Journal title Wasit Journal for Pure sciences Indexing Online ISSN Print ISSN 2790-5241 2790-5233 Frequency 4 issues per year DOI prefix 10.31185 Editor in Chief Managing Editor Prof. Dr. Ali Khalaf Hussain , College of Education for Pure Science, Wasit University, Iraq Email: alhachamia@uowasit.edu.iq Assist. Prof. Dr. Abdul Hadi M.Alaidi, College of Computer Science and IT, Wasit University, Iraq Email: alaidi@uowasit.edu.iq Organized by Wasit University - College of Computer Science and IT Citation Analysis <a href="https://www.scopus.com/results/results.uri?s=REF%28%22wasit+journal+for+pure+science%22+OR+%22Wasit+J.+Pure+Sci.%22+OR+%22+Wasit+J+Pure+sci%22+OR+%22Wasit+J.+Pure+Sci.%22+OR+%22Wasit+Journal+of+Pure+sciences%22+OR+%22+Wasit+J+Pure+Sci%22+OR+%22+Wasit+J+Pure+Sci%22+OR+%22+Wasit+J.+Pure+Sci.%22+OR+%22+Wasit+J.+Pure+Sci.%22%29&limit=10&origin=searchhistory&sort=plf-f&src=s&sot=b&sdt=b&sessionSearchId=2cd33205ae7d7106b9940c3e5d8db675">Scopus | Google Scholar Acceptance Rate: 40% Review Speed: 90 days Before submission, You have to make sure that your paper is prepared using the WJPS paper Template, or Latex Template has been carefully proofread and polished, and conformed to the author's guidelines. Online Submissions Do you already have a username/password for WJPS? GO TO LOGIN Need a username/password? GO TO REGISTRATION Registration and login are required to submit items online and check current submissions' status.
Cobalt-60 Nuclear Source Decay and Gamma Photon Dose Delivery in Radiosurgery: A Radiobiological Comparison of Single- and Multi-Session Irradiation for meningioma
Gamma Knife radiosurgery (GKRS) using Cobalt-60 sources has found wide application in the treatment of intracranial meningiomas because it is used to deliver highly conformal dose distributions with steep gradients. This study was aimed at performing a radiobiological comparison of single-session and multi-session meningioma cases by including dose rate decay, sublethal damage repair, and analytical modeling of probabilistic outcomes. Methods: One hundred intracranial meningioma cases were studied (single-session, 50 cases; multi-session, 50 cases) in this prospective study at Ghazi Alharriri Gamma Knife Center, Medical Complex, Baghdad, Iraq, between January 2025 and March 2026. Dosimetric parameters and beam-on time were extracted, and radiobiological calculations were made in accordance with the linear-quadratic model. BED, EQD2, dose-rate-corrected BED, and Lea-Catcheside repair factor (G) were calculated. Tumor control probability (TCP) as well as normal tissue complication probability (NTCP) were estimated using logistic and Lyman-Kutcher-Burman models, respectively. The statistical comparisons were performed with independent t-tests. Results: Multi-session treatments demonstrated significantly lower effective dose rates and higher beam-on times (p < 0.01), reflecting time-dependent Cobalt-60 decay effects. Single-session radiosurgery yielded higher BED and EQD2 (p < 0.0001); however, TCP remained comparable between groups (p = 0.312), indicating a dose–response plateau. NTCP was significantly reduced in the multi-session group (p = 0.021). A strong inverse correlation was observed between dose rate and repair factor (r = −0.62, p < 0.001), while beam-on time was negatively correlated with NTCP (r = −0.41, p = 0.01). Conclusion: Time-dependent dose delivery significantly influences radiobiological response in GKSRS. Multi-session treatment achieves equivalent tumor control with improved normal tissue sparing, supporting its role as a physics-optimized strategy for meningioma management.
Copyright (c) 2026 Lubna mahmood https://creativecommons.org/licenses/by/4.0
Luminescent Zinc-Based Metal-Organic Frameworks for Fast and Selective Optical Detection of Pharmaceutical Pollutants in Aqueous Solution
This paper reports the development of luminescent zinc-based metal-organic frameworks (MOFs) as optical sensors for monitoring pharmaceutical pollutants in water specifically diclofenac and ibuprofen. The MOFs were synthesized under solvothermal conditions and exhibited crystalline, microporous structures with strong fluorescent properties, enabling analyte-induced quenching for sensitively detection. Under optimized conditions a rapid response was realized in 2 min with high selectivity towards common interferents. Stern -Volmer analysis revealed that they behaved in a linear manner and their detection limits were 0.8 µg/L and 2.1 µg/L when using diclofenac and ibuprofen respectively. Recovery tests in tap, river and wastewater sample produced 92-106% recovery values which prove to be effective. The sensors have been found to be stable with a 90 % performance when they were used repeatedly. These findings demonstrate luminescent MOFs as high-quality, reusable sensors in seconds of environmental surveillance of new pharmaceutical pollutants.
Copyright (c) 2026 Mohammed Rashid Znad, Mohammad Reza Khakzad https://creativecommons.org/licenses/by/4.0
Fake Instagram Account Detection Using Single and Stacking Ensemble Models on Integrated Static and Temporal Features
This research examines the detection of fake Instagram accounts by comparing single machine learning classifiers with a stacking ensemble model applied to integrated static and temporal features. A dataset of 7,594 accounts was collected over five months using multiple scraping tools, ensuring a balanced representation of real and fake profiles. Key behavioral, engagement, and account-level features were extracted to capture users’ activity patterns. Among the individual classifiers, Random Forest achieved the highest performance; however, the stacking ensemble model outperformed all single models, reaching an accuracy of 0.9888 with superior precision, recall, and F-measure values. The findings demonstrate that integrating temporal metrics with ensemble learning significantly enhances detection accuracy and provides a more reliable approach for identifying fraudulent accounts on social media platforms.
