Baghdad Science Journal

Due to common used of disposable food containers cuurrently.This study evaluated the toxicity resulted from the interaction between the boiling tap water, microplastic container , aluminuim foil with or without microwave radiation on some physiological and histological parameters in Pigeons. Forty pigeons were classified into four groups. The birds in group 1 drink tap water without boiling placed in the usual drinking bowl. The birds in group 2 received tap water in thermoplastics containers previously boiled in microwave oven for 5 minutes, whereas in group3, the birds drink boiled tap water that put in thermoplastics containers for 5 min and group 4 the birds drink boiled water placed in a covered bowl completely with aluminum foil for 5 minutes, in the last three groups when the water temperature drops to 27Cº placed in the usual drinking bowl .The experimental continued for 30 days. The blood test findings hemoglobin levels dramatically dropped in G2, G3, and G4 compared to G1. Granulocytes Percentage dramatically decreased at all test groups compared to G1, WBCs, Lymphocytes and Monocytes significantly increased in all treated groups. Lipid profile results showed LDL levels increased statistically significant in G2 than in G1, while liver profile only increased in ALT value at G3 compare G1. Histopathological sections of liver, kidney and testicular tissues revealed pathological changes in whole treated groups compared to control . It can be concluded that using of plastic container and Aluminum foils can be toxic when increased temperature with or without using microwave oven.

Prostate cancer is a frequent masculine disease, especially when becoming older. Paraoxonase-1 (PON-1) is an enzyme present in the human body that plays an important function in preventing oxidative stress and inflammation. It is mostly linked to high-density lipoprotein (HDL). Dipeptidyl peptidase-4 (DPP-4) is an enzyme regulating blood sugar levels. It is found on many cells surfaces: immune cells, epithelial cells, and pancreatic cells. The aim of this cross-sectional study was to determine the biochemical role of PON-1 effect and DPP-4 enzymes with the anti-cancer ability of vitamin D. Research samples were collected from Iraqi prostate cancer patients (male aged 45-80 years) diagnosed after receiving chemotherapy (30 samples G2), and hormonal treatment (30 samples G3), compared to the healthy control (G1). ELISA technology was used to estimate serum levels of the enzymes with vitamin D. The results recorded an increase in PON-1 levels for G2 (115.33±15.92) (p-value <0.0001), and a slight increase for G3 (65.85±6) (p-value >0.05), compared to the G1 (61.99±9.82). There was an increase in the effectiveness of the DPP-4 enzyme for patients after chemotherapy and hormonal treatment (22.79±2.12) (p-value <0.0001) and (10.93±1.96) (p-value <0.0001), respectively, compared to the G1 (8.51±1.09). There was a severe loss of vitamin D for G2 (4.39±0.69, p-value <0.0001) and G3 (11.85±1.61, p-value <0.0001) compared to the G1 (18.92±2.36). The results indicate the role of these enzymes as vital indicators for patients with prostate cancer and its development, and vitamin D deficiency is a risk factor for these patients.

Many new chemicals, including chalcones, derivatives of 5,6-dihydropyrimidin-2-ol and 5,6-dihydropyrimidin-2-thiol, are synthesized in this work. The starting materials are substituted acetophenone derivatives involving 1,2,3-triazoline (1). Chalcone derivatives (2a-h) were obtained by condensing compound (1) with some substituted aromatic aldehydes in the presence of 40% potassium hydroxide KOH. The process of producing pyrimidin-2-ol derivatives (3a–e) by cyclizing produced chalcone derivatives (2a–e) urea in the presence of sodium hydroxide. Equivalent pyrimidin-2-thiol derivatives (4a-d) were produced when chalcone derivatives (2a-d) reacted with thiourea in the presence of sodium hydroxide. The target compounds are characterized using 1H-NMR, 13C-NMR, and FT-IR. The findings demonstrate the target compounds' strong biological activity, including their antioxidant and antibacterial properties. The studies on molecular docking.

The hydrophilic interaction liquid chromatography (HILIC) conjugated with UV spectrometry was employed to the determination of two antihistaminic drugs (Acrivastine and fexofenadine) in pharmaceutical formulations using two lab-made zwitterionic HILIC stationary phases (ZIC-2 and ZIC-5). The study aimed to investigate the impact of separation parameters including acetonitrile (ACN) content, pH of the eluent, and acetate buffer concentration on the retention behavior of Acrivastine and fexofenadine. A mixture of 3.0 mM acetate buffer (pH 4.75) and ACN (30:70) was used as eluent, with a flow rate of 0.6 mL/min. The detection wavelength was controlled at 267nm. The linearity of Acrivastine and fexofenadine is 0.09-13.5 and 0.09-9 µg/mL on the ZIC-2 and ZIC-5 columns, respectively. The proposed methods exhibited a high level of precision (RSD ≤ 0.82), these methods determined the LOD for Acrivastine in ZIC-2 and ZIC-5 to be (0.030 – 0.008 µg/ml) and LOQ (0.090 – 0.024 µg/ml), respectively. LOD values for fexofenadine were determined to be (0.050 – 0.020 µg/ml) for ZIC2 and ZIC5, respectively. Additionally, LOQ values were found to be (0.1515 – 0.060 µg/ml) for ZIC2 and ZIC5, respectively.

