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jcoeduw-1432
Digital Citizenship and its Relationship to the Level of Health Awareness of Corona Virus (Covid-19) among a Sample of Palestinian University Students: محمود عبد المجيد عساف
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The study aims to identify the degree of appreciation for the level of digital citizenship of a sample of Palestinian university students in the governorates of Gaza, and its relationship to the level of health awareness about the emerging coronavirus (covid-19). To achieve the objectives of the study, the researcher followed a descriptive approach by applying two questionnaires; the first, which consists of 30 items, was used  to measure the level of digital citizenship.  The second, which consists of 19 items, was used to measure the level of health awareness. Both questionnaires  were applied on a sample of 367 students who were electronically selected using the manner simple randomness. Results have shown that the degree of appreciation for the level of digital citizenship was high with a relative weight of 76.08%. Besides, the level of health awareness of coronavirus was high with a relative weight of 81.83%. Additionally, it has been found that there is a direct relationship with a statistical significance between the degree of sample appreciation of the level of digital citizenship, and the degree of  its evaluation of the level of health awareness of coronavirus, where the correlation coefficient was 0.468. Moreover, there has been shown that there are no statistically significant differences between the mean scores of the individuals appreciation of the level of health awareness of coronavirus (Covid-19) due to the gender variable. That is; the level of digital citizenship was in favor of females.The study recommended that e-university platforms should include health awareness messages for preventive purposes

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Publication Date
Thu Sep 08 2022
Journal Name
Al-khwarizmi Engineering Journal
Performance Prediction in EDM Process for Al 6061 Alloy Using Response Surface Methodology and Genetic Algorithm
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The Electric Discharge (EDM) method is a novel thermoelectric manufacturing technique in which materials are removed by a controlled spark erosion process between two electrodes immersed in a dielectric medium. Because of the difficulties of EDM, determining the optimum cutting parameters to improve cutting performance is extremely tough. As a result, optimizing operating parameters is a critical processing step, particularly for non-traditional machining process like EDM. Adequate selection of processing parameters for the EDM process does not provide ideal conditions, due to the unpredictable processing time required for a given function. Models of Multiple Regression and Genetic Algorithm are considered as effective methods for determ

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Publication Date
Thu Mar 16 2023
Journal Name
Wireless Personal Communications
Technology Applications in Tracking 2019-nCoV and Defeating Future Outbreaks: Iraqi Healthcare Industry in IoT Remote
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Publication Date
Sun Oct 31 2021
Journal Name
Iraqi Geological Journal
Use Conventional and Statistical Methods for Porosity Estimating in Carbonate Reservoir in Southern Iraq, Case Study
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Porosity is important because it reflects the presence of oil reserves. Hence, the number of underground reserves and a direct influence on the essential petrophysical parameters, such as permeability and saturation, are related to connected pores. Also, the selection of perforation interval and recommended drilling additional infill wells. For the estimation two distinct methods are used to obtain the results: the first method is based on conventional equations that utilize porosity logs. In contrast, the second approach relies on statistical methods based on making matrices dependent on rock and fluid composition and solving the equations (matrices) instantaneously. In which records have entered as equations, and the matrix is sol

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Publication Date
Fri Sep 26 2025
Journal Name
Applied Data Science And Analysis
Deep Learning in Genomic Sequencing: Advanced Algorithms for HIV/AIDS Strain Prediction and Drug Resistance Analysis
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Genome sequencing has significantly improved the understanding of HIV and AIDS through accurate data on viral transmission, evolution and anti-therapeutic processes. Deep learning algorithms, like the Fined-Tuned Gradient Descent Fused Multi-Kernal Convolutional Neural Network (FGD-MCNN), can predict strain behaviour and evaluate complex patterns. Using genotypic-phenotypic data obtained from the Stanford University HIV Drug Resistance Database, the FGD-MCNN created three files covering various antiretroviral medications for HIV predictions and drug resistance. These files include PIs, NRTIs and NNRTIs. FGD-MCNNs classify genetic sequences as vulnerable or resistant to antiretroviral drugs by analyzing chromosomal information and id

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Publication Date
Mon Sep 30 2019
Journal Name
College Of Islamic Sciences
Imam Ahmed bin Mohammed bin Yazid                Bin Harun Abu Bakr Alkhalal             And his jurisprudence in transactions
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The message of our Prophet Muhammad (peace and blessings of Allaah be upon him) was the most complete of the messages, and the conclusion of the messages and completed by the mercy of this nation. Al-Khalal al-Baghdadi al-Hanbali (may Allaah have mercy on him) was a scholar who updated his writings. And The Nha Brhnha and became an independent and arranged doctrine Such a world of Galilee Hambali must show his character and his knowledge and its impact on the Islamic arena.

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Publication Date
Thu Jun 01 2023
Journal Name
Medicinal Chemistry
New Niflumic Acid Derivatives as EGFR Inhibitors: Design, Synthesis, In silico Studies, and Anti-proliferative Assessment
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Background:

1,3,4-oxadizole and pyrazole derivatives are very important scaffolds for medicinal chemistry. A literature survey revealed that they possess a wide spectrum of biological activities including anti-inflammatory and antitumor effects.

Objectives:

To describe the synthesis and evaluation of two classes of new niflumic acid (NF) derivatives, the 1,3,4-oxadizole derivatives (compounds 3 and (4A-E) and pyrazole derivatives (compounds 5 and 6), as EGFR tyrosine kinase inhibitors in silico and in vitro.

Methods:

The designed compounds were synthesized using convent

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Publication Date
Sun Feb 01 2015
Journal Name
The European Physical Journal A
Analytic view at alpha clustering in even-even heavy nuclei near magic numbers 82 and 126
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Publication Date
Tue Feb 16 2016
Journal Name
The Iraqi Journal Of Agricultural Sciences
MEASURING VIBRATION AT TRACTOR PLATFORM , STEERING WHEEL AND SEAT EFFECTIVE AMPLITUDE TRANSMISSIBILITY FACTOR DURING OPERATION TILLAGE
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Field experiment conducted to measure vibrations on three axes longitudinal X, lateral Y and vertical Z on steering wheel, platform tractor and vertical vibration in seat tractor and seat effective amplitude transmissibility (SEAT) factor during operation tillage in silt clay loam soil with depth 18 cm in Baghdad. Split – split plot design under randomized complete block design with three replications least significant design 5 % used. Three factor were used in this experiment included two types of plows included chisel and disc plows which represented main plot, three tires inflation pressure was second factor included 1.1 ,1.8 and 2.7 bar, and three forward speeds of the tillage was third factor included 2.35 , 4.25 and 6.50 km/hr. Resu

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Publication Date
Sun May 01 2022
Journal Name
Optical Fiber Technology
Optical fiber sensor network integrating SAC-OCDMA and cladding modified optical fiber sensors coated with nanomaterial
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Publication Date
Tue Dec 05 2023
Journal Name
Baghdad Science Journal
AlexNet Convolutional Neural Network Architecture with Cosine and Hamming Similarity/Distance Measures for Fingerprint Biometric Matching
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In information security, fingerprint verification is one of the most common recent approaches for verifying human identity through a distinctive pattern. The verification process works by comparing a pair of fingerprint templates and identifying the similarity/matching among them. Several research studies have utilized different techniques for the matching process such as fuzzy vault and image filtering approaches. Yet, these approaches are still suffering from the imprecise articulation of the biometrics’ interesting patterns. The emergence of deep learning architectures such as the Convolutional Neural Network (CNN) has been extensively used for image processing and object detection tasks and showed an outstanding performance compare

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