Al-Kut University College Journal

The research aims to test the role of operations strategy as an independent variable in achieving strategic agility as a dependent variable, by presenting a problem for the researched organization, which is the factory (General Company for Petrochemical Industries in Taji), which forces the company's management to follow systems that provide it with the ability to continue and compete, and by relying on resources that have the ability to achieve strategic agility, and the research tool was relied upon, which is the questionnaire distributed to the research sample that includes workers and managers, and the adoption of the five-point Likert scale, based on the SPSS statistical program, and the use of relevant statistical methods to find the correlation and regression of the obtained data, and the forms were distributed in number (65) forms and (60) were retrieved, to verify the validity of the hypotheses, and the results showed the impact of operations strategy has a significant impact on strategic agility by relying on indicators, cost, quality, flexibility and delivery.

Most Iraqi economic units suffer from high rates of environmental pollution and a shortage of available resources and energy, which causes high production costs, in addition to the lack of these units in using modern cost techniques to determine and measure the quantities and costs of negative products. Among these techniques is the material flow costing technique (ISO 14051 standard), which works To track and analyze inputs and outputs and divide them into (positive products and negative products), as well as reducing environmental impacts and the amount of emissions and waste, optimally exploiting available resources and energy, improving the environmental performance of the unit, and preserving the environment from pollution. Therefore, this research aims to explain the conceptual framework of the ISO 14051 standard in light of cost-effective material flow technology, clarify the impact of the standard on the carbon footprint, optimal exploitation of available resources and energy, and provide viable alternative solutions that would limit the emission of gases and vapors. For the purpose of achieving the research goal and testing its hypothesis, a factory was chosen. Batteries - Babylon 2 Laboratory and approving the financial and non-financial data included in the reports, records and documents for the year 2022. The research concluded that the use of the ISO 14051 standard in light of cost technology for material flow helps reduce the carbon footprint in the battery industry.

The research aims to identify the levels of causal attribution among football coaches as well as to identify the differences between internal and external attribution between coaches for the research sample through the construction and application of the causal attribution scale among football coaches. The researcher assumed a discrepancy between the levels of causal attribution among coaches and the ownership of a large percentage of coaches for internal attribution. The researcher used the descriptive method for its suitability for the research sample, which included football coaches for Iraqi league clubs for the central and southern regions, and the researcher used in his study the causal attribution scale built by the researcher.
One of the most important conclusions reached by the researcher was: the construction and application of the causal attribution scale among football coaches, and the results showed a high level of causal attribution in the internal field more than the external field. The results also showed a difference in the level of causal attribution among the members of the application sample, and also the winning trainers attribute their victory to internal reasons such as effort and ability, and attribute their loss to external causes such as luck and coincidence.

This research aims to analyze the impact of using the Balanced Scorecard in evaluating the professional performance of accountants and auditors at Wasit and Kut universities. The research adopted a descriptive analytical approach, and data were collected through a questionnaire distributed to the research population. A total of (130) questionnaires were distributed, of which (112) were valid and suitable for analysis. The data were analyzed using a set of statistical methods, including descriptive statistics, Pearson correlation coefficient, multiple regression, and the ( T-test).
The results of the t-test for the correlation coefficient between the first independent variable (achieving the strategic objectives of administrative units using the Balanced Scorecard) and the second dependent variable (measuring the professional performance of accountants and auditors using the Balanced Scorecard) showed a value of (0.741) and a probability value of (0.000), indicating a highly significant correlation between the two variables. The study recommends the necessity of implementing the Balanced Scorecard in Wasit and Kut universities and that their senior management make the necessary decisions in this regard.

Walkability emerges as an important efficiency measure in street design and can be interpreted based on its relevance and effectiveness of cities in promoting pedestrian mobility and sustainability. The current study attempts to find out the walkability of Al Jamiya Street in Babylon, Iraq, through Morphological Evaluation and Pedestrian Observation method conducted on a random sample of 50 pedestrians. The results show an enormous gap between design and service delivery efficiency. The street has enough sidewalks, but over-crowding by vendors and absence of proper crossing points mar its efficiency. More alarmingly, almost complete absence of something as natural as shade and trees almost ensures that walking during summer season is impossible; and all pedestrians agreed to this observation. The current problem and future studies section concludes that designing walkability in hot and arid climatic conditions is strongly interconnected and cannot be provided by just designing wider pavement but requires heavy emphasis upon climatic comfort and pedestrian priority management practices.

Over time, the need for creativity grows. We live in an environment characterized by continuous growth and escalating transformations. It is essential for individuals to understand the conditions required for progress in both the current context and future endeavors. Given the complex and interconnected nature of the components of change, it is crucial to have a comprehensive understanding of the actions taken, whether individually or in part, which should begin within a coherent framework defined by scientific rigor and methodological precision. Creativity within organizations is a fundamental necessity demanded by prevailing economic and societal transformations.