The complex of (2,2\'-(7,7\'-dimethyl-2,2\'-dioxo-1,1\',2,2\'-tetrahydro-3H,3\'H-5,5\'-biindole-3,3\'-diylidene) dihydrazinecarbo thioamide), denoted as L, was prepared by the reaction of o-tolidine with chloral hydrate and hydroxylamine hydrochloride in the presence of sodium sulfate and a co-solvent, absolute ethanol. The novel complexes of Ni(II) and Cu(II) with the ligand, L, have been synthesized from a reaction of one mole of L with two moles of NiCl2 and CuCl2 to produce complexes with the general formula [M2LCl2] [where M= Ni(II) or Cu(II)]. All three complexes are novel and have a square planar geometry. The metal ions in the two metal complexes coordinate with the ligand via nitrogen, oxygen, and sulfur centers. FT-IR spectroscopy, 1H-NMR spectroscopy, UV-visible spectra and elemental analysis (CHNS) studies were employed for characterization. Existing anticancer agents often face limitations and challenges such as resistance and adverse side effects. This study aims to bridge this research gap by synthesizing and characterizing novel nickel(II) and copper(II) complexes as potential anticancer agents and evaluating their in vitro anticancer activity against the MCF-7 breast cancer cell line. Both compounds proved to be more potent against the MCF-7 cell line than widely used anticancer drug cisplatin. In assessing in vitro anti-proliferative activity against the MCF-7 cell line, [Cu2(L)Cl2] exhibited notable cytotoxicity, with an IC50 of 8.43 µM (5.53 µg/mL), surpassing cisplatin (34.29 µM or 10.29 µg/mL). The IC50 for [Ni2 (L)Cl2] was 26.73 µM (17.45 µg/mL). These results highlight the potential of [Cu2(L)Cl2] as a promising candidate for further investigation in cancer treatment.

The present paper reports the development and validation of an analytical method for doxycycline hyclate (DOX) quantification in pure and dosage forms by normal and reverse flow injection analysis (FIA) techniques. Two types of FIA approaches were employed to automate a diazotization coupling reaction of DOX with diazotized benzidine dihydrochloride. Red azo dye was formed rapidly and measured at maximum wavelength of 488 nm. Calibration curves were prepared over different DOX concentrations with linearity ranges of 10-250 and 1-180 µg/mL for normal and reverse FIA systems respectively with the limits of detection of 2.95 and 0.46 µg/mL and limits of quantitation of 9.83 and 1.55 µg/mL, respectively. The developed approaches were then used to determine DOX in pharmaceutical dosage forms with high percentage recoveries and low %RSD. In concordance with the standard method, the proposed techniques demonstrated rapid detection and applicability in pharmaceutical analysis.

In this work, a sample of mesoporous silica (MPS) with a high surface area was prepared by hydrothermal synthesis procedure. To investigate the properties of the prepared sample, it was characterized using different techniques like N2 Adsorption-desorption, Scanning electron microscopy (SEM), X-ray diffraction (XRD) and Atomic Force microscopy (AFM). In order to evaluate the efficiency of the prepared MPS, adsorption study was carried to examine the ability of MPS to remove crystal violet dye (CV) from aqueous solution. The study was done in different conditions of initial concentration (25-60 mg/L) of dye, dosage of MPS (0.01-0.05 g) and temperature range (293-323K). The results were applied to three adsorption isotherms which are, Langmuir, Freundlich and Temkin models and gave a very high match with Freundlich equation (R2 = 0.9678). The kinetics of adsorption study was also applied using first order and second order kinetics model in addition to intra particle diffusion model. It reveals that removals of CV dye by MPS is a second order reaction and diffusion step is not the only limiting steps as the linear equation of intraparticle diffusion did not pass through origin. Thermodynamic investigation reveals spontaneous behavior and endothermic nature of adsorption process.

