Journal of Education for Pure Science-University of Thi-Qar

In this work, we investigated the generalized Laplace transform method (GLTM) for obtaining an exact solution to some linear partial differential equations of fractional order. It is shown that under specific conditions for our method, other related methods are deduced. Theorems about the existence and uniqueness of transform is established. Furthermore, we provide examples to demonstrate the method’s applicability.

Finding the ideal number of superior features to optimize the learning algorithm\'s performance is the ultimate aim of feature selection. However, when a dataset\'s features count rises, this issue gets more difficult to solve. The Marine Predators Algorithm (MPA) is a novel metaheuristic that has proven effective in solving many optimization issues.In MPA, the fundamental exploratory and exploitative procedures are modified to choose the best and most significant features in order to get the most accurate categorization. The outcomes demonstrate the exceptional ability of the suggested MPA-SVM strategy to choose the most important and ideal attributes. Support vector machines (SVMs) are an essential method that are skillfully used to address classification problems. In this work the MPA is adjusted using the SVM as classifier. The present study proposes MPA-SVM as a solution to the issue of feature selection in high dimensional datasets. The suggested method\'s efficacy was confirmed using ten high-dimensional datasets acquired from Arizona State University (ASU) repository. The proposed method was compared with six state-of-the-art feature selection algorithms, including ASO and EO, demonstrating superior performance. Standard deviation is a measure ofthe robustness and stability of optimization algorithms, it is used in MPA-SVM and achieved lowest values. Across all datasets, MPA-SVM produces the lowest average error rates, minimum classification standard deviation (STD) values , FS rates and runtime.

This paper present a new concept of intuitionistic fuzzy rectangular n-normed spaces, with some fundamental definitions. Subsequently, we present the Cartesian product of intuitionistic fuzzy rectangular n-normed spaces, study its effect on the properties of intuitionistic fuzzy rectangular n-normed spaces, and prove that the Cartesian product of intuitionistic fuzzy rectangular n-normed spaces is also an intuitionistic fuzzy rectangular n-normed spaces. Finally, we establish the completeness of the Cartesian product of complete intuitionistic fuzzy rectangular n-normed spaces and provide some theorems related to these spaces.

Nowadays, one of the most important uses of molecular biology for disease detection is microarray data analysis. Gene selection, which searches for a subset of genes with the lowest internal similarity and highest ortant task in microarray data processing. Data dimensionality can relatedness to the target class, is an impbe reduced by removing duplicate, annoying, or unnecessary information. This study uses graph theory to ls, this study proposes support the gene selection strategy for disease diagnosis. To improve diagnostic skila gene selection technique to evaluate DNA microarray data that takes advantage of graph theory and social network analysis. Gene selection effectively addresses this problem because it reduces computational ing the classification accuracy of microarray data. This research proposes a unique complexity while increasgene selection method based on social network analysis. The two primary goals of the proposed approach genes. This algorithm iteratively are to reduce redundancy and increase its relevance for the selected determines the maximum population size in each cycle. Then, using node clustering, relevant genes are selected from this community\'s list of genes currently present. According to the published results, the new on method will reduce time complexity while improving microarray data\'s classification gene selectiIt is a bipolar disorder (Bip) dataset. Thirty of the 61 observations in the sample had bipolar accuracy.SVM classifier was used to -The LStions. disorder, while the remaining thirty were observational observatest the suggested approach on several sets of data, with the main focus of the study being classification SVM, the findings demonstrated notable -accuracy. With an average classification accuracy using LSin classification accuracy. The study demonstrated that the suggested methodology can efficiently increasesidentify genes and greatly increase classification accuracy by utilizing a variety of metrics to assess them.

In this paper, we present an approximate solution for the Fornberg–Whitham equation using fractional operators, specifically the Atangana–Baleanu derivative, and apply the Yang AdomianDecomposition Method (YADM). The results indicate high efficiency and show a significant agreement with the exact solution.

