Mustansiriyah Journal of Pure and Applied Sciences

Study and calculation of the mass absorption coefficient for X-rays of zinc oxide and cadmium oxide compounds

Hanaa A. Aziz — 2026-03-30

the theoretical results were than compared
with the results predicted by using the programs (xcom, ffast) noticeable differences were
observed in the values of the theoretical results and the values of the experimental or semiexperimental results from programs (Xcom, Ffast) because the theoretical equation considers
the material to be homogeneous and ideal and depends on the absorption resulting from the
photoelectric effect, this is in addition to the effect of absorption edges. The equation
theoretical followed the same experimentally proven course: with increasing X-ray energy, the
mass absorption coefficient of material decreases,.All calculations and graphical
representations were performed using MATLAB 2020. The linear correlation coefficient
between the theoretical and experimental results was also calculated using one of advanced
MATLAB, algorithms were used to demonstrate the degree of agreement between the results
MATLAB 2020 theoretical and experimental statistical method using function Cftool.

The new search direction of four term conjugate gradient method for unconstrained optimization

Ali Kassim — 2026-03-30

In this paper, we suggested a new four-term CG method, by expanding the three-term CG method to the four-term CG method. The search direction p, with some newly proposed parameters, is a four-term hybrid form for modified traditional CG methods. This hybridization yielded a newly created hybrid CG method with four terms as its ultimate result. The new method proposed ( the four-term CG method ) fulfills the conjugacy condition by the Dai-Laiao method and possesses sufficient descent property. Also, it was confirmed that the suggested new method would globally converge to the general Wolfe conditions beneath an adequate assumption. Finally, a comparison was made between the three terms CG that were adopted in the expansion and the proposed four-term formula, the computational results demonstrated the new method's robustness and effectiveness.

Evaluating the Association Between Oxidized Low-Density Lipoprotein (OxLDL) and Nitric Oxide Levels in Iraqi Atherosclerosis Patients

shams fahad — 2026-03-30

Background: Atherosclerosis is a chronic condition where plaques composed of lipids build up in arterial walls, causing them to harden and narrow. Oxidized low-density lipoprotein (OxLDL) plays a crucial role in this process by triggering inflammatory responses and contributing to plaque formation. Nitric oxide (NO) is essential for maintaining vascular health, but its bioavailability is often reduced in atherosclerosis, exacerbating the condition. Objective: Evaluation of levels of oxLDL as an indicator of oxidative stress and assessment its effect on nitric oxide levels. Methods: This study included determination of the levels of oxLDL and nitric oxide in sera samples from 100 atherosclerosis patients in comparison with age and sex matched healthy donors. Results: The results showed that there is a statistically significant increase (p<0.01) in oxLDL levels in atherosclerotic individuals as compared to control group, while a statistically significant reduction (p<0.01) was observed in nitric oxide levels in patients’ group. Conclusion: The data indicates that elevated levels of oxidized low-density lipoprotein (OxLDL) and reduced levels of nitric oxide (NO) are significant markers in the pathogenesis of atherosclerosis. Oxidative stress acts as a critical trigger for the formation of OxLDL, which in turn amplifies pro-inflammatory responses within the vascular system. High OxLDL levels contribute to endothelial dysfunction and promote inflammation, exacerbating plaque formation. Concurrently, decreased NO levels impair vascular relaxation and increase oxidative stress, further aggravating the condition. This interplay between oxidative stress, high OxLDL, and decreased NO highlights the complex mechanisms driving atherosclerosis and underscores the potential for therapeutic interventions targeting these pathways.

Reliability of a coherent multicomponent system with strength falling between two stresses following Chen distribution

Ali Attia — 2026-03-30

This research presented reliability formula for multicomponent strengths falling between two stresses of a coherent system , based on Chen distribution with unknown parameters and known common parameter for the two stress random variables and component strengths , respectively. Three methods were discussed for estimating Chen distribution parameters that used to estimate the reliability function using maximum likelihood, least squares, and weighted least squares estimation methods. These estimates are compared via a simulation study using mean square error criteria to large, medium, and small samples. The comparison’s most significant results are that the estimator’s performance for maximum likelihood is better in most of these experiments that have been studied.