The study involved synthesis of four new complexes involving bidentate ligand [(E)-4-((benzo[d]thiazol-2-ylimino) methyl)-1,5-dimethyl-2-phenylpyrazolidin-3-one] with the general formula [M(L)2Cl2].xH2O or [MLCl2]. xH2O, where M represents Mn2+, Co2+, Ni2+ and Cu2+. The ligand was synthesized by treating the 4-Antipyrinecarboxaldehyde and 2-aminobenzothiazoles in an alcoholic solution. The structural characteristics of ligand and complexes were characterized quantitatively and qualitatively by using various techniques including elemental analyses, magnetic susceptibility measurement, molar conductivity, infrared, ultraviolet absorption, Gc-Mass, and 1H-NMR spectroscopy studies. The physical measurements indicated that the prepared complexes are non-electrolyte and showed that the ligand is bidentate when coordinated with metal ions through the nitrogen of azomethine (-C=N-) and oxygen atom of carbonyl (-C=O) for antipyrine, octahedral geometry for Mn2+ and Ni2+ complexes but tetrahedral geometry of Co2+ and Cu2+ 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 Psedomonas ,E. coli, strepto and staph as bacterial strains using doxycycline, tetracyclin, 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.

The polymer was used to inhibit the corrosion of copper metal in salt media in different concentrations at room temperature using potentiometric polarization measurement. The polymer was prepared by mixing (0.1M) 4-Hydroxy aniline (C6H7NO) with (0.25M) of ammonium persulfate as the initiator using the electro-deposition technique. The polymer's results showed that copper in (3.5%) NaCl had good corrosion resistance. The findings demonstrate that the %IE for polymer-induced copper corrosion is 89.32% at 10 ppm concentration as a result of the 4-hydroxy aniline polymer's adsorption from salt solution on the surface of copper metal. The numbers from the polarization method and the acquired standard data agree well. The coated copper by polymer lowered the current density icorr compared to uncoated alloy.

Cancer is a multistage defect and very complex disease. It is distinguished by particular genetic mutations that lead to a loss of management of cellular functions during uncontrolled growth of normal cells leading to malignant cells. CA18 occurs by the abnormal growth of cells in colon. SPP1 is multifunctional protein. The aim of this study is to study genetic polymorphism of SPP1gene in CA18 patients along with healthy individuals to find the relationship with CA18 and compare both globally. DNA was extracted and the genetic polymorphism of the SPP1gene was determined. Genetic analysis of SPP1 (in rs11730582 T/C region) polymorphism for the two groups showed of (CC) 1 (6.7%) in healthy control and 6 (20.0%) in patients. The results of (TC) showed 8 (53.3%) in healthy control and 14 (46.7%) in patients. The result of (TT) was 6 (40.0%) in healthy control and 10 (33.3%) in patients, it indicates no significance between the two groups in this region at (p>0.05). The results showed SPP1 (in rs11439060 GG/G- region) for the two groups 2 (13.3%) dilation/dilation in healthy control and 14 (46.7%) in patients, while they gave 8 (53.3%) insertion/dilation in healthy control and 12 (40.0%) insertion/dilation in patients. A result of insertion/insertion was 5 (33.3%) in healthy control, while 4 (13.3%) in patients. These results mean that there was no significance between two groups in this region at (p>0.05). The result of rs11730582 SNP of SPP1 gene was close to the results of East Asian.

This study aims to evaluate the levels of coffee lipids, diterpenes, chlorogenic acid (CGA), and caffeine in coffee samples with different roasting levels. Additionally, it aims to assess the impact of coffee extracts with/without omega-3 supplementation on lipid profile parameters. Liquid-liquid and soxhlet extraction, and quantitation were performed using HPLC-DAD. Two groups of Wistar rats were used: non-hyperlipidemic and hyperlipidemic. Blood samples were collected before and after hyperlipidemia induction after six weeks. The lipid profile: total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglyceride (TG), non-high-density lipoprotein (non-HDL), and total cholesterol-to-high-density lipoprotein ratio (TC/HDL) were determined. A positive correlation between coffee lipids, diterpenes, and caffeine levels with the coffee’s roasting degree was found, and a negative correlation for CGA. The study was conducted on rats fed with a healthy and hyperlipidemia-induced diet. Green coffee had a significant effect on the lipid profile of non-hyperlipidemic rats. It lowered the levels of TG, LDL, and TC/HDL, and raised HDL and non-HDL levels significantly compared with the control group. In combination, green coffee and omega-3 lowered TC, TG, LDL, non-HDL, and TC/HDL levels. Green coffee alone or in combination with (omega-3) lowered lipid profile parameters but increased non-HDL levels in non- and hyperlipidemic induced-rats.

