Journal of Basrah Researches (Sciences)

The use of Artificial Intelligence (AI) in the field of mathematics education as a novel tool has introduced new capabilities in the learning and teaching processes. This technology not only assists teachers and students in enhancing the learning process but also provides the latest teaching methods.
One of the main advantages of AI in mathematics education is the ability to offer personalized learning. By analyzing individual data related to each student's learning style, AI systems can precisely adjust educational programs. This implies that each learner will follow a learning route that is suited to their knowledge level and needs. These technologies can help to diversify the learning process, catch students' interest, and boost their excitement for active engagement in the learning process. In this post, course content was personalized using the Protus 2.1 educational system.

Staphylococcus aureus causes critical infections in humans such as urinary infections, mastitis, pneumonia, meningitis, endocarditis, and osteomyelitis. It represents the main cause of nosocomial infection in surgical wounds. This study aimed to investigate the prevalence of S.aureus resistance and its virulence by using genetic markers. 150 samples were collected from vagina swabs for pregnant and nonpregnant women admitted to Ibn Al-Baladi and Al-Imamain Al-Kadhimain hospitals/Iraq. The study was performed from July to October 2023.The results showed that 13 isolates of S.aureus have mecA genes at ( 86.7%),11 isolates have icaA gene (73.3%), and 8 isolates have the sea gene (53.3%), while hla gene was found in all 15 isolates (100%). S.aureus isolates showed histopathological changes in liver ,lung and vagina of mice .There was a prevalence of methicillin resistance S. aureus due to the presence resistance gene in most isolates. S.aureus isolates in this study showed their virulence through the occurrence of histopathological changes in liver ,lung and vagina .

We introduce the latent diffusion model used in medical ultrasound image synthesis. We point out that precision is the issue, and installing ultrasound images was completed with an accuracy of 97.47% since ultrasound demands greater accuracy. It has some particular disadvantages because it operates in real-time and requires operator settings. Considering these challenges, our model has a lot of promise to provide accurate and lifelike ultrasound images. Even though it is hard to calculate the precise answer for this optimization, applying the backpropagation method merely once can produce an approximation. In order to train a diffusion model with the value and outcomes (FID: 2.870, CLIP: 0.209, SSIM: 0.9923, and LPIPS: 0.92) that we promised, we generated synthetic images of roughly 300 ultrasound images. were acquired. Expanding the use of artificial intelligence in medical imaging is the aim of this endeavor. Since this is a novel problem, the study will serve as a foundation and source of inspiration for researchers looking into possible applications of diffusion models in medical image production. The URL https://www.kaggle.com/datasets/suraahmed56/computer-vision-medical-images provides access to synthetic images.

A total of 24 species were recorded in Al-Hawizeh marsh during from June 2004 to Novmber 2018, belonging to 11 families and 22 genera, including 16 native species and (8) alien species, of which 17 species were recorded in the first study (2008) and 16, 13, 15, and 19 species were in the second, third, fourth and fifth studies2019)) respectively, Cyprinidae family recorded the highest number with nine species. Eleven common species were recorded between the studies. The cluster analysis results of the species composition in the Al-Hawizeh marsh using the Jaccard Similarity Index showed three main groups. The highest similarity percentage was among the first and second studies, which amounted to 88.2, and the lowest was between the fourth and fifth studies, which amounted to 50.0.

Globally, technology has developed in various sectors to improve individuals' quality of life and safety. Monitoring and surveillance systems are critical components in different industries' infrastructure. Monitoring and surveillance systems based IoT have enhanced promptly in recent years, integrating with sophisticated technologies such as blockchain, deep learning, cloud computing, and edge computing. To the best of our knowledge, there are few reviews in the field of monitoring and surveillance-based blockchain. For that reason, we conducted a literature review to discuss different methods for addressing security and privacy problems in monitoring and surveillance systems based IoT utilizing blockchain technology. Our research divides the papers into five sections which are surveillance systems, authentication mechanisms, artificial intelligence, security mechanisms / Monitoring in different smart industries, and detection mechanisms. It also focuses on the use of blockchain technology and its types, the employment of external data storage that integrat with the blockchain for supporting its storage, and the type of tools used, to compare the previous studies. Additionally, the review reaearch compares current methodologies in terms of shortcomings such as lack of security analysis, performance evaluation, malicious attacks, and data security during transmission. Our research introduces a comparison that includes security features such as mutual authentication, Anonymity, the employing of external storage, performance analysis Also, the research gave a summary and analysis of previous works. Lastly, the study benefits beginner researchers in terms of saving time and effort.

