Mustansiriyah Journal of Pure and Applied Sciences

JACOBSON-SMALL COMPRESSIBLE MODULE

2026-04-05T21:24:14+03:00

For an R-module Qand a commutative ring (with identity) R. In this paper, we introduce the
notion Jacobson-small compressible ( for short J−Small compressible) AnR −module Q is
J −Small compressible if Q can be embedded, in each of its non-zero J-small sub-module.

A Density Functional Calculations for InxGa1-xAs Nanostructure

2026-04-05T21:43:22+03:00

The electrostatic and structural properties of InₓGa₁₋ₓAs nanocrystals were investigated with
density functional theory (DFT) in conjunction with large unit cell (LUC). The calculations were
carried out for 16 and 64 atom nanocrystals with indium compositions of x = 0.00, 0.25, 0.50,
and 1.00 in 3D periodic boundary condition. All the calculations were carried out on the
B3LYP functional and 6-31G basis set with the assistance of Gaussian 09 software. The results
demonstrate that an increase in indium is accompanied by a systematical growth of the lattice
constants and a reduce in the electronic band gap, which is in agreement with quantum
confinement and the alloying effect. Other properties, such as the total and cohesive energies,
electron affinity, ionisation energy and ionicity, also showed significant size-dependent and
composition-dependent differences. The results indicate strong atomic-configuration dependent
electronic properties of the material that shed light on its potential application in nextgeneration nanoelectronics and photonics

A novel structure for measurable functions sequence convergence based on approach measure space

2026-04-05T21:53:11+03:00

In measure theory, there are several types of convergence of sequences like convergence in
measure, almost everywhere and almost uniformly we presented definition of approach
measure and approach measurable functions also we defined new concepts which
convergence of sequences of approach measurable functions almost everywhere with
respect approach measure and convergence of sequences of approach measurable functions
in approach measure , we studied the relationship between them, We also raised the topic of
"almost bounded" and what its relationship to "bounded"? We will discuss the relation
between uniform convergence and convergence of sequences of approach measurable
functions in approach measure, the relation between convergence of sequences of approach
measurable functions almost everywhere with respect approach measure and convergence of
sequences of approach measurable functions in approach measure.

The Hybrid Mathematical Model for Interpolation and Prediction Based on FBGS Algorithm to Determine Optimal Weights with Real Data Application

2026-04-05T22:26:20+03:00

This study aims to improve prediction accuracy by applying a hybrid method based on
BFGS algorithm to select optimal weights that the best evaluate the solution weight at each
point. Newton and Lagrange's interpolation methods were used to construct the proposed
hybrid model structure. The performance of the three methods (the basic methods and
hybrid method) was evaluated through mean square error analysis. The results, applied to
real data representing the radon gas spread rate in Baghdad, showed that the hybrid method
clearly outperformed traditional methods, achieving a mean square error of 0.001501,
compared to 0.00876 for Newton method and 0.003216 for Lagrange method. This approach
contributes to improving prediction accuracy and reducing error by dynamically distributing
weights based on BFGS algorithm. This study a solid foundation for developing more
efficient models in various fields, such as temporal data forecasting and dynamic systems
analysis.

The equivalent theorems for best approximation in weighted spaces

2025-04-24T09:41:28+03:00

The modulus of smoothness is used to demonstrate the direct and inverse trigonometric
polynomials estimation theorem in weighted space with the degree of best approximation of
unbounded functions. Also, we approximate of derivative function by derivative of
trigonometric polynomials in weighted spaces. Moreover, an unbounded function is better
the degree of best approximation for derivative functions in same space.

Chaotic Image encryption using Arnold’s cat map and mod function with an auxiliary image key

2025-08-13T22:02:44+03:00

In the last decade a significant progress has been achieved by computer specialist in the field of information security and guarantee confidently deliver the information Especially the images to authorized persons, the article in our hand introduces a procedure for Image encryption and decryption based on hybrid chaotic system which it organized into two primary steps, firstly using Arnold cat map (ACM) on an image as a chaotic system. Secondly, enhanced the first step with use modulo function and (XOR) logical function of auxiliary image with the image of the first step of obtain extra security level , in evaluating the proposed algorithm, three key performance indicators are considered, the MSR, PSNR and the Entropy adding to these a visual inspection of the result images, Analysis reveals that the algorithm's extensive key space and heightened sensitivity to minimal key changes make it robust against brute-force attacks, and it also immune against statistical attacks due to the entropy values results exceed 7.3 for the result images.