The study revealed that histochemical examination of the lens and retina in the fish Poecilia latipinna reflects adaptation and acclimation to dark and light conditions. The tissue sections of the lens and retina were stained with different stains and examined by a light microscope. The lens was found to be spherical in shape, transparent, hard, and avascular. The lens was surrounded by a cellular homogeneous capsule and followed by cuboidal epithelial cell layer, which was situated beneath the capsule. The epithelial cells elongated into lens fibers that were arranged in concentric rings, and the lens fiber membranes took a serrated shape that overlapped with each other. The study showed that the retina was composed of two layers: the inner, known as the neural retina, and the outer, known as the pigmented epithelial layer. The neural retina also consists of the visual cell layer, the external limiting membrane, the outer nuclear layer, the outer plexiform layer, the inner nuclear layer, the inner plexiform layer, the ganglion cell layer, the nerve fiber layer, and the internal limiting membrane. The visual cell layer was found to consist of rods and rich in cones, which included single and double cones. The internal limiting membrane was distinguished by the presence of vitreal blood vessels that extended within this membrane. Thus, visual acuity plays a main role in the life of the sailfin mollyfish.

The heavy metals are non-biodegradable and could be toxic and carcinogenic, thus, their improper concentration in water could result in critical health issues for living organisms. Therefore, this study aimed to remove three heavy metals (Cadmium, lead and copper) by biological treatment especially using environmental nonpathogenic bacteria such as Staphylococcus epidermidis and Escherichia coli. Eight samples were collected from Tigris River from different regions of Mosul city. Two containers were used for each sample to measure the concentration of minerals before and after treatment by bacteria. The results showed that E. coli was more efficient in removing two heavy metals (cadmium and lead), while the Staph. epidermidis was more capable of removing copper. This may benefit the sustainable development of the environment in the future by using these bacteria to remove hazardous metals if they are present in large concentrations in the water.

Acinetobacter baumannii is one of the most clinically significant pathogens that cause severe nosocomial infections. Rapid identification of this bacterial species is essential so that proper treatment can be provided and outbreaks can be controlled. In this study, a multiplex PCR was used to identify A. baumannii. Twenty clinical isolates of A. baumannii and three each of Gram+ve (Staphylococcus aureus, Bacillus licheniformis, Micrococcus yunnanensis) and Gram−ve (Proteus mirabilis, Pseudomonas aeruginosa, Klebsiella pneumoniae) bacterial isolates were subjected to singleplex and multiplex PCR reactions using three sets of species-specific primers (P-Ab-ITS-F, P-Ab-ITS-R; OXA-51-like-F, OXA-51-like-R; sp2-F, sp4-R). Additionally, 24 Acinetobacter spp., including A. baumannii genomes, were retrieved from the National Center for Biotechnology Information GenBank database and aligned with each primer using the Blast program. The complementary bases of the primers with the bacterial genome were determined, and their melting temperatures were calculated using the Promega Tm calculator. Multiplex PCR successfully amplified three amplicons in all the tested A. baumannii isolates; however, it did not amplify other bacterial species. Furthermore, in comparison to other Acinetobacter spp., there were significant differences in the mean annealing temperatures of the primer sets for A. baumannii (p<0.01). Taken together, multiplex PCR can be considered a relatively simple, rapid, and specific method for the identification of A. baumannii. Moreover, it is feasible in most laboratories and is essential for clinicians, the control of outbreaks, and epidemiological studies.

This research examines microplastic contamination patterns in three ecologically significant freshwater gastropods—Pomacea canaliculata, Filopaludina javanica, and Sulcospira testudinaria—in Indonesia's Brantas River system. Through systematic sampling at three downstream locations, we quantified and characterized microplastics within the digestive tracts of these molluscan species. The results demonstrate universal microplastic ingestion across all taxa, though with notable interspecific variation. The invasive P. canaliculata showed the highest contamination levels (4.5 microplastic particles per individual, average weight 15.2g), predominantly comprising textile fibers and plastic fragments. In contrast, F. javanica specimens (average 10.8g) contained primarily cosmetic microbeads and packaging films (2.7 particles/individual), while the native S. testudinaria (8.5g average) accumulated industrial pellets and synthetic microfibers (3.2 particles/individual). This particle diversity—spanning six distinct microplastic categories—points to multiple pollution pathways within the watershed, from laundry effluent to agricultural runoff and improper waste disposal. The species-specific accumulation patterns likely reflect differences in feeding ecology and microhabitat preferences, with surface-feeding snails more exposed to buoyant films and water column feeders ingesting suspended fibers. These findings not only establish baseline contamination data for tropical freshwater systems but also underscore the urgent need for targeted mitigation strategies addressing the dominant microplastic sources identified. The demonstrated bioaccumulation in edible species raises particular concerns for local communities relying on river resources, highlighting the interconnected nature of aquatic ecosystem health and human welfare in developing watersheds.