Breast cancer (BC) is a commonest disease that causes death among women worldwide, the current study was designed to identify the different types of BC and find out the most common type. The pathological and medical data were collected from the medical profile of1497 patients diagnosed with BC from January 2011 to December 2022. The results showed a significant increase in the number of BC cases from 46 in 2011 up to 149 in 2022. The mean ± standard deviation of BC cases significantly increased from (77.1 ± 22.8 ) case/year in (2011-2017) up to (191.4 ± 34.1) case/year in the later five years (2018 –2022). Out of 1497 cases of BC, 1343 case (89.7%) of them with ductal carcinoma, 86 case (5.7%) lobular carcinoma, 17 case (1.1%) metaplastic carcinoma, 17 case (1.1%) paget’s disease, 15 case (1%) phylloids tumor, 14 case (0.9%) mucinous carcinoma, and 5 cases (0.4%) sarcoma.The average age of all patients is (51.2 ± 12.1) yearswith non significant difference between those with different types of BC and in the age intervals of BC patients, the majority of them (84.6%) at age interval ≥ 40 year in comparison with 15.4% at young age interval (20 –39). Statistical analysis of BC patients based on their menopause status reveals that (50.2%) are premenopausal and (49.8%) are postmenopausal, and there is no significant difference between patients with different types of BC.

Background: The current study designed to investigate the influence ofparoxetineand escitalopramon gametogenesis of rats; Method: The current study was conducted in the animal house of Biology department-College of Education for Pure Sciences-Thi Qar University, for the period from 25April to25 May, 2024.The experiment included three groups (5 females and 5 males), control group: Inject with normal saline, second group: Inject with paroxetine(10 mg/kg ), whereas third group: Injected by escitalopram(20mg/kg); Results:The results showed histological changes in the testicle and ovary. Results indicted a non-significant down in the numbers of (Spermatogonia,Primary spermatocytesandSpermatids)in the injected groups.The resultsindicates a significant low in the numbers of primordial follicles,non-significant differences in the second group and a significant differences in the third group for primary follicles,non-significant differences in the second group and significant differences in the third group forsecondary follicles; Conclusion: We concluded the negative histological effect of these drugs on testicular and ovarian tissue was reflected in the gametogenesis, which led to a decrease in this process

Background: In recent decades, research has focused on finding an alternative antibacterial agent due to the spread of resistance isolates and the side effects of antimicrobial agents. In the present study, the antibacterial activity of gold nanoparticles (AuNPs) against clinical bacterial isolates was studied.Method: In this study, the supernatant of Bacillus subtiliswas used to synthesize (AuNPs), and the resulting (AuNPs) were characterized by spectroscopic techniques. also, theantibacterial activity of (AuNPs) against Enterobacter cloacae, Pseudomonas aeruginosa, Proteus mirabilis, and Staphylococcus haemolyticusisolates were studied by disk diffusion method.Results: The synthesis of (AuNPs) was confirmed by a color change. which was check were by UV-spectroscopy at a maximum absorption peak of 535nm. Field Emission Scanning Electron Microscope (FE-SEM) confirmed the presence of small spherical nanoparticles with an average diameter of 21.86-31.94nm. X-ray diffraction (XRD) shows the crystalline nature of (AuNPs) was verified with an average particle diameter of 41.14nm. AuNPs showed antibacterial activity with inhibition diameters of P.aeruginosa13mm, P. mirabilis10mm, S. haemolyticus25mm,E. clocae25mm.Conclusion: It was found that the antibacterial activity of (AuNPs) was shown against both Gram-negative and Gram-positive isolates. based on the result, it was concluded that AuNPs are effective against pathogenic bacteria

This study employs the natural transformation approach combined with the homotopy perturbation technique, a new and effective hybrid method that integrates the homotopy approach with the natural transformation, using a simplified iterative procedure that reduces computational demands. This method provides fast, convergent, and sequential solutions. The reliability of the method is verified through its application to two case studies of ST-TE within the context of the Caputo derivative, which involves the definition of non-singular kernel functions. The study also includes comprehensive comparisons between approximate and exact solutions, utilizing relevant scientific literature to assess the accuracy and effectiveness of the method. Graphical representations highlight the impact of incorrect temporal and spatial parameters on the solution\'s behavior. The findings suggest that this method is easy to implement and well-suited for studying complex physical models governed by nonlinear partial differential equations with fractional time components

