The research aims for the study about Ibn Tulun's personal and scientific biography to the
scholar, scientist and historian Ibn Tulun Shams Id- Din Mohammad Ibn Ali al-Dimashqi al-
Salihi (953 A.H. / 1546 A.D.) Ibn Tulun was a prominent Muslim historian in Blad al-
Sham.
At the first deals with Ibn Tulun's personal biography, author's name, Lineage, and
nickname, his nativity; his upbringing, and edification, his moral character, Finally, his death.
As to Ibn Tulun's scientific biography, at the first deals with his initiation into education
and learning , sheds light on his tutors and his authorities , scientific stations and travels , his
scholarly status , and Ibn Tulun's alumni or his students .
Los nombres propios nombran a un ser o a un objeto, distinguiéndolo de los demás seres de su misma clase, se escriben siempre con letra mayúscula a principio de palabra. Los lingüistas hacen mayor hincapié en las divergencias de referencia, entre nombres propios y nombres comunes. Así, suele decirse que el sustantivo propio no tiene como referente ningún concepto. El asunto de la traducción de los nombres propios parecería una cuestión de gusto personal del traductor pero vemos también que en algunas épocas es más frecuente traducirlos, y en otras, por el contrario, se prefiere dejar esos nombres en su forma original, tal vez con algunas adaptaciones ortográficas. Parecería entonces cuestión de modas. Pero, eviden
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The size of parasites and the shape of its cephalothorax and ovaries changed according to the age. The monthly observations on egg production indicated multibree
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The research problem has
... Show MoreIron is one of the abundant elements on earth that is an essential element for humans and may be a troublesome element in water supplies. In this research an AAN model was developed to predict iron concentrations in the location of Al- Wahda water treatment plant in Baghdad city by water quality assessment of iron concentrations at seven WTPs up stream Tigris River. SPSS software was used to build the ANN model. The input data were iron concentrations in the raw water for the period 2004-2011. The results indicated the best model predicted Iron concentrations at Al-Wahda WTP with a coefficient of determination 0.9142. The model used one hidden layer with two nodes and the testing error was 0.834. The ANN model coul
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