the Restoration of degraded images by using the Straubel pupil function filter mask

murooj Tawfeek — 2026-03-30

Restoration is a process by which an image suffering from degradation can be recovered to its original form, Image restoration is an important issue in high-level image processing which deals with recovering an original and sharp image using a degradation and restoration model, removing blur and noise from image is complicated problem to solve, to get an enough efficient method to remove the noise from the images is a greater challenge for the researchers, Sometimes, the captured image gets blurry and there is noise from the environment, Degradation may occur due to noise, motion blur, Gaussian blur, camera mismatch, non-uniform illumination, and low-contrast image. Depending on the pupil function technique for images of point objects, the proposed method integrates the design of a new amplitude filter mask to improve image quality. our filter was intended to restore blurry and noisy images using MATLAB software, The performance of this algorithm has been compared with CLAHE and HE algorithms. the statistical image quality measurement is based on the signal-to-noise ratio (PSNR), Error Relative Global Dimensionally de Synthase (ERGAS), image entropy, Structural Similarity Index (SSIM), Root Mean Square (RMSE), and Enhancement Measure Estimation (EME). The experiment results show when all parameters are compared that our filter implementation technique for image restoration recovers the damaged image.

A Color Image Encryption Using E0 Keystream Generator with Chaotic Map

Rusul Basheer — 2026-03-30

Development and advancement of technology and the transfer and sending of images over the Internet, it has become necessary to protect images from brute-force attacks. Therefore, one crucial step in securing images from intrusion or unwanted access is image encryption. In this paper, the E0 algorithm is used in Bluetooth encryption, but here we will use the keystream generator of this algorithm in image encryption once and use the logistic map and the E0 key stream generator again, and then we will compare the results of these methods. The new methods take advantage of the disordered movement of logistic map to further improve the confusion and diffusion aspects of image encryption processes. The E0 algorithm can produce a stream that appears to be random, which is then used to image encryption, which leads to higher entropy and lower correlation with the image. Several different tests this was done to evaluate the performance of the encoder scheme generated using E0 algorithm and logistic map, including randomness tests, correlation coefficient analysis parameters, uniform average intensity of change (UACI) ,number of pixels change rate (NPCR), and other tests that prove its efficiency. The paper concludes that the E0 algorithm enhanced by the logistic map not only provides more secure and efficient image encryption but also focuses on preserving the confidential information in the image more effectively.

A Vehicle Speed Estimation Using Faster RCNN

salih khalid — 2026-03-30

The transport network, especially the road system, has faced huge pressures due to increased population growth and urbanization. One of the negative side effects of traffic growth is road accidents. Intelligent Transportation system (ITS) is like the Human vision system (HVS). Deep learning-based classification and detection algorithms have emerged as powerful tools for vehicle detection in intelligent transportation systems. The limitation of the number of high-quality labeled training samples makes the single vehicle detection in intelligent transportation systems. This paper presents the detection and tracking of vehicles on the recorded dataset by utilizing the Faster RCNN architecture and Matlab 2023 programming language. After training the object detection model using Faster RCNN the detection accuracy 100% and then applying Kalman Filter to track the vehicles detected by the Faster RCNN. The system has proven its effectiveness in detecting and distinguishing vehicles.

On Soft Strongly Ƅ-Closed Sets and Soft Strongly Ƅ-Continuous Functions in Soft Topological Spaces

Saif Zuhair Hameed — 2026-03-30

In this paper, we introduce and study a new class of soft sets, called soft strongly closed and soft strongly open sets, and we present and study a new generation of soft strongly continuous functions, we obtain a several characterizations and properties and relationships between them.

Evaluation antibacterial Efficacy of different Calcined Temperature of the MgO NPS Prepared by Hydrothermal Methods Against Serratia. marcescens Isolated From Post-Operative Wound Infection