Copyright (c) 2026 Samar Sadeq Hassoun, Nahla A. Flayh https://creativecommons.org/licenses/by/4.0
Immunohistochemical Evaluation of Ki-67 and Galectin-3 as a Diagnostic marker for Thyroid Carcinoma in Wasit Province
Thyroid Cancer is a malignant tumor that arises from the cells of the thyroid gland. Biomarkers can help detect thyroid cancer before obvious clinical symptoms appear, improving early diagnosis. Therefore, the purpose of this study was to assess the role of Ki-67 and Galactin-3 (Gal-3) proteins as a biomarker for thyroid cancer diagnosis and to ascertain how these proteins relate to the type and stage of the tumor. This study includes forty formalin-fixed, paraffin-embedded thyroid carcinoma tissue samples were collected from the archives of Histopathology Department at Al-Karama Teaching Hospital in Wasit Governorate, Iraq which diagnosed by a specialist physician. The streptavidin-peroxidase method was used for immunohistochemical staining. Gal-3 expression was evaluated based on staining intensity and the distribution of positive cells, whereas K-67 expression was evaluated based on the proportion of positively stained nuclei. The mean age of the patients was 44.85 ± 10.3 years, with ages ranging from 24 to 72. Males made about 15% of cases, with women making up the bulk (85%). The most prevalent histological pattern was papillary thyroid carcinoma (85%), which was followed by multifocal papillary thyroid cancer (15%). Stage I diagnoses accounted for 80% of cases, whilst stage II diagnoses made up 20%. 65% of cases had elevated Ki-67 protein levels, which were strongly correlated with the type and stage of the tumor. In 70% of cases, especially papillary thyroid carcinoma, galectin-3 protein levels were substantially positive. They also demonstrated a statistically significant link with the type and stage of the tumor. The findings suggest that Gal-3 and Ki-67 are useful markers for thyroid cancer diagnosis. While Gal-3 may be a crucial diagnostic marker for distinguishing malignant thyroid cancers, Ki-67 shows tumor growth activity and may suggest its aggressiveness.
Copyright (c) 2026 Saja Hussain Delfy https://creativecommons.org/licenses/by/4.0
On Unitary Quasi-Square Equivalence and Related Classes of Operators
This paper introduces and systematically investigates the notion of unitary quasi-square equivalence for bounded linear operators on Hilbert spaces. This equivalence relation, defined through the unitary equivalence of operator squares, provides a classification that preserves essential spectral and structural properties while capturing higher-order similarities not detectable through classical unitary equivalence. We establish that this relation defines a genuine equivalence relation and explore its connections with fundamental operator classes including square normal operators, <!-- [if gte msEquation 12]><m:oMath><m:r><m:rPr><m:nor/></m:rPr>n</m:r></m:oMath><![endif]--> <!-- [if gte vml 1]><v:shapetype id="_x0000_t75" coordsize="21600,21600" o:spt="75" o:preferrelative="t" path="m@4@5l@4@11@9@11@9@5xe" filled="f" stroked="f"> <v:stroke joinstyle="miter"/> <v:formulas> <v:f eqn="if lineDrawn pixelLineWidth 0"/> <v:f eqn="sum @0 1 0"/> <v:f eqn="sum 0 0 @1"/> <v:f eqn="prod @2 1 2"/> <v:f eqn="prod @3 21600 pixelWidth"/> <v:f eqn="prod @3 21600 pixelHeight"/> <v:f eqn="sum @0 0 1"/> <v:f eqn="prod @6 1 2"/> <v:f eqn="prod @7 21600 pixelWidth"/> <v:f eqn="sum @8 21600 0"/> <v:f eqn="prod @7 21600 pixelHeight"/> <v:f eqn="sum @10 21600 0"/> </v:formulas> <v:path o:extrusionok="f" gradientshapeok="t" o:connecttype="rect"/> <o:lock v:ext="edit" aspectratio="t"/> </v:shapetype><v:shape id="_x0000_i1025" type="#_x0000_t75" style='width:5.4pt; height:12pt'> <v:imagedata src="file:///C:/Users/Zpc/AppData/Local/Temp/msohtmlclip1/01/clip_image001.png" o:title="" chromakey="white"/> </v:shape><![endif]--> -quasi-normal operators, hyponormal operators, and various isometric structures. Our main contributions include complete characterization of spectral invariants preserved under this equivalence, demonstration of preservation theorems for advanced operator classes and their <!-- [if gte msEquation 12]><m:oMath><m:sSup><m:sSupPr><m:ctrlPr></m:ctrlPr></m:sSupPr><m:e><m:r><m:rPr><m:nor/></m:rPr>C</m:r></m:e><m:sup><m:r><m:rPr><m:nor/></m:rPr>∗</m:r></m:sup></m:sSup></m:oMath><![endif]--> <!-- [if gte vml 1]><v:shape id="_x0000_i1025" type="#_x0000_t75" style='width:9.6pt;height:12pt'> <v:imagedata src="file:///C:/Users/Zpc/AppData/Local/Temp/msohtmlclip1/01/clip_image003.png" o:title="" chromakey="white"/> </v:shape><![endif]--> -algebraic structure, applications to concrete operator families, and development of decomposition theorems revealing the canonical structure of equivalence classes. The theory developed provides a tool for operator classification with applications to invariant subspace problems, similarity theory, and the structural analysis of non-normal operators.
Copyright (c) 2026 Victor Wanjala https://creativecommons.org/licenses/by/4.0
Hybrid CNN–LSTM Framework for DDoS Detection in 5G-Enabled IoT Environments
The increasing deployment of 5G-based Internet of Things (IoT) infrastructure brings about unparalleled opportunities and the exacerbated threat of malicious Distributed Denial-of-Service (DDoS) attacks. Conventional signature-based or static rule-based intrusion detection systems are still inadequate for the dynamic and high-volume traffic in such systems. In order to address these issues, in this paper, we propose a hybrid model with CNN and LSTM. The network features spatial correlations, which are captured by the CNN layers, and the LSTM units learn sequential dependencies for detecting attack behavior evolution. Experimental evaluations performed on the CICDDoS2019 and BoT-IoT datasets show how each module helps, leading to an overall detection accuracy of more than 99% and low false positive rates, and classification latency in real time, 19.5 ms for CICDDoS2019 and 13.6 ms for BoT-IoT), comparisons to relevant existing machine and deep learning baselines establish the trade-off made between accuracy, scalability and practical deployment efficiency. The results show that the proposed model is a suitable and practical choice for protecting 5G-enabled IoT systems against DDoS attacks.
