Iraqi Journal of Science

The current study aimed to evaluate the impact of hepcidin levels on the diagnosis and management of T2DM patients and to investigate the association between their levels and insulin resistance. The study included 100 T2DM patients, which are divided into three groups according to disease duration: D1 (n=38; < 5 years), D2 (n=37; 5-10 years), D3 (n=25; >10 years), and 50 healthy persons as control (C). Fasting blood glucose (FBG) was determined by glucose oxidase-peroxidase and glycated haemoglobin A1c (HbA1c) by immunoturbidimetric assays. Serum insulin and hepcidin were evaluated using ELISA. Also, HOMA-IR and HOMA-β were calculated from FBG and insulin. The FBG, HbA1c, insulin, and HOMA-IR levels increased significantly, while HOMA-β and hepcidin levels decreased significantly in all patient groups compared to C. A significant difference in hepcidin levels was recorded between males and females in all groups. There was a significant negative correlation between HOMA-IR and hepcidin levels in all patient groups. Based on the results, it can be concluded that these biochemical markers are important in diagnosing and managing T2DM.

The study evaluates the potential of powdered olive kernels as a natural adsorbent for extracting basic violet 10 dye from contaminated water using adsorption methods. The adsorbent material was characterized using Fourier transform infrared spectroscopy (FTIR) and atomic force microscopy (AFM). The impact of various operating variables namely, dosage of olive kernels powder, adsorption period, starting BV10 dye concentration, temperature and pH were investigated. The experimental data obtained from the equilibrium adsorption studies fitted better with the Freundlich adsorption isotherm model compared to the Langmuir isotherm model. Kinetics analysis for adsorption process revealed that the pseudo-second-order model (PSOM) was more appropriate to describe the sorption process constructed to pseudo-first-order (PFOM). Thermodynamics data for the adsorption of BV10 dye onto kernels powder surface display that this process is spontaneous and exothermic in nature.

Background: Empirical knowledge on the cautious application, safety and efficacy of medicinal plants is limited especially in developing countries around the world.
Objective: This study is aimed at investigating the cytotoxic, antioxidant and antimicrobial potential of Gardenia angustifolia root bark with a comparative objective on ethyl acetate and methanol extracts.
Methods: Compound elucidation was done using Gas Chromatography-Mass Spectrometry (GC-MS) analysis and Thin Layer Chromatography (TLC). Cytotoxic evaluation was conducted using Artemia salina and Human Embryonic Kidney-293 (HEK-293) cell line. Antioxidant assay was conducted using 2,2-diphenyl-1-picrylhydrazyl (DPPH) and Ferric Reducing Antioxidant Power (FRAP) models. The agar-well diffusion method was employed for antimicrobial assay.
Results: Both crude extracts constitute thirty-five (35) bioactive components each by GC-MS analysis. The extracts showed a non-toxic effect toward brine-shrimp and HEK-293 cell line at a moderate concentration and displayed significant antimicrobial effects on some strains of microorganisms. Furthermore, the in vitro antioxidant analysis conducted using the DPPH and FRAP models revealed a concentration dependent antioxidant activity of the extracts.
Conclusion: The findings indicate that ethyl acetate extract of Gardenia angustifolia root bark is a better radical and microbial growth inhibitor, attesting to its antioxidant nature and as a promising target in antimicrobial drug design.

Contemporary developments have indicated an escalating demand for plant-derived pharmaceutical products. Apart from their economic value, the utilization of medicinal plants is believed to pose a considerably reduced risk of adverse effects when compared to the use of synthetic drugs. One of the approaches to determine the potential of a medicinal plant against biological diseases is the quest for novel medications using natural constituents. In this present investigation, we sought to determine the immunomodulatory and hepatoprotective properties of Hydnophytum formicarum in BALB/c mice. The study used 6-week-old healthy BALB/c mice. The treatment consisted of giving different doses of H. formicarum tuber extract. To observe the immunomodulation activity, we compared the relative numbers of various immune cell subsets, such as CD4, CD8, CD4CD25, and B220 cells. Furthermore, hematoxylin and eosin staining were conducted to see how the H. formicarum extract affected liver damage. The results showed that the H. formicarum affects CD8+, CD4+, CD4+CD25+, and B220+ cells in a few spots. Importantly, the histopathological view showed that there were no significant differences between the control group and the treatment group. These results indicate that the H. fomicarum tuber extract did not harm the liver. Finally, to understand the role of H. fomicarum