Farah Noori Ibraheem hadibe — 2026-03-30

The main aim of this study was to develop a straightforward procedure for synthesizing magnesium oxide nanoparticles (MgO NPs) that are used as antimicrobial agents against Serratia.marcescens, gram negative bacteria were isolated from post-operative wound infection. A high-purity MgO NPS has been prepared by applying different calcinations temperatures (200ºC, 400ºC, 650ºC) using the hydrothermal chemical method. The hydrothermal technique is a process used to create a crystalline nanomaterial’s from high-temperature aqueous solutions with high vapour pressures. This way can be prepared more shape of nanoparticles that are highly stable. The structural, morphological and optical properties ‘of MgO NPs were conducted using : XRD, FE-SEM and UV-vis analysis respectively. The crystallinity it increases with an increase of calcination temperature that reducing the effect on antibacterial activity were done on pathogenic bacterium as result of increase in particle size with sharp intense peak. The antimicrobial effects of the prepared MgO NPs were examined against MDR strains using Micro-dilution titer method with various doses of minimum inhibitory concentration (MIC) of MgO NPs (1500, 1000, 500, 250, 125, 62.5, 31.5 µg/ml^-1). The higher value was decreased the bacterial growth was recorded at 1500µg/ml^-1concentration at 650 ºC, and the lower value was recorded as 31.5 µg/ml^-1 concentration at 200ºC. This preparation procedure yielded a higher nanoparticles effectiveness in fast, simple and economical manner that have active role against microbial growth.

Glaucoma Disease Classification based on Convolutional Neural Network Architecture Method

Fatin Ezzat Muhy Al-Dean Al-Obaidi — 2026-03-30

Glaucoma refers to a class of eye disorders which often associated with the increase in intraocular pressure that can damage the eye by destroying the nerve cells in the retina in addition to the optic nerve of the eye. The need for detecting and recognizing glaucoma eye disease increased worldwide. The current paper presents a novel Convolutional Neural Network (CNN) based on an intelligent pattern categorization method upon ACRIMA dataset images. The analysis involved using 95% of the dataset in training and 5% for testing. The proposed technique for detecting and classifying the glaucoma in fundus images consisted of a multi-layer deep neural network model with convolutional and classification layers. The originality of the current study aims to verify the impact of the number of filters (nf) upon 50 and 100 Epoch values in classifying glaucoma disease. Results gave a state-of-art performance in classifying glaucoma and recorded a unity value for sensitivity, 99.35% for specificity, and 99.29% for accuracy. The variation of the number of filters recorded higher values for all estimators, and hence the proposed model at final succeeded with significant advancement in comparison with other related studies.

the Study of the effect of temperature and concentration variations of some additives in some polymers on the Seebeck effect coefficient and its energy gap

Najla Elgheryani — 2026-03-30

The objective of this research is to study the Seebeck effect on certain polymers as well as the effect of temperature on their energy gap, therefore the research is divided into two parts, the first concerning the Seebeck effect, and its samples were prepared by adding different concentrations (0.0, 0.028, 0.054, 0.079%) of sodium chloride, sodium carbonate and potassium sulfate to solutions of each of PVA, PEG, CMCHV, PVP and PAM in twice-distilled water. The Seebeck effect coefficient remained practically unchanged with the addition of different concentrations of additives in PEG solutions, but increased with increasing concentrations and temperature in PVP solutions. As for CMCHV solutions and PAM solutions, it decreased with increasing concentrations and temperature .The second part measures the change of energy gap of thin polymer films to which different concentrations of citric acid have been added (40, 57, 66, 72%) due to the change in temperature. The energy gap decreased for all samples with increasing temperature and citric acid concentrations. These results will allow to improve the thermoelectric and electrical properties of polymers and use them in many fields such as industry, medicine and science.

Preparation of Pure ZnO and CNP/ZnO nanostructures as efficient Bio Sensor

Mohammed Alsalami — 2026-03-30

This study describes a promising approach for producing pure Zinc Oxide (ZnO) films and Carbon nanoparticles (CNPs) decorated ZnO (CNP/ZnO) nanostructured films for glucose sensors. Zinc oxide nanoparticles with an average particle size of (11.14 nm) were grown on Zn foil using the anodization method, while carbon nanoparticles were synthesized using the hydrothermal method. The decoration process of carbon nanoparticles on ZnO film nanocomposites was conducted using the ultrasonication method. Several tests were performed, including microstructure observation, crystallinity analysis, and electrochemical property investigation. The nanostructures were determined using energy-dispersive X-ray spectroscopy (EDX), field-emission scanning electron microscopy (FE-SEM), transmission electron microscope (TEM), UV-vis spectroscopy, photoluminescence (PL) measurements, and X-ray diffraction (XRD). The electrochemical analysis of the CNPs/ZnO nanostructures significantly refined the sensitivity to glucose compared to pure zinc oxide nanostructures. The CNP/ZnO nanostructures increased glucose sensing with a sensitivity of (2372 µA.mM^-1.cm^-2) the low detection limit is (1.15 mM), with a linear range of 0.3 mM to 1 mM.