Copyright (c) 2026 Noor Alyassiri, Mohammed Mahdi Salih Altufaili, Fatima Adel Nama https://creativecommons.org/licenses/by/4.0
On Nano g-Connectedness Via Graph with Medical Application
The importance of nano-topology via graphs lies in its ability to represent data within a small yet meaningful topological framework, which enhances the accuracy of classification and decision-making, and supports comparison and categorization. This makes any model applicable in various fields such as healthcare and data analysis. The main objective of this paper is to introduce a new concept of nano-connectivity, called nano-g connectedness via graph. Which defined as the disjoint union of any pair of open sets. We also present a new application to identify the main factors contributing to atherosclerosis.
Copyright (c) 2026 Ahmed Hussien Nasir, Nisreen Abd Alokbi https://creativecommons.org/licenses/by/4.0
A Continuum Model Based on SIR Equations for Epidemic Spread Analysis
To comprehend how epidemics develop and assess the efficacy of medical treatments, infectious disease modeling is crucial. The SIR model, which separates the population into susceptible, infected, and recovered groups, is one example of the mathematical and statistical models that scientists and policymakers use to describe the dynamics of infection transmission among individuals within a society. These models may also be expanded to incorporate other components, such vaccination, alterations in social behavior, and the results of medical treatments. This work focuses on analyzing the spread of Oropouche fever using the SEIR model with a protective factor that simulates the impact of interventions. The results demonstrate how interventions contribute to reducing the number of infections and delaying the peak of the epidemic. The work also examines the use of the SIRD model in two versions: the classic (without vaccination) and the modified (with vaccination). The numerical results clearly demonstrate the role of vaccination in reducing morbidity and mortality rates, highlighting the importance of mathematical modeling in designing effective strategies to combat epidemics.
Copyright (c) 2026 Alhusna Baqir, Ali Hussein Shuaa https://creativecommons.org/licenses/by/4.0
Interleukin-6 Dynamics in Host Immune Response: A Cross-Species Study in Humans and Camels During Coronavirus Infections
This study investigates the role of interleukin-6 (IL-6) as a key pro-inflammatory cytokine in coronavirus-infected humans and camels. A total of 75 serum samples from each species were analyzed using ELISA. The results revealed a significant elevation of IL-6 levels in infected individuals compared to controls (P<0.0001), indicating a strong inflammatory response. Notably, this is the first study to quantify IL-6 protein levels in dromedary camels during natural coronavirus infection, addressing a gap in camel immunology. The findings underscore IL-6 as a critical biomarker of inflammation across species. Further research is warranted to explore its mechanistic role in disease severity.
Cytokines, Interleukin-6 (IL-6), Inflammation, Immune response, Pro-inflammatory cytokines
Copyright (c) 2026 Ibrahim Hasan, Hussein A. Mohamed, Rasha M. Othman https://creativecommons.org/licenses/by/4.0
Solving Linear Programming Problems Using Sensitivity Analysis and Heptagonal Ranking Function
This research presents an integrated approach that combines sensitivity analysis with fuzzy logic to address uncertainty in operations research models. The study focuses on the right-hand side (RHS) of constraints as a key parameter subject to variation. To capture this uncertainty, a symmetric heptagonal fuzzy membership function is proposed, where the seven representative values are governed by a single weight parameter (r). A corresponding fuzzy ranking function is developed to transform the fuzzy outcomes into comparable crisp values. The methodology is applied to two illustrative linear programming examples drawn from textbooks, covering both maximization and minimization problems. The results demonstrate that the fuzzy ranking approach, particularly under specific weight configurations, provides more robust and realistic solutions than traditional sensitivity analysis. These findings confirm the effectiveness of integrating fuzzy logic into sensitivity analysis for enhancing decision-making in linear programming under uncertainty.
sensitive analysis, linear programming problem, fuzzy numbers, heptagonal function, ranking function
Copyright (c) 2026 Aya Safaa Fadhil, Iden Hassan Al-kanani https://creativecommons.org/licenses/by/4.0
Fabrication of a Nanocarbon-Reinforced Nanoclay Membrane for Purification of Drinking Water Contaminated with Escherichia coli
Ultrafiltration systems are considered clean and environmentally acceptable for removing bacteria and contaminants from drinking water. This study used poly sulfone polymer (PSU), DMF, Kaolinite Nanoclay with carbon nanoparticles synthesized from date seeds using reverse-phase technology. In this case, the performance of an ultrafiltration (UF) membrane was improved. Carbon nanoparticles (0, 0.025, 0.050, and 0.075%) were examined, and the polymer concentration was maintained at 17%. The membrane was tested for its ability to remove Escherichia coli from contaminated drinking water. FTIR, AFM, SEM, and EDX tests were used to characterize the membrane composition and structure. As the carbon nanoparticle concentrations varied, the membrane's morphology changed. Various factors were also studied, such as the membrane's ability to remove bacteria from drinking water. The synthesis of membrane (nanofilm) was confirmed using FTIR which conformed presence of active groups responsible for the synthesis process. AFM show revealed an array of homogeneous particles with a regular surface, SEM show a smooth spherical appearance with a diameter range of the particles between 27.69-63.74 nm, EDX spectra shown the main components of the extract were carbon (C), and oxygen (O), , with percentages of 64.8%, and 24%, respectively. The presence of minor components such as Al, Si, S, and Cl with percentages of 0.6% , 0.7% , 8.7%, and 0.3%, respectively. The results of removing contaminated bacteria from drinking water using membranes indicated significant statistical differences (P≤0.05) between the membranes. The efficiency of the membranes was in the following order: F4>F3>F2>F1 compared with sampled without treatments. The study demonstrated that the fabricated nanoclay membrane reinforced with nanocarbon exhibited high efficiency in removing Escherichia coli from contaminated drinking water.
Copyright (c) 2026 Batool Sh. Razij, Mohammed M. Sharqi, Mazin A. Alalousi https://creativecommons.org/licenses/by/4.0
Real-Time Lightweight Emotional Face Recognition Framework Based on CNN Algorithms
In this study, emotional face recognition uses the CNN algorithm. CNN is employed because of its ability to process local tissue and high-resolution connections. This design is planned to authenticate emotion scores for real-time and user-friendly implementations. The preliminary work begins in the neighboring part of the study. Software is utilized to preprocess the data in this study. A CNN model is created using a framework. The process consists of three phases: preprocessing, classifying, and evaluating data. The system was tested on several datasets. The lightweight CNN's performance for training and testing images showed approximately 88.67%. The performance of the system was also compared with existing methods. The research highlights the CNN model's ability to provide state-of-the-art performance in real-time applications, with potential implications for medical and safety applications. However, limitations such as data imbalance and a lack of multimodal approaches were identified for future exploration.
