Journal of University of Babylon

Background: In clinical oncology, precise segmentation of the target tumor is essential. The positron emission tomography (PET)/computed tomography (CT) scanner effectively combines anatomical information from computed tomography with functional information from PET for accurate tumor identification, which can comprehensively describe tumor volumes, cancer is an acute disease that kills a large number of people around the world, so early detection is a vital need.
Materials and Methods: In this study, proposed techniques, hybrid 2 and the HSV-based hybrid techniques are presented to isolate and extract abnormal regions in PET and PET/CT images, these methods were implemented on eight, images.
Results: Results showed that the applied methods were sufficient to detect, isolate and extract areas of tumor.
Conclusion: The calculated tumor area was compared with that of the radiologist delineation it was most percent relative differences are acceptable, for PET and PET/CT images.

---

Background: Stress has been linked to a number of psychological conditions, including major depressive disorder, schizophrenia, and Alzheimer's disease (AD). Serum serotonin levels and serotonin receptor gene polymorphism were thought to be linked to psychological stress-related illness symptoms.
Materials and Methods: In this study, 60 male patients with psychological stress were divided into 20 groups: those with Alzheimer's, schizophrenia, and major depression. Thirty-nine individuals were included as a healthy group. Serum serotonin levels were measured by ELISA, and the results were correlated with the serotonin 1 A receptor (5-HT1A) (rs6296G/C) gene polymorphism.
Results: The findings indicated a statistically significant (p≤0.05) reduction in serotonin levels across all patient groups when compared to the healthy group. Additionally, the serotonin receptor (rs6296G/C) gene polymorphism results demonstrated that the majority of patients with Alzheimer's and schizophrenia had the highest percentage (50%) of 5-HT1A genotype GC when compared to the healthy group.
Conclusion: It was determined that serotonin levels and 5-HT1A gene polymorphism are related in psychological stress disorders, such as depression, schizophrenia, and Alzheimer's disease.

Nitrogen is basic and necessary element contributing to plant growth and development. It is one of the most nutritious fertilizers used in agriculture. Adding a sufficient amount of Nitrogen will increase production, but the excessive use of nitrogen fertilizers has led to environmental pollution and the deterioration of the ecosystem. The plants use 50% of the added nitrogen and the remaining portion is lost to the environment in various ways, and thus it will affect the farms and pollute the soil, air, and water. Consequently, it has negative effects on humans and animals, as the emission of NH3, N2O, and NO in gaseous form through denitrification and volatilization, which causes dangerous environmental problems, as well as an increase in the value of production due to the loss of fertilizers from the ecosystem. Therefore, nitrogen use efficiency (NUE) must be developed and enhanced through agricultural management to improve plant production. This review describes several factors that participate in the loss of nitrogen from soil-plant systems, such as nitrification, volatilization, filtration, denitrification, ammonium fixation, surface runoff, and stabilization, plant factors, temperature, rainfall and irrigation, tillage, soil texture, Crop residues and soil quality, nitrogen management.

Background: Klebsiella pneumoniae is bacteria gram-negative opportunistic pathogen that causes a variety of infectious diseases.
Aim of the study: The present study aimed to isolate K. pneumoniae, and finding out the extent of the spread of the blaOXA -1 and blaOXA -48 genes responsible for antibiotics resistance.
Materials and methods: the detection of Klebsiella pneumoniae on morphological and biochemical tests and then assured with VITEK-2 system, and genotyping of blaOXA-1 and blaOXA-48 genes in isolates was done by PCR technique.
Results: One hundred and fifty-eight samples were collected randomly from adults who suffers from respiratory tract infection, urinary tract infection, Inflamed burns and wounds including both sexes from different hospitals in Baghdad especially Teaching Laboratories of Medical City, Al-Mahmodia health centre, Al-Yarmouk hospital,and Al-Mahmodia hospital during the period from September to November 2020.Shown the results by VITEK-2 system for all isolates that 50 (31.6%) isolate from the total of isolates were K. pneumonia .Molecular test was detected via Polymerase Chain Reaction (PCR) conventional proved to be effective for the detection of diffuse blaOXA -1 gene at 66.7% and the present results demonstrated a high significant presence at P≤0.0001 among isolates. While the prevalence of 5 isolates of the blaOXA -48 gene was 16.6% showed significant difference between the isolate types with p value ≤ 0.01.
Conclusions: K. pneumoniae was the most bacterial species isolated from patient with pneumonia and our results indicate the spread of the OXA genes.