This research aims to study the impact of managing the foreign currency auction window on monetary stability in Iraq, focusing on the relationship between the Central Bank of Iraq and its sales of US dollars or other foreign currencies, exchange rates, banking liquidity, and foreign exchange reserves. The study is econometric in nature, employing daily and monthly secondary data for the period 2010-2025 from the Central Bank of Iraq, the Ministry of Finance, and other official reports. A standard linear regression model was used to analyze the effect of the volume of sales through the Central Bank's auction window on the exchange rate, with liquidity and foreign exchange reserves included as covariates. Correlation tests, dynamic regression, and time-sequence analysis were also applied to gain a precise understanding of the dynamics of the Iraqi money market. The results showed that selling foreign currency through the Central Bank's auction window significantly reduces exchange rate volatility and enhances monetary stability, particularly when adequate levels of banking liquidity and foreign exchange reserves are available. The study also demonstrated a positive relationship between foreign exchange reserves and market volatility control, highlighting the central bank's pivotal role in managing economic shocks. The results also indicate that effective monetary intervention policies can help reduce inflation and currency volatility, and maintain macroeconomic stability.

This research aims to build and test an integrative model analyzing the impact of Total Productive Maintenance (TPM) across three dimensions (preventive maintenance, autonomous maintenance, and training & development) on improving Production Efficiency (PE) across three dimensions (productivity, breakdown reduction, and product quality), through the mediating role of Organizational Culture (OC) across three dimensions (continuous improvement culture, teamwork, and organizational commitment), at the Wasit Textile and Knitting Factory as a case study. The researcher adopted a descriptive-analytical approach combined with a case study methodology. A 48-item questionnaire was designed and distributed to a census sample of (115) individuals out of (140) with a response rate of (82.1%). Data were analyzed using SPSS V.26 and AMOS V.24. Results revealed a significant positive impact of TPM on PE (β = 0.718, p < 0.001), and organizational culture plays a partial mediating role with an indirect effect of (0.274) and mediation ratio of (38.2%), while R² increased from (0.516) to (0.614) upon introducing the mediator.

This research aims to analyze the role of digital maturity in enhancing the resilience of supply chains in the oil sector, through a field analysis study in the Buzurgan oil field. The importance of this study stems from the strategic nature of oil supply chains and the operational and logistical challenges they face, which require high levels of flexibility and rapid response considering accelerating digital transformations. The research adopted a descriptive analytical approach, and data was collected using a questionnaire distributed to a sample of 120 engineers and technicians working in the oil field, specializing in and experienced in oil supply chains. The questionnaire included two main sections: the first measured the dimensions of digital maturity, namely: (digital strategy, digital technology, digital processes, and digital culture), while the second measured the dimensions of supply chain resilience, which include: (supplier resilience, production resilience, delivery resilience, inventory resilience, and response to market demands). The statistical analysis revealed a statistically significant positive role for digital maturity in enhancing supply chain resilience. A higher level of digital maturity contributed to improved supply chain adaptability to operational changes and mitigated the impact of disruptions. The results also showed that digital processes and technology were among the most influential factors in enhancing production and delivery resilience, while a digital culture supported improved market responsiveness and stakeholder coordination. The research concluded that promoting digital maturity is an effective approach to supporting the efficiency and resilience of supply chains in the oil sector and recommends a systematic investment in developing employees' digital capabilities and technical skills.

In this paper , the time-fractional nonlinear Schrödinger differential equations are solved using Jacobi's space-time spectral coherence technique (JSC method) under suitable beginning and boundary conditions. The examined issue is first converted into a related system of single weak kernel non-linear integral partial differential equations with the definition and characteristics of integral and fractional derivative operators. Thus the issue reduces to a collection of nonlinear algebraic equations by integrating the system associated with integral differential equations in both spatial and temporal variables together with the approximation of the integral in the equation using Jacobi Gauss quadratic method. Several reliable iterative solutions may be used to solve the problem. Examining the suggested method's convergence, After the study's conclusion, we computed their soft L2 and soft L1 and gave a few numerical examples.

The Burgers' equation is a time-dependent, nonlinear partial differential equation that finds applications in various scientific and technical domains, such as heat conduction, traffic flow, gas dynamics, and non-linear acoustics. This research article provides an overview of Burgers' equations, including their basic form and the time fractional form. The conditions under which exact solutions are available are discussed, and an approximation analytic Adomian approach is introduced to address the standard Burgers' equation.
The paper highlights the challenges in numerically solving the Burgers' equations due to their nonlinear behavior and low viscosity. Analytical solutions require infinite series with slow convergence for low viscosity coefficients. Consequently, numerical approaches are of significant interest for obtaining solutions to the Burgers' equation.
Furthermore, the article discusses how Burgers' equations can be derived from more complex models, making them commonly referred to as toy models. Examples of such simplifications are presented, and the concept of the outer product of vectors is introduced to aid in the understanding of Burgers' equations.

