Wasit Journal for Pure Sciences

This study investigates a nonlinear predator–prey system modeled by reaction–diffusion equations in a convex, open domain with Robin boundary conditions. The system is discretized and solved using the Finite Element Method (FEM), providing an effective framework to capture the spatiotemporal dynamics of predator–prey interactions. An error analysis is performed to assess the accuracy of the numerical solutions relative to the exact continuous solution. The results demonstrate the reliability and efficiency of the proposed numerical scheme, highlighting its capability to handle the complexities associated with Robin boundary conditions.

Let G=(V,E)G = (V, E)G=(V,E) be a graph. An Italian dominating function (IDF) is defined as a mapping f:V→{0,1,2}f: V \rightarrow \{0, 1, 2\}f:V→{0,1,2} satisfying the condition that, for any vertex mmm with f(m)=0f(m) = 0f(m)=0, there exists either at least one neighbor vvv with f(v)=2f(v) = 2f(v)=2 or at least two neighbors n,sn, sn,s with f(n)=f(s)=1f(n) = f(s) = 1f(n)=f(s)=1. The weight of an IDF is the sum of the function values over all vertices, and the minimum weight among all IDFs of GGG is denoted by γI(G)\gamma_I(G)γI(G).
A total Italian dominating function (TIDF) is an IDF in which every non-zero vertex is adjacent to at least one other non-zero vertex. The minimum weight of any TIDF is referred to as the total Italian domination number, denoted by γtI(G)\gamma_{tI}(G)γtI(G).
In this study, we investigate TIDFs for a graph GGG and its middle graph M(G)M(G)M(G), obtained by inserting a new vertex on each edge of GGG and connecting two new vertices if they share a common adjacent vertex in GGG. For selected standard families of graphs, we determine the exact values of these domination parameters.

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 of characteristic whose subsets of zero divisors satisfy the condition . The ring is constructed over its subring as an idealization of the - 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.

Artificial Intelligence (AI) is rapidly transforming medical diagnostics by enhancing accuracy, efficiency, and decision-making across radiology, pathology, laboratory medicine, and robotic-assisted procedures. AI-driven models, including deep learning, convolutional neural networks, vision transformers, and multiple instance learning, have demonstrated expert-level performance in disease detection, tumor classification, hematology analysis, and microbiological diagnostics. Intelligent robotics and autonomous systems further optimize workflow efficiency, enable real-time interventions, and facilitate remote diagnostics. Expert systems and clinical decision support tools integrate AI with medical knowledge to support differential diagnoses, treatment planning, and personalized medicine. Despite these advancements, challenges remain in data heterogeneity, algorithmic bias, interpretability, regulatory compliance, and integration into clinical workflows. Emerging solutions, such as explainable AI (XAI), federated learning, self-supervised learning (SSL), and multimodal AI integration, aim to address these challenges, promoting transparency, security, and equitable outcomes. The convergence of AI with clinical workflows, data governance frameworks, and human-AI collaboration has the potential to transform diagnostics into a precise, efficient, and ethically guided component of modern healthcare.

Cloud computing offers scalable, on-demand computational resources; however, efficient CPU utilization remains a critical challenge, as insufficient provisioning can lead to increased latency and higher energy consumption. This study aims to evaluate and optimize CPU performance in cloud systems using two machine learning models—Random Forest (RF) and Long Short-Term Memory (LSTM)—combined with the Multi-Objective Grey Wolf Optimizer (MOGWO) for hyperparameter tuning in time-series prediction. The models were assessed on the Google 2019 cluster trace using standard evaluation metrics, including Accuracy, Precision, Recall, and F1-score. Experimental results demonstrate that MOGWO-based optimization significantly improves model performance. The accuracy of the RF model increased from 65.15% to 89.70%, while the LSTM model improved from 95.02% to 98.60%, with corresponding gains in precision, recall, and F1-score. These findings confirm the effectiveness of the optimized models in predicting CPU usage patterns and enhancing CPU resource management in cloud computing environments.

