Baghdad Science Journal

Diabetic retinopathy (DR) is the major cause of blindness due to microvascular damage from high blood glucose and lipid levels. Arginase1 is an enzyme that regulates β-cell activities, insulin resistance, and vascular complications, inflammatory responses, and oxidative stress. This study aimesto investigate the role of Arginase1 in Iraqi individuals with obesity, T2DM and DR and investigating the relationship with GST and MDA in the development of inflammation and oxidative stress that causes diabetic complication. This research included 120 participants: 40 obese controls, 40 diabetes Type 2 patients, and 40 diabetic retinopathy patients, aged 30–65, from both sexes. All groups were subjected to tests: measuring FBG, HbA1c, Lipid profile, serum total Arginase1, MDA, GST, BMI, waist-to-hip ratio. The study revealed that diabetic patients had significantly higher Arginase1 levels (113.75±4.12 ng/mL) compared to diabetic retinopathy (85.03±4.90 ng/mL) and control groups (41.80±1.66 ng/mL) at (P≤0.05) diabetes retinopathy patients had significantly higher MDA levels (627.23±22.71 ng/mL) compared to diabetes Type 2 patients (581.67±16.53 ng/mL) and the control group (366.71±13.24 ng/mL). GST activity was substantially higher in diabetic retinopathy patients (16.95±2.69 IU/L) compared to the control group (7.67±0.94 IU/L) and diabetes Type 2 patients (6.47±0.75 IU/L). This research concluded that there is a direct relationship between Arginase1 and oxidative stress. Under the curve for ROC test, Arginase1, MDA, and GST had excellent and good values, but Arginase1 was not an indication for diabetic retinopathy but was an early parameter for Type 2 diabetes, where its levels rise at the beginning of the disease.

The research includes the preparation of several complexes of the transition elements and zinc where M(II) = Co, Ni, Cu, and Zn, containing the 3d shell, with ligand (LH)was synthesized by treating the (Z)-3-hydrazineylidene-1-phenylpyrazolidine and Salicylaldehyde. Ligand (LH) was identified using FT-IR spectra,1H-13C-NMR, UV-Vis spectroscopy, elemental microanalysis CHN and mass spectrometry. The complexes were studied and identified with elemental microanalysis CHN, FT-IR spectroscopy, UV-Vis spectroscopy, conductivity measurement, and magnetic sensitivity. The result showed that, these complexes were classified as homogeneous tridentate complexes with the formula [M(LH) (H2O)2Cl] or [M(LH)2]. xH2O. The physical measurements indicated that the prepared complexes are non-electrolyte and showed that the ligand is tridentate when coordinated with metal ions through the nitrogen of azomethine (-C=N-), oxygen atom of (O-H) and the nitrogen(N-H) of Pyrazolidinone octahedral geometry for all complexes with good yield. This study emphasized the importance of using a metal complexation method to stabilize ligands and increase their bioactivity. The biological screening of the complexes demonstrates that the Schiff base metal complexes exhibit remarkable efficacy in combating microorganisms, by utilizing Pseudomonas, E. coli, strepto and staph as bacterial strains using doxycycline, tetracycline, clindamycin, rifampin, clarithromycin, levofloxacin, amikacin, ceftriaxone, vancomycin, ampicillin as reference to determine Pharmacological resistance to negative and positive bacteria. Hence, their results were good in inhibition. Then, the potential of these prepared compounds as antioxidants was determined by inhibiting free radicals using DPPH as a free radical.

Heart and blood vessel illnesses are collectively referred to as cardiovascular diseases (CVD). Although there is, till now, no known cure for the deadly disease known as CAD, it is extremely predictable, treatable, and preventative. Dipeptidyl peptidase 4 (DPP4) is a single-pass transmembrane protein that regulates glucose levels and has several roles in the immune system, cell migration and proliferation, and other areas. The purpose of the study is to measure the DPP 4 levels in the sera of Iraqi patients with CAD. The DPP4 was measured in Iraqi patients\' serum in the current investigation to evaluate, which included forty patient’s CAD who were compared with 40 control. In comparison to the control group, the results revealed a substantial rise in DPP4 levels (p>0.05) in CAD. The DPP4 ROC curve clearly showed that the cut-off value for people with cardiac problems was 15.715. The results of the study indicated that there are several critical indicators between the sera of patients with CAD and controls. These markers included fasting blood glucose (FBG), lipid profiles, urea, creatinine and uric acid. Finally, serum DPP4 is an accurate marker that is able to differentiate CAD and patients from control.

