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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 molecule. A sequence DL model based on a bidirectional gated recurrent unit (GRU) is implemented. The model is applied to the Stanford COVID-19 mRNA vaccine dataset to predict the mRNA sequences deterioration by predicting five reactivity values for every base in the sequence, namely reactivity values, deterioration rates at high pH, at high temperature, at high pH with Magnesium, and at high temperature with Magnesium. The Stanford COVID-19 mRNA vaccine dataset is split into the training set, validation set, and test set. The bidirectional GRU model minimizes the mean column wise root mean squared error (MCRMSE) of deterioration rates at each base of the mRNA sequence molecule with a value of 0.32086 for the test set which outperformed the winning models with a margin of (0.02112). This study would help other researchers better understand how to forecast mRNA sequence molecule properties to develop a stable COVID-19 vaccine.

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Publication Date
Thu Apr 30 2020
Journal Name
Journal Of Economics And Administrative Sciences
The Role of E-Learning in enhancing Total Quality Management in Educational Institutions: an exploratory study of the opinions of a sample of the staff of the Directorate of Education in the province of Nineveh
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Purpose: The research aims to determine the relationship between E-Learning and Total Quality Management (TQM) in Educational institutions in Nineveh Governorates.

Methodology / Design: The researchers distributed (30) questionnaires to employees (teachers and administrators) of Nineveh Governorate education who represent the community of the research sample, as they were analyzed using the SPSS V.20

The importance of research: The importance of the research in the fact that it focuses on one of the educational methods represented in integrating the traditional method and relying on modern technologies using computers and the Internet in the field of education to improve the reali

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Publication Date
Sun Mar 31 2024
Journal Name
Iraqi Geological Journal
Permeability Prediction and Facies Distribution for Yamama Reservoir in Faihaa Oil Field: Role of Machine Learning and Cluster Analysis Approach
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Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, F

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Publication Date
Sun Sep 01 2013
Journal Name
Journal Of Economics And Administrative Sciences
Using median rule in determining the establishment of service station between Al hasaka and Al kamishly serian cities
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Research indicates that the second half of the twentieth century marked large interests in the service industry by government and private organizations in that one, and the service industry has become the bedrock of plans in achieving economic and social development. From this standpoint felt specialists and researchers the importance of transport modes, including rail, which should be available between Almnltq Civil populated as services organized by the competent authorities to achieve the active participation of citizens in economic and social development in the region and that the term services means economic activities, which are the results Pollack concrete such as accepting the situation and satisfaction them or satisfacti

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Publication Date
Mon Jun 01 2020
Journal Name
Al-khwarizmi Engineering Journal
Analysis of Different Hand and Finger Grip Patterns using Surface Electromyography and Hand Dynamometry
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Recording an Electromyogram (EMG) signal is essential for diagnostic procedures like muscle health assessment and motor neurons control. The EMG signals have been used as a source of control for powered prosthetics to support people to accomplish their activities of daily living (ADLs). This work deals with studying different types of hand grips and finding their relationship with EMG activity. Five subjects carried out four functional movements (fine pinch, tripod grip and grip with the middle and thumb finger, as well as the power grip). Hand dynamometer has been used to record the EMG activity from three muscles namely; Flexor Carpi Radialis (FCR), Flexor Digitorum Superficialis (FDS), and Abductor Pollicis Brevis (ABP) with different

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Publication Date
Wed May 10 2023
Journal Name
Journal Of Planner And Development
The impact of population density in sustainable housing - An applied study in the city of Baghdad (Mahalla 817), Al-Bayaa as a model
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Population density represents an important indicator for determining the growth of the size of urban areas. Population density has a direct impact on the quality of life, and its excessive rise may lead to the deterioration of basic service facilities. The problem of the research is that the increase in the total population densities of the residential neighborhood may reflect negatively on the nature of the services provided to the residents in these shops, and thus the failure to achieve sustainability in the residential locality. The research assumes that the increase in population densities negatively affects the achievement of sustainability in the residential neighborhood.

 

      

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Publication Date
Fri Feb 08 2019
Journal Name
Journal Of The College Of Education For Women
A Unit Plan Based Upon The MI Theory: A theoretical study with an application Inside The Classroom
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This research basically gives an introduction about the multiple intelligence
theory and its implication into the classroom. It presents a unit plan based upon the
MI theory followed by a report which explains the application of the plan by the
researcher on the first class student of computer department in college of sciences/
University of Al-Mustansiryia and the teacher's and the students' reaction to it.
The research starts with a short introduction about the MI theory is a great
theory that could help students to learn better in a relaxed learning situation. It is
presented by Howard Gardener first when he published his book "Frames of
Minds" in 1983 in which he describes how the brain has multiple intelligen

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Publication Date
Thu Jul 01 2021
Journal Name
Journal Of Physical Education
The effect of e-learning on learning the skills of short Service and front-dimensional blow for badminton players under the age of 15 years.
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AO Dr. Ali Jihad, Journal of Physical Education, 2021

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Publication Date
Wed Mar 10 2021
Journal Name
Baghdad Science Journal
A Comparison Between the Theoretical Cross Section Based on the Partial Level Density Formulae Calculated by the Exciton Model with the Experimental Data for (_79^197)Au nucleus
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In this paper, the theoretical cross section in pre-equilibrium nuclear reaction has been studied for the reaction  at energy 22.4 MeV. Ericson’s formula of partial level density PLD and their corrections (William’s correction and spin correction) have been substituted  in the theoretical cross section and compared with the experimental data for  nucleus. It has been found that the theoretical cross section with one-component PLD from Ericson’s formula when  doesn’t agree with the experimental value and when . There is little agreement only at the high value of energy range with  the experimental cross section. The theoretical cross section that depends on the one-component William's formula and on-component corrected to spi

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Publication Date
Fri Jul 30 2021
Journal Name
Iraqi Journal For Electrical And Electronic Engineering
EEG Motor-Imagery BCI System Based on Maximum Overlap Discrete Wavelet Transform (MODWT) and Machine learning algorithm
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The ability of the human brain to communicate with its environment has become a reality through the use of a Brain-Computer Interface (BCI)-based mechanism. Electroencephalography (EEG) has gained popularity as a non-invasive way of brain connection. Traditionally, the devices were used in clinical settings to detect various brain diseases. However, as technology advances, companies such as Emotiv and NeuroSky are developing low-cost, easily portable EEG-based consumer-grade devices that can be used in various application domains such as gaming, education. This article discusses the parts in which the EEG has been applied and how it has proven beneficial for those with severe motor disorders, rehabilitation, and as a form of communi

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Publication Date
Mon Apr 27 2026
Journal Name
Applied Fruit Science
Predicting Bitter Orange (Citrus aurantium L.) Maturity by Machine Learning Based on Picking Force in Smart Picker
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Manual fruit picking is labor-intensive and can damage fruit. Fully mechanized picking is efficient, but it also risks fruit damage. Therefore, semi-automated tools are needed to improve bitter orange picking. This paper presents a smart manual picker designed to facilitate picking while predicting fruit maturity based on picking force as well as various chemical and physical parameters using machine learning (ML). The study methodology consists of five stages: (1) manufacturing the smart picker, (2) picking 50 bitter orange samples, (3) measuring the characteristics of the bitter oranges in the laboratory, (4) training different ML models, and (5) identifying the most accurate model for predicting fruit maturity. The results indicate that

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