Face recognition, Lightweight face detection, Pattern Recognition, Real-Time Systems
Copyright (c) 2024 Suryanti Awang, MOHAMMED AHMED TALAB https://creativecommons.org/licenses/by/4.0
Fractional Diffusion Equations Solved via Log–Laplace Residual Power Series
In this paper, fractional diffusion equations of Caputo-Hadamard presented as interesting classes of fractional differential equations have not studied before. For the first time, the solution to a diffusion equation of this type was calculated. Therefore, it is considered a new addition to the diffusion equation with the presence of this form of fractional derivatives, with its initial conditions. Additionally, this paper presents a suitable Laplace logarithmic formula for the given fractional equation, derived from the generalized Laplace formula and its established conditions. This also relies on the formula for the fractional derivative as a transformation. The first time this type of fractional derivative was established, it helped in finding an approximate formula for the solution. The residual power series method used is effective in solving many fractional equations and was even more effective when used with the Laplace logarithmic formula. The Log(t+1) –Laplace residual power series method combines the two concepts, formulating and evaluating the method, shown that from any changing of the values of the parameters involving in formulation of derivative as well as the values of fractional order which found in tables and figures for illustrative examples which illustrates the presented method.
Copyright (c) 2025 Ali Zeki, Sameer Qasim Hasan https://creativecommons.org/licenses/by/4.0
Marine Predator Optimized BiLSTM Framework for Real-Time Intrusion Detection in IoT Environments
The rapid expansion and interconnectivity of Internet of Things (IoT) environments have intensified the risk of advanced cyber-attacks, necessitating the development of intelligent and resource-efficient Intrusion Detection Systems (IDS). This study introduces a novel hybrid IDS framework that integrates the Marine Predators Algorithm (MPA) with a Bidirectional Long Short-Term Memory (BiLSTM) network to enhance both detection accuracy and computational efficiency. Within the proposed model, MPA is employed for feature selection and hyperparameter optimization, thereby refining the learning dynamics of the BiLSTM architecture. Experimental evaluations conducted on two benchmark IoT datasets, CICIoT2023 and TON_IoT, demonstrate that the MPA-BiLSTM model consistently achieves detection accuracies exceeding 99.5%, while maintaining low inference latency and minimal memory consumption—characteristics that ensure suitability for real-time edge deployment. Additionally, SHAP-based explainability mechanisms are integrated to provide interpretability of feature importance, thereby fostering transparency and trust in the decision-making process. The results confirm that the synergistic integration of MPA with BiLSTM significantly improves both predictive performance and operational efficiency, establishing the proposed framework as a scalable and practical solution for IoT security.
Copyright (c) 2025 Huda Kadhum Ayoob, Haneen Hasan Hadi Rubaye, Sarah Hayder Hashim https://creativecommons.org/licenses/by/4.0
Experimental Analysis of Random Forest and LSTM Models for Optimizing CPU Efficiency in Cloud Computing
Cloud computing provides scalable and on-demand computational resources; nevertheless, effective utilization of the CPU is a critical issue because insufficient provisioning can result in higher latency and increased energy consumption. This research aims to measure, assess, and optimize CPU performance in cloud systems through the use of two machine learning models, namely, Random Forest (RF) and Long Short-Term Memory (LSTM), and subsequently using the Multi-Objective Grey Wolf Optimizer (MOGWO) to optimize the hyperparameters of the models to predict time series. The models were evaluated on the Google 2019 cluster trace using common metrics (Accuracy, Precision, Recall, and F1). The experimental findings demonstrate that optimization using MOGWO is an effective method for enhancing the performance of the models. Specifically, the accuracy of the Random Forest model increased from 65.15% to 89.70%, while the LSTM model improved from 95.02% to 98.60%, with consistent gains in precision, recall, and F1-score. These results verify the usefulness of the optimized models in forecasting the patterns of CPU usage and optimizing the use of CPU in cloud computing systems.
Keywords: Cloud computing, CPU efficiency, Random Forest, LSTM, machine learning, resource optimization, performance prediction, time-series analysis.