Many bio-chemical and molecular assays should be used to obtain the right identification of Staphylococcus aureus. Molecular assay is the best method for identification. The aim of this study was to optimize the best methods of Staphylococcus Aureus identification in pregnant women. This study has been done by collection of 100 samples from pregnant women (aged from 20- 30 years) were collected from Al-Karama Teaching Hospital and Al- Zahra Teaching Hospital. Only 50 (0.5 %) clinical samples of positive culture bacteria for Staphylococcus aureus by using different biochemical and molecular tests. PCR assay was the best method for identification. Usually, many assays should be done to ensure the right identification and exclude the false positive results.

The goal of this paper is to present how to calculate an approximate semi-analytical approach to magneto-hydrodynamic (MHD) Jeffery–Hamel flow using a unique form of the perturbation iteration algorithm (PIA) . At one and both grid points, the approximate solution of this issue is determined in the form of a quickly converging series. For various values of Hartmann and Reynolds numbers, results for velocity profiles in divergent and convergent channels are shown. (P IA) and fourth-order Runge Kutta are used to solve the problem (RK4). Both solutions are compared to the variance of the parameter technique (MADM) and (DTM) to see if the method is effective (PIA). The effects of the settings are visualized via graphical simulation for both divergent and converging, channels.

The second most common cause of cancer-related fatalities in women is breast cancer. Breast cancer is a multi-step process that involves several cell types, and it is still difficult to prevent globally. In Iraq, breast cancer is the most frequent kind of cancer. The study included a total of 60 patients. Approximately 31 of the breast cancer patients were diagnosed at age 50 or older; 58.3% came from urban areas. (43.4%) patient with breast cancer who is obese. Positive family history was recorded at 18.3%. Accordingly, 33.3% of these patients presented in advanced stages (IV). The main histological type was invasive ductal carcinoma, in which pathological changes of grade II and III were observed at 76.6% and 16.7%, respectively. We also noticed that women with breast cancer on the left side (55.0%) are more likely. According to the data for ABO blood groups, type O and type B are the most common. Only 71.7% of our tumors were ER-positive, 61.7% were PR-positive, and 33.3 percent were HER2. The majority of tumors (50.0%–20.0%) are luminal. The results of this study support stepping up efforts to create comprehensive strategies for controlling breast cancer in Iraq. In the lengthy battle against breast cancer, these findings are but a minor step forward.

Weather forecasting is inherently complex; however, scientists are endeavoring to leverage the latest computational technologies to enhance its accuracy. By using huge amounts of data, scientists hope to be able to predict potential weather patterns as accurately as possible. If their efforts succeed, meteorologists may be able to predict major events for years or even decades to come. This could provide tremendous potential to save the lives of many people from severe weather conditions such as hurricanes, droughts, and floods. Many different methods and algorithms for deep learning and machine learning have been used to predict weather, including RNN, LSTM, GRU, RN-Net, the CRNN model, and BLSTM-GRU, and machine learning algorithms including ANN, SVM, Random Forest (RF), and K-Nearest Neighbor. . In this survey, we will briefly summarize the methods used to build models to predict climate change.

The E. granulosus is recognized as a biologically distinct subspecific variant or strain that may exhibit varying degrees of infectivity toward domestic animals and humans. This research examined the influence of the IL-32 gene polymorphism rs2283468 in patients with Echinococcus granulosus. This study has been ongoing from January 2024 until the end of May. Five ml of blood samples were collected from thirty subjects, including fifteen patients with cystic echinococcosis from the hospitals in Baghdad city and fifteen healthy persons as a control group, and fifteen hydatid cyst samples were collected from infected humans that had undergone a surgical operation. Conventional PCR with specific primers, followed by Sanger sequencing, was used to characterize the SNP. We analyzed the gene using Sanger sequencing for the SNP rs2283468 of IL-32, and found that patients with Echinococcus granulosus had genotype frequencies of AA (13.3%), CC (73.3%), and AC (13.4%). Also, in healthy people, there was AA (80.0%), CC (0.0%), and AC (20.0%). The CC genotype got the highest score in patients (73.3%) and the lowest score in healthy people (0.0%). This was in contrast to the dominant allele, which got the highest score in healthy people (80%) and the lowest score in patients (13.3%), with very different (p<0.05) and odd ratios (0.008). The C allele is more common in patients (75.0%) and least common in healthy people (16.67%), with a significant difference (p< 0.05).

