In this research, a Co-polymer (Styrene / Allyl-2.3.4.6-tetra-O-acetyl-β-D-glucopyranoside) was synthesized from glucose in four steps using Addition Polymerization according to the radical mechanism using Benzoyl Peroxide (BP) as initiator. Initially, Allyl-2.3.4.6-tetra-O-acetyl-β-D-glucopyranoside monomer was prepared in three steps and the reaction was followed by (HPLC, FT-IR, TLC), in the fourth step the monomer was polymerized with Styrene and the structure was determined by FT-IR and NMR spectroscopy. The reaction conditions (temperature, reaction time, material ratios) were also studied to obtain the highest yield, the relative, specific and reduced viscosity of the prepared polymer was determined, from which the viscosity ave

This paper presents a new design of a nonlinear multi-input multi-output PID neural controller of the active brake steering force and the active front steering angle for a 2-DOF vehicle model based on modified Elman recurrent neural. The goal of this work is to achieve the stability and to improve the vehicle dynamic’s performance through achieving the desired yaw rate and reducing the lateral velocity of the vehicle in a minimum time period for preventing the vehicle from slipping out the road curvature by using two active control actions: the front steering angle and the brake steering force. Bacterial forging optimization algorithm is used to adjust the parameters weights of the proposed controller. Simulation resul

The educational function of television is one of the basic functions in light of the technical development that included the specialized satellite channels in all its fields, including the educational field, as its role became parallel to the role of educational institutions. These studies are among the descriptive studies in terms of the type of study methodology that describes the phenomenon, interprets its and extract the results and relationships between the variables. The study sample was multistage (random and intentional) included the students of the sixth academic and literary preparatory stage in the city of Baghdad.
The study problem was summarized by the following main question:
( What are the motives for the exposure of

In this paper, we present multiple bit error correction coding scheme based on extended Hamming product code combined with type II HARQ using shared resources for on chip interconnect. The shared resources reduce the hardware complexity of the encoder and decoder compared to the existing three stages iterative decoding method for on chip interconnects. The proposed method of decoding achieves 20% and 28% reduction in area and power consumption respectively, with only small increase in decoder delay compared to the existing three stage iterative decoding scheme for multiple bit error correction. The proposed code also achieves excellent improvement in residual flit error rate and up to 58% of total power consumption compared to the other err

This paper proposes an on-line adaptive digital Proportional Integral Derivative (PID) control algorithm based on Field Programmable Gate Array (FPGA) for Proton Exchange Membrane Fuel Cell (PEMFC) Model. This research aims to design and implement Neural Network like a digital PID using FPGA in order to generate the best value of the hydrogen partial pressure action (PH₂) to control the stack terminal output voltage of the (PEMFC) model during a variable load current applied. The on-line Particle Swarm Optimization (PSO) algorithm is used for finding and tuning the optimal value of the digital PID-NN controller (kp, ki, and kd) parameters that improve the dynamic behavior of the closed-loop digital control fue

A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an