Copyright (c) 2025 Sajjad Adnan Ajeel, Sundos A. Hameed https://creativecommons.org/licenses/by/4.0
Characterization of Group of Invertible Elements of Six Index Zero Completely Primary Finite Rings of Characteristic p
The study of finite extension of Galois rings in the recent past have given rise to commutative completely primary finite rings that have attracted much attention as they have yielded important results towards classification of finite rings into well-known structures. In this paper, we give a construction of a class of completely primary finite ring<!-- [if gte vml 1]><v:shapetype id="_x0000_t75" coordsize="21600,21600" o:spt="75" o:preferrelative="t" path="m@4@5l@4@11@9@11@9@5xe" filled="f" stroked="f"> <v:stroke joinstyle="miter"/> <v:formulas> <v:f eqn="if lineDrawn pixelLineWidth 0"/> <v:f eqn="sum @0 1 0"/> <v:f eqn="sum 0 0 @1"/> <v:f eqn="prod @2 1 2"/> <v:f eqn="prod @3 21600 pixelWidth"/> <v:f eqn="prod @3 21600 pixelHeight"/> <v:f eqn="sum @0 0 1"/> <v:f eqn="prod @6 1 2"/> <v:f eqn="prod @7 21600 pixelWidth"/> <v:f eqn="sum @8 21600 0"/> <v:f eqn="prod @7 21600 pixelHeight"/> <v:f eqn="sum @10 21600 0"/> </v:formulas> <v:path o:extrusionok="f" gradientshapeok="t" o:connecttype="rect"/> <o:lock v:ext="edit" aspectratio="t"/> </v:shapetype><v:shape id="_x0000_i1031" type="#_x0000_t75" alt="" style='width:16pt; height:16pt;mso-width-percent:0;mso-height-percent:0;mso-width-percent:0; mso-height-percent:0' o:ole=""> <v:imagedata src="file:////Users/hadi/Library/Group%20Containers/UBF8T346G9.Office/TemporaryItems/msohtmlclip/clip_image001.wmz" o:title=""/> </v:shape><![endif]--> <!-- [if gte mso 9]><xml> <o:OLEObject Type="Embed" ProgID="Equation.DSMT4" ShapeID="_x0000_i1031" DrawAspect="Content" ObjectID="_1819869786"> </o:OLEObject> </xml><![endif]-->of characteristic<!-- [if gte vml 1]><v:shape id="_x0000_i1030" type="#_x0000_t75" alt="" style='width:16pt;height:13pt;mso-width-percent:0; mso-height-percent:0;mso-width-percent:0;mso-height-percent:0' o:ole=""> <v:imagedata src="file:////Users/hadi/Library/Group%20Containers/UBF8T346G9.Office/TemporaryItems/msohtmlclip/clip_image003.wmz" o:title=""/> </v:shape><![endif]--> <!-- [if gte mso 9]><xml> <o:OLEObject Type="Embed" ProgID="Equation.DSMT4" ShapeID="_x0000_i1030" DrawAspect="Content" ObjectID="_1819869787"> </o:OLEObject> </xml><![endif]-->whose subsets of zero divisors <!-- [if gte vml 1]><v:shape id="_x0000_i1029" type="#_x0000_t75" alt="" style='width:31pt;height:20pt; mso-width-percent:0;mso-height-percent:0;mso-width-percent:0; mso-height-percent:0' o:ole=""> <v:imagedata src="file:////Users/hadi/Library/Group%20Containers/UBF8T346G9.Office/TemporaryItems/msohtmlclip/clip_image005.wmz" o:title=""/> </v:shape><![endif]--> <!-- [if gte mso 9]><xml> <o:OLEObject Type="Embed" ProgID="Equation.DSMT4" ShapeID="_x0000_i1029" DrawAspect="Content" ObjectID="_1819869788"> </o:OLEObject> </xml><![endif]--> satisfy the condition <!-- [if gte vml 1]><v:shape id="_x0000_i1028" type="#_x0000_t75" alt="" style='width:150pt;height:23pt; mso-width-percent:0;mso-height-percent:0;mso-width-percent:0; mso-height-percent:0' o:ole=""> <v:imagedata src="file:////Users/hadi/Library/Group%20Containers/UBF8T346G9.Office/TemporaryItems/msohtmlclip/clip_image007.wmz" o:title=""/> </v:shape><![endif]--> <!-- [if gte mso 9]><xml> <o:OLEObject Type="Embed" ProgID="Equation.DSMT4" ShapeID="_x0000_i1028" DrawAspect="Content" ObjectID="_1819869789"> </o:OLEObject> </xml><![endif]-->. The ring <!-- [if gte vml 1]><v:shape id="_x0000_i1027" type="#_x0000_t75" alt="" style='width:12pt;height:13pt;mso-width-percent:0; mso-height-percent:0;mso-width-percent:0;mso-height-percent:0' o:ole=""> <v:imagedata src="file:////Users/hadi/Library/Group%20Containers/UBF8T346G9.Office/TemporaryItems/msohtmlclip/clip_image009.wmz" o:title=""/> </v:shape><![endif]--> <!-- [if gte mso 9]><xml> <o:OLEObject Type="Embed" ProgID="Equation.DSMT4" ShapeID="_x0000_i1027" DrawAspect="Content" ObjectID="_1819869790"> </o:OLEObject> </xml><![endif]-->is constructed over its subring <!-- [if gte vml 1]><v:shape id="_x0000_i1026" type="#_x0000_t75" alt="" style='width:79pt;height:22pt; mso-width-percent:0;mso-height-percent:0;mso-width-percent:0; mso-height-percent:0' o:ole=""> <v:imagedata src="file:////Users/hadi/Library/Group%20Containers/UBF8T346G9.Office/TemporaryItems/msohtmlclip/clip_image011.wmz" o:title=""/> </v:shape><![endif]--> <!-- [if gte mso 9]><xml> <o:OLEObject Type="Embed" ProgID="Equation.DSMT4" ShapeID="_x0000_i1026" DrawAspect="Content" ObjectID="_1819869791"> </o:OLEObject> </xml><![endif]--> as an idealization of the <!-- [if gte vml 1]><v:shape id="_x0000_i1025" type="#_x0000_t75" alt="" style='width:15pt;height:18pt;mso-width-percent:0;mso-height-percent:0; mso-width-percent:0;mso-height-percent:0' o:ole=""> <v:imagedata src="file:////Users/hadi/Library/Group%20Containers/UBF8T346G9.Office/TemporaryItems/msohtmlclip/clip_image013.wmz" o:title=""/> </v:shape><![endif]--> <!-- [if gte mso 9]><xml> <o:OLEObject Type="Embed" ProgID="Equation.DSMT4" ShapeID="_x0000_i1025" DrawAspect="Content" ObjectID="_1819869792"> </o:OLEObject> </xml><![endif]-->- modules. A thorough determination and classification of the structure of the group of invertible elements using fundamental theorem of finitely generated abelian groups is given.
Completely Primary Finite Rings, Group of Invertible Elements, Six Index Zero
Copyright (c) 2025 Hezron Were, Mr. Nicholas Waweru, Dr. Edward Njuguna, Dr. John Matuya https://creativecommons.org/licenses/by/4.0
Comparison Between Different Probability Distributions for Modeling Exchange Rate Volatility Behavior in Oil Countries for the Period 1990 – 2022
this study examines the distributional properties of exchange rate volatility for major oil-exporting developing economies from 1990-2022. Three probability distributions [ Normal, Fr'echet, and Log Normal] are compared using moment method and maximum likelihood approaches.The Bayesian information criterion (BIC) and the Akaike information criterion (AIC) are used to assess goodness-of-fit. The results indicate that the Normal distribution tends to provide the greatest fit for many countries [Iraq, Kuwait, Libya, Iran, Algeria, Oman, and Bahrain]. The Log Normal distribution is found to be optimal for[ Saudi Araia, Qatar, and Egypt] , while the Fr'echet distribution fits best only for United Arab Emirates. This analysis provides insights into the underlying distributional characteristics of exchange rate returns in these countries, which can inform appropriate model selection for volatility forecasting and risk management applications . The research adds to existing literature by conducting a entire distributional comparison across a broader set of oil- exporting developing economies over an extended time.