Background: Proteus mirabilis known for its motility, swimming and swarming motility and involvement in various infections, these two types of motility are important for tissue colonization, thus increase the pathogenicity of the bacteria.
Materials and Methods: Twenty-one isolates from different clinical and animal sources belong to P. mirabilis were collected. The isolates identified using biochemical tests and the Vitek-2 compact system.
Swimmer and swarmer cells media used to detect the effect of antibiotics, sodium azide and tannic acid.
Results: Sodium azide inhibited swimming more effectively than swarming, with higher concentrations (0.1%) showing the greatest inhibition. Tannic acid also inhibited swimming and swarming, with greater effects on swimming cells, particularly from clinical isolates. Swarming motility, especially in animal-derived isolates, exhibited resistance to tannic acid. Additionally, the isolates showed varying resistance levels to antibiotics.
Conclusions: This study demonstrated that, P. mirabilis exhibit different responses to antibiotics, sodium azide and tannic acid according to the type of cells whether it was swimmer or swarmer. Sodium azide and tannic acid inhibit swimming more than swarming cells. Antibiotics resistance patterns was differ according bacterial isolate and type of antibiotic. These finding could be used in future to enhance the activity of antibiotics especially those used to treat gram-negative bacterial infection.

In this review, the methods of synthesis and applications of zinc oxide nanoparticles (ZnO NPs) were presented for the period from 2002 to 2024. Zinc oxide (ZnO) has a high economic value because of its inexpensive cost, natural availability, environmental friendliness, simple manufacturing process, and so on. ZnO has developed novel laser and optoelectronic device approaches that will increase the density of data storage and the speed of optical communication recording. Because of the changes in mechanical, chemical, and optical properties with decreasing size, that is generally assumed to arise from quantum confinement effects who is scale-dependent, there is a lot of research going on with ZnO, particularly in the area of nanotechnology. Low defect density ZnO nanostructures might be useful. Because their stress may be successfully regulated via flexible relaxation on free-side surfaces rather than plastic relaxation, nanostructures are more likely to generate faultless structures than epilayers

Background:
Abnormal PWD expressed the impaired atrial electrical conduction which occurs as a result of structural changes (remodelling) in the atria caused by hypertension )HT). Diastolic function of the left ventricle (LV) have significant role in determination LV filling. Diastolic dysfunction (DD) related with fatality and hospitalization due to heart failure (HF).
Patients and Methods:
The study involved 100 individuals with HT. PWD was calculated from 12- leads surface ECG by determining the disparity betwixt the broadest and thinnest P waves. LV mass and left ventricle mass index ( LVMI) and diastolic function measurements were performed.
Results:
Correlation studies between PWD and parameters of LV diastolic function (E/A ratio and e`) showed statistically highly significant negative correlation (r = - 0.401, r = - 0.244) subsequently, p,< 0.05).
Correlation study of PWD with E/e` showed a positive correlation which was statistically highly significant (r = 0.241, p < 0.05). While the Correlation studies of PWD with left atrial volume (LAV) and left atrial volume index (LAVI) showed statistically no significant positive correlation (r = 0.116, r = 0.029, p,>,0.05).
Correlation studies of PWD with LVM and LVMI: showed statistically highly significant positive correlation with (r = 0.474, r = ,0.340respectively, p,<,0.05).
Conclusions:
This research concludes that PWD obtained from ECG is a nonintrusive determiner, for diastolic function in hypertensive patients, that can reflect LV diastolic function and the effects of hypertension on LV remodelling.