Magnesium oxide nanoparticles (MgO NPs) have been prepared by a green synthesis using the leaves of Ficus carica extract, which can be easily used for various biomedical applications. Magnesium oxide nanoparticles (MgO NPs) prepared by a green synthesis are non-toxic, biocompatible, and environmentally friendly. Fig extract functions as a reducing and stabilizing agent. The precursors used for preparation are from Magnesium Nitrate MgO(n) and bulk magnesium oxide MgO(b) by two different methods. The MgO(n) prepared from Magnesium Nitrate and MgO(b) prepared from bulk magnesium oxide nanoparticles were characterized using UV-visible spectroscopy (UV-Vis) to analyze the absorption patterns, The energy band gap (Eg) decreased from 7.8 eV to 4.7 eV for MgO(n) and from 7.8 eV to 5.3 eV for MgO(b). X-ray diffraction Spectroscopy (XRD) analyses show that the cubic phase structure is well-represented in both MgO NPs. showing that crystallite size was at 6.3 nm for MgO(n) and 7 nm for MgO(b). Field-Emission Scanning Electron Microscopy (FE-SEM) analysis for both MgO NPs showed a decrease in average particle size, with particles taking on a spherical form. The Energy Dispersive Spectroscopy (EDX) revealed patterns originating from Mg and O for both MgO NPs. Atomic force microscopy (AFM) Digitized pictures taken using this method have surface characteristics like root mean square roughness (Rq) and average roughness (Ra) quantified., it was found that the root mean square roughness (Rq) and the average roughness (Ra) for MgO(n) are much higher than MgO(b), which makes the MgO(n) prepared from magnesium nitrate more suitable for use in antibacterial applications.

In this study, the nanocomposite thin films were prepared from polyvinyl alcohol (PVA) that reinforced with different proportions of carbon nanotubes (CNTs) via hand casting method . The current work aims to create the connection between improving surface characteristics for high efficiency electronic covering and biodegradability. The nature of bonding between the PVA and CNTs were studied by Fourier-transform infrared spectroscopy (FTIR). Wettability properties were studied via contact angle analyzer. The two dimensions\' morphology of nanocomposites was studied using atomic force microscopy (AFM). Biodegradable tests of nanocomposites were performed via burial in soil for seven days. According to the contact angle data, adding PVA to CNTs causes the contact angle decreased from 95 degrees to 56 degrees at a concentration of 0.001 wt% of CNTs, and it then increases to 78 degrees at a concentration of 0.03 wt% of CNTs. The results of the infrared analysis showed that the physical interaction between the carbon nano tubes and the poly vinyl alcohol matrix, as well as, the IR intensity transmittance decreases with the addition of carbon nano tubes. Atomic force microscopy results prove that the roughness average decreases with increasing the CNTs ratios from 0.01 up to 0.03 wt% percent, bearing index increases with increasing the carbon nano tubes concentrations, and surface flatness increases with increasing the carbon nano tubes ratios. The best sample for this study was TH2 (0.002 wt of CNTs.

Two simple innovative green methods have been developed to synthesize precise phase of cobalt oxide nanostructures Co3O4; the first method included using cobalt hydroxide carbonate (extra pure) Co2CO3(OH)2 with aqueous plants extract of syzygium aromaticum (clove) bud extract (SABE) once; then again with pomegranate peel extract (PPE) both samples were submitted to microwave irradiation for 5 minutes followed by discrete calcination at 500 oC and then 850 oC an interested shapes of doted semi-cubic of Co3O4-NPs was successfully obtained in both cases; the samples donated as (B1) and (B2) with average particles size calculated via Debye-.Scherrer formula found to be of 20.93 nm and 17.25 nm respectively. The second method included using the same process however; the procedure was conducted in a basic medium to synthesize the controlled phase of Co3O4 nanostructure by adding potassium hydroxide to cobalt hydroxide carbonate (extra pure) Co2CO3(OH)2 and aqueous plants extract of syzygium aromaticum (clove) bud extract (SABE) and again with pomegranate peel extract (PPE) mixture. Crystal shapes of semi-doted cubic of Co3O4-NPs were also achieved in this process the samples donated as (B3) and (B4) with average size calculated via Debye-.Scherrer formula was found to be of smaller size12.66 nm and 5.64 nm respectively; The synthesized cobalt-oxide NPs were characterized using X-ray powder diffraction (XRD), filed emission scanning electron microscopy (FESEM),energy-dispersiveX-ray spectroscopy (EDS), atomic force microscopy (AFM), and fourier transform infrared spectroscopy (FT-IR) techniques were used to confirm the design of cobalt based nanostructures and reveal their characteristic features, their different sizes and morphologies.