Toll-like receptors trigger both innate and adaptive immune responses against viral infections by inducing the release of inflammatory cytokines and antiviral mediators. This study aims to examine the role of TLR8 and genetic variants thereof in the progression and recovery of COVID-19 disease. It was conducted on 90 patients, (including 45 severe-critical cases, 45 mild-moderate cases),50 recovered cases, and 89 healthy people using quantitative real-time and allele-specific PCR for gene expression and gene polymorphisms determination, respectively. TLR8 gene expression did not show a significant difference among the study groups, while its median level showed statistically significant differences with respect to disease severity and Ct values. Interestingly, it showed a negative correlation with respect to the concentrations of some clinical parameters, which constituted a significant difference with respect to C-reactive protein and lactate dehydrogenase. However, in COVID-19 patients, low frequencies of the TLR8 G and C alleles of rs3764879 in male patients and the G allele of rs3764880 in female patients were seen. Additionally, the A allele of rs37648801 in male patients and the A allele and its AA genotype in female patients increasingly affected their frequency. An association between males and females regarding increased frequency of the A allele of rs3764880 in patients and the G allele of rs3764880 in healthy subjects, constituting a risk factor and a protective factor, respectively, in both sexes. The frequency of certain alleles was found to be associated with either increased or decreased susceptibility to COVID-19 infection and severity. However, the TLR family has been shown to exhibit a particular kind of extremely accurate gene regulation.

Hypertension is the primary cause of cardiovascular disease and early mortality globally, it is a complex feature Determined by many factors endogenous mechanisms, genetic factors, physiological systems and environmental exposures variances. In recent years, there has been growing scientific interest and research focused on the immunological aspects of antihypertensive medications. Among these, Candesartan has emerged as a prominent subject of study. It has been demonstrated to reduce the inflammatory reactions associated with hypertension. The present study was conducted at Imam Hussein Teaching Hospital in the holy city of Karbala from December 20, 2022 to May 22, 2023. There were three distinct groups in the study which are (G1) healthy people, (G2) those receiving medication other than candesartan for their hypertension and (G3) patients receiving candesartan treatment for hypertension to evaluate various physiological parameters in order to ascertain the impact of candesartan on these samples such as serum sodium, serum potassium, and Adiponectin, as well as the investigation of some immunological markers like Interleukin1( IL-1), Interleukin18( IL-18) and Tumor Necrosis Factor(TNF). The study findings showed statistically significant differences in the mean levels of TNF, Interleukin-1, Interleukin-18, serum sodium, serum potassium, and adiponectin between the study groups. The current study was designed to obtain more clarification of some physiological and Immunological parameters in Iraqi hypertensive patients treated with candesartan, and determine the essential role of some physiological parameters such as, (serum sodium, serum potassium, and adiponectin), and Immunological parameters such as, [pro- inflammatory cytokines like, Interleukin-1(IL-1), Interleukin-18, and Tumor Necrosis Factor alpha (TNF-α)]. The result of physiological parameters showed a high significant increasing (P<0.05) in the concentration of serum potassium level in group of hypertension patients treated with candesartan (G3) while the concentration of serum sodium and adiponectin showed a significant decreasing (P<0.05) in the same group (G3) compared with control healthy group(G1), and positive control group (G2). The results of Immunological parameters including Interleukin-1, Interleukin 18 and Tumor necrosis factor- alpha showed that there was a high significant increasing (P<0.05) in the concentration of control healthy group(G1), and positive control group (G2) compared with hypertension patients treated with candesartan (G3).

Chronic liver disease (CLD) can potentially cause disruptions in the normal functioning of various endocrine organs responsible for producing hormones. As a result, individuals suffering from CLD may experience fluctuations or imbalances in the levels of certain hormones within their bodies. As well as they frequently have suppressed immune systems making them more vulnerable to parasite infections. The primary objective of this study was to investigate the association between Toxoplasma gondii infections and liver function by analyzing the interplay between these parasites and hormones. This study was conducted in Baghdad, Iraq from December 2021 to May 2022. One hundred and twenty male patients with Chronic liver disease (CLD) (age:14-75 years) and 120 control males (age: 24-70 years) participated in this study. Stool and serum samples were collected from all individuals and were then analysed for intestinal protozoan parasites and anti-Toxoplasma antibodies respectively. Hormonal tests were conducted for all participants which included (Cortisol, testosterone, prolactin, insulin, and thyroid-stimulating hormone TSH). Biochemical tests included (Prothrombin time PT, international normalized ratio INR and albumin); liver enzymes were (aspartate aminotransferase AST, alanine aminotransferase ALT, alkaline phosphatase ALP and gamma-glutamyl transferase GGT) and interleukins (Interleukin 13 IL-13 and transforming growth factor TGF). The findings indicate that among the control group participants, 34 individuals, which constitute 28.33% of that group, tested positive for protozoan parasitic infections. In contrast, a higher proportion, 69 individuals or 57.5%, of the participants diagnosed with CLD were found to be positive for protozoan parasites. Four sub-groups were formed in response to prior results: Control-parasites positive, control-parasites negative, CLD-parasite positive and CLD-parasite negative. The status of the protozoan parasites did not affect the hormones levels. The results of liver enzymes showed that parasite positive status was significantly related to all enzymes among CLD patients except the GGT. As well as parasite positive status was not correlated with the other biochemical (PT, INR, and albumin) and immunological parameters (IL-13 and TGF). There was no correlation between the positive status of parasites and cortisol, testosterone, insulin, prolactin, or TSH. The liver enzyme results showed a high correlation (p<0.05) between the presence of parasites and all of the enzymes among CLD patients, with the exception of the GGT enzyme. PT, INR, albumin, and the other biochemical and immunological markers (IL-13 and TGF) did not correlate with the presence of parasites.