Protection study of the corrosion behavior of Copper surface was conducted with several concentrations of drug. Experimentally, voltammetric measurements were used to check the inhibition efficiency (% IE) in saline solution of 3.5% NaCl. The results showed an increase in the inhibition efficiency with increasing the concentration of the drug was 95.90%. Theoretical treatment of the drug in the gas phase was performed using the (hyperchem-8.07) program for molecular mechanics and semi-empirical computations. The (PM3) approach was used to determine the heat of formation (ΔH˚f), binding energy (ΔEb), and total energy (ET) for durq at 298 K. To explore the reactive sites of the compound, the electrostatic potential (L) was computed

This research included using an environmentally friendly method to synthesize nano particles of zirconium oxide using chamomile plant extract as a reducing agent in basic medium at pH 12, and then preparing the metal oxide using various techniques such as FTIR spectroscopy, scanning and transmission electron microscopy (SEM and TEM), energy-dispersive X-ray spectroscopy (EDX) to calculate the percentages of elements. The infrared spectrum showed the appearance of spectral bands at 559 and 441 cm-1 due to the formation of the bond between the oxygen and zirconium. Depending on AFM analysis, the average mean size of ZrO2 Nano partials was 45.11 nm, the measurement of crystal size in nanoparticles was conducted using Debye Scherer\'s equation in x-ray diffraction, resulting in a value of 7.59 nm. XRD showed that the ZrO2 nanoparticles had a tetragonal crystal structure. The zirconium oxide was also studied theoretically using the Hyper Chem-8.0.7 program, where Vibration frequencies of t-zirconium dioxide were calculated at 567 cm-1, which matches the practical value, draws the tetragonal geometry optimization. Antimicrobial activities of ZrO2 nanoparticles have been studied against Bacillus, Klebsiella and Candida fungus in different concentrations of (25%, 50%, and 100%) in DMSO solvent medium, ZrO2 nanoparticles showed different efficacy towards these antimicrobial. The DPPH radical scavenging activity in different concentrations (12.5, 25, 50, 100, and 200 ppm) were used to analyze the antioxidant scavenging activity of ZrO2 nanoparticles, the potential antioxidant activity of ZrO2 NPs could be useful as anticancer agents and other deleterious diseases in future.

In this research, the theme for employing a simple and sensitive method is to employ a new Schiff base ligand (Nʹ-(4-(dimethyl amino) benzylidene)-3, 5-dinitrobenzohydrazide) to estimate Ni (II) to form orange complex (N-(4-(dimethyl amino) benzylidene)-3, 5-dinitrobenzohydrazide nickel (II) chloride) in acid medium (hydrochloric acid), it gives an absorption peak at the wavelength 485 nm. The preferred conditions were studied to form the complex and obtain the highest absorbance including concentration of Schiff base ligand, the best medium for complex formation, effects of addition sequence on complex formation, the effect of temperature on the absorbance of the complex formed, and the setting time of the formed complex. The obtained results show the extent of the scatter plot 0.03-6 ppm and linear range 0.03-5 ppm. The relative standard deviation RSD % was calculated and the ratio was less than 4% for n=8, the percentage recovery was calculated and the results were within acceptable percentages, correlation coefficient equal to 0.99995 was obtained, while the estimation coefficient was equal to 0.9999 and percentage capital R-squared explained variation as a percentage / total variation equal to 99.99. The method is considered one of the successful methods for estimation of Ni (II) in its aqueous solution also in different specimens in which it is found at the lowest cost, and by using a newly prepared Schiff base ligand and for the first time it is used in the estimation of Ni (II).

Methods with high precision and sensitivity have been developed to estimate Hesperiden (HSP) and Naringenin (NAR) levels in various samples, including capsules for medicines and urine. These techniques utilize a flow injection device and an alkaline medium to perform a coupling reaction using diazotized metoclopramide (DMP) as chromogenic reagent. The utilization of the diazotized reagent of metoclopramide is regarded as an environmentally friendly chromogenic reagent. The peak absorption wavelengths for HSP and NAR are 442 nm and 416 nm, respectively, indicating that yellow dyes exhibit the most outstanding absorption at these specific wavelengths. Calibration curves were generated using a range of concentrations of HSP and NAR, ranging from 3 to 120 µg/mL and 2.5 to 120 μg/mL, respectively. The detection limits for HSP and NAR in both supplements were found to be 0.904 and 0.865 μg/mL, respectively. All analytical variables included in the FIA technique were evaluated and optimized for maximum efficiency. The determination of HSP and NAR in medicines and urine samples was successfully conducted using the approved procedures.