A Comparative Study on Image Steganography based on Deep Learning

2025-09-07T21:09:22+03:00

With the expansion and growth of technology in a sophisticated and noticeable way the protection of transmitted data has come to be one of the most important and prominent issues that have occupied specialists in the purview of data security. one of the most prominent fields used in information security is the technology of images steganography, as this technology enables us to hide data in images in a secret manner so that no one can notice it. Deep learning technology has gained more attention lately as it has become a potent tool in many applications, such as image steganography to secure and protect data transferred. this scientific paper aims to investigate and debate the different deep learning techniques that are available in the purview of image steganography. Image steganography deep learning techniques can classified into three principal groups: CNN-based, GAN-based, and traditional methods.

Double Core PCF Sensor with Au-HfO2 Hybrid layer Based on SPR

2025-08-11T18:46:19+03:00

Double core photonic sensor design based on surface plasmon resonance (SPR) that is the base of A photonic crystal fiber sensor is designed with a hexagonal structure that employs a hybrid material design incorporating Au and HfO2 Layer. The latter is considered as active plasmonic material at which SPR is rapidly excited. COMSOL Multiphysics software is used to design and analysis the obtained data for suggested sensor. Finite element method (FEM) that is a full-wave numerical technique is carried out to obtain the approximate solutions for electromagnetic boundary value problems The proposed sensor has a detection range from which optimize in spectrum range from to . The sensitivity (S_λ) has a large value at that associated with the highest sensor resolution of . The trending data of resonance wavelength peak ( ) and peak loss exhibit non-linearly increased. Concerning on FWHM, the findings show that the non-linearity tendency is dominant; it increases rapidly with RIs of analyte. lastly, figure of merit ( ) has larger value at maximum ) where the highest values of FOM attains maximum values of . Fine-tuning air hole diameter, core size, layer thickness dimensions of the fiber to achieve good performance of proposed sensor. Leveraging the unique optical properties of HfO2 and Au, the sensor demonstrates superior performance metrics, achieving confinement.

Advanced Fixed point Analysis in Partial Metric Type Spaces under Fuzzy soft set Theoretic Constraints

2025-06-28T21:49:09+03:00

In this paper ,the concept of fuzzy soft -partial Hausdorff Metric are introduce and the fixed point theorems of Banach contraction principle in fractional form are generalized to complete fuzzy soft b-partial metric space, and prove that a single valued fss- mapping ,multi valued fss- mapping have a fss- fixed point in this space.

Enhancement the sensitivity and temporal response of porous silicon gas sensor via integrating gold nanoparticles

2025-08-21T17:25:34+03:00

In this research, two specific types of planner mode room temperature gas sensors involve a bare porous silicon (PS), and PS modified with gold nanoparticles PS/AuNPs were synthesized and tested extensively. The bare PS layer, was prepared by laser assisted electro chemical etching of N type (100) silicon wafer. The performance of these sensors, were studied by analyzing the FE-SEM, AFM images, XRD patterns and the gas sensing features of NO₂ at different gas concentrations from 10ppm to 40ppm. The results showed, strong modifications in sensitivity and temporal response of bare PS sensor after incorporating AuNPs. For the bare PS gas sensor, the sensitivity was 7.5% at gas concentrations of 10 ppm and 13.1% at gas concentrations of 40 ppm. While for modified AuNps/PS gas sensors, the sensitivity was increased steadily from 55.6% at gas concentration of 10ppm to 83.6% at gas concentration of about 40ppm. The as compared with the bare PS gas sensor, relationship between the sensitivity of the sensor and the gas concentration after adding gold nanoparticles is as close as possible to a linear relationship without saturation effects. The temporal response Rst and Rct of bare PS and modified (AuNps/PS) gas sensors are about 96 sec and 122 sec and for modified AuNps/PS is much lower than that of bare PS gas sensor of about 58 sec and 82 sec respectively improvement of Rst and Rct after incorporating AuNPs are about 65% and 49% respectively. This significant improvement in modified AuNps/PS gas sensor, in both of the temporal response and sensitivity is due to the thermal conductivity of AuNps and the additional surface area of AuNps to porous silicon layer. The main objective of this research is to improve the performance of gas sensors based on a porous silicon layer by incorporating gold nanoparticles. This is based on the distinct thermal properties of gold nanoparticles, as well as their high surface area. Increasing the sensitivity to detecting NO₂ gas, in addition to increasing the detection speed, is the focus of this study.