Due to the rapid increase in data collected through wearable devices and smartphones, there is a high demand for developing Human Activity Recognition (HAR) techniques. In response, this paper proposes an innovative technique for Human Activity Recognition (HAR). This proposed methodology comprises three key phases: feature extraction, feature selection, and features classification using an ensemble model. First: an Empirical Mode Decomposition (EMD) is applied to decompose the time series data into several modes. Then, a vector of features is extracted from each mode to form the final features vector. The extracted features are sent into several classification models to classify the input time series into different activities. Public datasets are used to evaluate the proposed model. The results demonstrated the ability of the proposed model to classify human activity

High-quality sleep plays a major role in improving brain development and lifestyle. Electroencephalogram (EEG) signals are the most important signals collected by polysomnography (PSG) used for sleep staging. Manual sleep scoring is a difficult task, however, developing an automatic sleep stage is important to support experts to detect sleep disorders in early stage. Method: In this paper, an automatic single-channel EEG signal sleep stages classification model is proposed. A Discrete Wavelet Transform (DWT) based EEG feature is suggested. Three types of features including entropy, linear, and statistical features are extracted and evaluated to score sleep stages. First, we applied the DWT to each 30-second epoch to decompose the signal into five bands. Then, EEG features are extracted from each band. EEG signals from two datasets named Dreams, and EFD sleep are used to evaluate the proposed model. Results: We interpreted the results using essential statistical criteria. The results showed that the use of combination features improves the sleep classification results. Based on the results, with the Dream dataset, the classification accuracy rate, Kappa coefficient, and F-score were found 0.97,0.92, 0.95 For the second database, we obtained 0.95,0.94, 0.93 for accuracy rate, Kappa coefficient and F-score respectively. Conclusions: We developed a method to score sleep stages that can be used by healthcare providers to identify sleep disorders

Decentralized environments are necessary for the complex and sensitive data required for land registration. The main focus of present technology is database storage, which is less secure and prone to fraud. This is because unstructured data and non relational databases present challenges due to the characteristics of the database. Fraud is one of the main problems and a persistent issue with the Malaysian land registration system public ledgers are created by block chain technology from all complex transactions, which hold great promise for replacing intricate systems with a single, straightforward database. As a result of current land office procedures, the land registration process is highly centralized, requiring multiple people to evaluate and authorize the data. Consequently, it is necessary to determine the block chain technology model.

The environment surrounding being organisms is a crucial factor that influences the level of biological exchange of these organisms. Our topic is related to the effect of mobile phones and home routers, were the electromagnetic waves and emitted from it this important on the biological structure of humans and animals. The study is divided into two main parts: the first part discusses the general framework of this effect; the second part focuses on the practical examination of the physiological effects of these waves on blood indicators in rabbits over three months with exposure for 22 hours daily and discusses the mechanisms of these effects. The current study results indicate that the waves emitted by mobile phones and routers have an effect on blood parameters. The results of the statistical analysis from this study indicate significant effects on the studied blood parameters about (p<0.05), despite some numerical changes, represented by a decrease in the number of WBC and RBC, In addition to other parameters(MID%, HGB, NEUT%, and LYM%)