Beta thalassemia is an inherited blood disorder characterized by inadequate or absence β-globin chains synthesis. β-thalassemia patients require blood transfusion to survive, but at the same time cause iron deposition in the endocrine glands, liver, and heart. Hypogonadism is the most prevalent endocrine disorder due to iron deposition in the hypothalamus, pituitary, and gonads. Ghrelin, a peptide hormone secreted by the gastric fundus mucosa and plays an important role in the regulation of body weight, energy balance, and reproductive functions. This study aimed to investigate the role of ghrelin level as a diagnostic tool for hypogonadism in females with beta thalassemia major. There were 35 splenectomized thalassemia patients, 35 non-splenectomized thalassemia patients, and 30 healthy controls. Determination of body mass index, red blood cell, hemoglobin, hematocrit, serum ferritin, luteinizing hormone, follicle stimulating hormone, anti mullerian hormone, and ghrelin were performed for all participants. The results showed that BMI, RBC, Hb, Hct, LH, FSH, and AMH levels were significantly lower in both splenectomized and non-splenectomized patients in comparison to healthy controls, whereas serum ferritin level was significantly higher in both splenectomized and non-splenectomized patients in comparison to healthy controls. Ghrelin level was significantly higher in both splenectomized and non-splenectomized patients in comparison to healthy controls, and the area under the receiver operator characteristic curve of ghrelin was high (99.5 %), therefore ghrelin, in addition to serum ferritin, and gonadal hormone analysis can be used as diagnostic tools for assessment of gonadal function, early detection, and prevention of hypogonadism in these patients

Nanotechnology is considered as a modern technique which provides useful characteristics to the nanomaterial including small size with high biological activity and the possibility to obtain novel nano-biohybrids through combining efficient and safe transport carriers with biological molecules. The nanohybrid chitosan has important applications in the food industry, due to its biochemical properties, such as biocompatibility, biodegradability, non-toxicity. The goal of this research is to develop environmentally friendly, economically feasible and a simple prepared natural nanohybrid antioxidant instead of artificial one, which is a main characteristic on a food industrial scale. The sol-gel ion exchange method was used to prepare a nanohybrid antioxidant from chitosan and magnesium oxide (Ct-MgO). Several techniques were used to characterize the prepared compound. FT-IR spectroscopy revealed the appearance of new groups in the Ct-MgO spectrum and shifting of some groups to high and low frequencies indicating the formation of a new compound. The X-Ray Diffraction (XRD) spectrum of the antioxidant understudy confirmed the intercalation of chitosan between MgO layers and synthesis of nanohybrid antioxidant. The image of Scanning Electron Microscope (SEM) showed that MgO granules turn into homologous morphology with highly porous surface after formation of Ct-MgO. Energy Dispersive Spectroscopy (EDS) confirmed the presence of Mg in the nanohybrid compound formula. The antioxidant activity of Ct-MgO was evaluated using three methods including ferrous ion-chelating, DPPH radical scavenging and Ferric reducing antioxidant power (FRAP). These antioxidant tests revealed that the nanohybrid chitosan possessed an efficient antioxidant activity greater than the free chitosan

Etoricoxib is a novel NSAID that belongs to selective COX-2 inhibitors. The aim of this research is to develop a precise, selective, cost-effective, and validated HPLC analytical method for the quantitative determination of Etoricoxib and its degradants. The analysis was implemented on a C18 column (150 × 4.6 mm, 5 μm particle size) using a mobile phase containing methanol and ammonium acetate (60:40) at a flow rate of 0.9 mL/min, with the Photodiode Array detector wavelength set at 234 nm. The method was validated according to USP43-NF38 guidelines and the International Conference on Harmonization recommendations. Linearity was established with a correlation coefficient value of 0.9999. The selectivity, accuracy, precision, and robustness of the method showed results within the acceptance criteria. Detection limit (DL) and quantification limit (QL) were 0.21 μg/ml and 0.72 μg/ml, respectively. Etoricoxib was subjected to several forced degradation factors, such as alkali and acid hydrolysis, heat degradation, photolytic degradation, and oxidative degradation. The degradants were successfully separated from the drug using the developed analytical method. Degradation of Etoricoxib was observed in oxidation, thermolysis, photolysis, and basic hydrolysis, while it was found stable in acid hydrolysis. Stress studies showed that maximum degradation of Etoricoxib was observed upon exposure to oxidation. Assessment of greenness was investigated using AGREE (Analytical greenness Metric approach). The Method approach’s functionality was evaluated using BAGI (Blue applicability Grade Index). Hence, the results indicated that the method can be used as an efficient tool for routine testing in QC laboratories and the pharmaceutical industry