This work presents a method for controlling the travel routes that tracked robots use to get around their environments. The algorithm utilized in model-predictive control could be implemented in a vehicle to make the vehicle capable of driving itself if an emergency arises. It is anticipated that soon, collaboration between edge clouds will become more commonly used to connect and control various pieces of rescue equipment. The Linear Model-Predictive Control algorithm, also known as LMPC, is an excellent choice for controlling the path that the rescue robots will take because it makes efficient use of the limited computational resources available and delivers accurate results in real-time. This makes it an excellent choice for controlling the rescue robots' route. Despite this, the driving conditions are hazardous, and there are significant shifts in how the road is curved at various points in this scenario. The level of accuracy that can be achieved when tracking a route with significant curvature variations is limited because only linearized feed-forward data can be entered into the LMPC. This places a restriction on the level of accuracy that can be achieved. The authors of this study suggest utilizing a preview linear MPC as a means of locating a solution to this problem and a means of addressing it. The test controller has had a simulation loaded into it that was created in MATLAB with the assistance of Simulink. Despite the significant variations in the curvature of the route, performance regarding route tracking was improved by utilizing the suggested strategy. This was possible due to more accurate route tracking.

Predicting and controlling environmental dynamics requires an understanding of the intricate interaction between hunting cooperation, fear, and other biological elements in eco-epidemiological models. Studying these models is crucial for environmental sustainability and conservation, as seen by the rise in infectious diseases caused by population growth and interactions among organisms. The goal of this study is to create a novel mathematical model that takes into account contagious diseases that impact both predators and prey, as well as how cooperative hunting behavior on the part of predators causes anxiety in the prey population. Important features of the model are examined, including the existence, boundedness, uniqueness, and positivity of solutions, as well as the determination of equilibrium locations and the local stability criteria that support them. Around the equilibrium points, bifurcation analyses are performed, exposing a variety of dynamic behaviors, including multi-stability events. The theoretical conclusions are confirmed and control settings are determined by numerical simulations using MATLAB R2021a's 4th-order Runge-Kutta method.

In the realm of mathematical analysis, the concept of a Multiset (in short mset), which allows for the inclusion of repeated elements within a collection, has garnered attention. This is particularly relevant in real-life scenarios where the presence of duplicates holds significance. The emergence of mset topology, a specialized branch of topology designed to accommodate the unique characteristics of msets, has provided a valuable framework for understanding the topological properties of these diverse collections. This paper delves into the nuanced exploration of mset topology, specifically focusing on various properties associated with minimal closed submsets and maximal closed submsets. These submsets are then scrutinized in terms of their interior, closure, and counts, providing a comprehensive understanding of their structural intricacies within the mset topology context. Expanding the analysis, this research also investigates submsets with diverse combinations of designations, including minimal open, maximal open, minimal closed, and maximal closed. This study contributes to the establishment of a detailed taxonomy of submsets within the mset topology framework, elucidating the interplay between openness and closedness in different contexts. By uncovering and explicating the properties of minimal and maximal closed submsets, as well as their varied combinations of designations, this paper makes a substantial contribution to the broader mathematical discourse on msets and their intricate topological characteristics.

Acute lymphoblastic leukemia in children is a bone marrow and blood cancer. Healthy blood cells begin to change and grow out of control when leukemia initially occurs. Acute lymphoblastic leukemia (ALL) is an immune system-related malignancy of the lymphocytes, a subtype of white blood cells. The most common form of cancer affecting children is ALL. In this work, the topological indices are derived from the structure of a chemical graph, and predict the physical, chemical, and biological characteristics of several anticancer drugs. ALL are usually treated with drugs such as methotrexate, 6-mercaptopurine (6-MP), vincristine, L-asparaginase, and/or prednisone, etc., which are used to treat cancer. The purpose of the Quantitative Structure-Activity Relationships (QSAR) and Quantitative structure-property relationships (QSPR) analysis is to ascertain the topological distance-based parameters of anticancer drugs. Using the SPSS tool, statistical measures are applied to distance based topological indices such as TIAC, TIMS & GDI of drugs that reveal a strong correlation (r = 0.9) with physicochemical properties like Average mass, Molar refractivity and Molar volume. The results of this study may assist to chemists in identifying the chemical, physical and biological activity associated with them.

