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NOISE EFFECTS IN SKILL DISCRETION AND MODELING
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Diesel generators is widely used in Iraq for the purpose of maintaining electric power demand. Large number of operators engaged in this work encounters high level of noise generated by back pack type diesel generators used for this purpose. High level of noise exposure gives different kinds of ill effect on human operators. Exact nature of deteriorated work performance is not known., in present research , quastionaire was adsministered 86 repondents in Baghdad city were exposured to wide range of noise level (80-110) dB(A) with different ages and they have different skill discretion levels. Noise levels A-weigthed decibles dB(A) were measured over 8 weeks two times aday during the 2019 summer using a sound level meter.For predicting the work efficiency deterioration fuzzy tool has been used in present research. It has been established that a fuzzy computing system will help in identification and analysis of fuzzy models fuzzy system offers a convenient way of representing the relationships between the inputs and outputs of a system in the form of IF-THEN rules.The paper presents a fuzzy model for predicting the effects of noise pollution on operators performance as a function of noise level, skill discretion and age of the operators.

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Publication Date
Mon Apr 03 2023
Journal Name
Journal Of Educational And Psychological Researches
Fitting Scoring Rubrics for Electronic Portfolio to Partial Credit Model According to the Number of Assumed Dimensions
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Abstract

 

The current research aims to reveal the extent to which all scoring rubrics data for the electronic work file conform to the partial estimation model according to the number of assumed dimensions. The study sample consisted of (356) female students. The study concluded that the list with the one-dimensional assumption is more appropriate than the multi-dimensional assumption, The current research recommends preparing unified correction rules for the different methods of performance evaluation in the basic courses. It also suggests the importance of conducting studies aimed at examining the appropriateness of different evaluation methods for models of response theory to the

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Publication Date
Wed Aug 30 2023
Journal Name
Baghdad Science Journal
Deep Learning-based Predictive Model of mRNA Vaccine Deterioration: An Analysis of the Stanford COVID-19 mRNA Vaccine Dataset
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The emergence of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has resulted in a global health crisis leading to widespread illness, death, and daily life disruptions. Having a vaccine for COVID-19 is crucial to controlling the spread of the virus which will help to end the pandemic and restore normalcy to society. Messenger RNA (mRNA) molecules vaccine has led the way as the swift vaccine candidate for COVID-19, but it faces key probable restrictions including spontaneous deterioration. To address mRNA degradation issues, Stanford University academics and the Eterna community sponsored a Kaggle competition.This study aims to build a deep learning (DL) model which will predict deterioration rates at each base of the mRNA

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Publication Date
Sat Oct 01 2022
Journal Name
Baghdad Science Journal
Design of a Simple Dust Removal System for a Solar Street Light System
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      Dust and bird residue are problems impeding the operation of solar street lighting systems, especially in semi-desert areas, such as Iraq. The system in this paper was designed and developed locally using simple and inexpensive materials. The system runs automatically. It Connects to solar panels used in solar street lighting, and gets the required electricity from the same solar system. Solar panels are washed with dripping water in less than half a minute by this system. The cleaning period can also be controlled. It can also control, sensing the amount of dust the system operates. The impact of different types of falling dust on panels has also been studied. This was collected from different winds and studied their impact o

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Publication Date
Wed Feb 08 2023
Journal Name
Iraqi Journal Of Science
Generation Contour Lines from Digital Elevation Model (1m) for AL-khamisah, Thi-Qar Government
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The DEM (Digital elevation model) means that the topography of the earth's surface (such as; Terrain relief and ocean floors), can be described mathematically by elevations as functions of three positions either in geographical coordinates, (Lat. Long. System) or in rectangular coordinates systems (X, Y, Z). Therefore, a DEM is an array number that represents spatial distributions of terrain characteristics. In this paper, the contour lines with different interval of high-resolution digital elevation model (1m) for AL-khamisah, The Qar Government was obtained. The altitudes ranging is between 1 m – 8.5 m, so characterized by varying heights within a small spatial region because it represents in multiple spots with flat surfaces.

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Publication Date
Sun Mar 04 2012
Journal Name
Baghdad Science Journal
The use of laurylamine hydrocholoride CH3(CH2)11 NH3 –Cl for secondary oil recovery
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Laurylamine hydrochloride CH3(CH2)11 NH3 – Cl has been chosen from cationic surfactants to produce secondary oil using lab. model shown in fig. (1). The relationship between interfacial tension and (temperature, salinity and solution concentration) have been studied as shown in fig. (2, 3, 4) respectively. The optimum values of these three variables are taken (those values that give the lowest interfacial tension). Saturation, permeability and porosity are measured in the lab. The primary oil recovery was displaced by water injection until no more oil can be obtained, then laurylamine chloride is injected as a secondary oil recovery. The total oil recovery is 96.6% or 88.8% of the residual oil has been recovered by this technique as shown

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Publication Date
Fri Jul 01 2016
Journal Name
Journal Of Economics And Administrative Sciences
مقارنة بعض الطرائق الجزائية في تحليل انموذج المؤشر الواحد شبه المعلمي مع تطبيق عملي
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ABSTRACT

In this research been to use some of the semi-parametric methods the based on the different function penalty as well as the methods proposed by the researcher  because these methods work to estimate and variable selection of significant at once for single index model including (SCAD-NPLS method , the first proposal SCAD-MAVE method , the second proposal  ALASSO-MAVE method ) .As it has been using a method simulation time to compare between the semi-parametric estimation method studied , and various simulation experiments to identify the best method based on the comparison criteria (mean squares error(MSE) and average  mean squares error (AMSE)).

And the use

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Publication Date
Sun Jan 14 2018
Journal Name
Journal Of Engineering
A Nonlinear MIMO-PID Neural Controller Design for Vehicle Lateral Dynamics model based on Modified Elman Neural Network
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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

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Publication Date
Fri Jan 26 2024
Journal Name
Iraqi Journal Of Science
Elastic Electron Scattering From Unstable Neutron-Rich P19PC Exotic Nucleus
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The ground state proton, neutron, and matter density distributions and corresponding root-mean-square (rms) of P19PC exotic nucleus are studied in terms of two-frequency shell model (TFSM) approach. The single-particle wave functions of harmonic-oscillator (HO) potential are used with two different oscillator parameters bRcoreR and bRhaloR. According to this model, the core nucleons of P18PC nucleus are assumed to move in the model space of spsdpf. The shell model calculations are carried out for core nucleons with w)20(+ truncations using the realistic WBP
interaction. The outer (halo) neutron in P
19
PC is assumed to move in the pure 2sR1/2R-
orbit. The halo structure in P
19
PC is confirmed with 2sR1/2R-dominant c

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Publication Date
Fri Jun 24 2022
Journal Name
Iraqi Journal Of Science
Determination the density of cometary nucleus material using gamma ray attenuation
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In this work, the technique of attenuation of gamma ray to calculate the density of comet nucleus materials (C/2009 P1 (GARRADD) at different range of energy (0.2- 0.9 MeV). also, the single scattering model for gamma rays has been assumed that photons reaching the detector with scattered only once in the material. The program has been designed and written in FORTRAN language (77 – 90) to calculate the density for molecules using Monte Carlo method was used to simulate the scattering and absorption of photons in semi- infinite material. Gamma ray interacts with the matter by three mainly interactions: Photoelectric effect, Compton scattering and Pair production (electron and positron). On the 137Cs source energy (662 keV), Compton scat

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Publication Date
Sat Apr 15 2023
Journal Name
Journal Of Robotics
A New Proposed Hybrid Learning Approach with Features for Extraction of Image Classification
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Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

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