Infertility is the inability to become pregnant after six months or more of consistent, unprotected sexual activity. There are several causes of infertility, including hormonal, genetic, and environmental factors. The current investigation focuses on the polymorphism of SNP rs 1611770 in Mucin one gene and expression in infertility, considering a few demographic variables including age, body mass index and circumference of the abdomen. The current study included 30 infertile females and 30 fertile females. The mean BMI recorded Significant differences 28.76±0.85 for infertile and 25.99±0.65 for fertile, There are no significant differences in age and abdominal circumference between infertility and fertility. Significant variations were seen in the expression of Mucin 1 between infertile females and those who were fertile, A positive association up-regulates Mucin one in infertility, increases Mucin one in the endometrium that may prevent implantation leading to infertility. Polymorphism was studied by sequencing segment from Mucin one gene 897 pb, In the population under research, the common genotype is rs1611770\'s CC genotype, For rs1611770, allele A may be the protective one, while allele C may be an etiological factor related to infertility. Allele C may increase the susceptibility to infertility in the study sample. The SNP rs1611770 polymorphism deviates the infertility group from Hardy-Weinberg Equilibrium. that may related with up regulation expression of Mucin one gene in infertility

The current study included the numerical taxonomy cultivars species of the genus Bougainvillea Comm. ex Juss. Cultivated in Nineveh/Iraq, consists of the cultivars of B. glabra Choisy species: (ʽFormosaʼ, ʽLesserʼ, ʽRoyal purpleʼ, ʽSanderianaʼ) and the cultivar ʽMary palmerʼ of the B. spectibilis Willd species, ʽ Thimma ʼ cultivar of B. peraviana Hum. and Bon species., ʽCalifornia goldʼ and ʽCherry blossomʼ cultivars of B. X buttiana species using the morphological characteristics. 12 qualitative morphological traits were selected to draw the polygonal diagrams for all cultivars under study and 27 morphological traits 7 quantitative and 20 qualitative were compared for cultivars species of the genus numerically to find the similarity matrix and dendrogram that illustrates the similarity and difference relationship between cultivars species belong of the genus Bougainvillea Comm. ex Juss. The results of polygonal diagrams and dendrogram showed the numerical taxonomy based on morphological characteristics can be used to diagnose, classify and isolate cultivars species of the genus studied, as an indicator to clarify them. The results of the similarity matrix indicate that there is a clear variation between the studied cultivars in levels of similarity, with the highest level of similarity at 78% (between B. glabra ʽLesserʼ and B. glabra ʽRoyal purpleʼ).The dendrogram also showed the closeness of the cultivars belonging to the same species significantly to each other, they were isolated from the cultivars of different species, and this is evidence of the differences shown by the cultivars Bougainvillea species among themselves.

The Moringa peregrina plant possesses various therapeutic properties. This study determined the immunomodulatory and cytotoxic activities of M. peregrina seed ethanol extract (MPSE). Based on using gas chromatography-mass spectrometry analysis MPSE is rich in flavonoids, isothiocyanate, tocopherols, triterpenoids, and phenolics compounds. The immunomodulatory effect of MPSE was determined on whole blood and polymorphonuclear (PMNs) cells and macrophages. The in vitro antiproliferative effect was determined on the non-small-cell lung cancer, NCI-H460, cell line. Real-time quantitative PCR and flow cytometry were used to determine the expression of apoptotic genes in the MPSE-treated NCI-H460 cells. MPSE significantly (p< 0.001) suppressed whole blood, PMN cells and macrophage ROS production with IC50 values of 40.3±0.3, 33.0±0.6, and 26.1±0.43 µg/mL, respectively. MPSE at the IC50 concentration of 46.1 µg/mL after 48 h treatment was significantly cytotoxic to the NCI-H460 cells but harmless to the normal murine fibroblast, NIH-3T3, cells. The NCI-H460 cells treated with MPSE underwent late apoptosis, which was also evident by the upregulation of the BAX and downregulation of the Bcl2, c-Myc, and survivin gene expressions. MPSE modulated the functions of cells of innate immunity by decreasing their ROS production. The extract also induced apoptosis of the NCI-H460 cells via the mitochondrial pathway