Normal Distribution , Fréchet Distribution ,Log Normal Distribution, Oil-Exporting Countries, Exchange Rate Volatility.
Copyright (c) 2025 Awatif Al-Dubaicy https://creativecommons.org/licenses/by/4.0
Synthesis, Characterization and Study of Liquid Crystal Behavior of Linear-Shaped Mesogens Containing Cyano and Alkyl as a Terminal Groups
A novel series of liner-shaped liquid crystalline compounds containing thiazolidine-2,4-dione moieties with cyano and alkyl as terminal groups were synthesized and characterized. The molecular design incorporated a thiazolidine-2,4-dione unit linked to flexible alkyl chains via Knoevenagel condensation, yielding mesogens with varying terminal lengths. Structural confirmation was achieved through FT-IR, ¹H-NMR, and mass spectrometry. Mesomorphic properties were investigated using polarized optical microscopy (POM) and differential scanning calorimetry (DSC). All compounds [V] 4,5,6,8 exhibit liquid crystal properties, while, compound [V]3 do not show mesomorphic behavior. Odd-even effects were observed in transition temperatures, highlighting the influence of alkyl chain length on mesophase stability.
liquid crystal, heterocyclic, Nematic phase, Smectic phase
Copyright (c) 2025 Hassan Jabori, Nisreen H. Karam https://creativecommons.org/licenses/by/4.0
Indirect Spectrophotometric Determination of Nitrate Ion in Groundwater Using an Organic Reagent
Low river water levels in summer and low rainfall in winter have led to desertification, leading to widespread use of groundwater in many areas of Iraq, especially in the southern and central regions. Groundwater is considered an alternative solution to address desertification, but in reality, many health problems have emerged due to the ions present in groundwater, which cause numerous diseases. Among these ions studied in this research is the nitrate ion (NO3⁻). This spectroscopic method was proposed to determine the concentration of nitrate ions in groundwater. Eriochrome Black T (EBT) colorimetric detector was used as a spectroscopic detector after complexing the detector with silver nitrate to determine the nitrate ion in groundwater. Several parameters and optimal conditions for this reaction were studied, and the assessment of nitrate levels was found to be consistent with the linear equation of the Beer-Lambert law in the range of 0.5–5.0 mg/L at a wavelength of (530) nm. When plotting the calibration curve, good linearity was recorded (R2 = 0.9979). The limits of detection (LOD) and quantification (LOQ) of the method were determined to be 0.269 and 0.816 mg/L, respectively, based on the standard deviation values. The precision measured in terms of relative standard deviation (RSD) over the specified time period at five concentration levels was less than 6.7%. The recovery rate was satisfactory, ranging from 96 to 103, Sandell's sensitivity equal to (0.0263) μg.cm-2. The rationale for using spectrophotometric methods lies in their simplicity of operation, low cost, and applicability in laboratories with limited resources, as opposed to more advanced techniques such as HPLC or LC-MS. Based on these results, the method was approved for the determination of nitrate ions.
Groundwater; Eriochrome Black T (EBT); Spectrophotometer; Silver Nitrate.
Copyright (c) 2025 Ryadh mohammed https://creativecommons.org/licenses/by/4.0
Total Italian Domination: Structural Results on Middle Graphs of Standard Families
If we have graph , an Italian dominating function (IDF) is defined as a mapping satisfying the condition, if for any vertex must be have neighbor there is no fewer than 1 vertex to be or no fewer than 2 vertices where The weight of a (IDF) is the sum of all these functions for all vertices . And the minimum weight selected from overall (IDF) of and indicated by A total Italian dominating function (TIDF) is an (IDF) if the collection of non-zero elements, each of which is contiguous to at least one non-zero element. A minimum weight of any (TIDF) is referred to as a total Italian domination number indicated by . We commenced the study of (TIDF) of graph and the graph can be issued by add a new vertex between every two old vertices in and draw the edges between every two added vertices provided that there exist common vertex binds them, we called middle graph of . For selected standard graph families. The accurate numerical values of these graph-related parameters were established. We determined these parameters' exact values.
Italian domination, total Italian domination, Middle graph
Copyright (c) 2025 Maryam ALTABATABAII https://creativecommons.org/licenses/by/4.0
Computational Approaches to Studying Reaction Mechanisms and Transition States in Quantum Chemistry
The Computational chemistry plays a crucial role in understanding reaction mechanisms and transition states at the atomic level. Quantum mechanical methods, particularly Density Functional Theory (DFT), provide insights into electronic structures, energy landscapes, and reaction kinetics. This study explores computational techniques such as Transition State Theory (TST), the Nudged Elastic Band (NEB) method, and post-Hartree-Fock approaches to identify reaction pathways and energy barriers. Molecular Dynamics (MD) simulations further examine reaction dynamics over time under various conditions. This analysis of model reactions highlights key transition states and activation energies, demonstrating that DFT-based methods balance computational efficiency and accuracy. NEB simulations effectively mapped minimum energy paths, while TST calculations provided reliable reaction rate constants. The integration of time-dependent DFT (TD-DFT) enabled the study of excited-state reaction dynamics, crucial for photochemical processes. These findings confirm that quantum mechanical simulations can accurately describe chemical transformations, aiding in the design of novel catalysts, materials, and pharmaceuticals.
Computational Chemistry, Reaction mechanisms, Transition states, Density Functional Theory (DFT).