The term \"endocrine disorders\" refers to a collection of conditions that are caused by issues that occur in a portion of the endocrine system.It is possible for these illnesses to manifest themselves when the endocrine system creates an excessive amount or an insufficient amount of a hormone, or when the body does not react appropriately to the hormone.It is the purpose of this study to investigate the impact that endocrine abnormalities have on the spleens of both male and female laboratory rats.The rats, totaling twenty-four, were divided into three groups.(G 1), which consisted of eight rats (four males and four females), a normal saline solution was administered to them for a period of twenty-one days.) (G 2), which consisted of eight male rats, metoclopramide was administered intraperitoneally at a dose of 0.6 mg ø kg on a daily basis for a period of 21 days in order to induce hyperprolactinemia.(G 3), which consisted of eight female rats, metoclopramid was administered intraperitoneally at a dose of 0.6 mg ø kg. This was done in order to induce hyperprolactinemia for a period of 21 days. Following the conclusion of the trial, which lasted for 21 days, a number of blod parameters were evaluated, and the proportion of the hormone prolactin was evaluated.Subsequent to the resection of the spleen, the three groups were separated into their respective groups.Hematoxylin, eosin, and masson stain were used throughout the process of sectioning and staining the tissue. the study demonstrated a noteworthy rise (p<0.05) in the concentration of the hormone prolactin among the male and female group as compared to the control group.Upon histological inspection of the spleen, it was observed that there was a discernible alteration. It is important to note that the white pulp increased region covered around fifty percent of the spleen area in comparison to the red pulp area.The germi centre of the secondary and lymph follicle was observed; however, as compared to the control, we saw that the white pulp region was reduced. Furthermore, the secondary follicle, which is the active follice, was not observed.It is concluded from this that endocrine disorder have a clear effect on the tissue immune status of the spleen through the changes shown in the results of the current study.

This study explores the use of machine learning techniques to classify blood pressure (BP) based on Photoplethysmography (PPG) signals. Three popular algorithms -decision trees (DT), random forests (RF), and support vector machines (SVM) -were evaluated and compared. The data preprocessing involved extracting relevant features from the PPG signals, including temporal, morphological, and frequency-domain characteristics. The RF model outperformed the DT and SVM models, achieving nearly 98% accuracy and F-score, demonstrating its ability to capture complex non-linear relationships between PPG features and BP. The RF model also showed robust performance in the presence of noise and variations in input PPG signals, making it a promising choice for real-world BP monitoring applications. The evaluation was performed using a publicly available dataset of simultaneous PPG and BP measurements

This paper aims to establish several coincidence and common fixed point theorems for self-mappings that satisfy contractive conditions,using the notion of the C-class function ofsymmetrical G-metric spaces.We also present some examples to demonstrate the validity of our results.Finally,we apply our fixed-point result to solve an integral equation

This paper explores approximate analytical solutions for a class of fractional differential equations involving the Caputo fractional derivative. The proposed method employs the Laplace transform in conjunction with the Picard iterativetechnique to derive solutions with improved accuracy and simplicity. The Caputo derivative\'s distinct formulation enables an intuitive representation of initial conditions, facilitating its application in various scientific and engineering problems. The study outlines the theoretical foundation of the approach, demonstrating its efficiency through illustrative examples. Results indicate that this methodology provides a reliable framework for addressing the complexities of fractional differential equations,offering insights into their behavior and practical applications.

In this paper, we construct a new sequence of bivariate Basakov –Bate operators based on parameter S . We discuss the rate of convergence of these operators using the partial and total moduli of continuity. A Voronovskaya-type asymptotic result is also established. Further, weintroduce theassociated GBS (Generalized Boolean Sum) operators and estimate the degree of approximation using the mixed modulus of continuity and a class of the Lipschitz of Bögel type continuous functions

Oil leaks on land and water surfaces from pipeline cracks cause severe damage to the environment. Pipeline leak detection is an essential step for pipeline safety management. This paper presents synthetic aperture radar (SAR) images as an approximate representation of the target scenes due to their expansive field of view and capacity to gather data at any time, irrespective of weather conditions. The present study presents a transfer learning framework that uses deep learning convolutional neural networks (CNNs) for pipeline leak detection. Where we worked on image SAR data that was spilt into 80% training data and 20% test data. The investigation of CNN transfer learning is carried out using two distinct approaches: parameter-based CNN transfer learning and hybrid feature-based mechanisms. The optimal transfer learning model is selected using ResNet-50, Xception and AlexNet algorithms pre-trained on ImagNet. The results demonstrate that the feature-based CNN transfer learning approach the Xception model, combined with anSVM classifier proposed in this study exhibits superior performance compared to parameter-based CNN transfer learning methods. The method the Xception model, combined with an SVM classifier achieved highest accuracy of 99.6%, recall 99.7%, precision 99.5 %and F1scor 99.6%.