Polypyrrole (PPy) is among the most widely used materials in many different sectors due to its electrical, optical, and mechanical properties. This work presents a study of pyrrole polymer structural and electronic properties pre- and post-doping with ZnO and TiO2, which were investigated by performing computational calculations based on density functional theory (DFT). Interpreting theoretical vibrational spectra and finding important information about chemical bonds, functional groups, molecular structures, natural bond orbitals (NBOs), the HOMO-LUMO energy gap, density of states (DOS), and Nuclear magnetic resonance (NMR). The results exhibit functional groups present for the three structures: N-H, O-H, and C-H. The frequency of N-H increased after adding ZnO, and the frequency was greater when adding TiO2, while the C-H stretching frequency showed a gradual increase consistent with the structures studied. The theoretically calculated energy gap was (3.865 eV) for PPy and (3.2734 eV) for PPy/ZnO, while (3.3235 eV) belongs to PPy/TiO2. Furthermore, the natural bond orbital analysis revealed that PPy/TiO2 had the highest energy among the three compounds (45.14) kcal/mol, indicating a stronger interaction between the donor and acceptor orbitals. The study found that the addition of TiO2 and ZnO to (PPy) contributed to significant improvements in electrical conductivity, thermal stability, and electrochemical performance. This helps to improve applications in various fields, such as sensors and supercapacitors.

This study examines how the microstructure of pure titanium is affected by Single Point Incremental Forming (SPIF) factors, such as step sizes (0.2, 0.4, 0.6 mm) and tool diameters (6, 10, 14 mm). According to the research, increasing step sizes cause more material to deform with each stride, which increases the accumulation of strain over shorter distances. By encouraging grain refinement and resulting in smaller, more evenly dispersed grains, this strain can improve mechanical qualities. Furthermore, localized heating is produced by the increased friction brought on by bigger step sizes, which further influences microstructural alterations.
The results show that, with respect to tool diameter, larger tools provide greater friction, which might lead to localised heating and encourage grain recrystallisation. This therefore has an impact on the material's residual stresses, which are crucial to the final microstructure. The study highlights the connection between tool diameter and microstructural evolution by analysing four different sections from each sample and observing significant changes in grain size and distribution along the sample wall. The findings highlight how crucial it is to optimise SPIF parameters in order to enhance the material's performance properties.
The analysis of fracture behaviour sheds light on the change from brittle to ductile fracture. SEM study of the fracture surfaces demonstrates that the number of pits increases as tool diameter and step size decrease, influencing the fracture behaviour

Single-image super-resolution (SISR) is an advanced and highly significant technique in computer vision and artificial intelligence fields. It aims to reconstruct high-resolution (HR) images from their low-resolution (LR) counterparts, presenting considerable challenges due to the loss of fine details during image downscaling. Applications of SISR range from medical imaging and satellite imagery to consumer photography and video streaming. Over recent years, the advent of deep learning has revolutionized the SISR domain, with convolutional neural networks (CNNs) emerging as the most promising approach. These networks have demonstrated remarkable accuracy, outperforming traditional image processing methods.
This paper focuses on the Very Deep Super-Resolution (VDSR) model, a specialized CNN architecture designed to improve the quality of single-image super-resolution. VDSR excels by learning the intricate relationships between low- and high-resolution images, particularly the mapping of the high-frequency details lost in LR images. This capability is achieved through a deeper network structure and the use of residual learning, which facilitates faster convergence and enhances performance. The paper conducted experiments to estimate HR images from LR inputs using the VDSR model and compared the results with traditional methods such as bicubic interpolation.
The findings reveal that the VDSR model delivers superior performance, producing HR images with greater accuracy and preserving finer details compared to bicubic interpolation. These results highlight the potential of VDSR in real-world applications where high-quality image reconstruction is critical. This study underscores the ongoing importance of deep learning in addressing challenges in SISR and paves the way for further advancements in the field.

Modern connectivity has been revolutionized by the Internet of Things (IoT). The limited computational capabilities of IoT devices make them vulnerable to various attacks including, Distributed Denial of Service (DDoS) attacks that compromise the availability of these devices and their services. This research introduces a machine learning-based approach to enhance DDoS attack detection in IoT environments. Using the CICIoT2023 dataset, we evaluated several machine learning models: Random Forest, XGBoost, Decision Tree, and K-Nearest Neighbors. The results demonstrated high classification performance across all models. XGBoost achieved the highest accuracy of 99.97% with a prediction time of 0.4735 seconds, while Decision Tree delivered the best prediction time of 0.1879 seconds, maintaining a high accuracy of 99.94%.
The results of the article confirm that the suggested machine learning models for DDoS attack detection in IoT networks are effective and improve the security of such networks.

This study aimed to shed light on the contamination of the most important foods with heavy metals, which were collected from vendors in the most crowded markets in the Wasit Province, Iraq. The content of lead and cadmium in eleven types of vegetables and fruits were estimated, as they are among the most dangerous toxic foods contaminated with heavy metals, which are among the most important components of the basic meals of Iraqi citizens. An atomic absorption spectrophotometer (Model Phoenix–986 AAS) was used to assess heavy metals. The results showed that the concentration of lead in some vegetables ranged between (0.021 to 1.05 μg/g) and in fruits it reached (0.25 to 0.39 μg/g). As for the cadmium element, the results showed that its concentration was less than the concentration of lead, as it reached (0.073 to 0.182 μg/g) in vegetables and (0.065 to 0.133 μg/g) in fruits. The study revealed potential health risks associated with the consumption of different types of vegetables and fruits. The results showed that the concentration of lead and cadmium in most of the studied samples exceeded the permissible limit recommended by the World Health Organization and the Food and Agriculture Organization, which may assist Iraqi regulatory bodies in developing new strategies to reduce risks to humans.