The objective of this study is to elucidate the environmental significance and potential health implications of airborne microorganisms through their molecular characterization, thereby enhancing our comprehension of atmospheric health and its broader impact on human well-being. Amplification of the 16sRNA gene was carried out based on the multiplex polymerase reaction and used Sanger method determining the nucleotide sequence of DNA to determine the evolutionary relationships, as it is a clear and reliable diagnostic method instead of the traditional inaccurate methods. The study included five stations from Basra Governorate, Iraq, represented by the areas of Qurna, Shuaiba, Tanumah, Khor Al-Zubair and Old Basra, as it revealed The study covers five genera belonging to 16 Gram-negative and Gram-positive bacteria species: Mammliicoccus lentus, Staphylococcus saprophyticus, Erwinia, Staphylococcus hominis, Lysinibacillus, Exiguobacterium aurantiacum, Staphylococcus epidermidis, Staphylococcus equorum, Staphylococcus succinus, Bacillus pumilus, Bacillus safensis, Staphylococcus xylosus, Pseudomonas aeruginosa, Atlantibacter hermannii, Bacillus subtilis, and Pantoea agglomerans). The isolated bacteria were submitted to the NCBI GenBank database for registration. Using the MEGA 11 software, a phylogenetic tree was constructed to analyze the evolutionary relationships among the study isolates and compare them to internationally available samples.

Recent research indicates that bacterial cellulose (BC) and bacterial nanocellulose (BNC) exhibit promising adsorptive properties for antimicrobial agents, owing to their complex network structure, high surface area, and substantial porosity. The aim of this study was to analyze the impact of the antimicrobial, antibiofilm, activity of BC and BNC on different bacterial isolates, were collected from wounds and burn wound patients. Also, a study examined the antioxidant activity. In this study, (BC) and (BNC) was produced from vingar mothers. The Pathogenic bacterial isolates were collected from wounds and burn wound patients of different age groups. The antibiotic susceptibility testing by disk diffusion method and Vitek 2 compact system method. The antimicrobial activity of BC and BNC on different bacterial isolates by the Resazurin-based 96-well plate microdilution method. Detection of biofilm formation and Inhibition of Biofilm Formation was quantified using the 96-well tissue culture plate method (TCP). Finally, the DPPH radical scavenging assay was used to investigate the ability of (BC) and (BNC) to scavenge free radicals. Seventy bacterial isolates were collected from wounds and burn wound patients of different age groups. It belongs to five species: Citrobacter freundii, ten isolates; Escherichia coli, 17 isolates; Klebsiella pneumonia, 16 isolates; Pseudomonas aeruginosa, 15 isolates and Staphylococcus aureus, 20 isolates. The Antibiotic susceptibility testing by disk diffusion method and Vitek 2 compact system method showed that most bacterial isolates were resistant to the antibiotics used. The antimicrobial activity of BC and BNC on different bacterial isolates by the Resazurin-based 96-well plate microdilution method showed different results in tests bacteria using the minimum inhibitory concentration (MIC). The study found that 60 out of the 70 bacterial isolates were able to produce biofilm, accounting for 85.71% of isolates, while 10 isolates (14.28%) did not produce biofilm. The effect of the BC and BNC on the biofilm formation using SUB-MIC 1 shows that BC and BNC prevent the biofilm formation in 17 (78%) and 25 (100%) isolates. Meanwhile, SUB-MIC 2 of BC and BNC prevent biofilm formation in 10 (42%) and 23 (80%) isolates. The results show that the % PDDH Radical Scavenging Activity for BC was (10.316 and 75.122) when the concentrations were (1.95 and 1000) μg/ml, respectively. The % PDDH Radical Scavenging Activity for BNC was (14.806 and 98.837) when the concentrations were (1.95 and 1000) μg/ml respectively. The % PDDH Radical Scavenging Activity for AA was (16.213 and 98.940) when the concentrations were (1.95 and 1000) μg/ml respectively. The bacterial cellulose and bacterial nanocellulose are characterized by their antimicrobial, antibiofilm, and antioxidant properties, which allow for a wide range of medical, industrial, and environmental applications.

The study aims to investigate and assess the indoor air quality within two renowned museums situated in Baghdad city, namely the Iraqi National Museum and the Natural History Museum, during the months of October and November 2023. A Temtop device was used to measure the main indoor environmental parameters in these museums, particularly (total volatile organic compounds (TVOC), formaldehyde (HCHO), particulate matter (PM), temperature, and humidity), as well as Air Quality Index (AQI). The average concentrations of different air pollutants measured in both museums were as follows: TVOC was 0.93 mg/m3 and 0.80 mg/m3, HCHO was 0.31 mg/m3 and 0.28 mg/m3, PM2.5 was 57.23 μg/m3 and 52.33 μg/m3, and PM10 was 85.11 μg/m3 and 72.82 μg/m3. The average values of TVOC were within the limits recommended by the World Health Organization (WHO) and Environmental Protection Agency (EPA), which is less than 1mg/m³. Meanwhile, the average values of the rest of the indoor pollutant concentrations exceeded the recommended limits, which for HCHO is 0.004 mg/m3 and for, PM2.5 is 35μg/m3, and PM10 is 150μg/m3, for the daily standard. The average values of indoor air temperature for the two museums were higher than the ASHRAE standards recommended limits. In contrast, Indoor RH is below the recommended limits. As for AQI, it was classified as unhealthy for sensitive groups for both museums.