Salmonella, a bacterial genus, is responsible for salmonellosis, a global disease that can impact both humans and animals. 80 clinical specimens were collected from individuals displaying signs of salmonellosis in Basrah province. The Salmonella isolates were obtained and identified using bacterial culturing on suitable conditions and validated techniques, such as the Vitek2 system and 16S rDNA gene amplification. A study was conducted to assess the susceptibility of antibiotics using the CLSI 2022 guidelines. The findings indicated that 7 specimens of the 80 patient specimens examined, tested positive for Salmonella. Depending on the method used to sequence the 16S rDNA gene's nucleotide sequence. Just six isolates were submitted to register their sequences in the Gene Bank-NCBI in order to diagnose them at the species and serovars level, receive accession numbers, and then determine their phylogeny. Salmonella Typhi is represented by four accession numbers (LC773417.1), (LC773418.1), (LC773419.1), and (LC773420.1). Salmonella Typhimurium is denoted by a single accession number LC773421.1. One accession number LC773422.1, is listed as Salmonella enterica only. Conclusion: PCR demonstrated a notable degree of precision when it came to identifying Salmonella spp at serovars level. Salmonella sreovars exhibiting multi and extensively drug resistance were successfully isolated and identified.

Background As a measure of a man's fertility, PAPP-A levels have lately been in the spotlight. Although most studies have focused on its role in pregnancy issues such low birth weight and gestational age, its potential impact on male fertility is starting to get more attention. Aim to determine if it is possible to use the levels of pregnancy-associated plasma protein-A (PAPP-A) in men's blood as a biomarker. The study's goal is to learn more about these levels' prognostic power and clinical assessment use by looking at the correlation with reproductive indices. Results Although there is a lack of evidence linking PAPP-A to sperm quality, there is some evidence that PAPP-A levels are associated with male reproductive problems. Future studies should look at the possibility of a stronger link between PAPP-A and male infertility. As a predictive biomarker, PAPP-A has a number of limitations when compared to other, more established fertility indicators like VAP-1. To summarize more study is needed to establish the reliability and therapeutic value of PAPP-A, while preliminary results indicate its promise as a biomarker for male fertility prediction. It will be vital to this effort to understand the possible pathways that link PAPP-A to male fertility. So, it's necessary to dig more into the link between PAPP-A levels and male infertility. As a conclusion: PAPP-A levels must be compared to other biomarkers to predict male fertility.

The emergence of such widespread pharmaceuticals as a pollutant has become one of the world\'s critical environmental problems that may lead to both the public\'s health and biodiversity deterioration. This article provides an exhaustive account of the current understanding of the environmental persistence of pharmaceutical contaminants following in-depth analysis of the additive effects of existing natural biodegradation pathways on the human health impact of these drugs. Paying special attention to biodegradation decomposing agents such as bacteria, fungi, and algae the paper estimates their ability to convert drug ingredients to compound that is eventually less toxic. Although these biologic systems contain an enormous potential for killing the unwanted pollutants, the variability in the complexity and endurance of the pharmaceutical substances overburden the degradation capabilities of these organisms thus necessitating improved biodegradation methods. Addressing the above-mentioned environmental factors, which include temperature, pH, and the occurrence of other contaminants, play a crucial role and have a direct impact that on the process of biodegradation, enhancing pollutants removal rate. To sum up, this paper enables the environmental science, microbiology and bioengineering enables creating progressively more functional and sustainable techniques of neutralizing these long-standing toxins; thus, protecting ecosystems, as well as human health.

