Journal of Basrah Researches (Sciences)

The two most prevalent and deadly types of cancer globally are lung and colon. Early and accurate detection of these cancers is necessary for improving patient outcomes. It was found that machine learning techniques, especially deep learning models, have the ability to generate accurate detection and classification results when trained on big databases. This paper introduces a novel framework to classify five classes of lung and colon cancer using ensemble learning of multiple Convolutional Neural Network (CNN) architectures. First, these models are trained and the predictions resulting from applying them on the test subset of images are preserved, then an ensemble voting is used to select the best result from each model. Our approach was applied on a challenging dataset of 25000 images. The classification accuracy achieved was 98.47% which is higher than the accuracy achieved using the standalone models applied to the same dataset. The potential of our ensemble approach can be further improved in the future.

Post-COVID-19 syndrome symptoms include circulatory abnormalities, respiratory problems, mental health concerns, and chronic fatigue. The burden that these illnesses placed on the global healthcare system led to a rise in complementary and alternative medicine, particularly medicinal plant therapies. Medicinal plants have long been used in traditional medicine for their antiviral, immunomodulatory, antioxidant, and anti-inflammatory properties. These properties are currently being studied. By lowering oxidative damage, immune response modulation, and chronic inflammation, these compounds with bioactive properties could assist in lessening the long-term consequences of COVID-19. Additional clinical trials were needed to verify the safety and efficacy of these medicines, even though initial study results suggested that medicinal plants might aid COVID-19 patients in recovering and living longer. The article provides comprehensive medical strategies that consider the needs of patients post COVID-19. It is recommended to use evidence-based medicinal herbs in addition to traditional therapy.

Three hundred samples of thigh meat and livers from local sheep (from both official and random slaughter) and imported Australian thighs were gathered in Basra city throughout the summer and winter seasons. The findings indicated an elevation in the mean concentrations of all examined components in the random slaughter source. The highest average concentration of lead was in the random slaughter source of thigh and liver in summer (1.38±0.404 and 2.2±0.71) mg/kg respectively. While in winter (1.55±0.43 and 2.06±0.6) mg/kg respectively.and, the highest average concentration of cadmium was for the randomly selected thigh and liver sources were 0.144 ± 0.06 and 0.19 ± 0.08 mg/kg, respectively. In the winter, it was 0.101 ± 0.06 mg/kg while in the summer, it was 0.16 ± 0.062 mg/kg. The copper concentration in the official slaughter source of thigh and liver during summer was (3.65 ± 10.92 and 27.883 ± 109.59) mg/kg, respectively. As for winter, it was (14.02 ± 16.57 and 32.003 ± 96.71) mg/kg, respectively. In contrast, The highest average concentration of copper was recorded for the random slaughter source during the summer (17.008± 31.13 and 51.963± 118.17) mg/kg, respectively. While during the winter, it reached (10.262± 16.494 and 42.101± 122.28) mg/kg, respectively. We computed the EDI of nutrients and compared it to the TDI, the THQ, and the HI for thigh lamb, as well as the risk quotient and risk index, respectively. Both adults and children had EDI values that were more than TDI, and THQ values that were lower than 1.0 (<1). That is, there are slight health risks that are not significant for adults, but they are almost significant for children. The HI values were less than 1.0 (<1) for adults. That is, the non-carcinogenic health risks are very small. However, the HI for children was greater than 1.0 (≥1), so there is a possibility of non-carcinogenic adverse health effects after consuming lamb meat.

This study investigates developing and optimizing hybrid algorithms for image processing and encryption using numerical optimization techniques based on heat diffusion methods. Using finite difference methods, we show the application to different types of images, which will be converted into arrays and treated as coefficients in the computational process. The paper aims to enhance image quality through algorithmic optimization and hybridization strategies. Experiments in one and two dimensions are conducted using both explicit and implicit methods to evaluate the impact of these techniques on image processing. The performance of the proposed approach is analyzed using statistical metrics such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Maximum Difference (MD), and additional quality assessment parameters.

