130 research outputs found
Bibliometric analysis of the results of Reiki research
No full textIntroduction: This study analyses bibliometric indicators to assess global research trends on Reiki, including publication patterns and key contributing countries. Methods: The publications included in Web of Science (WOS) databases between 1970 and 2024 were reviewed. The WOS database was searched using TS= “REIKI” and all WOS indexes were included. This search identified 414 studies. Among them, letters (n = 7), editorial materials (n = 19), book reviews (n = 9), corrections (n = 2), art and poetry (n = 1), news (n = 1), and retracted publications (n = 1) were removed. The remaining 374 articles were included in this bibliometric analysis. The R-package for bibliometric analysis (Bibliometrix) was used. Results: The bibliometric analysis found that researchers published Reiki-related studies between 1983 and 2024, with a publication growth rate of 4.47 %. On average, articles were 9.5 years old and received 13 citations per document. The total number of keywords identified by the authors was 553, the number of authors in the articles was 1124 authors, the number of single-author articles was 70, the average number of publications per author was 3.5, and international co-authorships were 7.219 %. The USA, Brazil, the United Kingdom, Canada, and Turkiye were the top five countries that published on Reiki, respectively. The USA, Canada, the United Kingdom, Australia, and Turkiye were the top five countries with the most cited articles, respectively. Vitale A. is the most cited author. Journal of Alternative and Complementary Medicine is the most cited journal. Conclusion: This study is the first of its kind to evaluate the articles written by researchers in the field of Reiki only. Despite the difficulties researchers experienced in the field of Reiki, the studies have increasingly been conducted in this field over the years and have been cited studies more. This study may be helpful for researchers to determine productive countries, journals, authors, and emerging trends in Reiki by providing comprehensive analyses and structured information on this subject. © 202
Complexities of the chemogenetic toolkit: Differential mDAAO activation by d-amino substrates and subcellular targeting
No full textExperimenting and thematically analyzing the project study to increase effectiveness of the case of the formation of seasons
No full textTeacher education is a critical component of the educational system, as it provides the skills and knowledge necessary for effective instruction in science and other related subjects. The purpose of this research is to enhance the incomplete and incorrect knowledge and skills of science teachers related to the formation of seasons by using a physical model that reveals the “change in the amount of energy per unit surface (CAEUS)”. Twenty-nine teachers from different regions of the country conducted experimental studies with physical models related to the formation of seasons, as part of a 5-day training program that included topics on astronomy. In this study, in which descriptive statistics, inferential statistics, and thematic analysis were used, it was observed that the comprehension of the causes and effects of the change in the amount of energy per unit surface was very successful in the development of mental models for understanding the seasons. Furthermore, the teachers reported that they were able to use the knowledge and skills they gained from the program in their teaching practice. The results obtained were compared with the literature and interpreted accordingly. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023
Evaluation of Spiritual Care and Well-Being Levels of Individuals Diagnosed with Lung Cancer in Turkey
No full textThis study aimed to assess the spiritual care needs and spiritual well-being levels of lung cancer patients undergoing chemotherapy (CT). This descriptive cross-sectional study was conducted with 110 patients in the outpatient CT unit of a university hospital. Data were collected using a personal information form, the “Three-Factor Spiritual Well-Being Scale” and the “Spiritual Care Needs Scale.” The average age of participants was 62.6 ± 8.0 years. Patients with a university or above education level, civil servants, self-employed individuals, those receiving only CT, and those with less than 5 CT cycles had significantly higher spiritual well-being scores (p < 0.05). Spiritual care needs scale scores were significantly higher for married individuals and those receiving only CT (p < 0.05). In conclusion, both spiritual well-being levels and spiritual care needs were observed to be high among lung cancer patients. © The Author(s) 2024