From soils exposed to anthropogenic load, a bacterial strain resistant to pollutants was isolated and characterized. The strain was identified as Pseudomonas trivialis 381 based on morphological, cultural, physiological and biochemical characteristics, as well as MALDI-TOF and 16S rRNA analysis. Its sequence was submitted to the NCBI GenBank (OR234684.1). Studies were conducted to evaluate the influence of the strain on seed germination and plant growth qualities. Laboratory studies have shown the stimulating effect of P. trvialis 381 on tomato seeds; seeds treated with the bacterial cultural liquid increased the energy of germination and seed germination to 100% (with the control being 64% and 60%), and also led to an increase in the length of the stem and root, and the number of roots. Microvegetation experiments have shown the stimulating effect of P. trivialis 381 on the growth of tomatoes and rice, with the particularly noticeable development of the root system, which enabled plants to absorb nutrients more quickly and made them more resistant to adverse conditions. The cultivation of seeds treated with cultural liquid on artificially saline soils (by adding 10% NaCl) enhanced the plants’ resistance to salinity, resulting in better growth of primed plants and an increase in chlorophyll content to more than 3 times in the leaves

Combined, plant sterols and stanols, substances structurally similar to cholesterol, are referred to as phytosterols. In plants, phytosterols serve the same purposes as cholesterol does in animals and people. This study\'s primary goal was to compare two different cell line types to the extracted steroid and nano-steroid. PHPLC was used to isolate the novel steroid molecule from the Euphorbia milii plant in its pure form, and FT.IR was used to confirm its identity. The chemical had a retention time of 13.87. By using the chitosan-maleic complex process, nano-steroids were created. To evaluate produced nanoparticles, the Philips PW1730 XRD instrument was utilized. The morphological characteristics are ascertained using scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Using the MTT assay, the anti-cancer activity of steroids and nano-steroids against prostate cancer cells (PC3), breast cancer cells (MCF-7), and healthy cell lines was investigated. Apoptotic indicators were examined using AO/EtBr double labeling and DNA fragmentation. Using flow cytometry, the dispersion of the cell cycle was ascertained. Recent research has confirmed that the cytotoxic effect on cell lines of prostate and breast cancer was significantly improved by the amounts of steroids and nano-steroids. These results suggest that steroids and nano-steroids may have a dose-dependent effect in stopping the proliferation of breast and prostate cancer cells where it induces apoptosis and halts development in the G0/G1 phase of the cell cycle. Based on the current data, treating cancer cells of different types could potentially benefit from the use of the steroid and nano-steroid combination as a therapeutic method.

In this research, measurements and calculations were conducted to determine the activity and radiation hazard indices of radioactive nuclides in 43 petrol stations across Anbar Province, using the high-purity germanium detector (HPGe) at the Radiation Protection Center in Baghdad Province. The highest specific activity was reported for uranium-238 at 64.90 ± 11.00 Bq/kg, while thorium-232 was 32.80 ± 6.00 Bq/kg. Cesium-137 had a specific activity of 12.90 ± 0.60 Bq/kg, and potassium-40 was present at 753.00 ± 49.00 Bq/kg. The lowest specific activity values for uranium-238 were 0.20 ± 0.70 Bq/kg, and for thorium-232, it was 2.00 ± 0.40 Bq/kg. The lowest specific activity for cesium-137 was 1.40 ± 0.10 Bq/kg, while potassium-40 had a specific activity value of 15.20 ± 19.00 Bq/kg. The average specific activity level of soil at most fuel stations was below the global average, except for the Anah fuel station, which exceeded the global average. Moreover, the equivalent radium activity (Raeq), absorbed dose rate in air (Dᵧ), annual effective dose for external exposure (AEDEout), annual effective dose for internal exposure (AEDEin), external hazard index (Hex), internal hazard index (Hin), and gamma radiation index (Iᵧ) in soil samples from gas stations in Anbar Province exceeded the global permissible limits. Despite these levels being below the global average, the accumulation of these activities could pose a harmful factor to public health. The findings underscore the need for continuous monitoring and remediation efforts to protect public health in affected areas

In this work, Au/ CdS: TiO2 film nanocomposites were prepared and deposited on a glass substrate for photocatalytic application. First, sol-gel dip coating thin films of CdS: TiO2 were synthesized. Next, the plasma sputtering technique was used to deposit Au on the CdS: TiO2 thin films. Composite CdS: TiO2 ratios were (1: 1, 1: 0.25, and 1: 0.5). The produced films were studied using X-ray diffraction (XRD) measurements, atomic force microscopy (AFM), field emission scanning electron microscopy (FESEM), and UV-visible spectroscopy. With the addition of TiO2, the energy band gap, as measured by UV-visible spectroscopy, decreased from 3.82 eV for pure Au:CdS to 3.76 eV with increasing TiO2. To evaluate the photocatalytic activity of pure Au/CdS and modified Au/CdS:TiO2 thin films, methylene red (MR) aqueous solution degradation was used. The outcomes showed that after just 50 minutes, Au/CdS (1): TiO2(1) had an effective photocatalytic activity of up to 57.95%.

