Recently, microalgae have become a promising source in the production of biofuel. However, the cost of production is still the main obstacle to develop of this type of source. Although there are many extensive studies on the requirements provided for the cultivation of the microalgae, the study of the process, via the variables that affect the cultivation of microalgae, being still one of the important tasks to improve the production of biofuel. The present article is a serious attempt to investigate of use commercial fertilizer NPK (20:20:20+TE N: P: K) as considered a cheap nutrient medium in growth Chlorella vulgaris by comparison with traditional nutrient (Chu.10 medium). In addition, the current study addresses effect of di

Recently, microalgae have become a promising source in the production of biofuel. However, the cost of production is still the main obstacle to develop of this type of source. Although there are many extensive studies on the requirements provided for the cultivation of the microalgae, the study of the process, via the variables that affect the cultivation of microalgae, being still one of the important tasks to improve the production of biofuel. The present article is a serious attempt to investigate of use commercial fertilizer NPK (20:20:20+TE N: P: K) as considered a cheap nutrient medium in growth Chlorella vulgaris by comparison with traditional nutrient (Chu.10 medium). In addition, the current study addresses effect of different spar

In this research the results of applying Artificial Neural Networks with modified activation function to
perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance
Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of
identification strategy consists of a feed-forward neural network with a modified activation function that
operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have
been trained online and offline have been used, without requiring any previous knowledge about the
system to be identified. The activation function that is used in the hidden layer in FFNN is a modified
version of the wavelet func

In this research the results of applying Artificial Neural Networks with modified activation function to perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of identification strategy consists of a feed-forward neural network with a modified activation function that operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have been trained online and offline have been used, without requiring any previous knowledge about the system to be identified. The activation function that is used in the hidden layer in FFNN is a modified version of the wavelet function. This approach ha

In this article, the solvability of some proposal types of the multi-fractional integro-partial differential system has been discussed in details by using the concept of abstract Cauchy problem and certain semigroup operators and some necessary and sufficient conditions. 

The study of triples seeks to deal with the comprehensive nature of the Qur’an texts, and the choice fell on the trilogy of great torment, pain, and humiliation in the Noble Qur’an - an objective study, the title of this research, in which I tried to shed light on these terms, and the nuances between them, and in particular torment The eschatological terminology varied, which can be summed up in three terms, namely the great, the painful, and the offensive. The types of torment, the pain is the painful one that is described by the severity of pain and its horror, as for the humiliating punishment, it is that which humiliates the one who has fallen on it, and the diversity of torment is due to the diversity of sins.

High vehicular mobility causes frequent changes in the density of vehicles, discontinuity in inter-vehicle communication, and constraints for routing protocols in vehicular ad hoc networks (VANETs). The routing must avoid forwarding packets through segments with low network density and high scale of network disconnections that may result in packet loss, delays, and increased communication overhead in route recovery. Therefore, both traffic and segment status must be considered. This paper presents real-time intersection-based segment aware routing (RTISAR), an intersection-based segment aware algorithm for geographic routing in VANETs. This routing algorithm provides an optimal route for forwarding the data packets toward their destination

The aim of this paper is to approximate multidimensional functions by using the type of Feedforward neural networks (FFNNs) which is called Greedy radial basis function neural networks (GRBFNNs). Also, we introduce a modification to the greedy algorithm which is used to train the greedy radial basis function neural networks. An error bound are introduced in Sobolev space. Finally, a comparison was made between the three algorithms (modified greedy algorithm, Backpropagation algorithm and the result is published in [16]).