Autoxidized Oleic Acid Bifunctional Macro Peroxide Initiators for Free Radical and Condensation Polymerization. Synthesis and Characterization of Multiblock Copolymers
No full textSecilmis Canbay, Hale/0000-0002-3783-8064; Hazer, Baki/0000-0001-8770-805XWOS: 000491549500023TARAMASCOPUSIndex: SCI-E, WOS, ScopusTARAMAWOSAutoxidation of unsaturated fatty acids gives fatty acid macroperoxide initiators containing two functionalities which can lead to free radical and condensation polymerizations in a single pot. The oleic acid macroperoxide initiator obtained by ecofriendly autoxidation (Pole4m) was used in both the free radical polymerization of styrene and the condensation polymerization with amine-terminated polyethylene glycol (PEGNH2) to obtain triblock branched graft copolymers. The narrow molar masses of the poly oleic acid-g-styrene (PoleS) and poly oleic acid-g-styrene-g-PEG (PoSG) graft copolymers were successfully obtained. The inclusion of oleic acid decreased the glass transition temperature of the polystyrene segment because of the plasticizing effect of oleic acid. In addition, a mechanical property of the copolymer was improved when compared with the pure PS. Structural characterization, morphology of the fracture surface, micelle formation, thermal analysis and molar masses of the obtained products were also evaluated.Kapadokya University Research Fund [KUN.2018-BAGP-001]; Bulent Ecevit University Research FundBulent Ecevit University [BEU-2017-72118496-01]This work was supported by the Kapadokya University Research Fund (KUN.2018-BAGP-001) and Bulent Ecevit University Research Fund (#BEU-2017-72118496-01). The Authors thank to Koray Alper and Fatih Pekdemir for taking SEM and FTIR spectra, respectively. The Authors thank to Serdar Coban, Sidika Sarac Tabakli and Gulsen Darici (Cilas Kaucuk, Devrek, Zonguldak, Turkey) for taking stress-strain measurements
A novel neighborhood generation method for heuristics and application to traveling salesman problem
No full textÖztürk, Melike (Dogus Author) -- Conference full title: International Conference on Intelligent and Fuzzy Systems, INFUS 2019; Istanbul; Turkey; 23 July 2019 through 25 July 2019.This paper presents a novel neighbor generation mechanism for heuristic algorithms in which a permutation solution representation is utilized. The mechanism, called cantor-set based (CB) method, is inspired by the recursive algorithm which is used to construct a famous fractal shape, namely a cantor set. CB method was embedded into the classical local search (LS) algorithm to show its advantage of escaping from local optima providing big jumps in the landscape. CB method benefits from the self-similarity aspect of the fractal shapes to generate neighbor solutions Several variations of the CB method were designed to find the most effective variation on the classical traveling salesman problem (TSP). To make comparisons, swap and insertion mechanisms were also embedded into LS separately for solving the TSP. Finally, the methods were compared using a set of benchmark problems with varying city sizes. The computational tests exhibit that CB method gives better results than swap and insertion mechanisms in terms of effectiveness
Helmholtz principle based supervised and unsupervised feature selection methods for text mining
No full textTutkan, Melike (Dogus Author) -- Akyokuş, Selim (Dogus Author)One of the important problems in text classification is the high dimensionality of the feature space. Feature selection methods are used to reduce the dimensionality of the feature space by selecting the most valuable features for classification. Apart from reducing the dimensionality, feature selection methods have potential to improve text classifiers' performance both in terms of accuracy and time. Furthermore, it helps to build simpler and as a result more comprehensible models. In this study we propose new methods for feature selection from textual data, called Meaning Based Feature Selection (MBFS) which is based on the Helmholtz principle from the Gestalt theory of human perception which is used in image processing. The proposed approaches are extensively evaluated by their effect on the classification performance of two well-known classifiers on several datasets and compared with several feature selection algorithms commonly used in text mining. Our results demonstrate the value of the MBFS methods in terms of classification accuracy and execution time
