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New Method for the On-Line Determination of Molybdenum Via New Microphotometric Home Mode Instrument, Using Hight Emitting Diode as a Variable Intensity Radiant Source with a Miniture Photosilicone Diode Detector for System: Molybdenum(VI)-Hydrogen Peroxide-Ammonium Solution
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This research work aims to the determination of molybdenum (VI) ion via the formation of peroxy molybdenum compounds which has red-brown colour with absorbance wave length at 455nm for the system of ammonia solution-hydrogen peroxide-molybdenum (VI) using a completely newly developed microphotometer based on the ON-Line measurement. Variation of responses expressed in millivolt. A correlation coefficient of 0.9925 for the range of 2.5-150 ?g.ml-1 with percentage linearity of 98.50%. A detection limit of 0.25 ?g.ml-1 was obtained. All physical and chemical variable were optimized interferences of cation and anion were studied classical method of measurement were done and compared well with newly on-line measurements. Application for the use of developed method on alloy samples, using standard addition method and agreed quite wall.

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Publication Date
Sun Jun 01 2008
Journal Name
Baghdad Science Journal
A New Derivatives of Benzodiazepine, Imidazole, Isatin, Maleimide, Pyrimidine and 1,2,4-Triazole: Synthesis and Characterization
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The synthesis of new benzodiazepine, imidazole, isatin, maleimide, pyrimidine and 1,2,4-triazole derived from 2-amino-4-hydroxy-1,3,5-triazine, via its cyclocondensation reaction with different organic reagents, is described. FT-IR, 1H-NMR and as well as 13C-NMR spectra disclosed the structures of the precursors and heterocyclic derivatives formed.

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Publication Date
Sat Jan 01 2022
Journal Name
Indonesian Journal Of Electrical Engineering And Computer Science
Increasing validation accuracy of a face mask detection by new deep learning model-based classification
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During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

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Publication Date
Tue Jul 01 2014
Journal Name
Bulletin Of The Iraq Natural History Museum (p-issn: 1017-8678 , E-issn: 2311-9799)
A NEW SPECIES OF GRAPE-VINE LEAFHOPPERS, GENUS ARBORIDIA ZAKHVATKIN, 1946 (HOMOPTERA: CICADELLIDAE) FROM IRAQ
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Among a collection of leafhoppers from Erbil Province in Kurdistan/Iraq, a new species of the genus Arboridia Zakhvatkin, 1946 was designated and described here as a new species  to  the science. The erection of this species was mainly built on the external characters included the male genitalia. Sites and dates of collections so as the host-plants were verified.

 

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Publication Date
Mon Dec 24 2018
Journal Name
Bulletin Of The Iraq Natural History Museum (p-issn: 1017-8678 , E-issn: 2311-9799)
A NEW RECORD OF COELASTRELLA TERRESTRIS (REISIGL) HEGEWALD & N. HANAGATA, 2002 (SPHAEROPLEALES, SCENEDESMACEAE) IN IRAQ
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    This study identified the genus Coelastrella Chodat, 1922 which was isolated from a sediment sample taken from the Tigris river in Baghdad Governorate, Iraq. The alga was isolated and cultured in modified Chu 10 media and the morphological features of the isolated algae were observed in light microscopy (LM); it showed some characteristic features of this genus, such as its ellipsoidal or lemon- shaped cells, a visible pyrenoid and the chloroplast parietal. To ensure correct identification of the isolated alga, a molecular analysis using 18S rRNA gene and DNA sequencing revealed a match with C. terrestris (Reisigl) Hedewald & N. Hanagata 2002. This species is a new record in Iraq

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Publication Date
Fri Jan 01 2016
Journal Name
Computational Intelligence And Neuroscience
A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)
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This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the appl

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Publication Date
Thu Jun 29 2023
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Determination of Reservoir Hydraulic Flow Units and Permeability Estimation Using Flow Zone Indicator Method
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   Reservoir characterization plays a crucial role in comprehending the distribution of formation properties and fluids within heterogeneous reservoirs. This knowledge is instrumental in constructing an accurate three-dimensional model of the reservoir, facilitating predictions regarding porosity, permeability, and fluid flow distribution. Among the various methods employed for reservoir characterization, the hydraulic flow unit stands out as a widely adopted approach. By effectively subdividing the reservoir into distinct zones, each characterized by unique petrophysical and geological properties, hydraulic flow units enable comprehensive reservoir analysis. The concept of the flow unit is closely tied to the flow zone indicator, a cr

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Publication Date
Fri Jul 21 2023
Journal Name
Journal Of Engineering
Decisions making for fraction functions By Using Goal Programming Method
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Decision making is vital and important activity in field operations research ,engineering ,administration science and economic science with any industrial or service company or organization because the core of management process as well as improve him performance . The research includes decision making process when the objective function is fraction function and solve models fraction programming by using some fraction programming methods and using goal programming method aid programming ( win QSB )and the results explain the effect use the goal programming method in decision making process when the objective function is
fraction .

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Publication Date
Fri Mar 01 2019
Journal Name
Al-khwarizmi Engineering Journal
Health Monitoring For Cantilever Crane Frame Using Residual Error Method
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          In the present research, a crane frame has been investigated by using finite element method. The damage is simulated by reducing the stiffness of assumed elements with ratios (10% and 20 %) in mid- span of the vertical column in crane frame. The cracked beam with a one-edge and non-propagating crack has been used. Six cases of damage are modeled for crane frame and by introducing cracked elements at different locations with ratio of depth of crack to the height of the beam (a/h) 0.1, 0.20. A FEM program coded in Matlab 6.5 was used to model the numerical simulation of the damage scenarios. The results showed a decreasing in the five natural frequencies from undamaged beam which means

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Publication Date
Wed Apr 01 2015
Journal Name
Journal Of Environmental Engineering
Using Granular Dead Anaerobic Sludge as Permeable Reactive Barrier for Remediation of Groundwater Contaminated with Phenol
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Publication Date
Sun Nov 01 2020
Journal Name
Journal Of Engineering
Convolutional Multi-Spike Neural Network as Intelligent System Prediction for Control Systems
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The evolution in the field of Artificial Intelligent (AI) with its training algorithms make AI very important in different aspect of the life. The prediction problem of behavior of dynamical control system is one of the most important issue that the AI can be employed to solve it. In this paper, a Convolutional Multi-Spike Neural Network (CMSNN) is proposed as smart system to predict the response of nonlinear dynamical systems. The proposed structure mixed the advantages of Convolutional Neural Network (CNN) with Multi -Spike Neural Network (MSNN) to generate the smart structure. The CMSNN has the capability of training weights based on a proposed training algorithm. The simulation results demonstrated that the proposed

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