IJASR Volume 6 – Issue 3 September – October 2018 Edition
All listed papers are published after full consent of respective author or co-author(s).
For any discussion on research subject or research matter, the reader should directly contact to undersigned authors.
Estimating the Parameters of GP Model and AR model using Gibbs Sampler for Spatio-Temporal Datawith Comparison Study
Abdulkader Joukhadar, Hadia Tohmaz, Mostafa Ranneh
ABSTRACT: In the last 2-decades, more attention has been paid to the analysis and modeling of complex stochastic systems of large database, particularly time-spatial data. This paper presents models which help researchers to represent temporal-spatio data, the most important models are GP and AR. It also illustrates the specifications of these models related to the data. was Bayes hierarchical method is used to divide these two models to observation and structure equations.
This paper provides comparison study between Gaussian process (GP) and auto-regressive (AR) models to a database of pollution measurements for five stations in Aleppo city-Syria, special attention is given to studying of carbon dioxide (CO), as well as investigating the side effect of covariates on the
ratio of air pollution, it has been found the environment temperature has a great effect on the increase of CO ratio in the air. A data-Base records for 2-months has been acquired for the 5-station installed in Aleppo area, this procedure is attended to be capable to determine the fitting model, the Data-Base of last two days of September in 2009 were used for the temporal forecasts. Three statistical criteria; the root mean square error (RMSE), mean absolute percentage error (MAPE) and bias (BIAS), used to
examine the validation of the studied models for being valid for forecasting. The statistical analysis showed that the performance of the Gaussian model is superior to the auto-regressive model with the average of RMSE = 0.032 and 0.067, MAPE = 1.935 and 2.515, and BIAS = -0.015 and -0.067, for GP and AR, respectively.
Modeling Electrospinning Jet of a Polymeric Solution in Steady State
Mohammad Yousef Al Hashem, Mohammad Karman
ABSTRACT: This paper discusses Modeling of steady state of electrospinning jet. Its importance comes from the possibilityof utilizingthe jet in later process by accurately control the collection point location and by providing an acceptable anticipating level of the jet radius at the collector.
The proposed model was tested against experimental results and the accuracy of the model is 93%.
The solution is relatively highly viscous and this high viscosity comes from the contributions of the polymer and not of the solvent as its viscosity relative to that of the polymer is negligible.this leads to the need to use a model that is able to account for the viscoelastic behavior of the solution One candidate is Giesekus model as it is suitable for relatively high viscous solution in addition to its applicability for viscoelastic polymeric solutions
The paper study the influence of five parameters namely (Voltage, nozzle radius, volumetric flow, spinning distance, solution viscosity) on the radius of the jet. It concludes that the radius of the collected jet increases with the increase of volumetric flow, solution viscosity and nozzle radius, while it decreases with the increase of, Voltage, andspinning distance.