Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Ersin Kivrak"'
Publikováno v:
EDUCATION AND SCIENCE; Cilt 46, Sayı 208 (2021)
EĞİTİM VE BİLİM; Cilt 46, Sayı 208 (2021)
EĞİTİM VE BİLİM; Cilt 46, Sayı 208 (2021)
In the current study, it is aimed to investigate the opinions of the social studies teachers participating in the project on the activities conducted within the context of an ecology-based nature education program and thus to determine the extent to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::067791395d07fe36bcf8438471bc9b23
https://aperta.ulakbim.gov.tr/record/234790
https://aperta.ulakbim.gov.tr/record/234790
Publikováno v:
Oceanological and Hydrobiological Studies. 41:74-84
The Akarçay is the most important stream in the Afyonkarahisar region in central Turkey. In this study, the relationships between epipelic diatoms, macrophytes, and the water quality of Akarçay Stream are presented. According to Canonical Correspon
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7f9a7fa38a32efb380710fd955721889
https://doi.org/10.2478/s13545-012-0009-z
https://doi.org/10.2478/s13545-012-0009-z
Autor:
Hasan Gürbüz, Ersin Kivrak
Publikováno v:
Ekoloji. 74:102-109
Epipelic diatoms and some physico-chemical features of Tortum streams were studied in the samples collected montly from four sampling-stations during June 2005-February 2006. A total of 113 taxa of epipelic diatom were determined. According to cluste
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3de49b8e7710f48a97f088cdb261911a
https://doi.org/10.5053/ekoloji.2010.7413
https://doi.org/10.5053/ekoloji.2010.7413
Publikováno v:
International Journal on Algae. 9:22-40
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7df00a4c66b84657aa9d5b55912accdc
https://doi.org/10.1615/interjalgae.v9.i1.20
https://doi.org/10.1615/interjalgae.v9.i1.20
Publikováno v:
Ecological Informatics. 1:431-439
Artificial Neural Networks (ANN) is computational architectures that can be used for estimating primary production levels and dominating phytoplankton species in reservoirs. Automata Networks (AN) were applied as a pre-processing method with subseque
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2e8cc363b18ca29d9b1d280eb59b2f49
https://doi.org/10.1016/j.ecoinf.2006.09.002
https://doi.org/10.1016/j.ecoinf.2006.09.002
Autor:
Ersin Kivrak
Publikováno v:
Biologia. 61:339-345
Seasonal changes in phytoplankton community structure of the lake Tortum were studied over one year period, from March 2002 to February 2003. The collected data were compared with the data collected 21 years ago. Chlamydomonas microsphaerella, Cyclot
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6c154cc7cfed165403cf69ffcfc1a94d
https://doi.org/10.2478/s11756-006-0062-4
https://doi.org/10.2478/s11756-006-0062-4
Publikováno v:
International Journal on Algae. 8:141-161
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::30399ebf657ef7100ba33d7449a32b57
https://doi.org/10.1615/interjalgae.v8.i2.30
https://doi.org/10.1615/interjalgae.v8.i2.30
Publikováno v:
Journal of Freshwater Ecology. 19:651-655
Seasonal changes in phytoplankton community structure of Kuzgun reservoir, a high mountain reservoir, were studied during the ice-free period in 2000 and 2001. Bacillariophyta was the dominant group, followed by Chlorophyta and Dinophyta. The dominan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d22c2182492c68e2f1c3e67a662178f9
https://doi.org/10.1080/02705060.2004.9664747
https://doi.org/10.1080/02705060.2004.9664747
Publikováno v:
Neural Computing & Applications. 12:166-172
A Neural Network (NN) modelling approach has been shown to be successful in calculating pseudo steady state time and space dependent Dissolved Oxygen (DO) concentrations in three separate reservoirs with different characteristics using limited number
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::34ba267777719fe613476cbee3189436
https://doi.org/10.1007/s00521-003-0378-8
https://doi.org/10.1007/s00521-003-0378-8
Publikováno v:
Hydrobiologia. 504:133-141
The Levenberg–Marquardt algorithm was used to train artificial neural networks to predict the abundance of Cyclotella ocellata Pant. and Cyclotella kutzingiana Thwaites using time, depth, temperature, pH, dissolved oxygen, and electrical conductivi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8e2db42822f891e3adf49b63e1ef9e27
https://doi.org/10.1023/b:hydr.0000008513.19329.29
https://doi.org/10.1023/b:hydr.0000008513.19329.29