Complete Arcs of Various Degrees in the Projective Space of Dimension Seven over the Galois Field F_2

Mohammed Hasan — 2026-03-30

The aim of this paper is to use the action of some cyclic subgroups of projective general linear group of order eight over the finite field of order two, , to construct complete arcs degree higher than three in . Therefore, a lower bound for maximum size of arcs of certain degree are founded in .

A Comparative Study on User Authentication Methods

Shaimaa Sattar Abood Al-azzawi — 2026-03-30

Everyone needs security, to use the internet, systems, and applications safely, protect their information and data, and prevent undesirable access. authentication plays critical role in security. There are many types of authentications, each one produces a unique method to confirm a user’s identity. Password-based authentication is the most popular method, despite vulnerabilities and breaches. In this paper, a brief overview of traditional user authentication methods (Passwords, Smart-Card Authentication, Digital Certificate Authentication) and Biometric Methods are provided. In addition, the Strengths and the Vulnerabilities of each technique are described. Biometric authentication is based on a unique physiological or behavioral character like user finger print, voices, retinas, and facial features. It provides powerful security depending on unique characteristics and features that are difficult to replicate or steal. So biometric authentication provides a reliable user identification than other methods despite that traditional methods like passwords are prevalent, biometric authentication offers highly reliable performance in security. The basis of the biometric system is a model recognition system.

Preparation and Characterization of CaZrO3 as a Bone Cement

Fadhil K. Farhan — 2026-03-30

The bio ceramic constant of zirconium dioxide and calcium oxide with a weight ratio of 1:1. It was prepared using a ceramic grinder with hardened ceramic balls for 10 continuous hours at a rotational speed of 350 rpm. The resulting powder was then collected and pressed into a mold with a pressing capacity of 5 tons, then sintering process was performed at a temperature of 850^oC for 4 hours the sintering samples of calcium zirconate subjected to milling for 24 hrs to obtain a very fine powder on the at nano scale. Structural examinations of CaZrO₃ powder were carried out by utilizing such as X-ray diffraction and scanning electron microscopy SEM, and Raman spectrum. A mixture of powder was prepared in the form of a paste by adding 30% of polylactic acid PLA for the purpose of making ceramic cement. It can be stored in ampoules for use in treating cracks in bones. The results of the hardness and toughness test of bone cement showed that the percentage of the binding material (polymer) is 30%.

Fabrication of Silver Oxide Nanoparticles using sol-gel technique and Their Role in Inhibiting Bacterial Biofilm Development

Ali Abbas — 2026-03-30

In this research, silver oxide nanoparticles were prepared using the sol-gel method, and measurements were made, namely XRD, FESEM, FTIR and EDX, and the nanoparticles were confirmed, and then these nanoparticles( Ag₂O) were used as a powder against the formation of the biofilm of bacteria ( bacterial adhesion measurement), where five types of bacteria were used (E. coli, Klebsiella pneumoniae, Pseudomonas aeroginosa , P. mirabilis and Stenotrophomonas maltophilia), where these particles showed their effectiveness significantly on the formation of the biofilm of bacteria, especially bacteria Klebsiella pneumoniae at 0.53O.D as well as Pseudomonas 0.4O.D , while bacteria E. coli, P. mirabili and Stenotrophomonas maltophilia showed resistance at different concentrations.

Apply XGBoost, Random Forest, and Neural Netwrk (MLP) Models for Predicting Employee Absenteeism Using Workforce Data Analytics

Mohammed H. Alars — 2026-03-30

Absence issues among employees create numerous problems for workplace productivity and operational continuity. The paper analyzes machine learning approaches to predicting employee absenteeism based on controlled workforce information. Scientists applied a thorough preprocessing pipeline to a realistic dataset, which involved dealing with missing values as well as encoding categories along with feature numerical scaling. An exploratory analysis of the data and feature importance evaluation helped identify proper predictive attributes.
Random Forest, together with XGBoost and Multi-Layer Perceptron (MLP) Neural Network, served as the classification models to create predictive models, which were assessed using accuracy and precision and recall, and F1-score metrics. XGBoost proved its superiority as a predictive model by reaching 81.03% accuracy during testing while showing balanced results. This study highlights that ensemble-based machine learning approaches present opportunities to boost data-driven decision-making for human resource management operations. The coming paper will concentrate on adding more content to datasets as well as improving model visibility while studying adaptive learning methods to enhance real-world systems and stability.