This research aimed to produce Zinc Oxide nanoparticles using bacterial isolates and examine their potent antibacterial properties and toxic effects on breast cancer cell. A clinical isolate of Pseudomonas aeruginosa was selected from 170 clinical bacterial isolates for the biosynthesis of nanoparticles. ZnO NPs were biosynthesized from the bacterial filtrate. The physiochemical properties of the synthesized ZnO NPs were then characterized using various techniques including UV–vis spectroscopy, Atomic Force Microscope (AFM), Fourier Transform Infrared (FTIR), Energy Dispersive Spectroscopy (EDS) and Field Emission Scanning Electron Microscopy (FESEM). The antibacterial tests were conducted using the agar well diffusion technique, employing ZnO NPs at concentrations ranging from 3.125 to 100 μg/ml. Additionally, the anticancer effects on MCF-7 breast cancer cells were examined using the MTT assay. The study also included an assessment of apoptotic activity using Annexin V, analyzed through flow cytometry. From FESEM analysis, ZnO NPs were found to have an average size of 32.5 nm. and the other characteristic analysis confirmed the production of nanoparticles. The activity of produces ZnO NPs was tested against three pathogens (Staphylococcus aureus, Escherichia coli and Pseudomonas aeruginosa) in solid media. ZnO NPs showed potential antibacterial activity against tested bacteria, at concentration 100 μg/ml of ZnO NPs which showed the highest inhibition zones were (28, 27 and 23mm ) for S. aureus , E. coli and P. aeuroginosa respectively , Whereas the lowest inhibition zones were observed at 3.125 μg/ml for the S. auereus and E.coli by diameter ( 9 and 10 mm) respectively and with no effect on P. aeuroginosa. These nanoparticles also showed a dose response anticancer activity against MCF-7 cells with IC50 72.87μg ml-1 while on normal HEF cell line showed less effective. Annexin-V / Propidium Iodide (PI) flow cytometry analysis confirmed that ZnO NPs induce apoptosis in MCF-7 cells.

The aim of this research is to create thin films of GO:TiO2 by the spray pyrolysis method. Hummer’s method was utilized to prepare graphene oxide (GO). Different GO concentrations of (1, 2, 3, and 4) % were used, while a TiO2 concentration was maintained at 4% to form films having various volume ratios of (1:4, 2:4, 3:4, and 4:4), which were then coated on FTO substrate at 400°C and later dipped into the pigment solution of anthocyanin (AD) obtained from red cabbage plant. Thin films’ composition, structure, morphology and optical properties were studied through energy dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), Fourier transform infrared (FTIR) microscopy, field emission scanning electron microscopy (FESEM) and UV-Vis spectrophotometry among other analytical techniques. The sizes of the crystals in these films were range from 15.6 nm to 19.38 nm and were characterized by polycrystalline structures with anatase phase. The FTIR spectra showed a functional group of all materials used in the range between 400 cm-1 and up to 4000 cm-1. The surface morphology for GO:TiO2 films showed randomly distributed aggregates or lumps of GO on the top surface of those films. As the GO concentration increased, a continuous network with TiO2 was formed.
The optical properties and optical constants of the prepared GO:TiO2 and GO:TiO2:AD thin films in the spectral range 300–1100 nm were examined. The absorbance increased, the direct energy gap decreased (its value is within 3.56–3.17 eV), and the optical conductivity increased with increasing the graphene oxide concentration. The energy gap values of GO:TiO2:AD thin films, were less than that for GO:TiO2. The results showed the presence of two energy gaps within the ultraviolet spectrum (3.31-3.12) eV and visible light ranges (2.32-2.14) eV. These results are good candidates for optoelectronic devices such as hybrid or dye-sensitized solar cells and photodetectors.

Pollutants resulting from oil refining operations cause harmful effects on public health, especially for workers exposed for long periods. Measuring the level of specific contaminants in the blood of refinery workers is necessary to understand the relationship between exposure to pollutants and health status. This study aims to determine the levels and nature of heavy metals in the blood of occupational exposure workers in the Al-Dora refinery, Baghdad. A total of 20 blood samples (15 exposed workers and 5 non-exposed individuals (control)) were investigated to assess the level of heavy metals. An atomic absorption spectrometer (AAS) was used to measure the levels of Cr, Cd, Pb, and V in the blood samples. The mean concentrations of heavy metals in the blood of the exposed group are significantly higher compared to the control groups, with Cr at 65.27 μg/dl, Cd at 163.93 μg/dl, Pb at 37.33 μg/dl, and V at 153.33 μg/dl. The results showed a significant difference between the exposed and control groups (p<0.001), which indicates a strong correlation between heavy metal levels in the blood and exposure status. There was also a correlation between the level of heavy metals in the blood of exposure groups, age, smoking, and duration of work. In particular, workers aged 15 years and more, smoking workers, and female workers had higher levels of heavy metals. Moreover, Cd and V levels exceeded permissible limits. Our findings highlight the importance of implementing comprehensive health and safety protocols to reduce the risk factors associated with age, smoking, and prolonged exposure among workers in oil refineries to improve workers' overall health and well-being.