Author, genre and gender identification using deep learning algorithms
No full textGünümüzde artan veri miktarı, bu verilerin sınıflandırılma ihtiyacını beraberinde getirmiştir. Sınıflandırma, benzer özellikte olan verilerin kategorize edilmesi işlemidir. Bu çalışmada, veri olarak Türkçe haber metinlerinin seçildiği ve bu verilerin yazar, tür ve cinsiyete göre sınıflandırılabilmelerini sağlayan, makine öğrenmesi ve derin öğrenme algoritmalarının sınıflandırıcı olarak kullanıldığı geniş kapsamlı bir modelleme çalışması yapılması amaçlanmıştır. Bu amaçla ilk olarak, bir gazetenin köşe yazarlarına ait köşe yazılarını içeren, yazar tanıma, tür tanıma ve cinsiyet tanıma işlemlerinde kullanılabilecek, büyük ölçekli ve çoklu sınıflara sahip, toplam 14 adet yeni veri seti oluşturulmuştur. Yazar tanıma için 7, tür tanıma için 6 ve cinsiyet tanıma için de 1 adet olan bu veri setleri, Türkçe diline özel, doğal dil işleme adımlarından geçirilerek, sınıflandırma işlemlerinin yapılacağı sınıflandırıcıların uygulandığı ve en yüksek doğruluk başarılarının araştırıldığı, modelleme aşaması için hazır hale getirilmiştir. Modelleme aşamasında, Türkçe metinlerde yazar tanıma, tür tanıma ve cinsiyet tanıma problemlerinin çözümüne yönelik makine öğrenmesi algoritmalarından Multinominal Naive Bayes (MNB) ve Random Forest (RF) algoritmaları, derin öğrenme algoritmalarından da Convolutional Neural Networks (CNN) ve Long Short Term Memory (LSTM) algoritmaları, sınıflandırıcı olarak veri setlerine uygulanmıştır. Ayrıca, bu sınıflandırıcılardan en yüksek performansın alındığı hiperparametre değerleri, uzun deneysel çalışmalar sonucunda bulunmaya çalışılmıştır. Modelleme sonucunda, her bir veri seti için en iyi modellere ait, doğruluk, kesinlik ve duyarlılık değerleri kullanılarak her modelin performansı bulunmuştur. Modelleme aşamasının sonucunda, yazar tanıma için, genel olarak tüm veri setleri arasında, en yüksek başarının alındığı en iyi model, % 95,81 doğruluk başarı değeriyle, AI-TNKU-7 veri seti için, CNN algoritmasının sınıflandırıcı olarak kullanıldığı model olarak bulunmuştur. Tür tanıma içinse, en yüksek başarının alındığı en iyi model, GI-TNKU-6 veri seti için LSTM algoritmasının sınıflandırıcı olarak kullanıldığı ve %96,73 doğruluk başarı değerinin alındığı model olmuştur. Cinsiyet tanıma için de, en yüksek başarının alındığı en iyi model, %88,68 doğruluk başarı değeriyle LSTM algoritmasının sınıflandırıcı olarak kullanıldığı model olarak bulunmuştur.Nowadays, the increasing amount of data has brought the need to classify these data. Classification is the process of categorizing similar data. In this study, it is aimed to make a modeling study in which Turkish news texts are selected as data and that these data can be classified according to author, genre and gender, machine learning and deep learning algorithms are used as classifiers. For this purpose, firstly, a total of 14 new data sets with large-scale and multiple classes, which can be used in author identification, genre identification and gender identification processes, containing columnists of a newspaper, were created. These data sets, which are 7 for author identification, 6 for genre identification and 1 for gender identification, have been made ready for the modeling phase, where the classifiers for identification are applied and the highest accuracy successes are investigated by passing through natural language processing steps specific to Turkish language. In the modeling phase, Multinominal Naive Bayes (MNB) and Random Forest (RF) algorithms, which are machine learning algorithms for the solution of author identification, genre identification and gender identification problems in Turkish texts, and Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) from deep