Distortions in astronomical images caused by Earth’s turbulent atmosphere can be corrected using Adaptive Optics (AO). A Zonal-AO computer simulation approach for the correction of wavefront distortions in long-range astronomical observations, including those for solar prominences is presented. The primary task is to test the performance of AO system with solar image data. The distorted PSFs (point spread functions) and wavefronts caused by atmospheric turbulence are generated in the simulation, after that measured as well as corrected. The results of analyzing the impact of deformable mirror (DM) actuator stroke on both Strehl ratio as well as root-mean-square error (RMSE) show a correlation between such elements, and their effect on image quality in solar imaging is verified. Covariance was also examined and its relationship to RMSE, Strehl ratio and the count of DM actuators. We note that as the number of DM actuators becomes higher, wavefront correction is better than can be achieved through lower density DMs, hence leading to a higher Strehl ratio and smaller RMSE. It is observed that the relationship between stroke in DM and the Strehl ratio are not linear and too large stroke values decrease correction effectiveness. Furthermore, the covariance analysis shows a close relationship between the number of actuators, RMSE and wavefront correction quality. Zonal AO experiences are enriched through this study and a viable approach for the improvement of data quality when obtaining solar images is given to astronomers.

This Paper introduces a new local function φ_S^* in a Grill topological space (Ẋ,,τ) by using a semi open set, which is a superset of the local function φ_ . Additionally, two equivalent operators, Ψ_s and Γ_S that define topology τ_S , were devised based on φ_S^*, along with the presentation of various characteristics and properties of φ_S^* and the two equivalent operators, Ψ_s and Γ_S. Additionally, newly created sets with weakly open properties (φ_S^*.open, s.α-open, s.pre.open, s.semi.open) were established and their interrelations were proofed and diagrams that illustrate the relationship between them. For speed and accuracy of completing counterexamples, creating a computer program with original codes we developed will be an exciting progression, assisting in saving time, effort, and errors when computing instances related to guiding principles of the study. This is done by using Wolfram Mathematica program. These generic codes can be utilized to implement more programs related to the topic. A program was developed to execute the necessary programming by creating an algorithm that clarifies how the program operates. Different images of the program interface illustrate the stages of its implementation and application

The aim of this paper is to discover the impact of the fear of predators in prey and time lag corresponding to the gestation period on the dynamics of a food web model including two competing predator species and one prey species. Existence, non-negativity and boundedness of the model solutions are examined. An attractive region for the model is found. The permanence of the model, under certain conditions is proved. The criteria for the existence of all biologically feasible equilibrium points of the proposed model are determined. Locally asymptotically stability as well as globally stability of the model equilibrium points without time lag has been explored. Locally dynamics and the Hopf bifurcation near planar equilibrium points of the model with existence of time lag are studied. It is determined critical value for time lag, where the model under goes Hopf -bifurcation near planar equilibrium points. Finally, with the help of MATLAB program, the model solved numerically, it was discovered that, the dynamics of the model may induce a transition from the stability situation to the state where the populations oscillate periodically, when the value of fear level decreases. But when the value of time lag increases, the dynamics of the system may induce a transition from the a stability situation to the state where the populations oscillate periodically

This work suggests a system of ordinary differential equations (ODEs) containing a three-species food chain model incorporating wind and fear effects. The properties of the solution, like positivity and bound-ness, were investigated. All equilibrium points (biologically feasible) have been obtained, and the local stability of these equilibriums has been carried out. The global stability outcomes on the equilibrium points under specific restrictions have been established. Also, the persistence restrictions have been investigated. By utilizing Sotomayor’s theorem, the local bifurcation of the suggested model has been inspected. Furthermore, numerical analysis was carried out to ensure the theoretical results obtained by utilizing MATLAB code (using version R2018b) and showed how changing parameter values affected the dynamic behavior of a food chain system. The wind is an important abiotic component in the environment that can affect predation. Our outcomes point out that fear has various effects on the system, which can stabilize and destabilize the system. Also, increasing the strength of wind flow acts as a barricade in the search for food and hunting efficiency of predators which leads to their extinction

This paper aims to utilize modified artificial neural networks under stochastic differential equations to improve the estimation of occurrence rates on models towards the growth of software reliability through such models. There has never been a time when reliable assessment methods have been more important as software systems grow ever more sophisticated. This study covers the risks arising from software bugs, security weaknesses, and the need to meet legal requirements. The authors use a new method that combines artificial intelligence techniques with classical reliability models to achieve their goal of a more efficient computational tool for predicting software reliability. It focuses on the assessment of the differentiation of the Goel Okumoto model for the reliability functions and then simulates the event rate to obtain further applications. The implications of this research are vast because it provides a sound method through which time to failure in crucial systems essential in electricity generation, such as reliability engineering, finance, and telecommunication, is forecasted. In the end, this research benefits the existing conversations on enhancing software quality and reliability as organizations strive to provide organizations with robust and secure software solutions to handle today’s advanced technologies