Polycyclic Aromatic Hydrocarbons (PAHs), which are chemical molecules, have been causing numerous health and environmental issues in water supplies. This study investigated the levels of PAHs in groundwater samples collected from the Diyala Bridge and Al-Ahdamiyah districts between March and June 2023. Ten groundwater samples were collected from different locations; six wells were distributed in the Diyala Bridge area and four in the Adhamiya area. The results show that the average total amounts of PAHs in each water sample in Diyala Bridge and Al-Ahdamiyah districts ranged between 16.0 – 472.63 ppm with a mean of 213.13ppm, which is significantly higher than the ten ppm control. During the study period, Several locations reported noticeably elevated concentrations of one or both of the chosen PAHs. The highest concentration of PAHs was recorded for the chrysene compound at the Abu-Hassan site of 284ppm, followed by pyrene and acenophthylene, with a concentration of 167.8 and 131.7ppm, respectively. The lowest concentrations were for Naphthalene, which were 0.0213 and 0.041 in Qassim and Abu-Hassan wells, respectively. It also noted that high concentrations were recorded for all PAHs at the Mohammed, Ahmad, and Qassim wells, which exceeded significantly for other sites. This research advice focuses more on the risk evaluation of PAHs for well water sources when used for drinking.

It is important to spotlight the non-oil Iraqi wealth used in industry, including the kaolin clays in western Iraq, specifically in Anbar Governorate, where they are found in large quantities suitable for investment. This study focuses on the red kaolin clays found in the Hussainiyat Formation (Lower Jurassic) in the southeast of the Hussainiyat Valley. These clays are important in various industries, including ceramics, cement, and bricks. Geochemical analysis plays a an important role in determining the suitability of these clays for industrial use.
Kaolinite was studied in the Northeastern Hussainiyat Area within the Hussainiyat Formation (Lower Jurassic). The presence of kaolinite and illite-smectite was noted, in addition to montmorillonite, quartz, hematite, and goethite. The concentrations of the main oxides in the study area reached Al2O3 23.35%, SiO2 51.33, Fe2O3 9.05%, MgO 1.9%, CaO 1.13%, K2O 1.34%, P2O5 0.04%, and LOI 10.2%. Aluminium in kaolin clay has a positive relationship with Cr2O3 and TiO2, a negative relationship with iron, potassium, magnesium, and sodium oxides, and a negative relationship with quartz, which increases with the decrease of these oxides. The deposits were chemically classified into shale and Fe-shale and formed in extensive weathering.

The current experiment set out to determine the level of heavy metal pollution in the studied soil. Sixteen sites were chosen to collect soil samples. ICP-MS was utilized to assess heavy metals in soil samples. The detected heavy metal concentrations were compared with the values of the Geochemical Background WHO recommendations. Based on the pH values, all soil samples in the research region are determined to be alkaline. The average pH of the research area is 7.30. The heavy metal pollution was evaluated using descriptive statistical analysis and a correlation matrix. The soil exhibits a range of heavy metal pollution, varying from very low to severe. The mean concentration of elements B, Cu, Hg, Cr, Ni, and Sr was higher than the global soil concentration. The Co level of pollution was moderate. The pollution levels of Ce, Zn, V, Ba, and As in the world soil were lower. Unlike the global average percentage of contaminated soil, the remaining elements exhibited lower pollution levels.

The study area is situated in the southwest of the governorate of Najaf, underwent a climatic analysis using data from Al-Najaf Meteorological Station spanning from 1980 to 2022. Findings reveal an annual rainfall of 134.2 mm, a relative humidity of 40.32%, an average temperature of 25.6°C, a wind speed of 1.52 m/sec, 8.45 hours of daily sunshine, and an evaporation rate of 5470.4 mm. Notably, The total amount of corrected evapotranspiration was 1038.75 mm. Water Surplus (WS) varies across months, with values of 6.59 mm (November), 30.11 mm (December), 11.93 mm (January), and 4.72 mm (February). The water surplus amounted to 53.35 mm, about 39.75% of the annual rainfall. This value represents the portion of rainfall that recharges groundwater, assuming no surface runoff in the study area. However, factors such as evaporation and plant uptake can reduce the actual amount of water that infiltrates the soil. The silty sand soil type and low rainfall contribute to limited surface runoff.

