Sunshine duration (SD) is an essential atmospheric indicator which is used in many agriculture, ‎architects and solar energy applications. In many situations where data of sunshine duration may not be ‎available due to temporal and financial constraints, developing alternative indirect methods based on ‎theoretical considerations for determining SD are essentially required. In this study, seven models were ‎developed using stepwise regression technique to estimate monthly sunshine duration for Libya. The ‎predictors which were used as inputs differ from one model to another and they included monthly ‎cloudiness index, total day length, mean relative humidity, depth of precipitation, mean maximum ‎temperature, altitude and longitude over 16 meteorological stations spread across Libya during the ‎period of 1961 – 2000 . The evaluation of the developed models was performed using a set of data of ‎four meteorological stations representing different physiogeographic and climatic zones during 2001 ‎and against Abdelwahed and Snyder (2015) equations which were developed for estimating sunshine ‎duration for Libya. The statistical parameters of evaluation criteria included mean absolute error (MAE), ‎root mean square error (RMSE), (RMSE %) and Nash and Sutcliffe Efficiency (NSE). The linear regression ‎equation relating predicted with measured data with intercept equals zero and determination coefficient ‎‎(R2) were also used for evaluation purpose. According to the performance indicators, it was detected ‎that six of the developed models were superior to the model with one parameter (cloudiness index) in ‎estimating the sunshine hours. It was also found that all the developed models have better performance ‎in estimating the sunshine duration as compared with Abdelwahed and Snyder (2015) equations. ‎However, due to its few required variables, a model with two parameters (cloudiness index and total ‎day length) is sufficient and can be used with confidence in estimating sunshine duration for Libya. ‎ Keywords: Sunshine duration, Stepwise regression, Statistical model.‎ arabic 15 English 69
ِAhmed Ibrahim Ekhmaj, Milad Omran Alwershefani(12-2016)

تهدف هذه الدراسة إلى معرفة تأثير التغير في ضغط الهواء داخل الإطارات الخلفية (القائدة) على أداء الجرار زراعي نوع الجدع 735 MASSEY FERGUSON MF735)) ثنائي الدفع مع محراث مطرحي قلاب ثنائي الأبدان في تربة رملية، تمثل أداء الجرار الزراعي في في السرعة العملية، نسبة الانزلاق، السعة الحقلية الفعلية، الكفاءة الحقلية، وحجم التربة المثارة، حيث تم إجراء تجربة حقلية في محطة أبحاث كلية الزراعة جامعة طرابلس ليبيا بداية شهر نوفمبر 2020 م، نفذت التجربة وفق التصميم الإحصائي العشوائي الكامل (CRD) بواقع ثلاثة مكررات لكل معاملة متمثلة في تغير ضغط الإطار، والتي تكونت من 1، 1.6، 2 بار. أظهرت النتائج أن الجرار أعطي أفضل أداء كلما قل ضغط الهواء داخل الاطارات الخلفية للجرار. فلقد ارتفعت السرعة العملية معنوياً (عند مستوى 0.05) للجرار من 4.73 كم/ساعة إلى 5.63 كم/ساعة وذلك عند انخفاض الضغط من 2 بار إلى1 بار. في حين انخفضت النسبة المئوية ٌللانزلاق معنوياً (عند مستوى 0.05) من 33.74% إلى 22.03% وذلك عند انخفاض ضغط الهواء داخل الاطارات الخلفية للجرار من 2 بار إلى 1 بار ، كما أظهرت النتائج تفوق ضغط الإطار عند 1 بار في الحصول على أعلى معدل للإنتاجية العملية 0.219 هكتار/ساعة وأعلى معدل للكفاءة الحقلية 77.91% وأعلى معدل لحجم التربة المثار 394.20 م3 / ساعة. arabic 127 English 0
د. أحمد إبراهيم خماج, المنتصر بالله مختار محمد القريقني(3-2021)

The infiltration rate is an important parameter in soil, hydrological, ecological and agricultural studies. It plays the main role as the input parameter in models for water flow and solute transport in the vadose zone. In this study, Multilayer Artificial Neural Network "ANN" using the backpropagation algorithm was selected to estimate the steady infiltration rate covering different types of Libyan soils. The activation function was selected LOGSIG in the middle and exit layers. The input data were the percentage of sand, silt and clay, bulk density, saturated hydraulic conductivity and the volumetric water content in soil at -10 kPa. The performance of the ANN models was evaluated against a set of data that never seen by the model during the training phase. Multivariate linear regression model (MLR) based on the percentage of silt, saturated hydraulic conductivity and volumetric water content in soil at -10 kPa was also developed to determine infiltration rate for evaluation purpose. The results obtained in this study showed a good agreement between the measured data and the ANN simulated. The values of mean absolute error and root mean square error were slightly smaller in ANN steady infiltration rate model compared to the developed Multivariable linear regression model to estimate the infiltration rate. Although the results of these comparisons encourage the using ANN in practice, it would be valuable to have large local soil database from many different sites, in order to make a stronger assessment of the ANN models.
Ahmed Ibrahim Ekhmaj(10-2010)