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bsj-4804
Effect of Salt and Osmotic Stresses on the Activity of Some Antioxidant Enzymes and Biochemical Traits in Catharanthus roseus
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Abstract

The experiment has been carried out in the Syrian National Commission of Biotechnology, during the growing season 2018/2019, to study the effect of abiotic stresses (salinity and osmotic stresses) on the activity of some antioxidant enzymes and biochemical traits in Catharanthus roseus. The experiment has been laid according to (CRD) with three replications. The seeds have been sterilized by NaOCl solution (0.5% v/v), then planted on MS medium. Plantlets have been moved to MS medium enriched with NAA (1 mg.L-1) and BA (2 mg.L-1). The callus has been initiated from leaves using MS medium containing NAA (1 mg L-1) and KIN (2 mg.L-1). After 60 days, callus has been transferred to MS medium supplemented with different concentrations of PEG 6000 (-0.2, -0.3, -0.4, -0.5 MPa), and NaCl (25, 50, 75, 100 mM) in succession as stimulating agents. The results show that the top value of solutes leakage has been in the salt and osmotic treatments (28.04 and 26.98% respectively) compared with the control (8.563%). MDA content has significantly been higher in salt stress (102.3 µmol.g-1 FW) followed by the osmotic stress treatment (79.41 µmol.g-1 FW), while it was significantly lower in the non-stressed treatment (37.76 µmol.g-1 FW). An increase in the proline content occurred in both the stress treatments (4.623, 4.243 mmol.g-1 FW, respectively) compared with the control (2.477 mmol.g-1 FW). The activity of antioxidant enzymes (CAT, APX, and SOD) have significantly been higher in salt stress treatment (506.9, 12270.02 mol.min-1.mg-1 protein, 191.4 U.mg-1 protein respectively), followed by osmotic stress (259.4, 7106.22 mol.min-1.mg-1 protein, 65.60 U.mg-1 protein, respectively), while it has been significantly lower in control (126.9, 1800.38 mol.min-1.mg-1 protein, 36.03 U.mg-1 protein, respectively).

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Publication Date
Tue Oct 25 2022
Journal Name
Minar Congress 6
HANDWRITTEN DIGITS CLASSIFICATION BASED ON DISCRETE WAVELET TRANSFORM AND SPIKE NEURAL NETWORK
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In this paper, a handwritten digit classification system is proposed based on the Discrete Wavelet Transform and Spike Neural Network. The system consists of three stages. The first stage is for preprocessing the data and the second stage is for feature extraction, which is based on Discrete Wavelet Transform (DWT). The third stage is for classification and is based on a Spiking Neural Network (SNN). To evaluate the system, two standard databases are used: the MADBase database and the MNIST database. The proposed system achieved a high classification accuracy rate with 99.1% for the MADBase database and 99.9% for the MNIST database

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Publication Date
Wed Dec 01 2021
Journal Name
Baghdad Science Journal
Fuzzy Convergence Sequence and Fuzzy Compact Operators on Standard Fuzzy Normed Spaces
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The main purpose of this work is to introduce some types of fuzzy convergence sequences of operators defined on a standard fuzzy normed space (SFN-spaces) and investigate some properties and relationships between these concepts. Firstly, the definition of weak fuzzy convergence sequence in terms of fuzzy bounded linear functional is given. Then the notions of weakly and strongly fuzzy convergence sequences of operators  are introduced and essential theorems related to these concepts are proved. In particular, if ( ) is a strongly fuzzy convergent sequence with a limit  where linear operator from complete standard fuzzy normed space  into a standard fuzzy normed space  then  belongs to the set of all fuzzy bounded linear operators

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Publication Date
Mon Apr 15 2024
Journal Name
Journal Of Engineering Science And Technology
Text Steganography Based on Arabic Characters Linguistic Features and Word Shifting Method
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In the field of data security, the critical challenge of preserving sensitive information during its transmission through public channels takes centre stage. Steganography, a method employed to conceal data within various carrier objects such as text, can be proposed to address these security challenges. Text, owing to its extensive usage and constrained bandwidth, stands out as an optimal medium for this purpose. Despite the richness of the Arabic language in its linguistic features, only a small number of studies have explored Arabic text steganography. Arabic text, characterized by its distinctive script and linguistic features, has gained notable attention as a promising domain for steganographic ventures. Arabic text steganography harn

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Publication Date
Sat Jul 01 2023
Journal Name
Int. J. Advance Soft Compu. Appl,
Arabic and English Texts Encryption Using Proposed Method Based on Coordinates System
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Publication Date
Sun Jun 02 2019
Journal Name
Baghdad Science Journal
Fog Computing Resource Optimization: A Review on Current Scenarios and Resource Management
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            The unpredictable and huge data generation nowadays by smart computing devices like (Sensors, Actuators, Wi-Fi routers), to handle and maintain their computational processing power in real time environment by centralized cloud platform is difficult because of its limitations, issues and challenges, to overcome these, Cisco introduced the Fog computing paradigm as an alternative for cloud-based computing. This recent IT trend is taking the computing experience to the next level. It is an extended and advantageous extension of the centralized cloud computing technology. In this article, we tried to highlight the various issues that currently cloud computing is facing. Here

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Publication Date
Thu Oct 07 2021
Journal Name
Rimak International Journal And Humanities And Social Sciences
children were exposed to animation on specialized satellite channels and verified gratifications
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Publication Date
Sun Jun 20 2021
Journal Name
Baghdad Science Journal
Arabic Speech Classification Method Based on Padding and Deep Learning Neural Network
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Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to

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Publication Date
Tue Feb 06 2018
Journal Name
International Journal Of Gynecology And Obstetrics
FIGO consensus guidelines on placenta accreta spectrum disorders: Prenatal diagnosis and screening
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Recent population studies have shown that placenta accreta spectrum (PAS) disorders remain undiagnosed before delivery in half to two-thirds of cases. In a series from specialist diagnostic units in the USA, around one-third of cases of PAS disorders were not diagnosed during pregnancy. Maternal

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Publication Date
Wed Mar 18 2020
Journal Name
Baghdad Science Journal
On Light Mapping and Certain Concepts by Using m_X N-Open Sets
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The aim of this paper is to present a weak form of -light functions by using -open set which is -light function, and to offer new concepts of disconnected spaces and totally disconnected spaces. The relation between them have been studied. Also, a new form of -totally disconnected and inversely -totally disconnected function have been defined, some examples and facts was submitted.

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Publication Date
Sun Dec 31 2023
Journal Name
Iraqi Journal Of Information And Communication Technology
EEG Signal Classification Based on Orthogonal Polynomials, Sparse Filter and SVM Classifier
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This work implements an Electroencephalogram (EEG) signal classifier. The implemented method uses Orthogonal Polynomials (OP) to convert the EEG signal samples to moments. A Sparse Filter (SF) reduces the number of converted moments to increase the classification accuracy. A Support Vector Machine (SVM) is used to classify the reduced moments between two classes. The proposed method’s performance is tested and compared with two methods by using two datasets. The datasets are divided into 80% for training and 20% for testing, with 5 -fold used for cross-validation. The results show that this method overcomes the accuracy of other methods. The proposed method’s best accuracy is 95.6% and 99.5%, respectively. Finally, from the results, it

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