The process of evaluating and recording student attendance in the classroom is the basis for giving the final evaluation of the student. Paper methods of recording student attendance and evaluating their performance suffer from problems such as inaccuracy, wasting time, and difficulty in obtaining reports when needed. The research provides a solution to the problems of these systems using Arduino Nano to build a system for evaluating and recording student attendance, where the laboratory teacher gives each student a grade for each project from 1, 2 .. 10, and these grades are stored in a text file that is accessed via Excel, and the time and date of the evaluation are saved, and the student is given (0) if he did not attend the laboratory. The system reduces the time of recording student grades and attendance manually, complies with academic accreditation requirements, provides access to real-time information, reduces administrative burden, facilitates obtaining final evaluation reports for students, facilitates obtaining student attendance statistics, accuracy of entered data, and reduces human errors. The research aims to eliminate paper systems in student assessment and attendance recording and move to electronic systems. The limitations of the system lie in the cost if the system is adopted on a wider scale in educational institutions, the insufficiency of the Arduino Nano memory if it is used to assess all students at all levels, as the Arduino Nano memory is not enough for the programming code, and the large number of student names.

The study examines unsteady nanofluid heat transfer in a squeezing flow between two parallel plates, using water as the base of fluid with gold (Au), and magnetite (〖 Fe〗_3 O_4) nanoparticles. A new analytical approach (LCP-q-HAM) combining the q-homotopy analysis method, Laplace transform, convolution theory, and Padé approximation is employed to solve nonlinear differential equations that have to do with magnetic field effects and thermal conductivity. Analytical and numerical solutions (using BVP4C) are compared through tables and graphs, analyzing temperature and velocity distributions for various parameters such as nanoparticle volume fraction, Hartmann number, squeeze number, magnetic field angle, and conductivity. The results confirm the effectiveness of the analytical method.

This cross-sectional study was conducted in Wasit province. collected 296 stool sample diarrheic children less than 16 years, selected 96 Randomly from some patients in hospitals and public health centers in Al-kut city and private clinics (from 1st December 2023 to 1st May, 2024 were included in the study. Method Finding the form of the parasite through direct smear microscopy to confirm the presence of an intestinal parasite infection, and diagnosis this parasite by molecular method polymerase chain reaction technique (PCR). The results showed that the cases were matched by age, gender, and where the person lived. The diarrhea was looked at using direct smear microscopy, which showed that Entamoeba histolytica, Giardia.lamblia, and Cryptosporidium parvum were found 23(24.0%), 5(5.2%) and 4(4.2%) respectively. While, Diagnosis by polymerase chain reaction(PCR). This result was 64(66.67%), 11(11.46%), 23(23.96%), respectively. In conclusion, PCR assay of molecular detection to protozoal infection in the present study is more accuracy than traditional methods like direct smear.

Nine drinking water samples of different stations were collected from Northern Basrah governorate. The radioactivity assessment of drinking water was achieved by using thermoluminescence technique applying calcium fluoride dysprosium dosimeters (TLD-200). The lowest values of gamma absorbed dose was 0.0001 mSv/y, while the maximum result of gamma absorbed dose 0.021 mSv/y. Annual effective dose equivalent and excess lifetime cancer risk have been calculated for all drinking water samples. All outcomes have been recorded to be lower than the standard values. Thus, drinking water samples of different stations are harmless to be consumed in Northern Basrah city.

Botanical remedies have long been used for medical purposes, and their use has grown alongside human civilization. Citrullus colocynthis plant is a member of the Cucurbitaceae family contain many secondary metabolites once of them is cucurbitacin E. Studies have revealed the significance of cucurbitacin E because of its diverse therapeutic properties like antibacterial and biological activity against bacterial combined , study antidiabetic activity and investigate the DNA protection effect of cucurbitacin E with study its effect on human erythrocyte of these plant and its metabolite (cucurbitacin E) to demonstrate its activity in this study. Extraction for Citrullus colocynthis fruits by hot continuous method using chloroform as solvent was done then detection of cucurbitacin E was used by High-performance liquid chromatography (HPLC), well diffusion method for antibacterial, glucose reuptake method for antidiabetic activity, Agarose gel electrophoresis was subsequently employed to evaluate the range of DNA damage also invitro study of the hemolytic activity assay. After successful extraction the results show the valuable antibacterial activity of cucurbitacin E detect by using bacterial isolates, and minimum inhibitory concentration of cucurbitacin E values for bacterial isolate were (0.1, 0.25, and 0.15) mg/ml for (Staphylococcus aureus, Klebsiella pneumoniae and Bacillus subtilis) respectively. Regarding antidiabetic effect was studying by glucose reuptake invitro assay in which metformin was used as stander and the results were the inhibition values found for cucurbitacin E, extract, and metformin were( 0.108, 0.313, and 0.377mg/ml), respectively and after successful extraction the DNA damage protection assay determined the optimal concentrations (1,0.5,and0.25) mg/ml give protection %87.52, 72.21, and44.92respectivlyand cause heamolysis for red blood cell in dose dependent manner.