Smoking is considered a pervasive global concern, negatively affecting health and leading to several diseases that cause millions of fatalities annually. The purpose of this research was to investigate the effects of heavy and chronic tobacco use on testosterone levels, hematological parameters, renal function, cholesterol, triglycerides, and liver enzymes in male smokers. Participants (n = 60) were recruited from a population cohort of men in Al-Ramadi city, aged 20–63 years. The smoker group (n = 30) consisted of individuals who had been addicted to smoking for 10 years or more, consuming 15–40 cigarettes per day. All subjects completed a detailed survey regarding their health status, medication intake, and any other symptoms or diseases. The following parameters were measured: sex hormones, RBC count, Hb, HCT, MCV, MCH, MCHC, WBC count, platelets, renal function markers, lipid profiles, and liver enzymes (ALP, AST, and ALT). Our findings demonstrated a statistically significant decrease in total blood testosterone levels (p < 0.001) among chronic smokers compared to non-smokers (1.653 ± 0.097 vs. 5.980 ± 0.195, respectively). Cigarette smoking also adversely affected hematological parameters: RBC count, hemoglobin, hematocrit, MCV, MCHC, and total leukocyte count showed a statistically significant increase (p < 0.05). No significant variations were observed in MCH and platelet count (p > 0.05). Furthermore, the study revealed a highly significant increase in urea and creatinine levels (p < 0.001), indicating impaired renal function. Lipid profiles and liver enzymes (ALP, AST, and ALT) were also significantly elevated (p < 0.001) in smokers compared to non-smokers. In conclusion, the present study highlights the detrimental consequences of prolonged smoking, including reduced serum testosterone levels, alterations in hematological parameters, increased risk of kidney dysfunction (as evidenced by elevated urea and creatinine), dyslipidemia (changes in total cholesterol and triglycerides), and abnormal liver enzyme levels. These findings suggest that chronic smoking is a major risk factor for multiple systemic disorders.

Gingivitis and periodontitis are prevalent oral diseases caused by the disruption of the oral microbiota, with Porphyromonas gingivalis and Prevotella intermedia recognized as key pathogenic bacteria. This study aimed to investigate the prevalence and copy number of P. gingivalis and P. intermedia in patients with gingivitis and periodontitis, and to examine their association with age and gender. Ninety dental samples were collected from patients aged 2–68 years in Diwaniyah Governorate and analyzed using quantitative real-time polymerase chain reaction (qPCR). The results showed that 34.4% of samples tested positive for P. gingivalis and 82.2% for P. intermedia. Mean age differences between infected groups were not statistically significant, nor were differences in prevalence by gender. In pediatric patients aged 2–12 years, P. intermedia was associated with halitosis, regardless of the presence of caries. Higher bacterial copy numbers were observed in older age groups, indicating an age-related increase in periodontal pathogen burden. These findings highlight the significant role of P. gingivalis and P. intermedia in the progression of gingivitis and periodontitis and underscore the importance of early detection and targeted preventive strategies to maintain oral health.

Understanding chemical reaction mechanisms at the atomic level is essential for predicting reaction outcomes and designing efficient chemical processes. This study employs advanced computational chemistry techniques, including Density Functional Theory (DFT) at the B3LYP/6-31G(d,p) level, the Unified Reaction Valley Approach (URVA), reaction force analysis, and molecular dynamics (MD) simulations, to investigate the structural, energetic, and dynamic aspects of chemical reactions. Continuum solvation models (PCM and COSMO) and hybrid QM/MM approaches are utilized to account for solvent effects, while kinetic modeling software (TA-KIN, NETZSCH Thermokinetics, KINETICS, and AKTS-TA) provides insights into reaction rates and energetics. Key findings reveal that even seemingly simple reactions progress through multiple phases, including bond-breaking, bond-forming, and van der Waals interactions, with critical electronic structure changes occurring along hidden transition points. The integration of energy profiling, vibrational mode analysis and curvature coupling offers a detailed understanding of reaction pathways and the forces governing them. These computational insights enable accurate prediction of activation energies, reaction feasibility, and rate constants, providing a foundation for applications in catalysis, drug design, material science, and biomolecular interactions.

A novel series of liquid crystalline compounds containing 2,4-thiazolidinedione units with varying terminal alkyl chain lengths was successfully synthesized and characterized. The chemical structures of the synthesized compounds were confirmed by FT-IR, ¹H-NMR, and mass spectrometry. The mesomorphic behavior was investigated using polarized optical microscopy (POM) and differential scanning calorimetry (DSC). Compounds [V]₄, [V]₅, and [V]₆ exhibited enantiotropic nematic phases, while compound [V]₈ displayed a smectic A (SmA) phase. No liquid crystalline behavior was observed for compound [V]₃. The liquid crystalline properties were found to depend on the terminal-to-lateral chain length ratio, molecular geometry, and the nature of the linking groups. Additionally, an odd–even effect was observed in the crystal–nematic and nematic–isotropic transition temperatures for certain members of the series. The results highlight the crucial role of terminal chain length and cyano substitution in stabilizing mesophases and tuning the thermal behavior of these 2,4-thiazolidinedione-based liquid crystals.