Copyright (c) 2025 Sarah Badri Jasim, Khalidah H. M. Al Furaiji, Asam Hussein Ali https://creativecommons.org/licenses/by/4.0
The Impact of Rheumatoid Arthritis on Cardiovascular Health
Rheumatoid arthritis is a chronic disease caused by immune system dysfunction, characterized by an inflammatory response that affects joints and may extend to other organs like the heart. The exact cause of the disease is unknown, but it is believed to be linked to genetic, environmental, and some types of bacterial infections. It affects both sexes and people of all ages, with a higher prevalence among women. Symptoms often begin in the joints of the hands and feet and then extend to the knees and arms. This study assessed the risk of heart disease among rheumatoid arthritis patients in Wasit Governorate. The sample included 90 participants aged between 30-60 years, divided into two groups: a patient group (60 individuals diagnosed with rheumatoid arthritis) and a control group (30 healthy individuals). The results showed that the percentage of women with rheumatoid arthritis was significantly higher than that of men (73.3% versus 26.7%). The majority of rheumatoid arthritis patients were obese (70.0%) or overweight (21.7%), with a small percentage of those with normal weight (8.3%). Hematological parameters showed a significant increase in the erythrocyte sedimentation rate (ESR) in patients of both sexes compared to healthy controls. There were also differences in basic hematological parameters, such as red blood cell count, white blood cell count, hemoglobin, and platelets. RA patients had higher levels of triglycerides (TG), total cholesterol (TC), very-low-density lipoprotein (VLDL), and low-density lipoprotein (LDL), while significantly lower levels of high-density lipoprotein (HDL) were observed compared to the healthy control group.
Copyright (c) 2025 Amal Azeez, Mohammed R.S.AL-Attabi https://creativecommons.org/licenses/by/4.0
t^p/p-Laplace transformation for Finite Time Stability for Composition and Riemann- Katugampola Fractional Integro-Differential Systems
In this research, the finite-time stability of Multi-Composition Caputo-Katugampola fractional Integro- Differential Nonlinear system with many values ​​of fractional derivatives is studied with some sufficient and necessary conditions as Lipchitz conditions for nonlinear functions involving Riemann– Katugampola fractional integral and the formulas derived from them with their respective bounded value as well as finding the solution under these conditions that contains Mittag Leffler functions which appeared through the use of the generalized Laplace formula which suitable with Caputo-Katugampola fractional derivative. Therefore, important conditions appeared that contain the parameters that played a good role in finding and computing the stability as in the attached tables of illustrative examples that explain the necessary time requirements for it.
Copyright (c) 2025 Mohammed Salah, Sameer Qasim Hasan https://creativecommons.org/licenses/by/4.0
Screening of C677T polymorphism in the methylenetetrahydrofolate reductase gene in Anbar, Iraqi patients with β-thalassemia major
The synthesis of proteins, DNA, and RNA as well as the metabolism of methionine and folate, depend on the enzyme MTHFR, a crucial regulator of the one-carbon cycle. This enzyme is necessary for the remethylation of homocysteine to methionine and transforms 5,10-methylenetetrahydrofolate into its active form, 5-methyltetrahydrofolate. Determining the degree of polymorphism in the gene for the enzyme methylenetetrahydrofolate reductase (MTHFR) in patients with beta-thalassemia major and comparing it to the control group, as well as sequencing, analyzing, and detecting mutations in the DNA nucleotides of the gene for the enzyme MTHFR, were the objectives of the study. 90 samples were taken from patients at the Al-Ramadi Teaching Hospital for Women and Children, both male and female. The study included 60 samples of individuals with β-thalassaemia major. The patients were between the ages of 3 and 52. The control sample consisted of 30 samples of each sex, ages ranging from 16 to 65. For every sample in the investigation, the MTHFR C677T gene band was amplified using the polymerase chain reaction (PCR). When compared to the 100 bp marker, all samples had a band size of 198 bp. The CHROMAS was used to examine the sequence data. The BLAST SEARCH tool was used to record the data, which had the same gene, C677T, between 9800 and 9930, and showed no alterations from the reference gene (NG_013351). These findings show that the participants in this research were wild type for this location, meaning they did not carry the C677T mutation
Copyright (c) 2025 Dalal Talak Ali Abtan, Hadeel Abdelelah Abdel Razaaq https://creativecommons.org/licenses/by/4.0
AI in Medical Imaging & Diagnostics: review
ABSTRACT: The application of artificial intelligence (AI) has transformed medical diagnostics for the better with the adoption and evolution of radiology, histopathology, clinical laboratory testing, robotic surgery, and expert systems. The implementation of deep learning algorithms using AI, such as Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Generative Adversarial Networks (GANs), have outperformed humans in the analysis of medical images, tumor categorization, and biomarker detection. AI powered biopsy systems, which are a type of robotic assisted diagnostics, have improved the accuracy and reduced the invasiveness of the procedure, which in return, enables for better detection of diseases at earlier stages. The use of pipetting robots and automated blood analyzers for AI powered automation of clinical lab diagnostics improves the speed and volume of diagnostic assays while reducing the rate of human error. Additionally, AI powered expert systems, like OncoKB for the analysis of genetic mutations and deep mind’s AlphaFold for predicting protein structures, are advancing precision oncology and personalized medicine by offering non-biased treatment suggestions. The issues that impede the further integration of AI in diagnostics include data diversity, model generalization, obtaining legal certification, and the absence of XAI which would heighten clinician acceptance. To overcome these barriers, there needs to be standard validation frameworks, federated learning for privacy-centric AI training, and multimodal AI that incorporates medical images, genomic data, and EHRs. Emerging directions in self-supervised learning (SSL), active AI robotics, and human-machine interaction will likely improve the precision of diagnoses, automate clinical processes, and expand healthcare access around the world. This review outlines the role of AI in transforming medical diagnostics while analyzing existing obstacles, emerging possibilities, and recommendations for AI in clinical decision support systems.
Artificial Intelligence (AI), Medical Diagnostics, Deep Learning, Computer-Aided Diagnosis (CAD), Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), Explainable AI (XAI), Generative Adversarial Networks (GANs), Robotic-Assisted Biopsy, Precision Oncology, Automated Blood Analyzers, Digital Pathology.
Copyright (c) 2025 Saif Hameed Abbood Al-Waeli1, Haza Nuzly bin Abdul Hameed https://creativecommons.org/licenses/by/4.0
The relationship between the presence of Prevotella intermedia and Porphyromonas gingivalis and increased severity of gingivitis
ABSTRACT: The study aimed to analyze the relationship between the presence of P.intermedia and P.gingivalis and the increased severity of gingivitis. The results are consistent with previous studies that demonstrated the multiplicity of periodontal pathogens and their varying prevalence according to geographical, genetic, and environmental criteria, confirming that the use of molecular analysis techniques such as polymerase chain reaction provides high accuracy in detecting disease-causing bacteria and contributes to a deeper understanding of how the presence of these microbes is associated with the clinical development of gingivitis and periodontitis.
