In this research the effect of grain size and effect of La2O3 doping on densification  rate for the  initial and intermediate  stages of sintering were studied .The experimental results for α – cristobilite powder are modeled using ( L2-Regression ) technique in studying  the effect of grain size and La2O3 doping using  three particles size (6.12, 8.92, 13.6 ) µm, with undoped  initial powder and with La2O3 doping . The mathematical simulation showes that the densification rates increase as the initial particles sizes decrease and vice versa. This shows that the densification depends directly on the initial compact density which reflects the contacts area between the particles . How

Since the introduction of the HTTP/3, research has focused on evaluating its influences on the existing adaptive streaming over HTTP (HAS). Among these research, due to irrelevant transport protocols, the cross-protocol unfairness between the HAS over HTTP/3 (HAS/3) and HAS over HTTP/2 (HAS/2) has caught considerable attention. It has been found that the HAS/3 clients tend to request higher bitrates than the HAS/2 clients because the transport QUIC obtains higher bandwidth for its HAS/3 clients than the TCP for its HAS/2 clients. As the problem originates from the transport layer, it is likely that the server-based unfairness solutions can help the clients overcome such a problem. Therefore, in this paper, an experimental study of the se

A multidimensional systolic arrays realization of LMS algorithm by a method of mapping regular algorithm onto processor array, are designed. They are based on appropriately selected 1-D systolic array filter that depends on the inner product sum systolic implementation. Various arrays may be derived that exhibit a regular arrangement of the cells (processors) and local interconnection pattern, which are important for VLSI implementation. It reduces latency time and increases the throughput rate in comparison to classical 1-D systolic arrays. The 3-D multilayered array consists of 2-D layers, which are connected with each other only by edges. Such arrays for LMS-based adaptive (FIR) filter may be opposed the fundamental requirements of fa

In this article our goal is mixing ARMA models with EGARCH models and composing a mixed model ARMA(R,M)-EGARCH(Q,P) with two steps, the first step includes modeling the data series by using EGARCH model alone interspersed with steps of detecting the heteroscedasticity effect and estimating  the model's parameters and check the adequacy of the model. Also we are predicting the conditional variance and verifying it's convergence to the unconditional variance value. The second step includes mixing ARMA with EGARCH and using the mixed (composite) model in modeling time series data and predict future values then asses the prediction ability of the proposed model by using prediction error criterions.