Examining the high school students' transfer levels of modern physics topics to daily life [Ortaöğretim öğrencilerinin modern fizik konularini günlük hayata transfer düzeylerinin incelenmesi]

This study was conducted to determine the 11th-grade high school students' transfer levels of the modern physics topics to daily life in the context of the physics course. The sample of the study figured in the form of the mixed method constituted 314 11th-grade students selected with the cluster sample method. The Modern Physics Success Test (MPST) and Modern Physics Transfer Test (MPTT) developed by the researchers were used in the study. The study process was conducted quantitatively and qualitatively. The correlation between the MPST and MPTT scores of the students was identified in the quantitative section while the students' transfer levels of the modern physics topics to daily life were determined in the qualitative section. As a result of the analyses of the study, a weak, positive and significant relationship was found between the MPST and MPTT scores. In addition, It was revealed that the students' transfer levels of the modern physics knowledge were zero transfer, deficient transfer, and complete transfer and it was identified that the level of complete transfer was considerably low. Regarding the transfer of the modern physics knowledge to daily life, the topics, which complete transfer levels were the highest, were found as "Photoelectric Incident" and "Black-Body Radiation", and the topics, which complete transfer levels were the lowest, were detected as "Matter Waves" and "Pauli Exclusion Principle". Furthermore, it was also identified that the students' transfer levels varied according to other modern physics topics

A comparison of supervised maximum likelihood and ellipsoidal classification for crop cover estimation from Landsat TM data

Two supervised classification methods, the maximum likelihood and ellipsoid classification methods [1] were used to classify the crop cover by using Landsat TM data. The accuracy and performance of both classification methods were compared. The results show clear improvements of the ellipsoid method in overall accuracy and computational speed.Two supervised classification methods, the maximum likelihood and ellipsoid classification methods [1] were used to classify the crop cover by using Landsat TM data. The accuracy and performance of both classification methods were compared. The results show clear improvements of the ellipsoid method in overall accuracy and computational speed

DEFPOS H alpha observations of HII regions

WOS: 000293117000002We present H alpha emission line measurements of northern bright HII regions selected from the Sharpless (1959) catalog near the Galactic plane (b <= +/- 6 degrees). A total of 10 HII regions were observed with DEFPOS (Dual Etalon Fabry-Perot Optical Spectrometer) system at the f/48 Coude focus of 150 cm RT1150 telescope located at TUBITAK National Observatory (TUG) in Antalya/Turkey. The intensities, the local standard of rest (LSR) velocities (V-LSR), and the linewidths (Full Width Half Maximum: FWHM) of the H alpha emission line from our observations were in the range of 84 to 745 Rayleigh (R [one Rayleigh (R) is 10(6)/4 pi photons cm(-2) sr(-1) s(-1) = 2.4110(-7) erg cm(-2) sr(-1) s(-1) at H alpha and corresponds to an emission measure (EM = integral n(e)(2)dl) of 2.3 pc cm(-6) for a gas temperature of 8000K, where n(e) is the averaged electron density within an emitting region in the interstellar medium; dl is distance element to the source region (Haffner et al., 2003; Reynolds et al., 2005), 3 to -43 km s(-1) and 30 to 73 km s(-1), respectively. The LSR velocities and the linewidths from the data were obtained and compared with early results. We found that our results are in close agreement with them. Moreover, associated stars of some of the HII regions were updated by analyzing their location, velocities, and brightness. Crown Copyright (C) 2011 Published by Elsevier B.V. All rights reserved.TUBITAKTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [09ARTT150-436-1]; TUBITAK (The Scientific and Technical Research Council of Turkey)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [104T252]; TUBITAK-BIDEB at Physics Department of Middle East Technical UniversityTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK); Academic Research Project unit of Cukurova UniversityCukurova University [TBMYO2010BAP4]All observations were performed from the RTT150 so we thank to TUBITAK for a partial support in using RTT150 (Russian-Turkish 1.5-m telescope in Antalya) with project number 09ARTT150-436-1. We also thank to TUBITAK National Observatory (TUG) and TUG stuff. The authors also would like to thank R. J. Reynolds from the University of Wisconsin for his valuable help in the optical design of the DEFPOS as well as to start this study. We are grateful to S.K. Yerli and M.E. Ozel for reading and correcting the manuscript and for their remarks. We special thank an anonymous referee for helpful comments. This work is supported by the TUBITAK (The Scientific and Technical Research Council of Turkey) with Grant No. 104T252. NA gratefully acknowledges support through a Post-Doc Fellowship from the TUBITAK-BIDEB at Physics Department of Middle East Technical University. This work also supported with Academic Research Project unit of Cukurova University with Grant No. TBMYO2010BAP4

A comparison of two solar radiation models using artificial neural networks and remote sensing in turkey

This study introduces artificial neural networks for the estimation of solar radiation using model 1 (latitude, longitude, altitude, month, and meteorological land surface temperature) and model 2 (latitude, longitude, altitude, month, and satellite land surface temperature) data in Turkey. Price method was used for the estimation of land surface temperature values. Scale conjugate gradiant learning algorithms and logistic sigmoid transfer function were used in the network. R2 with model 1 and model 2 values have been found to be 96.93 and 97.24% (training stations), 80.41 and 82.37% (testing stations), respectively. These results are sufficient to predict the solar radiation. © Taylor & Francis Group, LLC