Meslek Yüksek Okulları / Vocational Schools

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Start date: 2020End date: 2023

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    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; 37536028
    In 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.
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    Anaesthetic Management for Orthotopic Liver Transplantation in A Patient with Glycogen Storage Disease Type Iiia
    (2023) Haka, D.; Cekmen, N.; 0000-0001-7448-8203
    Glycogen storage disease (GSD) type III is a metabolic disorder caused by a deficiency in amylo-1,6-glucosidase enzyme, which is responsible for the breakdown of the glycogen molecule, resulting in glycogen accumulating in the organs, hypoglycaemia, muscle weakness, liver dysfunction, delayed anaesthetic recovery, excessive surgical bleeding, cardiomyopathy and end-organ dysfunction. This case report presents a child with GSD type IIIa who underwent orthotopic liver transplantation (OLT) with her mother as a donor. A multidisciplinary approach should be provided to optimise the preoperative period and minimise complications in these patients.
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    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-9613-2023
    Using 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.
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    Sequential Decision Making for Elevator Control
    (2023) Tartan, Emre Oner; Ciflikli, Cebrail; 0000-0002-5688-4226; JVD-9650-2023
    In the last decade Reinforcement Learning (RL) has significantly changed the conventional control paradigm in many fields. RL approach is spreading with many applications such as autonomous driving and industry automation. Markov Decision Process (MDP) forms a mathematical idealized basis for RL if the explicit model is available. Dynamic programming allows to find an optimal policy for sequential decision making in a MDP. In this study we consider the elevator control as a sequential decision making problem, describe it as a MDP with finite state space and solve it using dynamic programming. At each decision making time step we aim to take the optimal action to minimize the total of hall call waiting times in the episodic task. We consider a sample 6-floor building and simulate the proposed method in comparison with the conventional Nearest Car Method (NCM).
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    The Effects of Using Artificial Intelligence and Robotics in Logistics Service Production: An Application in 3pls and 4pls
    (2023) Caglar, Macide Berna; Taskin, Bihter Karagoz
    Background: The purpose of this study is to investigate how artificial intelligence (AI) and robotic awareness, perceived organizational support, and competitive psychological climate approaches relate to turnover intention. In the literature, studies on robotic awareness and turnover intention have been undertaken in a variety of industries. In this respect, this study aims to address the absence in the literature of research on logistics services providers. This study aims to help businesses understand how to retain employees and foster a more inclusive and supportive workplace.Methods: The study utilizes survey information from 100 senior managers in the operations function of logistics service providers. The outcomes are obtained by modeling structural equations with SmartPLS. Data from the survey were gathered using the snowball sampling technique.Results: The results of the research reveal the effect of artificial intelligence and robotic awareness on competitive psychological and turnover intention.Conclusions: The study aims to explore the role of a competitive psychological climate and organizational support in mediating the relationship between AI and robotics awareness and turnover intention. We identify that awareness of AI and robotics has a considerable, favorable effect on the psychological climate of competition and turnover intention. We also find that the competitive psychological atmosphere has a substantial, favorable effect on turnover intention. In addition, organizational support has been demonstrated to have a substantial, favorable effect on turnover intention. However, it was not possible to identify the mediating role of organizational support and the psychological environment of competition in moderating the association between awareness of AI and robotics and turnover intention. On the basis of the research's findings, suggestions were made.
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    Bibliometric Analysis of Articles Published in Cukurova Medical Journal Between 2011 and 2022
    (2023) Polat, Sema; Tunc, Mahmut; Ozsahin, Esin; Tamam, Lut; Goker, Pinar; 0000-0003-1373-4700
    Purpose: This study aimed to perform a bibliometric analysis of the abstracts and keywords in articles published in the Cukurova Medical Journal from 2011 to 2022. Materials and Methods: We compiled and analyzed all articles published in the Cukurova Medical Journal between 2011 and 2022, totaling 1734 articles, using VOSviewer software (version 1.6.19). This analysis focused on the terms in the abstracts and the keywords of these articles. Results: The study identified 27,409 unique terms and 4,040 unique keywords in the abstracts of the 1734 articles. The most common terms were 'surgery' (333 occurrences), 'pain' (263), 'infection' (201), 'measurement' (192), 'rat' (185), 'tumor' (177), 'covid' (157), 'pregnancy' (148), 'questionnaire' (144), and 'drug' (142). The top keywords were 'children' (43 occurrences), 'quality of life' (37), 'covid-19' (34), 'nursing' (32), 'pregnancy' (28), 'depression' (27), 'mortality' (26), 'anxiety' (24), 'child' (22), and 'obesity' (17). Conclusion: This is the first bibliometric analysis of keywords and terms used in the Cukurova Medical Journal, offering insights into the evolving topics of interest in the journal's publications. It also provides valuable information for researchers looking to submit articles to the journal, highlighting prevalent themes and content areas.
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    Rates of Weighted Statistical Convergence for A Generalization of Positive Linear Operators
    (2023) Ilbey, Reyhan Canatan; Dogru, Ogun
    In 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.
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    Daily Life Activities of Children during the Pandemic
    (2021) Yersel, Beyhan Ozge; Gunes, Luegen Ceren; Luegen, Ceren
    The 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.
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    Brain Tumor Prediction with Deep Learning and Tumor Volume Calculation
    (2021) Karayegen, Gokay; Aksahin, Mehmet Feyzi
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    Deep Learning Based Multi Modal Approach for Pathological Sounds Classification
    (2020) Ankishan, Haydar; Kocoglu, Arif
    Automatic 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.