This study aimed to determine the input layer neurons for the Generalized Regression Neural Network (GRNN) model by using various classical methods, namely 1) Partial Autocorrelation Function (PACF), 2) frequency-based method, 3) frequency and Forward Selection (FS), 4) frequency and Backward Elimination (BE), 5) frequency and step-based methods, and 6) frequency method combined with the Least Absolute Shrinkage and Selection Operator (LASSO). These classical methods were combined with various parameters within GRNN, including smoothing parameters, forecasting strategies, and transformations. The most accurate model, with the lowest RMSE, MAE, MAPE, and SMAPE values, resulted from the combination of frequency and BE, rolling origin, MIMO, and additive transformation parameters. Additionally, a further approach is proposed by using binary dummy neurons in the input layer. Each best model obtained from the classical approach is given additional neurons in the input layer in the form of binary dummies. Thus, this approach combines the autoregressive lag approach to capture stochastic seasonal patterns and binary dummies to capture deterministic seasonal patterns. The empirical study results show that the GRNN model with the frequency and stepwise approach, and binary dummies, provides the best results. This is demonstrated by the lowest RMSE, MAE, MAPE, and SMAPE values. The results of this study also indicate that the forecasting accuracy of the proposed GRNN model significantly differs from the exponential smoothing, ARIMA, FFNN, and GRNN models. Based on these results, the approach in this study is an effective way to improve forecasting accuracy.

The Kingdom of Saudi Arabia is the gathering place of most of the nationalities of the Islamic world. When a certain disease spreads, it will be important to know which governorate has the greatest influence on the spread of the disease and take the necessary precautions to limit its spread, and this is the goal of this study. COVID-19, The SRS-COV-2 coronavirus that caused the most recent pandemic is known as the Corona pandemic. To determine which Saudi governorates had the greatest influence on the epidemics spread, data was gathered for thirteen governorates over two months (July and August). The data was analyzed by using cluster analysis. The Saudi governorates were divided into cluster (groups) and cluster centers, these centers represent the main characteristics of each cluster (group) by using the Hard K-Means (H.KM.) clustering technique, and the optimal number of clusters (groups) was calculated by applying the validity clustering methods to identify the group that has the greatest influence on the epidemic\'s propagation. We employ variance analysis (ANOVA table) to determine the governorate that has the greatest influence on the spread of the disease by knowing the variances within each cluster (group) and between clusters. The goal of ANOVA is to determine whether there are statistically significant differences between the governorates (clusters) in terms of the spread of the disease. The conclusion of the study suggests that the governorates of ( Riyad, Maka and Eastern) have had the most impact on the COVID-19 pandemic spread.

The transportation problem (TP) is a classical optimization problem in operations research and logistics. Due to several factors, real-life situations may have inconsistent supply, demand, and unit transportation costs. The fuzzy numbers represent these inaccurate data. In the present scenario, the decision-maker handles several objectives simultaneously. This paper presents a simple method to solve linear Bi-Objective Transportation Problems (BOTP) using (η, ξ) interval-valued fuzzy numbers (IVFN), offering a more realistic way to model uncertainty than normal fuzzy numbers. Using the signed distance ranking, the Fuzzy BOTP was transformed into the equivalent crisp BOTP. In this paper, A method is developed based on assigning the allocation in the objective's minimum cost, corresponding to the row and column cells with the minimum objective value. A unique, efficient solution is obtained directly, leading to an optimal compromise solution that the decision-maker prefers using the proposed method. The proposed method aims to assign the allocation in a way that minimizes the total objective value. The compromise (η, ξ) fuzzy efficient solution as well as the crisp efficient solution of the fuzzy BOTP is provided by this method, which has a minimum distance from the ideal solution. This proposed method is less time-consuming and simple to use. A numerical example is used to illustrate our proposed method and to compare the results with some other existing methods. The proposed method provides a non-degenerate efficient compromise solution for the example, which has a minimum distance (33.95) from the ideal solution.

Early and precise detection of Coronavirus related anomalies in lung computed tomography scans is necessary for implementing preventive measures and initiating proper treatment. However, the procedure of visually examining lung computed tomography (CT) images is demanding particularly when it comes to detecting tiny anomalies. This work intends to use artificial intelligence (AI) to help radiologists improve their illness diagnostic efficacy in reading computed tomography images by localizing, detecting, and quantifying COVID-19 pulmonary lesion severity to predict patient outcomes. A public dataset has been acquired from 20 Corona patients, which includes manually annotated lung and infection masks, to train a parallel quantification system (PSACOVID Net) that consists of two networks. The first one (UCGAN) is dedicated to identifying lung parenchyma, while the second one (ECGANCOVID) focuses on identifying areas affected by COVID-19 lesions to predict the disease severity stage. An additional data augmentation method in the preprocessing step is introduced in the proposed method to improve the diagnostic performance and provide a way to accurately quantify the infected area of the lung.Consequently, our framework has outperformed various baseline methods by achieving the highest sensitivity of 98.80% in COVID-19 detection performance and Enhance- Mean Absolute Error of 0.53% in quantifying infected lung regions.