The swift and unforeseen global spread of COVID-19 has intensified the focus on mathematical modeling of the disease worldwide. This study presents a stochastic differential equation, specifically the Ornstein-Uhlenbeck process, representing Iraq's COVID-19 time series data. This dataset includes the number of infected individuals, fatalities, and vaccinated cases. We estimated the process parameters using the maximum likelihood estimator for the counts of infected, deceased, and vaccinated individuals.
We utilized the Euler approximation and the Milstein method within MATLAB to simulate the epidemic's time series based on estimated and actual data. Finally, we compared the effectiveness of the Euler and Milstein methods in approximating the processing of the Ornstein-Uhlenbeck process concerning the numbers of infected, deceased, and vaccinated individuals in Iraq. We evaluated actual and estimated cases, highlighting the reliability and precision of the two numerical methods.

In this investigation, soil samples were taken from 25 locations in the northern Basra Governorate. The concentration of uranium was determined using the inductively coupled plasma mass spectrometry (ICP-MS) technique. Following impurity removal, they were crushed and sieved through a 75-micron sieve. The findings indicate that the uranium concentrations in the oil fields in North Rumaila and West Qurna range from 0.05 parts per million to 6 parts per million. The findings are examined and contrasted with those of earlier studies. Regarding the other regions, all of them were within this range of uranium concentration in the investigated samples. The findings are accompanied by comparisons with other research. The examined soil samples contained less than 100 parts per million (ppm) of uranium, indicating that the quality of the surface soil and fill is more significant than the mineable reserves. The uranium content in the northern Basra region was collected and evaluated, as detailed in this report. The investigation found that all surface soil samples had uranium levels below the hazardous threshold. the investigation found. According to the 1993 recommendation of the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR), the uranium concentrations in the study's surface soil samples were below the allowable limit of 11.7 parts per million.

Medical imaging has seen a significant advancement in recent years, with the introduction of multi-spectral X-ray imaging being one notable development. However, traditional image fusion methods such as weighted averaging or maximum intensity projection may not be sufficient to address the complexities of X-ray images. In response, a new deep learning-based multi-spectral X-ray image fusion method has been developed. This method utilizes Convolutional Neural Networks(CNNs) utilizes deep learning algorithms to combine multiple images obtained from different X-ray energy levels into a single image with higher resolution and improve quality. The problem statement highlights the limitations of traditional X-ray imaging methods and the challenges in developing an effective image fusion method. The proposed approach’s contribution is a step towards improving the quality and accuracy of medical imaging, leading to better patient outcomes and more efficient healthcare practices. The provided results indicate that the proposed model achieved an accuracy of 95% on the training data and 90% on the test data, with room for improvement. The limitation of the dataset and the algorithm used for classification are discussed as potential reasons for not achieving higher accuracy. Further research is required to develop specialized deep learning models for X-ray image fusion and explore other algorithms and techniques to address the challenges related to the dataset and the algorithm used for classification.