Simulation Study for Optical Sensing Based on Self-Mixing Effect

2025-08-24T16:27:13+03:00

This simulation study involves a unique investigation into optical feedback or the self-mixing within a single-longitudinal-mode laser cavity, essential to a novel interrogation technique for a fiber Bragg grating (FBG) based temperature sensor

A Perturbation Iteration Algorithm-Based Semi-Analytical Blood Flow Model for the Jeffrey Hamel

2025-08-30T17:41:32+03:00

This study presents an enhanced approach for solving the magneto- hydrodynamic nonlinear
Jeffrey-Hamel (MHD-JHF) problem, modeling human arterial blood flow, using a hybrid
metaheuristic combining Particle Swarm Optimization (PSO) and the Perturbation Iteration
Algorithm (PIA). The model is formulated from third-order ordinary differential equations
derived from the nonlinear MHD partial differential equations of Jeffrey-Hamel flow.
Optimal artificial neural network weights are obtained by minimizing the fitness function
through the PSO-PIA algorithm. The proposed method is evaluated across four MHD-JHF
scenarios, considering different Reynolds numbers and channel angles. Numerical results
demonstrate excellent agreement with reference solutions and highlight the importance of
accurately characterizing arterial blood flow. Statistical analyses based on multiple
performance metrics confirm the method’s accuracy, efficiency, and reliability. The
framework offers potential for future extension to related problems in science and
engineering applications.

"How can natural language processing improve cybersecurity threat detection?"

2025-08-12T13:38:16+03:00

Natural language processing (NLP) is being rapidly applied in cyber space, as its capacity
efficiently enables the process and the ability to analyze large versions of unnecessary text
data, acceleratory detecting and more effective and more effective danger. This paper
examines the integration of NLP in the manner of detecting the danger; the risk focuses on
its role in intelligence information, fishing detection, malware identification and user
behavioral analysis to identify discrepancy activities. Taking advantage of various machines
learning patterns, the NLP can increase the identification and accuracy of the danger and
prediction, including learning-pilgrimage, unheard, semi-conceptual, and reinforcement
learning. Many cases study email detection, expediting the reaction of the event and user
and unit behavioral analytics (UEBA) display their application. However, the study also
identifies challenges such as data quality issues, rapid development of dangers, language
ambiguity and adverse manipulation. Conclusions highlight that NLP has an important
ability to strengthen cyber security systems by combining the NLP such as advanced
approaches and combining with emerging technologies, making them more adaptive and
intelligent

Deoxygenation-Controlled Synthesis of ZnO Nanoparticles via Non-Thermal Plasma Jet for Optoelectronic Applications

2025-08-21T02:41:16+03:00

The present study explores the impact of oxygen reduction on the structural, electrical, and optoelectronic properties of zinc oxide nanoparticles (ZnO NPs) synthesized using non-thermal plasma jet (NTPJ), which used deionized water (DW) as the interaction medium. Four methods were used to test that. The first one was left DW without any treatment (M1). The second was DW treated with argon treatment for 10 min before use in NPs synthesis (M2). In contrast, in the third method, argon treatment was applied to DW during the entire synthesis process (M3). In the fourth one, DW was heated at 100°C and subjected to continuous argon flow during the synthesis process (M4). The characterization of zinc nanoparticles on glass and silicon was conducted using X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDX), and scanning electron microscopy (SEM). The EDX analysis revealed zinc: oxygen atomic ratio of 1:1, which is the stoichiometric ratio, at method M4, reducing defects and improving material properties.

The results showed highly crystalline hexagonal wurtzite of synthesized ZnO NP, with varying crystallite sizes depending on the substrate type. SEM images showed improved air permeability and aerosolization properties compared to samples with less oxygen reduction. Si/ZnO NP photodetector performance is more significant in the case of ZnO NPs synthesized in method M4, with a responsivity of 18.3 A/W and a quantum efficiency reaching nearly 40% at 330 nm wavelength.

Using Deep Learning Technology to Detecting Kidney Disease

2025-05-19T23:40:19+03:00

Deep learning is gaining significant importance due to data interpretation and its application to a wide range of diseases in general, especially kidney disease detection. Machine learning is widely applied in healthcare. Providing a sufficient number of samples (images) and providing the appropriate statistical software were major research challenges. Image preprocessing was then performed to convert the original medical image data into medical image data free of some unwanted distortions (noise). This was done to enhance the images, which must be of the same size and dimensions, to reveal certain image features, and transform them into high-quality medical images for use in kidney disease detection and classification. Ten statistical image metrics were used: SC, AD, MD, SSIM, RFSIM, FSIM, RMSE, LMSE, MAE, and PCC. These metrics were used to evaluate the performance of the two classification algorithms (CNN and RNN). The classification process used precision, accuracy, recall, and F1 metrics. High accuracy results were achieved, indicating that the resulting results are very good. The evaluation metrics provide the information needed to determine the performance of a classification model based on a given score (correct or incorrect). The study focused on the use of deep learning in the recognition and classification of kidney diseases. The results of the current study indicated that the CNN and RNN algorithms used in the proposed kidney disease recognition and classification system achieved acceptable results. The results achieved high classification accuracy for the proposed method for kidney diseases (90.52%, 64.64%) and for normal cases (94.05%, 77.12%) for the CNN and RNN algorithms, respectively.