By harnessing the classic anti-Lobatto four-point quadrature rule of degree of precision three, the order of convergence of the Newton-Raphson method has been profoundly enhanced. This newly developed iterative scheme reveals a remarkable order of convergence, reaching three and far surpassing the original Newton-Raphson method. It operates as a two-step iterative method that elevates Newton's approach by integrating the quadrature rule. A theorem related to this innovative iterative method has been formulated, confirming that its order of convergence is indeed three. In the numerical computation section, insightful scenarios demonstrate the remarkable numerical efficiency of the new method. Through the power of MATLAB software, a series of iterations for both the traditional Newton method and this groundbreaking scheme were conducted. Comparisons illuminate the strength of the new method, revealing that it converges to the root in fewer iterations than its predecessor. Thus, this new approach can be confidently declared more efficient. Furthermore, a fascinating correlation has emerged: as the order of convergence rises to three, the number of iterations decreases. This insightful finding reveals that, in essence, the number of iterations varies inversely with the order of convergence of an iterative method

With the rapid advancement of computational power, deep learning has become integral to medical imaging. Convolutional Neural Networks (CNNs) are widely applied to brain MRI scans for tumor classification due to their strong localization and recognition capabilities. However, their limited ability to capture long-range dependencies often leads to information loss and reduced performance in complex feature extraction. To overcome this, the Swin Transformer introduces a hierarchical attention mechanism with shifted windows that preserves spatial relationships and enhances the modeling of global contexts. This architecture improves the detection of intricate tissue textures and supports high-resolution reconstruction of MRI images. Furthermore, integrating the Swin Transformer with the Gray Wolf Optimization (GWO) algorithm enables fine-tuning of model parameters, enhancing accuracy and overall stability. The proposed hybrid framework demonstrates superior results in classification metrics, including accuracy, F1 score, precision, sensitivity, specificity, AUC, and minimized false rates. This synergy between Swin Transformer and GWO contributes to more reliable and efficient tumor classification, assisting radiologists in achieving faster and more precise diagnostic outcomes. Future work will focus on extending this model’s applicability and refining its adaptability for real clinical environments.

Safety in chemical and biological laboratories requires face masks. Nevertheless, the masked face images present obstacles to accommodate the wide pose variations, low resolution, changes in size and incomplete cropping, and varying emotive expressions, making feature extraction and recognition robust difficult. To decern between faces with proper, improper, and no facemask, Deep Learning (the use of Convolutional Neural Networks (CNNs)) and classifiers such as SoftMax, Support Vector Machines (SVM) and K-Nearest Neighbors (KNN) were used. The methodology utilized the 2075 AR face images that contain various resolutions, orientations, and occlusions. The paper develops a Deep Learning-based model utilizing CNNs and classifiers to detect proper, improper and no facemask usage. The results were promising, especially with occlusions in the moderated level: SoftMax with an accuracy of 88.41%, SVM with 80.19% and KNN of 47.58%. This methodology will assist in addressing challenges in detecting masked faces especially the wrong mask, which could help monitor compliance and ensure safety in other environments as well. The significance of the problem addressed is of utmost importance as it helps develop and ensure accurate detections of proper usage of facemasks. This issue is critical for safety in the chemical and biological laboratories and has implications for compliant enforcement, for safety, and for other industries especially those reliant on facial recognition technologies

Transformers are a specific category of neural network design. Transformers often depend on extensive pre-training on a large scale and exhibit a notable degree of computational complexity. The disadvantage of using this method is a significant increase in computational complexity, which necessitates a significant commitment of time and computing resources in order to successfully work with these models. Transformer networks possess the desirable benefit of extracting distant characteristics effectively via their self-attention mechanism. In this paper, the Global Self-Attention Transformer module is applied to tackle these issues. The model is based on a segmentation problem called Brain-GS that works as a mechanism and encompasses several forms, one of which is global self-attention. The aim of the experiment is to attain the best precision in segmentation lesions. Unlike localized self-attention, global self-attention assigns equal importance to all items within a given sequence. Global attention mechanism was used that demonstrates high efficiency Unet, making it suitable as the fundamental component of a deep neural network. The model is able to comprehend and accurately reflect the long-range relationships that are present in the data. Using the densnet and Resnet50 backbones, our approach is compared to the recommended architecture in the context of multimodal brain tumor segmentation. The proposed models may have a big effect on the prognosis and treatment of people with glioblastoma, a type of brain cancer that is very likely to be fatal. Our own model achieved a 0.896 dice score and an accuracy of 0.987, and Jaccard achieved 0.901 for validation data and tumor core