Within the scope of this study, a numerical approach is presented for solving the space-time fractional telegraph equation in multi-dimensional domains, based on the Caputo fractional derivative and utilizing the Yang Transform as an effective analytical tool. Simple and easily implementable numerical methods are employed, demonstrating high efficiency in obtaining accurate and satisfactory approximate solutions. The achieved results highlight the effectiveness of the proposed methodology in handling this class of complex fractional differential equations, paving the way for its application in various fields of physical andengineering modeling.

Hymenolepiasis is a parasitic Infection in humancaused by one of the tapeworms, such as Hymenolepis nana (H. nana). The infection most often occurs as an autoinfection where it is transmitted directly from the feces to the mouth without the intermediate host. In the current study, we present two cases of Hymenolepiasis, both cases were pregnant women. \"Samples were collected from patients who visited Bint Al-Huda Maternity and Children\'s Hospital. The first sample was obtained from a 27-year-old pregnant female in her 32nd week of gestation. She presented with symptoms of abdominal pain, diarrhea, vomiting, and general fatigue. The diarrhea occurred at a frequency of four to five times per day. General physical examination and ultrasound assessment of fetalhealth were within normal limits. Blood analysis revealed anemia with a hemoglobin level of 8.6 g/dL. Stool examination indicated the presence of intestinal parasites, and based on the morphological characteristics of the parasite, the diagnosis was H. nana infection.The second sample was obtained from a 22-year-old pregnant female in her 19th week of gestation, residing in a rural area. She exhibited similar symptoms to the first case, including recurrent vaginal discharge. Stool analysis confirmed the presence of H. nanaeggs. The stool sample showed a higher concentration of H. nanaeggs compared to other parasitic worms, suggesting that H. nanais likely the primary causative agent of the patient\'s symptoms. Additionally, the patient was diagnosed withanemia, with a hemoglobin level of 8.2 g/dL

In this paper, the Homotopy Perturbation Method (HPM) is employed to solve nonlinear partial differential equations, which often present significant analytical and computational challenges due to the complexity of their nonlinear terms. To address these difficulties, a newassumption for treating the nonlinear components is introduced, offering a simpler and more practical alternative to the traditional Adomian decomposition approach. While this new assumption enhances the applicability of HPM to a broader class of problems, it may have limitations when dealing with equations exhibiting highly singular behavior or strong nonlinearity

Forecasting the height of the significant wave (SWH) accurately and reliably is crucial for maritime and engineering purposes.This work designs a novel deep learning system to forecast the daily significant wave height (SWH).This work combines two techniques: Multivariate Variational Mode Decomposition (MVMD) and Long Short-Term Memory (LSTM). The oceanic time series data are decomposedinto intrinsic mode (IMFs) functions using the MVMD technique. The LSTM uses the IMFs as inputs.Applying the hybrid MVMD-LSTM model to two stations Albatross Bayand GladstoneQueensland, Australia\'s stations.The outcomeindicates thatthe proposed hybrid model improved the SWH forecasting. It scored acceptable performance NSE=0.09999, NSE=0.99999, WIE=0.9999,WIE= 0.999998,LME=1.7995, LME= 2.3894for Albatross Bay and Gladstone stations respectively. This work is essential for monitoring and managing clean energy resources to maximize sustainable energy production

n this work, we have discussed and prove the different properties and theorems of A new method notion, named the Martinez–Kaabar fractal–fractional (MK FrFr) Elzakitransform like Linearity property, Convolution theorem property, and provides two problems to support our methodology application to solve of Martínez–Kaabar MK Abel Integral Equation