The study explains the effect of the copolymer overlay ratio on optical properties that can be utilized in solar cell applications. Copolymers prepared by additive polymerization were characterized. The X-ray examination results characterized the samples and showed that the prepared samples of pure and composite polymers possess a high degree of crystallinity. The prepared samples were also characterized by FT-IR spectroscopy, which showed that these films had clear active groups. The optical properties of all prepared films were also studied, such as absorbance within the spectrum range (200 - 800 nm) as a function of wavelength, and Band gab Energy Eg, Excitation Energy Eo, Dispersion Energy Ed, Refractive index (no) 2, Dielectric constant at high frequencies E∞, Moments of The Optical Spectra for Crystal M-1 M-3, The Average Oscillator So, Third-order visual effect X^3. The study showed that the absorbance spectrum of the films of the materials under study is within the visible spectrum region, where the most significant peak was recorded for the pure and composite polymers at the wavelength of 485 - 495 nm. The study showed that the optical energy gap of P3HT polymer (2.78 eV) and for Thiophene (2.3 eV) and the value of the gap decreased with the doping rate increased until it reached (1.88 eV) at the copolymer rate of 70 % P3HT 30% thiophene. It showed varying thicknesses ranging from approximately 49.1 to 10.2 nanometers, and the recorded roughness of the polymer surfaces ranged from 20.7 to 5.19 nanometers.

In this study, we developed a theoretical model to study the effect of electron-phonon coupling on the energy gap between the HOMO-LUMO levels in an artificial molecule composed of six quantum dots with a geometric shape similar to the benzene ring, where three different configurations of the benzene ring were studied (para, meta, and ortho) depending on the location of the donor and acceptor atoms, and the effect of different locations of the phonon bath was also taken into consideration. Our model is illustrated, through a comprehensive and compact Hamiltonian approach, that specifies the dynamics of electronic and phonon states within these quantum systems. Our results demonstrated the important role of phonon coefficients in modifying the electronic properties of molecules. Based on these results, we observed that the electron-phonon coupling strength and phonon energy can be manipulated to modulate the energy gap, providing promising ways to develop advanced nanoelectronics applications.

Composite materials with potential applications
were formed by reinforcing polymeric blends containing 70% unsaturated polyester (UP) and 30% natural rubber (NR) with zinc oxide nanoparticles (ZnONPs) at weight fractions of (0, 1, 1.5, 2, 2.5, and 3%). The morphology and crystalline structure of ZnONPs were examined by using scanning electron microscopy (SEM) and powder X-ray diffraction (XRD) techniques. In addition, the ZnONPs sample exhibits a remarkably crystalline and wurtzite crystal structure, as indicated by the X-ray diffraction results. The antibacterial activity of the synthesized nanocomposites were evaluated against two types of bacterial growth , namely S. aureus ATCC 25923 and E. coli ATCC 25922, with the aim of assessing their Potential applications. The experimental findings demonstrated that ZnONPs nanocomposites showed antibacterial activity against E. coli and S. aureus bacteria. The cytotoxic assessment of ZnONPs on healthy white blood cells (WBCs) was tested.The results indicated that ZnONPs do not show any toxic effects on normal white blood cells. Finally, cytological criteria, such as the blastogenic index (BI), mitotic index (MI), and total chromosomal abnormalities (TCA) were used to evaluate the genotoxic potential of ZnONPs against peripheral blood lymphocytes (PBLs). In addition, the toxicity of metal nanoparticles on various mammalian cells has been assessed

To solve a lacunary interpolation problem with a range of (0, 2, 4), we first look at an appropriate class of insufficient splines. Under suitable assumptions, we prove the existence and uniqueness of the solution. We describe the convergence analysis and show that the approach is of order six and that it can be constructed of any order, with any intermediate lacunary requirements.

With continuously rising morbidity and mortality, the scientists focus their efforts to outcome novel therapeutics due to improve cancer patients’ survival. The major undesirable features of conventional therapies were low selectivity, high toxicity, drug resistance, and economic expenses. Opposite, natural compounds of medicinal plants play a crucial role in treatment of several diseases. Currently, many studies investigated the potential of secondary metabolites to fight cancer cells through enhanced apoptotic cell death or energy production pathway as a targeted and smart therapy strategies. The study aimed to evaluate and investigate of antitumor activity of Citrullus colocynthis fruit oil extraction against cancer and normal cell lines. AMGM-5 human glioblastoma and HBL-100 human breast epithelial cells were used to determination cytotoxic effect of C.colocynthis oil by MTT assay and detection of IC50 dose. Furthermore, this study evaluated apoptosis using fluorescent staining. In addition, we investigated of cell migration, invasion, and rapid rate proliferation of cancer cells by wound healing assay. The results showed significant impacts of essential oil accompanied by toxicity, apoptosis, and inhibition of cell proliferation, migration, and invasion against cancer cells, but, normal cells not affected. Our findings suggest natural compounds have possible cancer drugs without side effect on normal cells.