Background: The world problem in medicine is multidrug resistance, so all researchers tend to investigate alternative antibacterial materials, for example, nanoparticles. This research aims to synthesize the MgO nanoparticles by using bacteria.
Materials and Methods: survey the ability of some bacteria collected from the Advance Microbial Lab ten isolates (E. coli, Pseudomonas aerogenosa, and Klebsiella pneumoniae). One isolate from each genus of bacteria was chosen for this research, and 10 ml of magnesium nitrate (Mg (NO3)2.6H2O) was added to the culture broth of E.coli at 37ᵒC in a dark shaker water bath. After 24 hours, two molars of sodium-hydroxide solution were added to the chemical reaction mixture to achieve pH 10. The solution color was changed to brown two hours after the addition of the alkaline solution, then MgO NPs were characterization by, ultraviolet -Visible spectrophotometer wave line at (405-630 nm), energy-dispersive x-ray-analysis (EDX) and scanning-electron-microscopy.
Results: The result of scanning electron microscopy found the diameters of nano were (31-35,104-109,37.43-40,22) nanometer for E. coli, K. pneumoniae, and P. aeruginosa. The MgO nanoparticle minimum-inhibition concentration detected was 125µg/ml, which affected both G+ and G- bacteria.
Conclusion: E. coli, Klebsiella, and Pseudomonas successfully biosynthesized MgO nanoparticles. These nanoparticles exhibited effective antibacterial activity. This suggests that these bacterial strains are capable of producing nanoparticles with potential antimicrobial properties.

Background
The Fabrication an optical sensing devices base on different polishing losses (0.37, 0.68& 1.28dBm) and integrated by Graphene Oxide (GO). The sides polished of single mode Fiber used to resemble D- shape Fiber with area (2mm2) by removing outer jacket and cladding to generate an evanescent field on polishing area that used as optical sensing region. The optical sensing devices integrated by coat D- shape fiber with few layers (~22-layers) of Graphene Oxide (GO) with a thickness of ~ 0.09µm. The Performance D-Shaped Fiber has been done experiment by two test. First test of D-Shaped Fiber without coat done by applied acetone on polished region that show increase in transmitted power. Second test of D-Shape Fiber coated thin layer of graphene oxide (GO) that show increase in transmitted power, thus open promising way to be applied in many applications such as biomedical sensor, humidity sensor, temperature sensor.
Materials and Methods: D- shape Fiber with different polishing losses and integrated Graphene Oxide (GO)
Results: testing of Acetone by applied on polishing area that shows an increase in transmitting power Thus, transmitting power increased immediately due to a change in the refractive index from dry air to Acetone, which caused decreasing in power leakage that covered the sensing region.
Conclusion: The testing D-Shape Fiber that coated with GO shows enhancement, fast response, highly sensitivity and stable measurement (without fluctuation) to acetone camper to D-Shape Fiber only.

Background:
Antiferromagnetic systems exhibit complex quantum behaviours that require accurate numerical methods to analyse. The Stochastic Series Expansion (SSE) is a quantum Monte Carlo technique that simulates these systems. This study examines the effect of the number of bins on the accuracy of physical property measurements using SSE.
Materials and Methods:
The SSE simulations were performed using Fortran 90 on a Ryzen 7 processor. The system was initialised with varying lattice sizes (64, 128, 256, and 1024) and dimensionless temperatures (1/32, 1/16, 1/2, and 4) to analyse different configurations. Nbins and Monte Carlo steps were adjusted systematically to investigate their impact on the results. Results were visualised in Origin Lab.
Results:
Increasing the number of bins (Nbins) reduced fluctuations, leading to reliable results, especially at low temperatures. Lower temperatures lead to higher fluctuations in susceptibility and specific heat of the system. The Neel temperatures were observed around dimensionless temperature. T/J=0.5 Indicating phase transitions.
Conclusions:
The study shows that Nbins are crucial for accurate results in SSE simulations. Although the method is effective for most properties, specific heat calculations require higher computational costs and present limitations in precision.