In this article, a new modification of the Adomian decomposition method (ADM) called Adomian decomposition ????-transform method (AD????TM) is presented for finding analytical and approximate solutions of nonlinear parabolic partial differential equations. The AD????TM is an aggregation of Adomian decomposition method and ????-transform. Comparison of obtained result with exact solutions, modified variational iteration algorithm-II (MVIA-II), Laplace Adomian decomposition method (LADM), B-spline technique, Adomian decomposition method (ADM), modified variational iteration technique (MVIA) and homotopy perturbation transform method (HPTM) show that the AD????TM is an accurate, efficient, and reliable method. All the iterative process in this work implemented via Wolfram Mathematica 13.

The investigate of the dynamic behavior of viral hepatitis to stop the illness from spreading have been studied in this paper. A novel stochastic mathematical model was created in order to conduct the study. We prove this proposed model has a positive unique solution. By looking up the basic reproduction number, we also demonstrated the requirements that must be fulfilled for the injured person to heal. If ????0
????<1 it means that the liver will get rid of the virus and heal the infected person. While if ????0
????>1, the infection grows and the disease can invade all liver cells. Computer simulations have also been used to illustrate these findings.

The present study considers to confirming the applicability of flow with double-sided square lid driven cavity flow by using the lattice Boltzmann equation with moment-based boundary conditions for no slip boundaries. The boundary conditions are applied over the hydrodynamic moments of the lattice Boltzmann equations locally at each node. The investigation is carried out numerically for both single and multiple relaxation time models. To simulate two-sided lid driven-cavity flow, the top and bottom walls are moving with constant velocity while other walls are stationary. Various Reynolds numbers are used in a range of 100 and up to 5000. The present method shows the effect of the moving boundaries on the two symmetrical cavities that is separated by one center line. An expected behavior is achieved here. For validation, a comparison between our method and benchmark data is inserted. The comparison is found in a very good agreement with the literature.

The mixing problem is one of the most important problem in optimization strategies that falls within a wide range of applications, especially productivity ones, which makes it face many challenges. The problem is to determine how much of each item should be obtained and mixed with others so that the properties of the mixture fall within specified parameters and the total cost is minimized and the total profit is maximized. In this paper, we presented the problem modelling according to the linear optimization technique to obtain optimal results, which was implemented using the Python Pulp library. The proposed approach shows its effectiveness through advanced computational analysis in achieving compliance with the specified standards and obtaining maximum profit margins.

This paper discussed fear and predator-dependent refuges in a harvested prey-predator model with disease in predator with using the relationship between a predator's consumption of prey and the presence of fear in the prey in the existence of an infected predator in the environment. The solution was consistently limited in order to assure the availability of a solution due to the availability of conditions. The solution points for the system were also found, and the conditions for the approach of estimating the solution has been studied stability. We studied the bifurcation of the system and illustrate that it works. This research examined the effect of harvest factor on prey impact, as well as the combined influence of death in the unwell predator and the harvest of its available resources. It makes a major effect on the outcome of the system. The outcomes obtained by varying the parameters are displayed in numerical work. And theoretical solution was confirmed using Mathematica graphing.

In this article, we will go over the basics of e*S–hollow modules, e*S–coessential submodules and e*S–coclosed submodules as a generalization of the concepts of hollow modules, coessential submodules and coclosed submodules, respectively. We shall demonstrate some characteristics of these ideas.

The behavior of the result involution graph associated with the finite simple group O'N was investigated in this article. This includes determining particular properties of the graph ΓO′NRI and showing their effect on the algebraic properties of the group O'N.

Fuzzy soft operator theory (FS-OT) is the connection between operator theory and fuzzy theory that has recently been raised and discussed, which has a central significant role in the evolution of functional analysis. In this sequel, a new general class of fuzzy soft operators on fuzzy soft Hilbert space (FS-H) are introduced, called fuzzy soft κ quasi-hyponormal operators. These operators are generalized fuzzy soft hyponormal operators. Then, a sufficiently equivalent major condition for these imposed operators called feature characterization is achieved. Besides, several interesting features of quasi-hyponormal operators are yielded along with other outcomes. Therefore, conditions concerning the achieving of those analytical features are also investigated.

This study proposed an integrated security system for electronic transactions, first specifying the type of e-banking request using the two-phase commit procedure (2PC). The idea of micro-segmentation was then used to isolate individual e-transaction processes in different segments; after this, every segment was controlled by a smart contract. The Yellow Saddle Goatfish Algorithm (YSGA) was employed to detect whether the smart contract conditions were met optimally. Finally, if the customer receives approval, a distinct hash records the entire transaction procedure in the blockchain's main ledger. In a simulation environment, the proposed system was tested, and the results were: 0.0031 ns as a time to detect the smart contract's terms, an overall execution time between 0.001 ns and 0.005 ns, and a level of immutability equal to 5. The results of the analysis of the proposed system may inspire researchers/developers to apply it to e-banking institutions and benefit from the robust security features and high performance of the proposed methods.

