Yazar "Aydin, A." seçeneğine göre listele

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    Öğe
    Calculations of Double-Differential Neutron Emission Cross Sections for 9Be Target Nucleus at 14.2 MeV Neutron Energy
    (Springer, 2015) Sahan, M.; Tel, E.; Sahan, H.; Kara, A.; Aydin, A.; Kaplan, A.; Sarpun, I. H.
    In this study, we investigated neutron-emission spectra induced by (n,xn) nuclear reactions for the Be-9 structural fusion material at 14.2 MeV neutron energy. We calculated double-differential cross sections () with ALICE-2011 codeor the angles of 30A degrees, 60A degrees, 90A degrees, 120A degrees, and 150A degrees. Hybrid Monte Carlo simulation model have been used to calculate the double differential emission spectra for these different angles. The obtained results were compared with the measured data taken from EXFOR library. The results show an acceptable agreement.
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    Öğe
    Holistic nursing competence scale: Turkish translation and psychometric testing
    (Wiley, 2019) Aydin, A.; Hicdurmaz, D.
    Aim This study aimed to culturally adapt and evaluate the reliability and validity of the Holistic Nursing Competence Scale for application in the Turkish context. Background Nurses are expected to assess well-being of individuals by considering physical, social, psychological, cultural and spiritual dimensions to enhance adaptation to diseases. In Turkey, no tools have been developed to date for the evaluation of competencies in holistic nursing in the country. Methods The study was conducted with 288 nurses working in two hospitals in Ankara equipped with over 500 beds. A confirmatory factor analysis was performed in order to identify whether the items and the sub-dimensions of the adapted scale complied with the original structure comprising 36 items and five sub-scales, namely 'general aptitude', 'staff education and management', 'ethically oriented practice', 'nursing care in a team' and 'professional development'. Cronbach's alpha value was used as an estimate for reliability analysis. Results Opinions of 11 experts were obtained for content validation of the scale, and the content validity index was 0.90. The adaptation was observed to be acceptable on the basis of structural equation model fit indices in confirmatory factor analysis. Cronbach's alpha value was estimated to be 0.97 and 0.90, respectively, for the complete scale. Conclusion The study identified the Turkish version of Holistic Nursing Competence Scale as a valid and reliable tool for the evaluation of competence in holistic nursing among nurses. Implications for nursing and nursing policy The instrument may now be utilized as a tool of measurement in nursing practice, as well as in education and research, for identifying the level of competence in the holistic nursing practices among the nurses in Turkey.
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    Predictive Performance of Artificial Neural Network and Multiple Linear Regression Models in Predicting Adhesive Bonding Strength of Wood
    (Springer, 2016) Bardak, S.; Tiryaki, S.; Bardak, T.; Aydin, A.
    The purpose of this study was to develop artificial neural network (ANN) and multiple linear regression (MLR) models that are capable of predicting the bonding strength of wood based on moisture content, open assembly time and closed assembly time of the joints prior to pressing process. For this purpose, the experimental studies were conducted and the models based on the experimental results were set up. As a result of the experiments conducted, it was observed that bonding strength first increased and then decreased with increasing the wood moisture content and adhesive open assembly time. In addition, the increased closed assembly time caused a decrease in bonding strength of wood. The ANN results were compared with the results obtained from the MLR model to evaluate the models' predictive performance. It was found that the ANN model with the R (2) value of 97.7% and the mean absolute percentage error of 3.587% in test phase exhibits higher prediction accuracy than the MLR model. The comparison results confirm the feasibility of ANN model in terms of predictive performance. Consequently, it can be said that ANN is an effective tool in predicting wood bonding strength, and quite useful instead of costly and time-consuming experimental investigations.