In this study, casting method has been used to create films by mixing hydrated copper sulfate CuSO4.5H2O with three different concentrations 0.5,1 and1.5 wt% with a combination of PVA/PVP in a 1:1 ratio. The three different samples of different concentration of 0.5, 1 and 1.5 have been denoted as case a, b and c respectively. In order to evaluate and confirm the crystalline quality of the final mixture, PPCu(PVA/PVP/CuSO4.5H2O), the required physical measurements were carried out, including x-ray investigations. Also, an FT-IR analysis was used to investigate the structure and chemical composition of the mixture for the three cases. The film's morphology was studied by use of SEM equipment, which showed that the PPCu mixture's particle distribution was uneven. The thermal conductivity of the mixture at different temperatures was measured using the hot disk technique. The results showed significant influence of CuSO4.5H2O concentration on thermal conductivity.

In this work, the reproducing kernel Hilbert space method (RKHSM) was used to find a numerical solution to nonlinear integro-differential equations (NIDEs) on the form of finite series. The results showed that the approximate solution are converges to the exact solution of the NIDEs, which confirms the effectiveness of RKHSM as a reliable and practical tool for solving this type of e. This method is of great importance in the field of numerical solutions of NIDEs, which has wide applications in many engineering and scientific fields

Machine learning (ML) is increasingly indispensable in modern medicine, particularly for disease prediction and improving patient outcomes. This study applies ML techniques to predict thyroid disorders in diabetic patients, a critical task given the frequent co-occurrence and complex interplay between these conditions. six ML classifiers namely Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), and Naive Bayes (NB) were evaluated across three experiments on a local dataset: (1) a balanced dataset using Random Under-Sampling (RUS), (2) a subset of Type 2 diabetes (T2D) patients, and (3) a subset of Type 1 diabetes (T1D) patients. Random Forest classifier consistently outperformed other classifiers, achieving the highest accuracy (0.85) and F1-score (0.83) in the T2D-focused dataset and showing robust performance on the balanced dataset using RUS. These results highlight the suitability of Random Forest for deployment in clinical settings and underscore the importance of balancing techniques like RUS in improving predictive accuracy. However, challenges remain in predicting thyroid disorders among T1D patients due to the low prevalence of thyroid disorders in this group. The findings reinforce the potential of ML in advancing diagnostics and personalized care in diabetic populations.

A case-control study was carried out at Wasit province, from July 2023 to June 2024, included 50 specimens of patients with rheumatoid arthritis were under treatment, and 50 specimens of control were collected. The serological tests were done including the Anti-ccp Abs test by an immunoenzymatic technique and RF, ESR, and CRP tests by a fluorescence Immunoassay (FIA) technique. Moreover, the ratio of females to males in the present study was 2.5:1. We found significant differences between patients and controls in all serological RF, Anticcp, CRP, and ESR tests. According to hematological tests, patients had a statistically significant lower mean Hb than controls, the mean WBC count was significantly higher in patients than in the control group, and platelets were not statistically different between the two study groups. In conclusion, the demographic, hematological, and serological characteristics of rheumatoid arthritis in Wasit province exhibited similarities to those observed in various national and international studies. The current research can help to increase understanding of the RA disease by analysing different clinical markers associated with the condition.

This study used a long-term toxicity test to investigate how the microalga Chlorella vulgaris responded to nanopolysteren exposure under different light spectrums. Our experiment involved subjecting algae to polystyrene doses of 0, 0.5, 5, and 10 mg/L. We design an incubator consisting of three sections, each offering a distinct red, blue, and white light spectrum, for the purpose of cultivating algal colonies. Chlorophyll-a and b were estimated at consistent time intervals (1, 4, and 10 days) during the 29-day incubation period, using three replicate samples for each treatment. Experimental results have demonstrated a reduction in both algal cell viability and chlorophyll concentration when exposed to (5 and 10 mg/l) of nanopolysterene. The findings of our study indicate that the growth and chlorophyll content of C. vulgaris are significantly affected by the red-light spectrum and with the increase of nanopolyesterne concentrations.

