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Register a new userComparative Efficiency of Using the Classical Artificial Way (Bucket) and the Programmed Nursing Machine* in Raising Calves
مقارنة بين التنشئة الاصطناعية التقليدية للعجلات الرضيعة و التنشئة الاصطناعية باستخدام جهاز الإرضاع المبرمج
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Damascus University ورقة بحثية
Publication date
1999
fields
Animal Production
and research's language is
العربية
Authors
صاموئيل موسى( باحث )
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هدف البحث إلى دراسة سلوك العجلات الرضيعة تجاه جهاز الإرضاع الآلي المبرمج ليصار إلى تطوير برمجته، بالإضافة إلى تنشئة عجلات بوزن جيد و سليمة صحيًا و بتكاليف مناسبة في مزارع الأبقار الحلوب الكبيرة.
No English abstract
References used
السبع، محمد مروان، ومحي المزيد. ١٩٨٢ . الجلسات العملية في أساسيات الإنتاج الحيواني، منشورات جامعة حلب، كلية الزراعة.
مصري، ياسين، وصاموئيل موسى، وجمال سكوتي. ١٩٩٢ . الحظائر والمباني الجزء النظري، منشورات جامعة دمشق، كلية الزراعة.
موسى، صاموئيل. ١٩٩٦ . الرضاعة الصناعية للعجول باستخدام الآلة الأوتوماتيكية. مجلة باسل الأسد لعلوم الهندسة الزراعية. العدد الثاني.
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Twenty four of Awassi lambs, Reared at Al-Kraim Center for Sheep
Breeding and Range Management, were used to test the efficiency of artificial
raising using the Programmed Nursing Machine (PNM) on the growth rate of
Awassi newborn lambs for eleven
weeks postlambing.
Lambs were divided equally and randomly into two groups. The lambs in
the first group (G١) were separated from their dams at ٧,٤ ± ٣,١ days old and
raised artificially on dried whole dairy milk using PNM, while the lambs in the
second group (G٢), the control group, were left with their dams to be raised
naturally during the studied period (١١ weeks).
Evapotranspiration is an important component of the
hydrologic cycle, and the accurate prediction of this parameter is
very important for many water resources applications. Thus, the
aim of this study is prediction of monthly reference
evapotranspiration using Artificial Neural Networks (ANNs) and
fuzzy inference system (FIS).
Rainfall is highly non-linear and complicated phenomena, which require nonlinear
mathematical modeling and simulation for accurate prediction. This study
comparing the performance of the prediction of one-day-ahead, where Two
Feed Forward Neural N
etwork FFNN models were developed and implemented
to predict the rainfall on daily for three months (December, January, February).
These models are Artificial Neural Network traditional (ANN) model and artificial
neural network technique combined with wavelet decomposition (Wavelet-
Neural) According to two different methods to build a model using two types of
wavelets of Daubechies family (db2, db5). In order to compare the performance
of the models in their ability to predict the rains on short-term (for one and two
and three-days-ahead) the last months of the period of study, used some statistical
standards, These parameters include the Root Mean Square Error RMSE,
Coefficient Of Correlation (R).
The stability analysis of coastal structure is very important because it involves many
design parameter s to be considered for the save and economical design of structure.
In the present study neural network technique is adopted to predict the stab
ility
number of rubble mound breakwater.
One model is constructed based on the parameters which influence on the stability of
rubble mound breakwater, the back propagation algorithm is used in training network .
Agood correlation is obtained between network predicted stabilityand estimated
ones.
Correlation coefficient=0.88.
Weather forecasting (especially rainfall) is one of the most important and challenging
operational tasks carried out by meteorological services all over the world. Itis furthermore
a complicated procedure that requires multiple specialized fields o
f expertise.
In this paper, a model based on artificial neural networks (ANNs) and wavelet Transform
is proposed as tool to predict consecutive monthly rainfalls (1933-2009) taken of
Homs Meteorological Station on accounts of the preceding events of rainfall data.
The feed-forward neural network with back-propagation Algorithm is used in the
learning and forecasting, where the time series of rain that detailed transactions and the
approximate three levels of analysis using a Discrete wavelet transform (DWT).
The study found that the neural network WNN structured )5-8-8-8-1(, able to predict
the monthly rainfall in Homs station on the long-term correlation of determination and root
mean squared-errors (0.98, 7.74mm), respectively.
Wavelet Transform technique provides a useful feature based on the analysis of the
data, which improves the performance of the model and applied this technique in ANNmodels
for rain because it is simple, as this technique can be applied to other models.
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