Background: Intestinal protozoal infections, particularly those caused by Entamoeba histolytica, Giardia lamblia, and Cryptosporidium parvum, pose serious health risks for immunocompromised individuals. Cancer patients are especially vulnerable, as these infections often lead to severe diarrhea and other gastrointestinal complications. Methods: Stool samples were examined microscopically using direct smear and modified Ziehl–Neelsen staining. In addition, a rapid antigen test was performed to detect antigens of E. histolytica, G. lamblia, and C. parvum. Results: The overall detection rate of intestinal protozoal infections was significantly higher using the rapid antigen test (53.3%, 48/90) compared with microscopy (18.9%, 17/90). Based on the rapid test results, G. lamblia accounted for 43.75% of positive cases, C. parvum for 37.5%, and E. histolytica for 18.75%. The highest infection rates were observed among patients aged 41–56 and 57–72 years. A statistically significant association was found between age group and infection rate with both diagnostic methods (Microscopy: p = 0.041; RAT: p = 0.003). Participants were evenly distributed among breast, gastrointestinal, and hematological malignancies (33.3%) each. Participants were evenly distributed among breast, gastrointestinal, and hematological malignancies (33.3%) each. Conclusion: The rapid antigen test demonstrated greater sensitivity than microscopy for detecting intestinal protozoal infections in cancer patients. The high prevalence observed highlights the need for regular screening of this immunocompromised population. For rapid diagnosis and improved patient management, it is strongly recommended that rapid antigen testing be integrated into standard diagnostic protocols.

Handwritten digit recognition (HDR) remains one of the most fundamental and widely explored tasks in pattern recognition and computer vision, serving as a benchmark for evaluating machine learning and deep learning architectures. This study presents a comparative evaluation of three neural network models—Simple Perceptron (SP), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN) for handwritten digit classification using the standard MNIST dataset. The methodology includes data normalization, reshaping, and one-hot encoding to ensure consistent preprocessing across models. The SP model achieved a baseline accuracy of 92.7%, demonstrating limited capacity to capture complex spatial relationships. The MLP model, incorporating two hidden layers with nonlinear activations, improved recognition accuracy to 97.2%, confirming the benefit of deeper, fully connected representations. The CNN model achieved the highest validation accuracy of 98.9% by leveraging convolutional and pooling layers to efficiently extract local spatial hierarchies. Training and validation performance analyses confirmed stable convergence and minimal overfitting. Overall, the results demonstrate that CNNs significantly outperform classical and shallow neural architectures for HDR tasks, offering superior generalization, computational efficiency, and suitability for real-world applications in optical character recognition (OCR) and document automation.

Background: Transfusion-dependent beta-thalassemia (TDT) patients are highly susceptible to repeated infections. Iron overload and disruptions in the immune system are the main causes. Gastroenteritis symptoms are a significant cause of morbidity in this group. Objective: This study aimed to determine the prevalence of bacterial enteropathogens in TDT children presenting with gastroenteritis and to evaluate the association between bacterial growth and clinical/laboratory parameters. Methods: A cross-sectional study was conducted on 100 TDT children (≤15 years) at the Center for Genetic Blood Disorders, Al-Kut Hospital, Iraq (August 2024–January 2025). Stool samples were analyzed using culture, VITEK® 2, and 16S rRNA sequencing. Results: Bacterial growth was confirmed in 20% of patients. Isolates were predominantly Gram-negative (95%), with Escherichia coli (50%) being the most frequent. A significant association was found between bacterial growth and younger age (<10 years, P=0.001) and higher serum ferritin levels (P=0.034). Conclusion: Younger age (<10 years) and iron overload are major predictors of bacterial gastroenteritis in TDT children.

Urinary tract infections are among the most common bacterial infections affecting humans, with Proteus species frequently identified as a primary cause. The increasing resistance of these bacteria to antibiotics has prompted the search for safe biological alternatives, such as probiotics. In this study, the antibacterial action of the cell-free supernatant obtained from Lactobacillus isolates was evaluated against Proteus spp. adjusted to 0.50 McFarland turbidity using the well diffusion technique. Out of 150 urine samples collected from patients showing UTI symptoms, 20 samples (13.3%) were confirmed positive for Proteus. The infection rate was highest among patients aged between 21 and 40 years, Pearson Chi-square p-value = 0.000, which is less than 0.05, (statistically significant), relationship between age groups and the isolation of Proteus isolates, whereas there was no statistically significant relationship (p = 1.000) between gender (male and female) and Proteus isolation Meanwhile, 200 specimens taken from both clinical sources (healthy urine and vaginal swabs) and non-clinical sources (fermented foods) yielded 20 Lactobacillus isolates (10%). The supernatant of Lactobacillus exhibited a clear inhibitory effect on all Proteus isolates, forming inhibition zones ranging from 9 to 18 mm. Overall, these findings suggest the potential use of Lactobacillus supernatant as a natural suppressive agent against resistant Proteus strains in urinary tract infections.