The study started in November 2023 and ended in March 2024. The study concentrates on the relationship between urinary tract infections caused by Pseudomonas aeruginosa and some biochemical criteria (triglycerides, cholesterol, liver enzymes). A total of 110 blood samples were collected from patients at Al-Qurna General Hospital in Basra province to diagnose the bacterial species causing the infection and isolating the pseudomonas aeroginosa bacteria, which is one of the most common types of urinary tract infections. Triglyceride level (TG) showed a significant decrease (P˂ 0.01) in UTI-P. aeruginosa patients (99.4±6.31: 90.43 ± 8.85 mg/dl) compared to the control group (148.65 ± 6.64 mg/dl). Cholesterol concentration also recorded a significant decrease (P˂ 0.01) in UTI-P. aeruginosa (113.52 ± 6.03: 119.41 ± 8.5 mg/dl) compared to the control group (148.65 ± 6.64: 162.91 ± 5.14 mg/dl). AST of UTI-P. aeruginosa patients rise (P<0.01) (25.12 ± 1.68: 37.52 ± 3.12 U/L) compared to the control group (13.84 ± 2.17: 11.84 ± 2.51 U/L). ALT enzyme level of UTI-P. aeruginosa patients recorded higher (P˂ 0.01) value (38.15 ± 2.58: 41.57 ± 3.15 U/L) compared to the control group (16.28 ± 1.82: 13.4 ± 1.37 U/L). As well as ALP enzyme level of UTI-P. aeruginosa patients showed similar trend (82.45 ± 3.15: 92.11 ± 5.62 U/L) in comparison with the control group (57.02 ± 2.14: 51.42 ± 5.19 U/L).

The nuclear structure of 226-23U isotopes has been investigated using the interacting boson model (IBM-1). The model space is extended to include the f-boson (L=3) to calculate the negative parity energy levels . The ground state band (g.s.-band), beta band (β-band), gamma band (γ-band), and two negative bands have been described. The predicted wave functions have been used to calculate the transition probabilities in-band and inter-band. The nuclear shape was determined by calculating the potential energy surface (PES) as a function of deformation parameters. The model results are compared with available experimental data, and the results show good overall agreement.

fluoride-doped dysprosium (TLD200) to radiation was studied in this research. The thermal treatments researched before the radiation was conducted included 100°C/h, 200°C/h, 300°C/h, and 400°C/h. The following similarity values were derived from the study's thermal treatments: 11.5%, 12%, 13%, and 17%, respectively. The results indicate that the 400°C/h heat treatment increased the response, which is one of the best treatments compared to the heat treatments under investigation.

The present study focuses on the numerical and graphical analysis of the mechanism of thermal diffusion and diffusion thermo over a mixed convection Casson fluid flow over a vertical cone with porous material under the effects of Brownian motion and thermophoresis. Using transformable variables, the proposed model is transformed into a system of non-linear ODEs. Then, the approximate solutions are established using polynomial models in conjunction with spectral collocation. The impact of some important parameters, such as the Brownian motion parameter, the Prandtl number, the magnetic parameter, the Casson parameter, the Lewis number, the Dufour parameter, the Soret number, the velocity profile, the temperature, the diffusion, the skin friction coefficient, the Nusselt number, and the Sherwood number, is examined numerically. Also, several cases are resolved and graphically compared, illustrating the improved precision of the current approach.

Wireless Multimedia Sensor Networks (WMSNs) and Mobile Ad Hoc Networks (MANETs) share important features, such as their decentralized topology and multimedia transmission without infrastructure. However, their dynamic nature subjects them to internal routing attacks, including but not limited to, selective packet drop attacks in which the malicious nodes block data flows without detections. This paper presents SA-DCBiGNet which is a hybrid deep learning model that implemented a combination of Dense Convolutional Neural Networks (DCNN), Bidirectional GRUs (Bi-GRU), and Channel Attention as a defense approach in the Optimized Link State Routing Protocol (OLSRP). Using behavioral logs and network metrics, the model identifies adversarial nodes with high precision. The model was evaluated using Python/NS-3 and the WSN-DS dataset and it identified adversarial nodes with high level of accuracy of 99.75% accuracy, 99.30% precision, 99.02% F1-Score, and 98.8% recall, which is better than traditional methods.

The bulk optical and electrical characteristics of VN are investigated using first-principles simulations. The optimized lattice constants for rock salt and zinc blend are 6.403 Å and 7.22 Å, respectively. Both structures exhibit half-metallic behavior with metallic conductivity for spin-up electrons and semiconducting behavior for spin-down electrons. The zinc blend contains a direct band gap of 6.18 eV, whereas rock salt has a direct band gap of 3.77 eV. The total magnetic moment of both structures is 2 μB per unit formula. In the bulk Heusler lattice, the magnetic moment value shows that the structure behaves half-metallically in both the rock-salt and zinc-blende configurations.
By calculating key optical parameters, including the dielectric function, absorption coefficient, and optical spectra within the 0-15 eV energy range, the material demonstrates significant potential for spintronic devices and optoelectronic applications.