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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
Mon Jul 10 2023
Journal Name
Journal Of Engineering
Experimental Study of Pre-Cast Reinforced Concrete Deep Beams (Hallow Core section) Retrofitting with Carbon Fiber Reinforced Polymer (CFRP)
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Experimental programs based test results has been used as a means to find out the response of individual elements of structure. In the present study involves investigated behavior of five reinforced concrete deep beams of dimension (length 1200 x height 300 x width150mm) under two points concentrated load with shear span to depth ratio of (1.52), four of these beams with hallow core and
retrofit with carbon fiber reinforced polymer CFRP (with single or double or sides Strips). Two shapes of hallow are investigated (circle and square section) to evaluated the response of beams in case experimental behavior. Test on simply supported beam was performed in the laboratory & loaddeflection, strain of concrete data and crack pattern of

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Publication Date
Tue Oct 31 2023
Journal Name
College Of Islamic Sciences
The fatwa based on the statement of Zafar In the Hanafi school of thought
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Each school of Islamic jurisprudence has principles and rules upon which the diligent work in these schools is based. This is due to the view of sanctification of these rulings, as they are divine rulings. Therefore, the goal is to reach a ruling that represents the intent of the legislator as much as possible.
Hence, these schools of thought established rules for issuing fatwas with the intention of restricting the performance of a fatwa to the hands of those who are qualified for it and have met its conditions, so they gave priority to the most knowledgeable person over others to perform the fatwa. In the Hanafi school of thought, for example, the saying of Imam Abu Hanifa (may God have mercy on him) is given precedence over others,

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Publication Date
Wed May 01 2013
Journal Name
2013 Fourth International Conference On E-learning "best Practices In Management, Design And Development Of E-courses: Standards Of Excellence And Creativity"
Students' Perspectives in Adopting Mobile Learning at University of Bahrain
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Publication Date
Tue May 07 2019
Journal Name
Acm Journal On Emerging Technologies In Computing Systems
Neuromemrisitive Architecture of HTM with On-Device Learning and Neurogenesis
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Hierarchical temporal memory (HTM) is a biomimetic sequence memory algorithm that holds promise for invariant representations of spatial and spatio-temporal inputs. This article presents a comprehensive neuromemristive crossbar architecture for the spatial pooler (SP) and the sparse distributed representation classifier, which are fundamental to the algorithm. There are several unique features in the proposed architecture that tightly link with the HTM algorithm. A memristor that is suitable for emulating the HTM synapses is identified and a new Z-window function is proposed. The architecture exploits the concept of synthetic synapses to enable potential synapses in the HTM. The crossbar for the SP avoids dark spots caused by unutil

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Publication Date
Mon Jun 01 2015
Journal Name
Journal Of Economics And Administrative Sciences
The phenomenon of negative behavior has studied as a social and psychological phenomenon that effect on the performance and life of workers inside and outside the organization. The adoption of this phenomenon is studied in terms of the role of the interna
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The phenomenon of negative behavior has studied as a social and psychological phenomenon that effect on the performance and life of workers inside and outside the organization. The adoption of this phenomenon is studied in terms of the role of the internal environment of the organization in addressing this behavior, being the variables belong to the field of organizational behavior to see the results of those variables on the Iraqi organizations, since the specificities of it differ from the rest of the Arab and foreign environments. Therefore, this study focused on testing the relationship of the internal environment of the organization and its role in addressing the negative behavior of the workers.

thi

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Publication Date
Wed Dec 11 2019
Journal Name
Journal Of The College Of Education For Women
The Impact of the Selective Model on the Acquisition of Kurdish Grammar Concepts among Female Students in the Eighth Grade
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This research aims to identify the impact of the selective model in acquiring the concepts of Kurdish grammar among female students in the eighth grade, and to achieve the goal of research, the researcher selected the experimental design with partial control  and dimensional testing; the sample includes basic schools in the Chim district of Chamal/ Sulaymaniyah and randomly selected the basic school (Maha Bad) to be the field of application of the experiment and the random drawing method was chosen: two out of three sections and the number of students of the two sections is (75) students; section (C) represents the experimental group that studied the rules according to the selective model and its number is (37) students, while secti

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Publication Date
Wed Jul 31 2019
Journal Name
Journal Of Engineering
Evaluation of the Accuracy of Digital Elevation Model Produced from Different Open Source Data
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This study aims to estimate the accuracy of digital elevation models (DEM) which are created with exploitation of open source Google Earth data and comparing with the widely available DEM datasets, Shuttle Radar Topography Mission (SRTM), version 3, and Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM), version 2. The GPS technique is used in this study to produce digital elevation raster with a high level of accuracy, as reference raster, compared to the DEM datasets. Baghdad University, Al Jadriya campus, is selected as a study area. Besides, 151 reference points were created within the study area to evaluate the results based on the values of RMS.Furthermore, th

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Publication Date
Tue Apr 30 2024
Journal Name
Iraqi Journal Of Science
Crescent Moon Visibility: A New Criterion using Deep learned Artificial Neural-Network
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     Many authors investigated the problem of the early visibility of the new crescent moon after the conjunction and proposed many criteria addressing this issue in the literature. This article presented a proposed criterion for early crescent moon sighting based on a deep-learned pattern recognizer artificial neural network (ANN) performance. Moon sight datasets were collected from various sources and used to learn the ANN. The new criterion relied on the crescent width and the arc of vision from the edge of the crescent bright limb. The result of that criterion was a control value indicating the moon's visibility condition, which separated the datasets into four regions: invisible, telescope only, probably visible, and certai

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Publication Date
Tue Aug 31 2021
Journal Name
International Journal Of Intelligent Engineering And Systems
FDPHI: Fast Deep Packet Header Inspection for Data Traffic Classification and Management
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Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c

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Publication Date
Wed Jan 01 2020
Journal Name
International Journal Of Computational Intelligence Systems
Evolutionary Feature Optimization for Plant Leaf Disease Detection by Deep Neural Networks
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