Clustering is one of the modern techniques that have been discovered to solve the problem of the degree of similarity and dissimilarity between data within the network. Clustering originates from unsupervised techniques whose main function is to organize data into subsets based on the degree of similarity between these data. The research conducted an analytical study on Fuzzy C-Means (FCM) and Gaussian Mixture Model (GMM), which are considered the most prominent clustering techniques and aims to compare them in terms of the time taken by each algorithm to cluster the data and the energy consumed. Experiments were conducted in four different scenarios. The experiments concluded that GMM showed variation in energy consumption when the number of clusters gradually increased, while FCM showed clear stability in most cases. In terms of time, GMM was generally faster with fluctuations in performance, while FCM's performance was stable but relatively slower. Ultimately, each algorithm is used in a specific environment. GMM is fast with fluctuations in performance, which is useful in applications that require speed in performance, unlike FCM, which is relatively stable but slower, which is useful in applications that require accuracy in results at the expense of time.

The research focused on the identification of a newly recorded species in Iraq, particularly in the southern region, through the collection of fifteen samples from seven locations within the Basra Governorate, situated northwest of Al Shafi District. The sediment types of these samples were analyzed, alongside the identification of foraminifera species and their relative abundance in the area under investigation. The grain size analysis of the sediments revealed the presence of four sediment types: mud, clay, sandy silt, and sandy mud, with mud being the predominant type in the region. The identified foraminifera species was Aubignyna perlucida, a benthic foraminifera that inhabits brackish to marine environments.. Its presence in the study area serves as significant environmental evidence of the marine influence on southern Iraq, particularly within the sediments dating back to the Late Holocene epoch.

Kaempferol is an active native flavonoid, has long been recognized for its curative effects. It has been observed to possess numerous pharmaceutical characteristics. High performance liquid chromatography (HPLC) was used as a quantitative evaluation method to determine the best extraction solvent for obtaining Kaempferol from Prosopis juliflora leaves. In this study, ethanol, ethyl acetate and methanol were used as solvents in the extraction of Kaempferol flavonoid from Prosopis juliflora by employing traditional methods of extraction (reflex and maceration). The plant leaves extract of Prosopis juliflora showed existence of Kaempferol compound. Among the three solvents used, it was obvious that ethyl acetate extract of leaves showed the most significant amount of Kaempferol (92.54 mg.L-1). In accordance to the HPLC results, reflex was the most efficient technique for extracting kaempferol from Prosopis juliflora, and ethyl ethanoate was the best solvent. This information may contribute to develop this flavonoid as a possible agent for the prevention and treatment of some diseases.

The use of efficient machines and algorithms in planning, distribution, and optimization methods is of paramount importance, especially when it comes to supporting the rapid development of technology. Cluster analysis is an unsupervised machine learning function for clustering objects based on some similarity measure. In this paper, we review different types of clustering algorithms for clustering data of different sizes and their applications. This survey reviews five primary clustering approaches—Partitioning, Hierarchical, Density-Based, Model-Based, and Grid-Based clustering—highlighting their strengths, limitations, and suitability for location-based optimization. Each algorithm is evaluated on key performance criteria, including noise handling, computational efficiency, scalability, and the ability to manage spatial constraints. Key evaluations demonstrate that DBSCAN achieved an average silhouette score of 0.76, indicating strong cluster cohesion and separation, while K-Means showed the fastest computational time for datasets under 10,000 points. The Grid-Based method excelled in scalability, handling datasets exceeding 1 million points with minimal computational overhead. Case studies and real-world applications demonstrate the practical utility of these algorithms in optimizing center placement across diverse industries. The results provide valuable insights for practitioners and researchers seeking to improve distributed network design, resource efficiency, and location optimization using advanced clustering methodologies.