Water represents the most important and main component on the surface of our planet. It is a basic and essential necessity for all living things on Earth, including humans. As a result, water that humans drink needs to be safe, readily available, sufficient, and uncontaminated. Moreover, water pollution is a severe environmental problem that jeopardizes ecological sustainability, human health, and economic development. Due to natural and human activities, water has become contaminated with various types of polluted. Water pollution by factories, agriculture, and sewage can it make it unfit for human use and is harmful to the environment and causes negative impact on human health because it contains organic and inorganic pollutants. Therefore, humans must work to maintain water quality and treat polluted water. The present review aims to identify water pollution through the use of biological, chemical, and physical indicators. It also classifies water pollution according to their many sources, origins, and effects on human health. Additionally, a brief discussion on different technological used to treat water pollutant

Artificial intelligence (AI) and machine learning (ML) have revolutionized a wide range of fields and sectors. Without explicit programming, machine learning methods allow artificial intelligence (AI) systems to examine massive volumes of data, spot trends, and make data-driven judgments. As a result, decision making has changed dramatically in industries such as marketing, banking, and healthcare, where AI can now deliver insights more quickly and accurately. Ultimately, the combination of artificial intelligence and machine learning has opened up a world of new possibilities by enabling AI systems to continuously learn, adapt, and change. Machine learning's influence on AI is changing how companies run, how services are provided, and how issues are resolved. This signals the arrival of an era in which intelligent systems will propel innovation and advancement in every industry.
This article discusses machine learning methods and impact of machine learning on artificial intelligence, such as making better decisions, personalization, data automation, and predictive analytics. Also, discusses applications of machine learning in artificial intelligence in various fields such as recommendation systems, healthcare, finance, self-driven artificial intelligence, and marketing and sales. In order for the reader to properly engage in this expanding sector, the review attempts to assist him in developing a solid knowledge basis and comprehending how these technologies impact different facets of life.

Background:
Given the importance of early and accurate detection of skin cancer in preventing its risks and improving its treatment, the study aims to develop automated models based on deep learning to detect skin cancer and accurately classify skin lesions into benign and malignant and identify different types of lesions, which helps doctors in early diagnosis and decision-making.
Materials and Methods:
Relying on pre-trained convolutional neural networks, which makes it possible to train models using only a small amount of available training data. Two datasets of images of skin tumors were used, one to detect the presence of a tumor and the other to classify the tumor among different types of skin tumors. Both datasets underwent a set of initialization and processing steps and were then trained using two pre-trained convolutional neural network models.
Results:
The evaluation results of randomly selected images from the set of test images demonstrate the accuracy and high effectiveness of the models in prediction. DenseNet121 hybrid model achieved an impressive 94% accuracy, with an average precision, recall, and F1 score of 0.94. The EfficientNet B0 model achieved a weighted accuracy of 93%, with a weighted average precision, recall, and F1 score of 0.93 across nine classes.
Conclusion:
Pre-trained deep convolutional neural networks have made significant progress in improving the ability to detect skin cancers using dermoscopic images. This study showcases the capability of artificial intelligence in swiftly and precisely diagnosing skin cancer, as well as detecting (pre)malignant skin cancer.

