The adsorption ability of Iraqi initiated calcined granulated montmorillonite to adsorb Symmetrical Schiff Base Ligand 4,4’-[hydrazine-1, 2-diylidenebis (methan-1-yl-1-ylidene)) bis (2-methoxyphenol)] derived from condensation reaction of hydrazine hydrate and 4-hydroxy-3-methoxybenzaldehyde, from aqueous solutions has been investigated through columnar method.The ligand (H2L) adsorption found to be dependent on adsorbent dosage, initial concentration and contact time.All columnar experiments were carried out at three different pH values (5.5, 7and 8) using buffer solutions at flow rate of (3 drops/ min.),at room temperature (25±2)°C. The experimental isotherm data were analyzed using Langmuir, Freundlich and Temkin equations. The monol

Achieving goals effectively reflects the success of the institution. However, unless this indicator is coupled with efficiency when achieving goals, the institution will be equal in its achievements, and distinction will remain unachieved. Perhaps the role of the teaching staff in pushing the institution or college towards brilliance focuses on their ability to motivate people on the one hand and their interest in achieving brilliance for the institution. On the other hand, the importance of the research lies in the institution’s reaching a prominent position through the brilliance and creativity of teaching and achieving competition between institutions that make it more brilliance. The study seeks to achieve the goal of the real

Optical Mark Recognition (OMR) is the technology of electronically extracting intended data from marked fields, such as squareand bubbles fields, on printed forms. OMR technology is particularly useful for applications in which large numbers of hand-filled forms need to be processed quickly and with a great degree of accuracy. The technique is particularly popular with schools and universities for the reading in of multiple choice exam papers. This paper proposed OMRbased on Modify Multi-Connect Architecture (MMCA) associative memory, its work in two phases: training phase and recognition phase. The proposed method was also able to detect more than one or no selected choice. Among 800 test samples with 8 types of grid answer sheets and tota

This paper proposed a new method for network self-fault management (NSFM) based on two technologies: intelligent agent to automate fault management tasks, and Windows Management Instrumentations (WMI) to identify the fault faster when resources are independent (different type of devices). The proposed network self-fault management reduced the load of network traffic by reducing the request and response between the server and client, which achieves less downtime for each node in state of fault occurring in the client. The performance of the proposed system is measured by three measures: efficiency, availability, and reliability. A high efficiency average is obtained depending on the faults occurred in the system which reaches to

This paper presents a hybrid energy resources (HER) system consisting of solar PV, storage, and utility grid. It is a challenge in real time to extract maximum power point (MPP) from the PV solar under variations of the irradiance strength.  This work addresses challenges in identifying global MPP, dynamic algorithm behavior, tracking speed, adaptability to changing conditions, and accuracy. Shallow Neural Networks using the deep learning NARMA-L2 controller have been proposed. It is modeled to predict the reference voltage under different irradiance. The dynamic PV solar and nonlinearity have been trained to track the maximum power drawn from the PV solar systems in real time.

Moreover, the proposed controller i

We study in this paper the composition operator of induced by the function ?(z)=sz+t where , and We characterize the normal composition operator C? on Hardy space H2 and other related classes of operators. In addition to that we study the essential normality of C? and give some other partial results which are new to the best of our knowledge.

In this article, we design an optimal neural network based on new LM training algorithm. The traditional algorithm of LM required high memory, storage and computational overhead because of it required the updated of Hessian approximations in each iteration. The suggested design implemented to converts the original problem into a minimization problem using feed forward type to solve non-linear 3D - PDEs. Also, optimal design is obtained by computing the parameters of learning with highly precise. Examples are provided to portray the efficiency and applicability of this technique. Comparisons with other designs are also conducted to demonstrate the accuracy of the proposed design.