Journal of Education for Pure Science-University of Thi-Qar

The present research aimed to investigate the potential of the ethanolic extract of Cladophora crispata algae for synthesizing AgNPs. The GC-mass technique was used in the identification of active compounds present in the algal ethanolic extract. The extract’s capability to synthesize silver nanoparticles was examined, as well as the formation of these nanoparticles was deduced by the color transformation of the mixture from light green to dark brown.Some of the techniques used to characterize the synthesized nanoparticles include an Ultraviolet-Visible spectrophotometer (UV-vis), Fourier Transform Infrared (FTIR) spectroscopy, a Scanning Electron Microscope (SEM), and Energy-Dispersive X-ray spectroscopy (EDX). They were also characterized by X-ray Diffraction (XRD). The antibacterial effectiveness was tested through the Agar well diffusion method. The GC-mass results indicated that the ethanolic extract of the algae contains different active compounds with different percentages of each, such as Oleic acid (11.7%), Neophytadiene (12.0%), and n-Hexadecanoic acid (283.1%). UV-vis absorption spectroscopy was used to confirm the formation of the silver nanoparticles, which appeared at a wavelength of 417nm. The XRD analysis confirmed the crystalline nature of the resulting silver nanoparticles, while SEM revealed that the AgNPs were spherical with particle sizes between 29-99 nm. EDX analysis revealed that the synthesized silver nanoparticles were composed of silver, carbon, oxygen, and chlorine.The efficacy of the synthesized silver nanoparticles was tested against four types of pathogenic bacteria, namely E. coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Staphylococcus aureus. The results demonstrated that the silver nanoparticles had a significant inhibitory capability against these pathogens

The current laboratory study was conducted to verify the possibility of biological treatment (Vermiremediation) of the earthworm Lumbricus terrestrisin soil contaminated with crude oil, four concentrations (1%-2%-5%-8%) were prepared from soil contaminated with crude oil that was brought from the Shuaiba area near the south refineries in Basrah Province, southern Iraq, after growing them with earthworms and examining them every 10 days for 40 days, the statistical results showed that the earthworms significantly reduced total petroleum hydrocarbons (TPH).Mean concentration of total petroleum hydrocarbons for the 1% pollution concentration before treatment was 16.636 μg/g dry weight, and after 40 days of treatment it reached 5.330 μg/g dry weight, while at a contamination concentration of 2% it reached 33.426 μg/g dry weight before treatment and at the end of treatment the mean concentration reached 5.730 μg/g dry weight, as for contamination concentration of 5%, it reached 51.370 μg/g dry weight before treatment and decreased after 40 days of treatment to 7.340 μg/g dry weight. Pollution concentration 8% it was 70.540 μg/g dry weight before treatment, and after the end of treatment it decreased to 11.350 μg/g dry weight. The statistical results also showed that there is a direct correlation between (TPH) and pH and an inverse relationship with soil temperature

Fractional differential equations have gained significant attention in recent years due to their ability to model complex phenomena in various scientific and engineering fields. This study focuses on solving fractional differential equations involving the nonlinear Caputo fractional derivative and the Caputo-Fabrizio fractional derivative. We employ the Daftardar-Jafari Method (DJM)and the Elzaki Daftardar-Jafari Method (EDJM) to derive approximate analytical solutions. The combination of these methods provides a systematic and efficient approach to addressing the challenges posed by nonlinearities and the memory effects inherent infractional derivatives. Through illustrative examples, we demonstrate the accuracy and applicability of the proposed methods. The results indicate that DJM and EDJM are powerful tools for solving nonlinear fractional differential equations, offering insights into their underlying dynamics

The rapid advancement of computer and network technologies has led to a surge in network security threats. To combat these risks, Intrusion Detection Systems (IDS) have become essential for detecting and mitigating network attacks. Machine learning-based technologies present interesting possibilities for improving the accuracy and reliability of intrusion detection. However, the effectiveness of these methods might be jeopardized by high-dimensional datasets with irrelevant or duplicated features, which can reduce model performance and lengthen training time. This study takes a multifaceted approach to addressing these difficulties. Firstly, we suggest a data balancing strategy that combines oversampling and undersampling techniques to provide a more balanced dataset, hence improving the detection of both normal and harmful actions. Second, we use dimensionality reduction approaches to select and extract useful features, minimizing the impact of irrelevant data on model performance. Third, we use advanced deep learning techniques to improve the detection accuracy of network intrusions. We evaluate our proposed methodology against the NSL-KDD dataset, a well-known network intrusion detection benchmark. The experimental results show that our methodology enhances intrusion detection accuracy greatly when compared to existing methods. Combining balanced data, feature selection, and advanced deep learning approaches effectively addresses network intrusion detection difficulties, resulting in more reliable and efficient IDS solutions

The approach for resolving a set of linear and nonlinearequations using fractional natural variation iterations is proposed in this article. According to the findings, this strategy is far more successful and promising than previousnumerical approaches.

In this paper several types of dominating parameters are applied on a discrete topological graph Gτ=(V,E) created from the topological space(X,τ), where V(Gτ)is the set of vertices in Gτthat includes all subset of τunless X, ∅, and E(Gτ)is a set of edges between any two vertices in which any two vertices in Gτsuch that |A|+|B|≥|X|;A≠B. Some dominating parameters on Gτare studied such as: general domination, connected domination, doubly connected domination, and polynomial domination. Besides the minimum dominating set is proved for any topological graph formed by corona operation or join operation between two topological graphs. Also, the inverse domination is discussed.