Keywords: P. gingivalis, P. intermedia, Real-Time Quantitative Polymerase Chain Reaction
Copyright (c) 2025 Hodaa Qassim, Rana Salih Al-Taweel https://creativecommons.org/licenses/by/4.0
Numerical Simulation of a Nonlinear Predator-Prey System with Robin Boundary Conditions
This study presents a numerical simulation of a nonlinear predator-prey system described by a reaction-diffusion model within a convex, open domain, subject to Robin boundary conditions. The Finite Element Method (FEM) is employed to discretize and solve the system, offering a practical approach to capture the spatiotemporal dynamics of predator-prey interactions. Furthermore, an error estimation is derived to evaluate the accuracy of the numerical approximation in comparison to the exact continuous solution. The results confirm the reliability and efficiency of the proposed numerical scheme, particularly in handling the complexities introduced by the Robin boundary conditions.
Copyright (c) 2025 Ghassan Al-Juaifri, Huda Al-Maliky https://creativecommons.org/licenses/by/4.0
Assessment of Testosterone Levels in Tobacco Smokers, Dependence Over the Long Term with Some Haemato-Biochemical Parameters
Smoking is considered a pervasive global concern, negatively affecting health and resulting in several diseases, causing millions of fatalities annually. The purpose of this research was to determine how blood smokers' testosterone levels, blood parameters, renal function, cholesterol, triglycerides, and liver enzymes were affected by heavy and chronic tobacco use. Participants (n =60) were obtained from a population cohort of men in Al-Ramadi city aged 20-63 years , Smokers (n=30) have been addicted to smoking for 10 years or more with number of 15-40 cigarette/ day . The subjects answered a detailed survey on their health, therapy or drug intake, and other symptoms or disease. The sex hormone, RBC Count, Hb, HCT, MCV, MCH, MCHC, WBC Count, Platelets, renal function, lipid profiles, ALP,AST and ALT were measured. Our findings have shown a substantial statistical difference in blood total testosterone levels (p < 0.001) among chronic smokers and non-smokers, with lower levels observed in the former group. 1.653±0.097 and 5.980±0.195 respectively , Cigarette smoking adversely affects Haematological parameters ( count of red blood cell , hemoglobin, hematocrit, mean corpuscular volume, mean corpuscular haemoglobin concentration, total leukocyte count) demonstrated a statistically significant increase (p < 0.05). No significance variations are observed on mean corpuscular haemoglobin and platelets (p > 0.05). The findings indicated a highly substantial increase in the levels of urea and creatinine (p<0.001), Furthermore lipid and liver enzymes it was also revealed that it a highly significant elevated (p< 0.001). Therefore, the current investigation indicates detrimental consequences of prolonged smoking on testosterone level in serum , hematological variables , moreover smoking are a risk factor for kidney disorders indicated by urea and creatinine , dyslipidemia was found including change in total cholesterol and triglyceride finally liver enzymes abnormalities were recorded in our study including ALP,AST and ALT in the addicted smokers
Tobacco smoking, Testosterone, Blood parameters, Renal function, Cholesterol, liver function
Copyright (c) 2025 Nbaa Mutea Abid Al Alh , Asmaa Wajeh Jumaa, Nagam Khudhair https://creativecommons.org/licenses/by/4.0
Recurrent Neural Network Optimized by Grasshopper for Accurate Audio Data-Based Diagnosis of Parkinson's Disease
Proposed here is a speech-based diagnostic framework for detecting Parkinson's disease that utilizes a Long Short-Term Memory neural network and the Grasshopper Optimization Algorithm. The framework aims to improve the detection of PD while ensuring accurate and efficient classification of speech-based signals. Incorporated within the system is a dimensionality reduction technique called Linear Discriminant Analysis. This approach allows the system to optimally utilize the features extracted from the given speech signal while ensuring that all the conditions necessary for successful classification are met. The LSTM neural network serves as the core deep learning model for this framework. It is trained on the speech signals produced by both healthy individuals and those with PD, allowing it to detect the class of a given speech signal based on the conditions necessary for successful classification. We assess the suggested technique on two speech datasets that are publicly available: NeuroVoz, a Castilian-Spanish body of work, and EWA-DB, a database in the Slovak language. The classification accuracy of our model on NeuroVoz is 99.45%, while on EWA-DB it is 99.71%. Moreover, these results exceed those from several contemporary approaches that were also tested on these two datasets. We interpret our results as strong evidence that uniting deep learning with nature-inspired optimization does yield an effective tool for providing, in a non-invasive way, a diagnosis of early-stage Parkinson's across diverse linguistic contexts.
Grasshopper Optimization Algorithm (GOA)
Copyright (c) 2025 Saif Wali Ali Alsudani, Ghassan Khudair Saud https://creativecommons.org/licenses/by/4.0
Evaluation of ADH4 gene expression and hematological parameters as biomarkers for the pathogenesis of ITP in the pediatric
This study investigates the role of the ADH4 gene expression and hematological biomarkers in the pathogenesis of Immune Thrombocytopenic Purpura (ITP) in pediatric patients. The study involved 100 children, 50 with ITP and 50 healthy controls, to evaluate blood parameters and ADH4 gene expression. Results showed the mean white blood cell (WBC) count was significantly lower in ITP patients (8.11 ± 0.73) compared to controls (11.94 ± 0.69), Similarly, lymphocyte counts were significantly reduced in ITP patients (3.29 ± 0.43) versus controls (5.65 ± 0.42), The platelet count was markedly lower in ITP patients (26.18 ± 2.77) compared to controls (277.40 ± 10.74), On the contrary, The results show there was a non-significant difference between ITP patients and healthy individuals according to both neutrophil count and hemoglobin (Hb) levels (P < 0.05), while, ADH4 gene expression was significantly higher in ITP patients shown The mean 13.29, 1.00 between the ITP patients and Control respectively, suggesting its involvement in immune dysregulation. These findings highlight the potential of the ADH4 gene and some hematological parameter as a biomarker and therapeutic target in ITP, emphasizing the need for personalized treatment approaches
Copyright (c) 2025 Hayder Dawood saleem, Muthana Ibrahim Maleek https://creativecommons.org/licenses/by/4.0