital for cooling engines in automotive industry. Scarce information is available in the literature for estimating the heat transfer coefficients (HTC) of EGWM using knowledge-based estimation techniques such as adaptive neuro-fuzzy inference systems (ANFIS) and artificial neural networks (ANN) which offer nonlinear input-output mapping. In this paper, the supervised learning methods of ANFIS and ANN are exploited for estimating the experimentally determined HTC. This original research fulfills the preceding modeling efforts on thermal properties of EGWM and HTC applications in the literature. An experimental test setup is designed to compute HTC of mixture over a small circular aluminum heater surface, 9.5 mm in diameter, placed at the bottom 40-mm-wide wall of a rectangular channel 3 mm x 40 mm in cross section. Measurement data are utilized as the train and test data sets of the estimation process. Prediction results have shown that ANFIS provide more accurate and reliable approximations compared to ANN. ANFIS present correlation factor of 98.81 %, whereas ANN estimate 87.83 % accuracy for test samples.Item Optimization of Waiting and Journey Time in Group Elevator System Using Genetic Algorithm(2014) Tartan, Emre Oner; Erdem, Hamit; Berkol, AliEfficient elevator group control is an important issue for vertical transportation in high-rise buildings. From the engineering design perspective, regulation of average waiting time and journey time while considering energy consumption is an optimization problem. Alternatively to the conventional algorithms for scheduling and dispatching cars to hall calls, intelligent systems based methods have drawn much attention in the last years. This study aims to improve the elevator group control system's performance by applying genetic algorithm based optimization algorithms considering two systems. Firstly, average passenger waiting time is optimized in the conventional elevator systems in which a hall call is submitted by indicating the travel direction. Secondly, a recent development in elevator industry is considered and it is assumed that instead of direction indicators there are destination button panels at floors that allow passengers to specify their destinations. In this case optimization of average waiting time, journey time and car trip time is investigated. Two proposed algorithms have been applied considering preload conditions in a building with 20 floors and 4 cars. The simulation results have been compared with a previous study and conventional duplex algorithm.Item Daily Life Activities of Children during the Pandemic(2021) Yersel, Beyhan Ozge; Gunes, Luegen Ceren; Luegen, CerenThe aim of this descriptive study was to examine the views of parents with children between the ages of 3-6 on their children's daily life activities during the pandemic. The study sample was composed of 65 parents, among whom 60 were mothers, and five were fathers, who were selected with the snowball method and who had children between the ages of 3-6 and voluntarily participated in the study. The data were collected through the General Information Form and the Family Interview Form, which were developed in line with expert opinions. The collected data were analyzed using percentage and frequency values. The findings suggested that, during the pandemic, the children's family relationships were positively affected; the duration of using technological tools increased; the children started to wash their hands more carefully; and duration of activities, such as drawing and chores, and plays increased. It was also found that the children mostly preferred piece assembly games; their physical movement needs were not fully satisfied; and there was no change in their health conditions, self-care skills, diet, sleep patterns, interactive book reading, and purposes of using technology. In line with the findings, parents, experts were given specific recommendations.Item Brain Tumor Prediction with Deep Learning and Tumor Volume Calculation(2021) Karayegen, Gokay; Aksahin, Mehmet FeyziItem Deep Learning Based Multi Modal Approach for Pathological Sounds Classification(2020) Ankishan, Haydar; Kocoglu, ArifAutomatic detection of voice disorders is very important because it makes the diagnosis process simpler, cheaper and less time consuming. In the literature, there are many studies available on the analysis of voice disorders based on the characteristics of the voice and subdividing the result of this analysis. In general, these studies have been carried out in order to subdivide the sound into pathological - normally sub - groups by means of certain classifiers as a result of subtraction of the features on frequency, time or hybrid axis. In contrast to existing approaches, in this study, a multiple- deep learning model using feature level fusion is proposed to distinguish pathological-normal sounds from each other. First, a feature vector (HOV) on the hybrid axis was obtained from the raw sound data. Then two CNN models were used. The first model has used raw audio data and the second model has used HOV as an input. Feature data in both model SoftMax layers were obtained as a matrix, and canonical correlation analysis (Canonical Correlation Analysis (CCA) was applied at feature level fusion. The new obtained feature vector was used as an input for multiple support vector machines (M-SVMs), Decision Tree (DTC) and naive bayes (NBC) classifiers. When the experimental results are examined, it is seen that the new multi-model based deep learning architecture provides superior success in classifying pathological sound data. With the results of the study, it will be possible to automatically detect and classify the pathology of these patients according to the proposed system.Item An Approach to the Classification of Environmental Sounds by LSTM Based Transfer Learning Method(2020) Ankishan, HaydarThis electronic Effective frequency extraction from acoustic environmental sounds in frequency and time axis increases the importance of voice recognition, sound detection, environmental classification in recently. For this purpose, there are many studies in the literature on the discrimination of acoustic environmental sounds. These studies generally perform these operations with the help of machine learning and deep learning algorithms. In this study, a new artificial intelligence architecture using two long short term memory networks (LSTM) is designed. The structure, which uses both raw data and the proposed feature vector at its inputs, is reinforced by the transfer learning approach. The obtained classification results were fused at the decision level. As a result of experimental studies, five different environmental acoustic sounds were subdivided into 97.15% test accuracy. In environmental studies conducted in pairs, it is seen that the environmental sounds have reached 100% accuracy. Experimental results have shown that the proposed artificial intelligence architecture with fusion support at decision level is capable of discriminating acoustic environmental sounds.