Impetigo is a common and contagious superficial skin infection, primarily caused by Staphylococcus aureus and Streptococcus pyogenes. This study aimed to assess the in vitro activity of selected antibiotics and probiotics against S. aureus and S. pyogenes isolated from impetigo cases in Wasit, Iraq, and to compare their antimicrobial effects. In a cross-sectional study conducted from August to November 2024, 102 skin swabs were collected from patients with impetigo. Bacterial isolates were identified using standard microbiological methods and the Vitek® 2 system. Antibiotic susceptibility was determined by the disk diffusion method for 31 S. aureus and 6 S. pyogenes isolates against 17 antibiotics. The inhibitory activity of Lactobacillus rhamnosus and Lactobacillus fermentum was evaluated using the well diffusion and agar spot methods.S. aureus was the predominant pathogen (48.0%), followed by S. pyogenes (5.9%). S. aureus showed high resistance to penicillin G (90.3%) but remained susceptible to levofloxacin (100%) and vancomycin (96.8%). S. pyogenes exhibited 100% susceptibility to penicillin G but high resistance to tetracycline (66.7%). Both probiotic strains demonstrated significant antibacterial activity. L. fermentum showed larger inhibition zones (18 mm against S. aureus, 16 mm against S. pyogenes in the well diffusion method) compared to L. rhamnosus. The agar spot method proved more effective, with inhibition zones reaching up to 26 mm for L. fermentum. The study reveals significant antibiotic resistance among impetigo pathogens in Wasit, Iraq, highlighting the need for updated treatment guidelines. The potent in vitro antimicrobial activity of L. rhamnosus and L. fermentum suggests that probiotics could be a promising alternative or adjunctive therapy for impetigo, warranting further clinical investigation.

Heart disease is one of the most common diseases and the principal cause of sudden death nowadays. According to the Iraqi Ministry of Health, 28.83% of deaths in Iraq are caused by some kind of heart disease. This is motivated by the worldwide increasing fatality of heart disease (HD) patients each year and the availability of statistical methods that can be used to extract convenient knowledge from data collected from patients, to aid physicians in diagnosing heart disease. A statistical model was developed using multinomial logistic regression (MLR) to predict heart diseases using a training dataset of 201 patients collected from Alkarama Teaching Hospital. The data were statistically analyzed to reduce bias and assess how much the attributes explain the diversity of the dependent variables, which resulted in 95.8%. The overall prediction model had an accuracy of 94.5%. The researchers concluded that the MLR model has high accuracy in predicting heart disease, and that echocardiographic findings of hypokinesia, chest pain, and electrocardiographic results are the top three attributes, in terms of accuracy, for diagnosing heart disease.

Despite the presence of many research papers related to the prediction of heart disease through the use of artificial intelligence, the significance of heart disease and the extent of its impact on the lives of people lead to the fact that the researchers must continue to work on building the best program to predict heart disease from an early age to avoid its development, in this paper the researchers are using the K-nearest neighbor algorithm for the prediction of heart disease. The dataset used in this paper is collected from local hospitals in Iraq and was cleaned and analyzed by the researchers. The diagnosis system that is proposed in this work is to assist physicians to diagnose heart conditions by converting medical factors of the patients in to numerical representations, the simulation results show that the proposed K nearest neighbor classifier has 86% accuracy in classifying 4 medical heart conditions when using controlled databases. The researchers concluded that the most common heart conditions that are found in Iraq are the ones classified in this algorithm, which are Coronary heart disease, Congestive Heart failure, and Arrhythmias. The researchers conclude that there is a direct relationship between the amount of training data and the algorithm’s accuracy and an inverse relationship between the number of neighbors K and the algorithm’s accuracy.

Adversarial attacks pos a critical threat to the reliability of deep learning models, especially in medical imaging, where small pixel-level perturbations can make severe diagnostic misclassifications. This research discusses the vulnerability of a breast cancer histopathology classification model to adversarial attacks and proposes a robust defense framework. Using the BreaKHis 400X dataset, a DenseNet121-based transfer learning model achieved 87.16% accuracy on clean data. But under a Fast Gradient Sign Method (FGSM) attack with ε = 0.05, accuracy decreased to 19.45%, with an attack success rate of 80.55%. To handle these issues, a highly accurate defense model was developed, integrating adversarial training with a denoising module and adversarial detection. The proposed model significantly improved robustness that raising sensitivity to 96.75% and specificity to 90.91% on clean data, while dramatically reducing false negatives. The results show that combining adversarial training with targeted preprocessing can effectively enhance model resilience by offering a practical pathway toward more secure and reliable deep learning systems in clinical settings.