learning algorithms have been applied to data sets as classifiers. In addition, hyperparameter values with the highest performance from these classifiers have been tried to be found as a result of long experimental studies. As a result of modeling, using the accuracy, precision and recall values of the best models for each data set, the performance of each model was found. As a result of the modeling stage for author identification, it was seen that the CNN algorithm achieved the highest 95.81% accuracy in the AI-TNKU-7 data set compared to other algorithms used. As a result of the modeling for genre identification, an accuracy of 96.73% was achieved with the LSTM algorithm in the GI-TNKU-6 data set. It has been observed that the success of deep learning algorithms is higher than machine learning algorithms in other data sets used in genre identification. As a result of the modeling phase for gender identification, the LSTM algorithm performed better than other classifiers and an accuracy success of 88.68% was achieved
Reklam çevirilerinin çeviribilim bağlamında incelenmesi ve reklam çevirilerinde kültürün önemi
No full textÖzbey, Melike (Dogus Author) -- 13.07.2018 tarihine kadar kullanımı yazar tarafından kısıtlanmıştır.Bu çalışmanın amacı, reklam çevirilerinde kültürün önemini çeviribilim bağlamında incelemektir. Çeviribilim, yazılı ve sözlü çevirinin teori, betimleme yerelleştirme ve uygulamasını konu alan bir bilim dalıdır. İletişim teknolojilerinin ve küreselleşmenin gelişimi, uluslararası ticareti kolaylaşmasına katkıda bulunmuştur. Bu yeni küresel pazarda reklam ve pazarlama teknikleri önem kazanmıştır. Bir reklamın temel amacı, potansiyel müşterinin dikkatini çekmektir. Küresel pazarda geniş reklam yelpazesinde reklam çevirileri önem kazanmıştır. Bu tez reklam çevirilerde kültürünün önemini tartışacaktır. Böyle bir argümana ulaşmak için, reklam ve pazarlama kavramları açıklanacak ve Çeviribilimin teorik geçmişi ile bilgi verildikten sonra bu çalışma, çağdaş çeviribilim teorileri bağlamında farklı reklam metinlerinden örnekleri inceleyerek reklam çevirilerinde kültürün önemi vurgulanacaktır.The purpose of this study is to examine the importance of culture in advertisement translations in context of Translation Studies. Translation Studies is a field that deals with the study of the theory, description, interpretation and application of translation and localization. The development of communication technologies and globalization contributed to facilitating the international trade. In this new global market advertisement and marketing techniques has come into prominence. The main purpose of an advertisement is to attract consumer's attention. Accordingly, a wide range of advertisements in the global market and the translation of advertisements gain importance. This thesis will discuss the importance of culture in advertisement translations. In building up this argument, the concept of advertisement and marketing will be explained. After giving the theoretical background of Contemporary Translation Studies Theories, different examples from different advertisement texts will be analyzed in the light of Translation Studies. Throughout the study, the importance of culture in advertisement translations will be highlighted
Author and genre identification of Turkish news texts using deep learning algorithms
No full textNowadays, the increasing amount of data has brought the need to classify the data. Text classification is the process of categorizing similar text data. This paper aims to make a modeling study for author and genre identification, which is one of the important challenges of text classification, for Turkish news texts by using machine and deep learning algorithms. For this purpose, firstly, a total of 13 large-scale datasets having multi classes are built as new datasets. In the modeling stage, Multinomial Naive Bayes (MNB), Random Forest (RF), Convolutional Neural Network (CNN), and Long Short Term Memory (LSTM) algorithms were applied to the datasets. Results showed that for dataset AI-TNKU-7, the CNN algorithm demonstrated the highest accuracy for author identification at 95.81%. In relation to genre identification, the LSTM algorithm for the dataset GI-TNKU-6 demonstrated the highest accuracy at 96.73%
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