The use of credit cards for online purchases has significantly increased in recent years, but it has also led to an increase in fraudulent activities that cost businesses and consumers billions of dollars annually. Detecting fraudulent transactions is crucial for protecting customers and maintaining the financial system's integrity. However, the number of fraudulent transactions is less than legitimate transactions, which can result in a data imbalance that affects classification performance and bias in the model evaluation results. This paper focuses on processing imbalanced data by proposing a new weighted oversampling method, wADASMO, to generate minor-class data (i.e., fraudulent transactions). The proposed method is based on the Synthetic Minority Over-sampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN), and weight adjustment to identify specific minority areas while retaining data generalization and accurately identifying patterns associated with fraudulent transactions. Experimental results obtained from two datasets with Autoencoder (AE), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) learning models show that wADASMO surpasses other oversampling methods in three evaluation metrics: accuracy at 95.6%, 98.8%, and 99.2%; detection rate at 90.4%, 93.38%, and 93.38%; and area under the curve (AUC) at 93%, 96%, and 96.3% for AE, CNN, and LSTM models, respectively.

Malicious websites are those that are created to harm visitors or exploit their information for illegal purposes. These websites are commonly utilized in attacks, such as phishing, malware distribution, and scams. Clicking on a malicious URL can result in catastrophic outcomes, such as data breaches, financial losses, and identity theft. Detecting and blocking these websites is essential for protecting individuals and organizations from online threats, preserving data security, and sustaining confidence in online platforms. Researchers presented several methods for detecting malicious websites. Due to the threat's evolution, the problem remains unsolved. This paper presents a machine-learning model for malicious website identification. To process the textual contents, the experiment uses four different text vectorization techniques such as Count, TF-IDF, Hashing and word embedding (average Word2Vec). Eight machine learning models are tested to assess performance. The outcome demonstrates that the average word2vec embedding with extreme gradient boosting and random forest obtains 94.76% and 94.70% accuracy, respectively.

Water is a precious, essential, and dwindling resource in both developing and developed nations. As a vital nutrition for human beings, it easily takes the cake as the planet's most valuable natural resource. Various wastes, including municipal, industrial, agricultural (including pesticides and fertilizers), medical, etc., contribute to geo-environmental contamination and render water unfit for human or animal use. Therefore, it is crucial to establish effective means to automate water suitability checking. In this investigation, the variables included are pH, hardness, solids, chloramines, sulphate, conductivity, organic carbon, trihalomethanes, and turbidity. These measurements serve as a feature vector to represent the state of the water. The article employed an enhanced deep learning model (IDL) to predict the water quality class. Normalization, spitting, and transformation are three of the models used in preliminary data processing. Long Short-Term Memory (LSTM) models take in preprocessed data as input, and their weights are ideally chosen using Artificial Fish Swarm Optimization (AFSO). Using a dataset obtained from Kaggle, tests were conducted with several levels of granularity. The findings showed that the LSTM classifier is superior to the rest. The results show that deep learning methods may effectively forecast the viability of water quality.

Atrial fibrillation (AtrF) is described as uncoordinated atrial activity and inefficient atrial contraction, a supraventricular tachyarrhythmia. 1-2% of the general population suffers from AtrF, which is more common with older people but may go undiagnosed for a long time. Effective methods of identifying AtrF are required due to the increasing occurrence and rising hospitalization expenses and treatment associated with AtrF. In this study, 6000 ECG signals were used to evaluate AtrF classification using different types of Support Vector Machine (SVM) classifiers (Linear, Quadratic, Cubic, Fine Gaussian, Medium Gaussian, and Coarse Gaussian). Parameters used for feature extraction related to RR interval are (RR Interval) the interval between two consecutive R-waves of the ECG's QRS signal, Standard Deviation (SDRR) of normal RR intervals throughout the 24-hr of all normal RR intervals, Standard Deviation of the Average (SDANN) of all 5-min RR interval segments during the 24-hour recording period, (pNN50) the percentage of the discrepancy between adjacent normal RR intervals, Root Mean Successive Square Difference (R-MSSD) between adjacent normal RR intervals. In this work, the quadric SVM classifier was the most proficient with 89.9% accuracy, 0.93 AUC, 97% sensitivity, and 61% specificity. While the least proficient classifier was the cubic SVM, with 61% accuracy, 0.84 AUC, 96% sensitivity, and 54% specificity).

Data Science and Machine Learning have been playing a major role in assessing,
predicting and maintaining the health of power transformers using data analysis. This
paper focuses on leveraging data science techniques to analyze and interpret Dissolved
Gas Analysis (DGA) datasets associated with power transformers to predict Health
Index (HI). The Exploratory Data Analysis (EDA) involving the correlation matrix
and heat maps showed the correlation among all the features and indicated that the
dataset considered is not balanced; hence, the data balancing technique of oversampling
is employed to balance the data. Principle Component Analysis (PCA) is used to
estimate the principal components of the data, helping in selecting the features that
are most useful in the prediction. Classifiers, namely Support Vector Machine
(SVM), Random Forest (RF), XGBoost, and k Nearest Neighbors (KNN), are
employed on both the balanced data as well as the imbalanced data, and the results
were compared. RF classifier outperformed all the other classifiers with an accuracy
of 96.9%.