The emergence of "Deep Learning" DL has revolutionized the scope of cybersecurity and computer vision. However, this technology is not immune to emerging challenges that can affect its performance and security. One major challenge is the availability of large datasets for training DL algorithms. Furthermore, there is a need for improved algorithms and architectures that can effectively process such datasets. Another challenge is the constant evolution of cyber threats, which require the development of new DL models to defend against them. Additionally, the interpretability and explain ability of DL models in cybersecurity pose a significant challenge, as their black-box nature can make them difficult to understand and mitigate against. Therefore, the emerging challenges in DL for computer vision and cybersecurity require a coordinated effort from researchers and practitioners in the felid of neural network specially with generative adversarial network to overcome handicaps and effectively leverage the technology to enhance security and surveillance in various domains.

Multitudes of Unmanned Aerial Vehicles (UAVs) are generally embraced in military and civilian applications. Yet, this physical-cyber method is intimidated by cyber-attacks. Recently, Machine Learning (ML) based attacks detection approaches have been effectively embraced to detect cyber-attacks. This paper presented the Intrusion Detection Security (IDS) approach. The proposed approach investigates UAVs' cyber and physical attributes under normal process and attack circumstances. Two types of cyber-attacks have been classified: Denial-of-service (DoS) and replay. This study developed IDS approaches established on ML and Deep Learning (DL) prototypes, including Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naive Bayes (NB), and One Dimensional Convolutional Neural Networks (1DCNN). The produced approach is trained using physical and cyber attributes individually. The finding results indicate that the 1D-CNN model achieved higher accuracy (99.79%) compared to the machine learning algorithms. The experimental results show the efficiency of the proposed method's performance.

Glaucoma, a chronic eye disease characterized by progressive optic nerve damage, remains a significant public health concern worldwide. Early and accurate diagnosis is crucial for timely intervention and preventing irreversible vision loss. In this paper, developed an approach for glaucoma classification utilizing pre-trained convolutional neural networks (CNNs), specifically ResNet-101, ResNet-18, and DarkNet-19. Also employed accuracy-based weighted voting as a classifier fusion technique. Leveraging the capabilities of these CNN architectures, extract high-level features from retinal fundus images and apply transfer learning to adapt the networks for glaucoma classification. Leveraging the capabilities of these CNN architectures, extract high-level features from retinal fundus images and apply transfer learning to adapt the networks for glaucoma classification. To enhance classification performance, a fusion strategy based on accuracy-weighted voting is introduced, where the contribution of each classifier is weighted by its individual accuracy on the validation set. Experimental evaluations conducted on three glaucoma datasets—ACRIMA, RIMONE-v2, and Drishti-GS—using an 8:2 ratio for training and testing yielded promising results: 99.3% accuracy for the ACRIMA dataset, 95.6% accuracy for the RIMONE-v2 dataset, and 90% accuracy for the Drishti-GS dataset. These results confirm the efficiency of the approach proposed in the current study, particularly with regard to the accuracy of glaucoma diagnosis across different datasets. By enabling intervention after early detection, this approach could have a significant impact on glaucoma management, which in turn improves patient outcomes.

In the diagnosis of skin cancer, dermoscopy images have to be evaluated for feature extraction. In this paper, the authors design a new method to enhance the skin dermoscopy image segmentation accuracy by eliminating artifacts and hair using photometric quasi-invariants, followed by a unique approach for skin lesion segmentation using histogram-based feature fusion. Histogram features are used to extract data range and mean images from the histogram, and these are fused with the free artifacts and hair image to generate the final image. the authors used the PH2 dataset for the proposed segmentation method because it contains the ground truth for each image. In contrast, tray to use the Skin_Hair dataset, which includes artificial hair generated with its corresponding ground truth. On the other hand, some PH2 dataset images have hair artifacts that the proposed pre-processing method can remove. According to the experimental results, our method outperforms existing methods in three aspects: accuracy, efficiency, and robustness, measured by Accuracy (Acc), Precision (Pre), Sensitivity (Sen), Specificity (Spe), Jaccard Index (JI), and Dice (D). Our proposed method achieved an average Acc 96.14, Pre 93.87, Sen 94.49, Spe 95.99, JI 88.19, and D 94.21. Furthermore, the Spe increases to 95.99%, up by about +3.2% over top performing methods. In the meanwhile, JI is brought to 88.19%, which increases by about 1.5%; D takes over and goes up to value of 94.21%. These findings suggest that our methodology can provide a more effective and accurate way of detecting skin cancer.