The study examined the effect of the hexane extract from Juncus rigidus roots on the human dermal fibroblast cell line (DHSF) with the aim of exploring and identifying safe therapeutic alternatives for normal cells.
Materials and Methods:
The active compounds (phenols and flavonoids) in the plant extract were quantified, yielding (15.87, 17.33) mg/g, respectively. The DPPH assay, using concentrations of (20, 30, 40) mg/ml, recorded inhibition rates of (21.3, 23.44, 26.24) %, respectively. The amount of reactive oxygen species (ROS) in the root extract was found to be very low, measuring 343 nml. The extract was tested on normal cells of the human DHSF line at five concentrations (25, 50, 100, 200,400) µg/ml and for three durations (24, 48, 72) hours.
Results:
The highest cytotoxicity rate was recorded at a concentration of 400 µg/ml, reaching 34.69% after 72 hours of exposure to the extract, with significant differences observed between the higher concentrations at a probability error rate of P ≤ 0.05. In contrast, lower concentrations showed less inhibition of the cells, with the lowest concentration of 25 µg/ml resulting in a minimal rate of 3.03%. No significant differences were observed between the lower concentrations at exposure times of 24 and 48 hours.
Conclusion:
The extract is relatively safe for normal DHSF cell lines even at the highest concentration used in the experiment, with the highest cell mortality rate being after three days of exposure.

Background Intrusion detection in SCADA systems is essential for ensuring operational security and preventing unauthorized access. Traditional methods often face challenges related to high dimensionality and inefficiencies in accurately identifying threats.
Materials& Methods: This paper introduces a novel methodology that optimizes intrusion detection performance through a series of systematic steps, beginning with data preprocessing to eliminate redundant features, reducing the initial dataset from 35 to 17 features while maintaining critical information integrity. The approach utilizes Principal Component Analysis (PCA) to reduce dimensionality, transforming variables that are related into unrelated main components and retaining 99% of the original data variance. For classification, a Radial Basis Network (RBN) is employed, with parameters such as spread value and the number of hidden layer neurons carefully selected to enhance model performance. To avoid the problem of vanishing gradient, Emperor Penguin Optimization is applied to the training process. It should be noted that the population size and maximum iterations are carefully adjusted for an optimized training procedure. The obtained performance is 99.22% for accuracy, 99.31% for precision, 99.09% for recall, and 99.20% for F1 score.
Conclusion: These results confirm the model's appropriate performance for the detection of intrusions while avoiding any false alarms and validate it as a very efficient solution for strengthening security on SCADA systems.

Background: Diabetic foot infections (DFIs) are identified as localized purulence or inflammation that appears at the site beneath the malleoli in a diabetic patient. The goal of this study was to measure the levels of glutathione (GSH) and superoxide dismutase (SOD) in healthy people in Karbala city as well as in patients with type 2 diabetes mellitus who had or did not have diabetic foot ulcers.
Materials and Methods: This study involved 120 participants ranging in age from 35 to 75 years, and was conducted at the Imam Al-Hassan Center for Endocrinology and Diabetes in Karbala, Iraq. To seperate the serum, five milliliters of the participants' blood were collected, put within gel tubes, and spun at 4000 xg in a cooled centrifuge. The serum was stored at -20˚C in Eppendorf tubes prior to utilizing it in determination of SOD and GSH levels.
Results: Superoxide dismutase (SOD) activity mean levels in DFI group where the result from patient were significantly lower (241.4±35.61) (U / L) (p≤0.001) than in diabetic patients and healthy individuals. Similarly, glutathione (GSH) mean levels in DFI group patients were lower than in diabetic patients and healthy individuals (26.586±2.77) µmole/ml (p≤0.001).
Conclusion: These results imply that diabetic foot infections may be influenced by decreased antioxidant levels. This suggests that these antioxidants may be used as possible markers to diabetic those who are more vulnerable.

Background:
Solar energy devices, after a time has passed since their use, become older, their exposure to failure increases, their maintenance costs increase, and their production decreases. Thus, one solution is to replace them with a new device; this is referred to as preventive device replacement.
Materials and Methods:
When a solar panel fails, its life is over, and it is replaced. Alternatively, when it reaches the age ????1, a preventative solar panel replacement is implemented.
Results:
This work aims to identify the optimal time for the preventive replacement of solar energy devices, as the continued operation of an old device increases costs and necessitates a replacement that reduces maintenance costs and maintains the device's production continuity. The current research identifies the optimal time for preventive replacement of solar energy devices synthesized from n similar tubes, as their failure and replacement costs increase as they age. As for devices consisting of large solar panel, the cost of panel failure is expensive and replacing them is expensive.
Conclusions:
The researcher studied the best time to replace panel with new panel, and that is before the panels fail. At the end of the paper, we identified some industrial and economic decisions that affect the life of replacing these two types of devices.