Because commodities are prone to large price swings, forecasting their prices can be extremely difficult. Similar to other commodities, the price of silver is determined by a complicated interplay between a number of factors, including time-lag. Precise forecasting of the silver price is essential for the stable and effective functioning of silver markets. The time series that metal prices follow is non-stationary, nonlinear, and contains periods that fluctuate due to possible growth. Accurate forecasting of silver prices is difficult since they are consistently highly nonlinear and non-stationary. Forecasting metal prices, in general, have been predicted by support vector regression (SVR). The SVR is a modification of the recently developed machine learning and statistical theory-based classification paradigm. However, the SVR performance is highly sensitive to the choice of its hyperparameters that usually needed to tune. Therefore, choosing such hyperparameters is an essential component of SVR. This paper proposing employing the golden jackal optimization algorithm (GJO) algorithm, a meta-heuristic approach, to improve SVR hyperparameters determination and as a result improving silver prices’ forecasting. Depending on the several forecasting criteria, our findings demonstrate that the suggested approach outperforms two benchmark approaches and can significantly raise the silver price prediction's accuracy. These findings have significance for investigating artificial intelligence in the commodities market and for quickening the recovery of the global economy

Preparing a collection of Punjabi-English social media text and creating a next word prediction system for Punjabi users are the main goals of this study. As the Gurmukhi script has a large character set, typing in Punjabi takes a lot of time. That is the reason behind using the Roman script for communication on social media platforms like WhatsApp, Facebook, and Twitter by many Punjabi users. For such users, we have proposed a sequential CNN-BiLSTM architecture to provide suggestions to improve typing speed and make communication convenient. In our proposed model, we used 128 filters and a kernel size of 5 for the 1D convolutional layer, and then we placed a bidirectional LSTM layer with 150 units. The model was trained with a batch size of 256, and training took 38.22 minutes. In this study, we also demonstrate the key challenges in collecting and preprocessing social media text. We collected 311271 WhatsApp sequences ranging from 2 to 33 words to train our model. Experimental evaluation of this study shows that the proposed sequential CNN-LSTM model achieved higher accuracy compared to other LSTM-based approaches, such as LSTM, BiLSTM, and CNN-LSTM. This proposed model efficiently learns and thus produces common linguistic patterns in Punjabi-English bilingual social media texts produced by Punjabi users. The results of this study explore the important role of deep learning techniques in solving complex linguistic challenges and improving language modeling in multilingual texts

The non-orthogonal multiple accesses (NOMA) together with Cognitive radio network (CRN) are critical frameworks in the 5G remote transmission framework. Nonetheless, the framework is an outstanding method for the cognitive clients to get a telecommunication from the interference signals in different info numerous yield (MIMO)- NOMA-relied cognitive radio network. For here project, a modern beamforming strategy is introduced for getting a data trade inside similar units as well as adjoining units about every tempering client. The intervention is brought about through a blemished range detecting of the optional clients (SUs). The SUs is expected to get to the essential communication channels. Simultaneously, the essential client moreover gets back to the channel previous to the SUs contact closes. Such comparative method of getting to the essential channel will cause interference between the clients. In this way, we anticipated that the effect of interference would be incredibly decreased by the suggested procedure, also that the recommended approach will boost the whole mystery ratio in the 5G-relied cognitive radio network. The recreation outcomes provide best proof for the presentation of the suggested strategy. The simulation results showed a bit error rate of 0.5% during transmission with a signal-to-noise ratio of 5 dB, in addition to achieving excellent reception accuracy, reaching 99.5%.

Smart grids are supposed to make electricity networks more reliable, yet in practice their behavior can be surprisingly unstable when the load changes too quickly. During this work, we tried to understand that instability and to build a model that predicts when a grid remains stable or begins to drift. We used an Artificial Neural Network and tuned it with a Tabu Search routine that keeps adjusting the number of neurons and the activation functions until the results stop improving. It was not a single run; several training attempts were needed before the model settled. After applying the Tabu optimization, the network reached 98.2% accuracy and about 96% recall, which is higher than what we obtained from SVM or KNN in the same conditions. This enhancement was mainly due to the fact that the search process was made to search with unusual network topologies and not specific configurations. In addition, the process was also faster since it did not require re-training every model until it was ready. Although the context of the investigation is on the predictability of stability, the same methodological framework can also be applied to other systems in need of expediency and accuracy in forecasting complex nonlinear dynamics