Pseudomonas aeruginosa is a common Gram-negative opportunistic bacterium that causes acute and chronic infections in humans, especially the immunocompromised ones and in hospitalized patients. Its exceptional adaptability coupled with inherent resistance to various antibiotics as well as its capacity to develop other determinants of resistance has placed it among the main global concerns in public health. Pathogenic success of P. aeruginosa is explained by a combination of virulence factors, such as outer membrane proteins, lipopolysaccharides, biofilm formation, secretion systems, toxins, lytic enzymes, siderophores, antioxidant enzymes, and quorum-sensing networks all of which contribute to the increase of colonization, immune evasion and tissue damage. Molecular typing procedures have been of paramount importance in attempting to explain the epidemiology and population structure of this pathogen. Of these, multilocus sequence typing (MLST) has become one of the standardized, reproducible, and globally comparable methods, which rely on the study of shared housekeeping genes. This review summarizes the existing information about virulence determinants and pathogenicity of P. aeruginosa and critically analyzes the advantages and drawbacks of MLST and the corresponding genomic typing methods. Dynamics between virulence, resistance, and population structure must provide a better understanding of the contribution to enhancing surveillance, treatment regimens, and transmission among patient of high-risk P. aeruginosa clones in healthcare settings.

The molecular relations between bacterial pathogens and their hosts form the basis of the development, progress, and cure of infectious diseases. The bacterial pathogens can be characterized by a wide variety of highly sophisticated virulence mechanisms, including adhesins, invasions, toxins, and distinct secretion systems, that help them to invade host tissues, modify cellular signaling cascades, and escape immune recognition. Simultaneously, the innate immune reactions are regulated and coordinated in the defense system of the body and mediated by pattern recognition receptors, signaling cascades, and effector mechanisms such as phagocytosis, cytokine generation, and antimicrobial peptides. The success of infection control or disease progression depends upon the continuous molecular communication between the host and pathogen. More recent developments in genomics, proteomics and molecular immunology have dramatically enhanced our insight into these complex connections, demonstrating significant elements that influence the degree of harm brought by dangerous bacteria and the probability of a host becoming ill. Gaining insight into the biology of bacterial infections by understanding the molecular pathways of host-pathogen interactions not only facilitates our understanding of the biology of bacterial infections but also can be instrumental in identifying new targets of treatment. The information is useful in developing new approaches to treating and preventing bacterial infections including anti-virulence medications, immune-based therapy, and the generation of new vaccines.

Application of zero knowledge proofs makes blockchain technology more privacy-enhancing, without jeopardizing its transparency. Due to the privacy implications of blockchain networks — whose openness of transaction data tends to concern users — zero-knowledge proofs provide a viable alternative that allows the involved parties to verify the validity of a transaction without necessarily exposing any private data. The focus of the examination is on zk-SNARKs and zk-STARKs that are two improved forms of ZKPs that enhance scalability along with privacy. Studies of already established blockchain protocols revealed that the implementation of zk-SNARKs/STARKs and zk-Rollup was successful in enhancing the privacy, safety, and scalability of applications like Zcash and Ethereum, in spite of diverse computational and regulatory challenges.

The research paper examines how the heat retention capacity (LHTES) of phase change material (PCM) can be improved through the use of wavy fins. A numerical study of wavy finned PCMs was conducted using a transient 2D CFD model of the melting of a PCM, including tracking the interface between the PCM and the surrounding media. The study analyzed the effect of the most important geometrical features, including wave height, wavelength and distance between fins, on how the rate of melting changed and natural convection currents developed. The results of this numerical study demonstrated that wavy fins offered better performance than straight fins by reducing the thermal boundary layer at the wave troughs and generating secondary flow patterns (Dean vortices) at those elevation points. This analysis showed that, for the optimal configuration of moderate wave amplitude and spacing, total melting time of the PCM was reduced by ~26.6% compared to straight fin configurations. However, the study presents a hydrodynamic boundary condition, beyond which too much fin spacing reduction or too high wave amplitude will create convective choking conditions and develop stagnation regions, which will impede the buoyancy generated flow and lower thermal performance of the system in the final stages of melting. These results will be useful in developing design criteria for the purpose of optimizing the geometric topology of extended surfaces so that they can achieve maximum heat transfer efficiency for use in Energy Storage Applications that utilize PCM