The accelerating pace of adoption of decentralized applications requires the use of efficient, high-performance blockchain infrastructures. These blockchains are supported by consensus algorithms that are critical determinants of scalability, transaction speed, costs, and security. Developers are still unaware of the most useful options because there is a large gap in information regarding the comprehensive evaluation of these algorithms in practice. This study aims to test and compare the performance of the three most popularly used blockchain consensus protocols - Proof of Work (PoW), Proof of Stake (PoS) and Delegated Proof of Stake (DPoS) with the aim to improve the application of decentralized systems. By deploying smart contracts on real blockchain test networks (Callisto for PoW, Sepolia for PoS, and Tron Nile for DPoS), the research explores key performance metrics such as block time, deploy gas fee, block gas limit, and block size. The experiments utilize tools like Remix IDE and Tron-IDE, highlighting the practical implications of consensus algorithms under varying real-world conditions, including network congestion and transaction volatility. Tron Nile excels in speed and capacity but incurs high gas fees, Sepolia balances performance with moderate costs, and Callisto emphasizes cost efficiency at the expense of speed and scalability. Insights derived from this study provide valuable guidelines for developers to choose suitable consensus mechanisms based on the specific requirements of decentralized applications.

In this study five compounds were synthesized that includedsuccinimidederivatives. New compounds (S1-S5) were produced by the reaction of hydrazide derivatives with N-substituted maleimide and were diagnosed by mass spectrometry, NMR spectra of 1H and 13C, infrared spectroscopy, and the melting point of the generated compounds. Five substances wereexaminedfor their ability to inhibit breast cancer (MCF-7) using the MTT assay. When tested on breast cancer cells, the substances S3, S4, and S5 showed anti-cancer activity.

In this study five compounds were synthesized that includedsuccinimidederivatives. New compounds (S1-S5) were produced by the reaction of hydrazide derivatives with N-substituted maleimide and were diagnosed by mass spectrometry, NMR spectra of 1H and 13C, infrared spectroscopy, and the melting point of the generated compounds. Five substances wereexaminedfor their ability to inhibit breast cancer (MCF-7) using the MTT assay. When tested on breast cancer cells, the substances S3, S4, and S5 showed anti-cancer activity.

The present study aimed to evaluate the role of thyme oil in reducing the histopathological effects in the kidneys of common carp (Cyprinus carpio) exposed to two different concentrations of cadmium chloride (16.86 and 56.2 mg/L). The experiment included 6 groups, each containing 16 fish: G1, G2, G3, G4, G5, G6. The results showed the presence of histopathological changes in the kidneys of fish. They were more severe in the two groups treated with cadmium chloride only. They decreased in the two groups treated with cadmium chloride and thyme oil together. The study also showed that the longer exposure period increased the severity of the changes in the two groups treated with cadmium chloride only, especially after 30 days from the beginning of the experiment. Histological changes included dilatation of renal tubules, expansion of Bowman's capsule space, necrosis and degeneration of endothelial cells and renal tubules, increased mast cells and melanomacrophage aggregates. In contrast, histological changes were less severe in the two groups treated with cadmium chloride and thyme oil. This indicates the protective role of thyme oil in reducing the severity of histopathological changes.

The use of efficient machines and algorithms in planning, distribution, and optimization methods is of paramount importance, especially when it comes to supporting the rapid development of technology. Cluster analysis is an unsupervised machine learning function for clustering objects based on some similarity measure. In this paper, we review different types of clustering algorithms for clustering data of different sizes and their applications. This survey reviews five primary clustering approaches—Partitioning, Hierarchical, Density-Based, Model-Based, and Grid-Based clustering—highlighting their strengths, limitations, and suitability for location-based optimization. Each algorithm is evaluated on key performance criteria, including noise handling, computational efficiency, scalability, and the ability to manage spatial constraints. Key evaluations demonstrate that DBSCAN achieved an average silhouette score of 0.76, indicating strong cluster cohesion and separation, while K-Means showed the fastest computational time for datasets under 10,000 points. The Grid-Based method excelled in scalability, handling datasets exceeding 1 million points with minimal computational overhead. Case studies and real-world applications demonstrate the practical utility of these algorithms in optimizing center placement across diverse industries. The results provide valuable insights for practitioners and researchers seeking to improve distributed network design, resource efficiency, and location optimization using advanced clustering methodologies.