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Peak Ground Acceleration Models Predictions Utilizing Two Metaheuristic Optimization Techniques
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Publication Date
Mon Jun 05 2023
Journal Name
Journal Of Engineering
Image Compression Using 3-D Two-Level Technique
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In this paper three techniques for image compression are implemented. The proposed techniques consist of three dimension (3-D) two level discrete wavelet transform (DWT), 3-D two level discrete multi-wavelet transform (DMWT) and 3-D two level hybrid (wavelet-multiwavelet transform) technique. Daubechies and Haar are used in discrete wavelet transform and Critically Sampled preprocessing is used in discrete multi-wavelet transform. The aim is to maintain to increase the compression ratio (CR) with respect to increase the level of the transformation in case of 3-D transformation, so, the compression ratio is measured for each level. To get a good compression, the image data properties, were measured, such as, image entropy (He), percent r

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Publication Date
Mon Mar 01 2010
Journal Name
Journal Of Economics And Administrative Sciences
Two side properties of EWMA control panels unilaterally
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This study, establishes two stochastic monotonicity results concerning the run length of an upper one –sided Exponentially Weighted Moving Average (EWMA) control charts, based on the logarithm of the sample variance, for monitoring a process standard deviation, these properties cast interesting light on the control chart performance, and their extension to other one –sided EWMA control charts.

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Publication Date
Thu Aug 01 2019
Journal Name
Nanomedicine: Nanotechnology, Biology And Medicine
Listeria innocua Dps as a nanoplatform for bioluminescence based photodynamic therapy utilizing Gaussia princeps luciferase and zinc protoporphyrin IX
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Publication Date
Tue May 13 2025
Journal Name
Energies
Thermal and Thermo-Hydraulic Performance of a Semi-Circular Solar Air Collector Utilizing an Innovative Configuration of Metal Foams
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The enhancement of the thermal and thermo-hydraulic performance of a semi-circular solar air collector (SCSAC) is numerically investigated using porous semi-circular obstacles made of metal foam with and without longitudinal porous Y-shaped fins. Two 10 and 40 PPI porous material samples are examined. Three-dimensional models are built to simulate the performance of SCSAC: model (I) with clear air passage; model (II) with only metal foam obstacles, and model (III) with metal foam obstacles as well as porous Y-fins. COMSOL Multiphysics software version 6.2 based on finite element methodology is employed. A conjugate heat transfer with a (k-ε) turbulence model is selected to simulate both heat transfer and fluid flow across the entir

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Publication Date
Tue Sep 30 2025
Journal Name
International Journal On Engineering Applications (irea)
Prediction of Trips Attraction to the Central Business District of Al Nasiriyah City Utilizing an Artificial Neural Network Model
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Estimation of trip attraction and analyzing its main influencing factors are powerful for offering different classifications for business districts and presenting recommendations for improving attractiveness in long term. This is beneficial for designing transportation facilities and infrastructures. The paper presents the prediction of trip attraction using an artificial intelligence technology due to the profits that the technology can possess in shortening time, lowering expenses and saving effort. The new model has utilized six input parameters that have not been considered previously within the area of Nasiriyah city including; age and educational level of the passengers, mode of transport that the passengers use, purpose of the trip,

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Publication Date
Tue Dec 15 2020
Journal Name
Journal Of Baghdad College Of Dentistry
Impact of two non-nutritive sucking patterns on the development of anterior open bite in children of two kindergartens in Baghdad city
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Background: Non-nutritive sucking habit (NNSH) is the main environmental causative factor that disturbs normal orofacial development. In spite of the harmful effect of pacifier as a NNSH, mothers aware from the other types of NNSH like thumb sucking far more than pacifier use. Open bite is one of the most challenging malocclusions in orthodontics due to the high prevalence of relapse after treatment, so preventing the causative factor of its occurrence is essential at early age of child life. This study aims to assess the impact of two non-nutritive patterns on the development of anterior open bite in primary dentition and to compare which of these habits mostly affect open bite development. Materials and Methods: The sample consisted of

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Publication Date
Wed Jun 04 2025
Journal Name
Kufa Journal For Agricultural Sciences – 2025:17(2) : 56- 65
Efficacy of different Conidial concentrations, fungal Filtrations and silver nanoparticles Mycosynthesis using two isolates of Metarhizium anisoplae against Two Stored-grain Insects
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Publication Date
Sat Nov 22 2014
Journal Name
Indian Journal Of Physics
Comparison between shell model and self-consistent mean field calculations for ground charge density distributions and elastic form factors of 12C and 16O nuclei
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Publication Date
Sat Nov 22 2014
Journal Name
Indian Journal Of Physics
Comparison between shell model and self-consistent mean field calculations for ground charge density distributions and elastic form factors of 12C and 16O nuclei
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Publication Date
Mon Jan 01 2024
Journal Name
Aip Conference Proceedings
Comparative analysis of deep learning techniques for lung cancer identification
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One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p

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