In many countries around the world, agriculture plays a crucial role due to rapid population growth and the resulting increasing demand for food. Therefore, there is an urgent need to improve crop quality, which has a clear impact on increasing the economic and financial growth of farmers. Important factors contributing to the decline in crop quality are diseases caused by bacteria, viruses, fungi and other agricultural pests. The impact of these diseases can be mitigated using plant disease detection techniques based on artificial intelligence techniques. Transfer learning models in such cases are particularly useful for early identification and detection of these diseases, as they are specifically data-centric and prioritize specific outcomes related to the task at hand. This study provides a comprehensive overview of the different stages of the general plant disease detection system and a comparative analysis of the temporal model used to classify plant diseases. This analysis aims to enhance agricultural economic growth and provide tangible benefits to farmers and agricultural businesses, which have a direct impact on the financial and economic income of countries.

---

Background Beans are an important seed legume crop and are a staple food for millions of people in poor countries. With the increasing demand, there is a large gap between production and consumption of the bean crop. The study aims to investigate the impact of fertilization on the growth and yield of beans.
Material and method: During the spring of 2023-2024, scientists performed a field experiment in the Muwailiha region, Babil Governorate. Using three fertilizers the first factor represents Mineral fertilization at three levels (0, 50, and 100)% of the fertilizer recommendation, and the second factor EM1 biofertilization at three levels. The third factor is spraying with Nano-iron at three levels, using a randomized complete block design (RCBD).
Result: The 50% treatment significantly exceeded the fertilizer recommendation for mineral fertilizer and gave the highest average characteristics of plant height, number of pods, and number of seeds per pod. The total yield of the plant is (103.46 cm, 21.52 pods -1, 4.61 seed pods -1, g of plants-1, and 193.72 g) respectively. moreover, treatment without adding mineral fertilizer gives the lowest averages. The triple intervention treatment (50% fertilizer recommendation + spraying nano-iron at 2 g L-1 + EM1 20 gL-1) excelled significantly and gave the highest averages for all the studied traits.
Conclusion:
Using the treatment 50% of the fertilizer recommendation for mineral fertilizer was significantly superior in addition to the triple intervention treatment and gave the highest averages for all the studied traits.

Background: Extended exposure to antimicrobial drugs can lead to genetic changes in pathogens, resulting in resistance to specific antibiotics and posing significant public health challenges. This study aims to address this issue by synthesizing and characterizing a novel Co(II) complex with ((2,2'-(7,7'-dimethyl-2,2'-dioxo-1,1',2,2'-tetrahydro-3H,3'H-5,5'-biindole-3,3'-diylidene)dihydrazinecar bothioamide), denoted as ligand(L). The potential antibiotic activity of this complex is evaluated in vitro against three pathogens: E. coli ATCC 25922, P. mirabilis ATCC 14153, and S. pneumoniae ATCC 49619.
Methods: The novel Co(II) complex [Co2 (L)Cl2] was synthesized by reacting one mole of L with 2 moles of CoCl2, yielding the general formula [Co2LCl2]. Characterization was performed using FT-IR, 1H-NMR, UV-visible spectra, and elemental analysis (CHNS). The antimicrobial activity was evaluated by determining the Minimum Inhibitory Concentration (MIC) and Minimum Bactericidal Concentration (MBC). Additionally, the synergistic effects of L and [Co2(L)Cl2] complex with ciprofloxacin (Cip) were tested against E. coli and P. mirabilis.
Results: The characterization confirmed the structure of the [Co2(L)Cl2] complex, indicating successful coordination via nitrogen, oxygen, and sulfur, exhibiting a square planar geometry. Both the L and [Co2(L)Cl2] complex exhibited antimicrobial activity against the tested pathogens. Additionally, when used in combination with ciprofloxacin, both the L and the [Co2(L)Cl2] complex showed synergistic effects against E. coli and P. mirabilis, with the [Co2(L)Cl2] complex exhibiting greater efficacy.
Conclusions: The novel [Co2(L)Cl2] complex demonstrated higher antimicrobial activity than the L and showed synergistic effects with ciprofloxacin against specific pathogens. As a result of this study, these two compounds show potential as antibacterial drugs.