The high rate of occurrence of heart disease these days needs immediate advancements implementation of predictive methodologies for early diagnosis with high precision to improve the disease treatment and reduce the mortality rate. This paper proposes an efficient prediction and alarming system that integrates the multi-model deep learning techniques, such as Radial Basis Function Neural Network (RBFNN), Support Vector Machines (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Backpropagation Neural Network (BPNN with the Artificial Intelligence of Medical Things (AIoMT) to enhance the ability of prediction of heart disease in the real-time environment. The proposed model offers a comprehensive solution that produces data-driven insights for accurate prediction and timely heart disease alerts. This study reveals the potential of AIoMT in transforming the healthcare sector by providing a forward-looking view into the control and management of cardiac health conditions of patients on a large scale. The results presented in this paper is evident to the effective impact of AIoMT technology on healthcare towards cardiac health management and preventive strategies.

The rapid expansion of the population is a significant factor in the quick transformation rate of land use and land cover (LULC) in various locations worldwide. This approach led to a substantial exhaustion of natural resources. Consequently, continuous monitoring of LULC alterations has become crucial for efficiently administering natural resources and assessing the consequences of climate change. Land cover change (LULC) is widely recognized as a significant driver of environmental change that can affect human activities. Evaluating LULC change is the most accurate approach to understanding past land use patterns, identifying changes that need assessment, and establishing their substantial influence on urban environmental planning and management. Machine learning methods have become more critical for rapidly and accurately mapping LULC using remote sensing (RS) data. The main reason is the growing demand for LULC estimation in ecosystem services, natural resource management, and environmental management. Hence, assessing and contrasting the efficacy of different machine learning classifiers is crucial to attaining accurate LULC mapping. This study was conducted in the Kufa district using eight images, three from Landsat-8 in 2013, 2016, and 2019, to monitor change detection in LULC. Five classes were obtained: residential buildings, water bodies, orchards, bare lands, and agricultural land from a supervised classification using ERDAS Imagine 2015 connected with Google Earth Pro and ArcGIS 10.8 software. For LULC change detection 2013-2016, residential buildings increased by 4.04 km2 to 0.77% of the total area, and bare lands decreased by 47.08 km2 (10.17 % of Kufa’s area), converted into agricultural lands. The orchard area decreased by 22.29 km2 with a percent of 4.88%, which transfers into agricultural lands, and the latter increased by 67.40 km, which is 14.36 % of the total area. However, for LULC change detection 2016-2019, the bare lands increased by 30.66 km2 to 6.63 % of the total area, while orchards increased by 29.67 km2 with 6.47 % of the total area due to agricultural land converted into orchards. The residential buildings decreased by 20.93 km2, with 4.41% of the total area transferred into agricultural lands. This study output is crucial for environmental scientists, land managers, decision-makers, and urban planners in Al Najaf province.

Monitoring vegetation change is essential to monitoring natural and human factors affecting vegetation area and land use. The research aims to detect the change in vegetation cover during 2008, 2010, 2013, 2015, 2018, 2019, and 2023 in the eastern and western parts of the Tigris River in Baghdad, Iraq. Landsat 5 (Thematic Mapper) and 8 (Operational Land) satellite images were used. They were classified using the NDVI index (Normalized Difference Vegetation Index) to detect changes in vegetation and the NBI index (New Urbanism Index) to detect urban expansion using ArcMap 10.8. The results showed that the largest vegetation cover area was 77.69 km2 in 2013, followed by 2023, which was 68.58 km2; then, in 2018, it was 68.36 km2. In 2008, 2010, 2015, and 2019, there was a significant decrease in the vegetation cover areas, reaching 61.92 km2 in 2008, 60.62 km2 in 2010, 46.14 km2 in 2015, and 46.04 km2 in 2019. The vegetation cover area decreased from 77.69 km2 in 2013 to 46.14 km2 in 2015 due to the decrease in annual rainfall rates from 296.7 mm to 190.9 mm. In addition, there was a significant increase in building areas for 2008-2023 from 156.26 km2 to 173.5 km2 due to the increased population density and urban expansion in Baghdad. Because plants are the home of living organisms, reducing green spaces threatens biodiversity and poses health risks, as plants contribute to purifying the air.

The most fundamental and crucial technique in face recognition is face matching. Although many different algorithms have been used in various types of research in the field of human face detection, the problem of face detection and recognition persists because it is difficult to achieve reliable face matching under a variety of shooting conditions such as lighting changes, face position differences, or viewing angle differences. In this research, two-dimensional Normalized cross-correlation NCC was used to match the image of suspects with the captured image. The designed recognition algorithm was implemented to recognize a target face in an image of several faces (a Group image). Several different cases were tested. In group image #1, when the target image existed, a high normalized cross-correlation (NCC) value of 0.9307 was significantly higher than the NCC values of all other images in the group. In group image #2, when the target image does not exist, a maximum normalized cross-correlation (NCC) value of 0.5638 is lower than the value obtained when the target image was included in the group image. Ultimately, a test was conducted to determine if the target image was present in the group image. The results indicated that the max NCC value was below 0.6, indicating no target image was identified. The designed recognition algorithm has proven successful in all cases.