Let ????be a commutative semiring with unity. The non-comaximal graph ᴏf a semiring ????, denoted by ????(????)is an undirected graph where ????iᵴthe ᵴet ᴏf vertices in ????(????)and????,????∈????remain adjacent if and only if ????????+????????≠????. We look at the connectednessand the diameter of this graph. The concepts of independent set, clique and the girth of ????(????)are discussed.

Epilepsy is classified as a chronic, non-communicable disease, but at the same time, it is a serious disease that can lead to death if the necessary measures are not taken on time. Many studies have been presented on how to benefit from machine learning techniques in diagnosing or predicting the disease. However, more studies still need to develop methods and algorithms for better performance and guaranteed results. This article highlights the latest techniques and algorithms that have been used from 2019 -2024 and which have been published in peer-reviewed scientific journals. This review of the modern literature aims to identify the latest developments in this field and provide work that helps researchers and specialists interested in this disease. Also, in this study, the scope, limitations, and recommendations of previous studies have been discussed.

Acollectionof (n×n) not-solematrices onFdomainthis collectioncombinea group beneathprocessof matrix multiplyinvitedthe general linear group of measurenondomainF, indicateGL(n,F). The kernel ofahomomorphismofGL(n,F)to F*isthe special linear group and indicateby ????ℒ(n,F)is the determinantof these matrices, and this homomorphism.Thus ????ℒ(n,F)contains all matrices of determinant one whichis the subgroup of GL(n,F).The objective of thestudy is to find the Artin directrix(A.d.)for the groups ????ℒ(2,23) and ????ℒ(2,29)from written the rational valued characters (r.v.ch.) of the rational representations(r.r.)as a linear combination of the induced characters (in.ch.) for these groups.

Fruit peels are often regarded as agricultural waste and are typically discarded instead of being utilized for their antimicrobial potential. Studies indicate that these peels harbor valuable compounds that could be beneficial for medical applications. This study assessed the antibacterial properties of ethanol and aqueous extracts from the peels of kiwi (Actinidia deliciosa), pomegranate (Punica granatum), and orange (Citrus sinensis) against four bacterial pathogens: Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Bacillus cereus. It was collected the fruits from local markets in the city of Nasiriyah. Then, it was removed the peels, washed, dried, and ground them, preparing both aqueous and alcoholic extracts of these fruits to studytheir effectiveness in inhibition. The findings revealed that the pomegranate fat extract demonstrated the highest antibacterial activity, with an inhibition zone of 39.66±1.60 mm against Staphylococcus aureus, followed by 34.00±1.20 mm against Pseudomonas aeruginosa. The ethanol extract of pomegranate also showed significant activity, with 32.33±1.60 mm against Pseudomonas aeruginosa, while the lowest inhibition was recorded at 14.66±1.80 mm against Bacillus cereus. The kiwi and orange extracts exhibited inhibition zones ranging from 11.66±1.20 mm to 6.33±1.00 mm. The present study shows that the examined fruit peel residues have potential therapeutic uses against multidrug-resistant pathogenic bacteria. This approach can also reduce waste by promoting recycling in an economically and environmentally friendly way.

Fractional differential equations have gained significant attention in recent years due to their ability to model complex phenomena in various scientific and engineering fields. This study focuses on solving fractional differential equations involving the nonlinear Caputo fractional derivative and the Caputo-Fabrizio fractional derivative. We employ the Daftardar-Jafari Method (DJM)and the Elzaki Daftardar-Jafari Method (EDJM) to derive approximate analytical solutions. The combination of these methods provides a systematic and efficient approach to addressing the challenges posed by nonlinearities and the memory effects inherent infractional derivatives. Through illustrative examples, we demonstrate the accuracy and applicability of the proposed methods. The results indicate that DJM and EDJM are powerful tools for solving nonlinear fractional differential equations, offering insights into their underlying dynamics.

In this paper, disconnected multi-effect domination is discussed, a new dominationmodel in graphs is introduced. Let G=(V,E)is a finite, simple, nontrivial, and undirected graph without isolated vertex. A dominating subset DVis a disconnected multi-effect dominating set in Gif for every vertexv∈D, |N(v)∩(V−D)|≥2and G[D]is disconnected subgraph. The minimum cardinality over all disconnected multi-effect dominating sets in Gis the disconnected multi-effect domination number of Gdenoted by γdm(G). In this work some graphs generated by corona operation will be studied.

Software testing is a critical and resource-intensive phase in the Software Development Life Cycle (SDLC), often consuming up to50% of development costs. This study introduces an innovative module prioritization approach that optimizes testing time and cost while maintaining software quality. By integrating the KLOC (Kilo Lines of Code) metric with advanced genetic algorithms (GA)and Deep General Regression Neural Networks (DGRNN), the proposed method efficiently allocates testing resources to prioritize critical modules. This ensures comprehensive testing within constrained budgets, significantly reducing both time and cost. The methodology was implemented using MATLAB simulations, where DGRNN was compared against traditional machine learning models, including Support Vector Regression (SVR), k-Nearest Neighbors (k-NN), and Random Forest (RF). The results demonstrate that the GA-KLOC approach reduces testing time by up to 30% and lowers costs by 25% while maintaining high defect detection accuracy of over 90%. This research highlights the effectiveness of evolutionary algorithms and deep learning in optimizing software testing processes, offering a practical solution to enhance software quality, reduce errors, and improve cost efficiency in software development. The study also addresses the gap in module-level defect density prediction by leveraging DGRNN, providing a robust framework for future research in software testing optimization.

The fractional reduce differential transform approach is used in this article to solve the fractional-order biological population model (FOBPM). The fractional derivative is defined using the fractional derivative of Caputo. The suggested technique provides several solutions for FOBPM.