Background:
The impact of laser light and pulse duration on the hydrodynamic properties of plasma was studied in terms of electron temperature (Te), ion temperature (Ti), and ion rate (Z*).
Materials and methods:
The study was carried out in two stages: in the first phase, five energy levels of the Nd:YAG laser (1, 3, 5, 7, 9 J) and a pulse duration of (10 ns) were used at Full Width at Half Maximum (FWHM). In the second phase, five energy levels of the CO2 laser (1, 3, 5, 7, 9 J) with a (10 ns ) pulse duration .
Results:
It was observed that at energy values of (1, 3, 5, 7, 9) J for the Nd:YAG laser with a 10 ns pulse duration, the electron temperature ranged from (214.79 to 690.97) eV. While the elec-tron temperatures as a result of Co2 laser is ranged between (84.93 to 414.99) eV.
Conclusion:
We observe the electron temperatures of platinum plasma produced as a result of the ND:YAG laser was produced with with the same levels were higher than the electron temperatures of platinum plasma produced using a CO2 laser.

Background:
Fixed-point theory is an important area of study in pure and applied mathematics, and it is a flourishing area of research in the paperwork, the concept of fuzzy S-metric space is introduced, and The fixed point theorem's existence and uniqueness over fuzzy S-metric space are examined. We will go over The fixed point theorem's existence and uniqueness for k contractive mapping in fuzzy S-metric space. These theorems will be seen in fuzzy S-metric space. fixed point theory The transcendence is the main cause of the fixed point theory's recent resurgence. The significance of this theory and the questions it allows us to address regarding the dimensions of space. Proof of the existence of solutions to the physical equations is possible thanks to the notion of the fixed point.
Materials and Methods:
We will discuss the existence and uniqueness of the fixed point theorem by using k contractive mapping in fuzzy S-metric space.
Results:
This study proved the existence and uniqueness of new fixed point theorem in fuzzy S-metric space using k contractive mapping.
Conclusions:
According to this study, we were able to prove the existence and uniqueness of fixed points in s-metric space by using k-fuzzy contractive mapping.

Background:
This study aimed to identify the impact of the information technology governance on the quality of the financial reports in the Iraqi commercial banks: The modified role of cloud accounting and the need to activate the mechanisms of information technology governance to reduce the risks accompanying the use of the information technology
Materials and Methods:
For achieving the goals of the study, the researcher adopted the descriptive and analytical approach in addition to a questionnaire collected and analyzed by the (SPSS). The study sample consisted of (251) male and female employees in the information technology and accounting departments in the commercial banks.
Conclusions:
The study concluded that, a statistically significant impact at sig. (α≤0.05) for the information technology governance with its dimensions (planning and organizing, owning and implementing, support and delivery, follow-up and evaluation, direction and control) on the quality of the financial reports in the banks. The study also found a statistically significant impact at sig. (α≤0.05) for the cloud accounting as a modified variable on the relationship between information technology governance and the quality of financial reports in the banks, where the addition of the modified variable (cloud accounting) to the regression model.
The study recommends that the departments in the commercial banks be interested in informing all the employees of their roles within the framework of the use of information technology in a clear and written manner.