Abstract: Persian literature, after the spread of Islam in Pars, received a lot of influence from the subtle and rhetorical details of the Holy Quran. This effect is more in poetry than in prose because the weights of Persian poetry are closer to the melody of the Qur'an and its weights.The effectiveness of most of the prose works is only in the Quranic themes and words. Persian poetry that has benefited from various sciences of rhetoric, including semantics, eloquence and rhetoric. The degree and manner of influence of each of the didactic, lyrical, epic and travelogue literary types is different from the rhetorical points of the Quran. The instructions under educational literature have benefited the least from Quranic rhetoric. The max

The densities and visconsities of solutions of poly(vinyl alcohol)(PVA) molccuar weight (14)kg.mol-1in water up to 0.035%mol.kg-1

In this paper, we will study non parametric model when the response variable have missing data (non response) in observations it under missing mechanisms MCAR, then we suggest Kernel-Based Non-Parametric Single-Imputation instead of missing value and compare it with Nearest Neighbor Imputation by using the simulation about some difference models and with difference cases as the sample size, variance and rate of missing data.      

Future wireless networks will require advance physical-layer techniques to meet the requirements of Internet of Everything (IoE) applications and massive communication systems. To this end, a massive MIMO (m-MIMO) system is to date considered one of the key technologies for future wireless networks. This is due to the capability of m-MIMO to bring a significant improvement in the spectral efficiency and energy efficiency. However, designing an efficient downlink (DL) training sequence for fast channel state information (CSI) estimation, i.e., with limited coherence time, in a frequency division duplex (FDD) m-MIMO system when users exhibit different correlation patterns, i.e., span distinct channel covariance matrices, is to date ve

Due to the huge variety of 5G services, Network slicing is promising mechanism for dividing the physical network resources in to multiple logical network slices according to the requirements of each user. Highly accurate and fast traffic classification algorithm is required to ensure better Quality of Service (QoS) and effective network slicing. Fine-grained resource allocation can be realized by Software Defined Networking (SDN) with centralized controlling of network resources. However, the relevant research activities have concentrated on the deep learning systems which consume enormous computation and storage requirements of SDN controller that results in limitations of speed and accuracy of traffic classification mechanism. To fill thi