The work listed here reports extensive numerical computational modelling of hydrodynamic processes and the nonlinear, coupled interactions between plasma and high intensity laser EXPLOsions (electron density dynamics are considered as an offshoot of ponderomotive forces) using numerical methods to model the interaction of the laser with plasma using two fluid (Plasma) relativistic hydrodynamic (RHD) codes coupled with time dependent Maxwells equations in order to compute the space-time evolution of the plasma density spatially at different wavelengths (i.e. < 〖10〗^(-10) m ) as a function of laser intensity (I, W/cm^2) which ranges approximately from 〖10〗^15 to 〖10〗^19 W/cm^2. The model examines the relative importance of ponderomotive and thermal electron fluid pressures while providing no physical justification for all relativistic corrections (i.e. electron mass) or influence of static electric fields. The simulations revealed (+) that where conditions are relativistic (I > 10^18 W/cm2), then the ponderomotive force is dominant and ejects a significant number of electrons from the area surrounding the focal point of the laser (which creates an elongate plasma channel which creates an abrupt change in electron scale-free electron density at the plasma surface free electron density). The simulation indicated there is a well defined critical value for the laser to produce (or near produce) the vacuum-like region along the axis of a focused laser beam which contributes to the formation of a slow light region and will enhance self-focusing of the laser beam, (while also); (-) it was also shown that the sharp increase in density associated with the laser beam is a gross modification of the efficiency of resonance absorption. These findings provide critical theoretical insights into the optimization of laser energy deposition for inertial confinement fusion (Fast Ignition) and laser-wakefield acceleration applications.

Bacterial cellulose (BC) is an extracellular biopolymer of Acetobacter xylinum, which is now known as Komagataeibacter xylinus. The polymer can be characterized as having outstanding purity, lacking lignin and hemicellulose, while exhibiting high crystallinity, superior mechanical strength, and a distinctive three-dimensional nanofibrillar network compared to plant cellulose. In the current study, BC was grown by inoculating a native isolate of K. xylinus in Hestrin–Schramm (HS) medium and incubating without stirring at 30°C over a period of 7 to 10 days. The gelatinous pellicle was created on the air-liquid interface due to the biosynthesis of bacteria.
The collected pellicles underwent an alkaline purification procedure in order to remove the rest of the bacterial cells and medium constituents. Purification was done by placing the sample in 0.5N sodium hydroxide (NaOH) then washing the sample with distilled water several times until a neutral pH was obtained. This procedure ensured the production of high-purity BC membranes.
Structural characterization was done to prove the successful synthesis of BC. The analysis by Fourier Transform Infrared Spectroscopy (FTIR) showed the typical cellulose functional groups (hydroxyl (-OH) and glycosidic (C-O-C) bonds) which are associated with high-purity cellulose. Scanning Electron Microscopy (SEM) showed a dense, interconnected nanofibrillar network with a large surface area, indicative of well-formed BC. Taken together, the findings indicate that high-quality bacterial cellulose could be effectively produced and purified within the local laboratory setting using cost-effective measures, which explains why this bacterial cellulose could be used in environmental cleanup and adsorption research.

Background: The solution of large-scale sparse linear systems remains a fundamental task in numerical analysis, with direct applications in the discretization of partial differential equations (PDEs), computational fluid dynamics, and inverse problems. Classical iterative solvers such as Jacobi, Conjugate Gradient (CG), and GMRES exhibits algorithmic elegance and low memory overhead; however, their practical efficiency is heavily constrained by matrix conditioning and parallel implementation quality. Unpreconditioned variants often converge slowly or stagnate, while naive parallelization can induce excessive communication overhead and numerical non-determinism, compromising reproducibility and scalability. Methods: We present a rigorous, mathematically grounded performance analysis of shared-memory (OpenMP) and distributed-memory (MPI) implementations of Jacobi, CG, and GMRES. Our study integrates theoretical convergence bounds—expressed via the spectral radius ρ(G), condition number κ(A), and polynomial minimization—with empirical benchmarking on matrices from the SuiteSparse Matrix Collection (n=〖10〗^4 to 〖10〗^6). We implement correct parallel variants featuring row-wise domain decomposition, explicit ghost-layer handling, and statistically robust validation (one-way ANOVA, Tukeys HSD, p<0.01). Crucially, we contextualize our hand-coded solvers against the state-of-the-art hypre library, which employs algebraic multigrid (AMG) preconditioning, to establish a reproducible theoretical-empirical benchmarking framework.Results: Our analysis confirms that unpreconditioned solvers require 6–7× more iterations than their AMG-preconditioned counterparts (e.g., 38 vs. 6 iterations for CG on thermal2), directly validating classical error bounds involving κ(A). While our OpenMP and MPI implementations achieve strong scaling speedups of up to 9.8× and 11.4× (on 32 cores), hypre outperforms them by ∼5× due to optimized SpMV kernels, communication-computation overlap, and near-optimal preconditioning. Communication overhead dominates MPI runtime (28% for Krylov methods), aligning with theoretical predictions on global reduction bottlenecks.Conclusions: This analysis demonstrates the primacy of algorithmic sophistication—particularly preconditioning—over raw parallel efficiency in large-scale linear solvers. The integration of numerical analysis (convergence estimates, conditioning theory) with high-performance computing methodologies yields actionable insights: educational implementations serve pedagogical purposes but are unsuitable for production; reproducible comparisons with libraries like hypre are mandatory for meaningful performance evaluation; and future solvers must co-design numerical robustness with communication-avoiding parallelism. Our open-source code and benchmarking data provide a transparent, reproducible foundation for scalable numerical linear algebra research.