Baghdad Science Journal

The incidence and prevalence of type 2 diabetes mellitus (referred to as diabetes hereafter) is increasing in worldwide. Due to hyperglycemia at long periods and uncontrolled diabetes, complications could occur. Diabetes complications include microvascular and macrovascular complications that target the kidneys, nerves, eyes and heart. Accordingly, the current study aimed to investigate the level of connective tissue growth factor(CTGF) and transforming growth factor-B1(TGF-B-) together with some of relevant biochemical factors in patients with diabetes and diabetic nephropathy compared with healthy controls. The study included 120 (male 60and female 60) ranging in age (30-65) years old. Ninety patients with type 2 diabetes subdivided into three groups according to ACR criteria normoalbuminuria, microalbuminuria, macroalbuminuria (30 patients for each group) and 30 healthy people served as the control group, who visited Baghdad Teaching Hospital / Medical City and Al-Yarmouk Teaching Hospital, at the period between December 2021 and May 2022. CTGF and TGF-B1 levels were determined using the ELISA technique. Lipid profile, FBS, urea levels were estimated using calorimetric techniques. The results showed significant differences between groups of patients and control group for both CTGF and TGF-β1. Also, a significant differences of ACR were found among all patient groups and control group. According to the recorded results in this study, it can be concluded that both CTGF and TGF-B1 can be applied as dependable indicative factors between disease case and normal condition. Moreover, ROC analysis data revealed that CTGF and TGF-B1 excellent markers for follow up diabetic nephropathy (DN) disease

The new Schiff base (L) was synthesized from the condensation reaction of 3-amino-4-(1-methoxy-1-oxo-3-phenylpropane-2-ylamino)-4-oxobutanoic acid (aspartame) with 4-chlorobenzaldehyde in 3 drops of glacial acetic acid. This ligand is characterized by techniques: UV-Vis, FT_IR, 1HNMR, 13CNMR, Mass and C.H.N.O. A series of complexes were prepared with 1,10- phenanthroline as mixed ligands complexes. All mixed ligands complexes of chromium, cobalt, nickel, copper, palladium and platin have been characterized by the same ligands techniques. In addition to molar conductivity and magnetic moments, the NMR spectrum and mass spectrum were for ligands only. Results showed through previous techniques that the ligand (L) behaves as tetra dentate chelated ligand and binds to the metal ions through (N, O) with all the prepared complexes except for the palladium complex with the (N) atom only. Whereas in 1,10-phenanthroline coordinated by two nitrogen atoms as bidentate ligand. Two varieties of bacteria-gram positive (Staphylococcus aureus and Bacillus subtilis) and gram negative (Escherichia coli and Acinetobacter baumannii) as well as one type of fungus candida albicans at a concentration of 0.02 M were used to study the biological activity. The findings demonstrated that, in comparison to standard drugs and 1,10-phenanthroline, the produced ligand and metal complexes exhibit stronger inhibition of all species of bacteria and fungi. The standard (∆Hf0) and (∆E0) of all prepared compounds were computed using the Hyper – Chem 8.0.7 program. Additionally, vibration frequencies, HOMO and LUMO for the ligand only to determine the active sites in molecule and the coordination regions that are likely to occur.

Understanding the nature and composition of stone formation as well as developing treatment strategies that prevent stones from forming in kidneys are dependent on researching the chemical makeup of kidney stones. Therefore, 11 samples of kidney stones were collected and digested, and the concentration of metal ions such as (Ag, Al, Cu, Mn, Ni, Ti, Zn, Ca, Na, K and Mg) was measured using the Inductively Coupled Plasma - Optical Emission Spectroscopy method and X-ray Fluorescence. it was found that Ca is the highest concentration and most abundant in all samples in both methods, followed by Na, Mg and Ni. Moreover, K is followed by Ag, Al, Cu, Mn, Ti and Zn respectively. The two methods were evaluated and compared via t and F tests. It was found that the calculated values are less than the tabulated values at the confidence level of 95%. This is an indication that the two results are considered to be different in significance. This difference is due to the unique interaction of the X-ray photon with the material when measured by X-ray fluorescence. The ICP-EOS method is considered the best in terms of accuracy, higher sensitivity, and analysis of a wide range of elements.

As a great chelating agent, Coiandrum Sativum extract was used in the biosynthesis of NiO nanoparticles, which was an environmentally friendly process. The nanoparticles were then investigated by x-ray diffraction, FT-IR, and SEM. The result of SEM suggests that the nanoparticles have some agglomeration but are primarily spherical. After that, powdered nano-particles were used to make thin films, which were annealed for 60 minutes at (300 and 400) °C in the air. Atomic force microscopy (AFM) and X-ray diffraction (XRD) were utilized to determine the structural properties and composition of nickel oxide films, the films' surfaces are smooth and that the crystalline geometry of the films is cubic. Following annealing at 400 °C, the nickel oxide layer's surface profile is composed of uniformly sized ultrafine grains. The grain size increases from 39 to 46 nm with increasing the annealing temperature of the thin layer.

Background: When dental impression materials come into contact with the patient’s saliva, blood, or plaque, they can become immediately contaminated with potentially harmful germs.This could result in cross-contamination. The spread of infection can be prevented through disinfection of the impression materials. However, the disinfection solution may change the properties of the dental impression materials.
Objective: The goal of this research is to find out how the detail reproduction and linear dimensional stability of zinc oxide-eugenol (ZOE) impression material change after being immersed in hypochlorous acid (100 ppm, 200 ppm, 400 ppm) and 2% glutaraldehyde for 10 minutes.
Materials and Methods: Fifty zinc oxide-eugenol impression material specimens were randomly classified into five groups, with ten specimens per group. A modified mold was made in accordance with ISO 4823:2015. The specimens were immersed in hypochlorous acid (100 ppm, 200 ppm, 400 ppm) and 2% glutaraldehyde for 10 minutes. Detail reproduction was evaluated using a Dino-Lite microscope, and dimensional stability was assessed using the ImageJ computer program. Statistical analysis was performed using GraphPad Prism version 10. One-Way Analysis of Variance (ANOVA) was used to analyze the data, with a probability value (P) greater than 0.05 indicating statistical insignificance.
Result: glutaraldehyde had no effect on the detail reproduction and linear dimensional stability of zinc oxide-eugenol impression paste compared to the control group (P > 0.05).
Conclusion: Considering the limitations of this investigation, hypochlorous acid appears to be an effective disinfectant for zinc oxide-eugenol impression paste without affecting surface details and dimensional stability.

Ubiquitin carboxyl-terminal hydrolase L1 (UCHL1) and S100B are proteins involved in neuronal development and damage. Their levels in serum have been proposed as potential biomarkers for various neurological conditions, including COVID-19 and Multiple Sclerosis (MS). The study aimed to compare serum levels of UCHL1 and S100B among COVID-19 patients, MS patients, and healthy controls, and to assess the correlation between these biomarkers in the context of COVID-19. This cross-sectional study included 91 COVID-19 patients, 29 MS patients, and 50 healthy controls from the Kurdistan Region (KRG) of Iraq. Serum levels of UCHL1 and S100B were quantified using enzyme-linked immunosorbent assay (ELISA). Statistical analyses included ANOVA, Pearson’s correlation, and Kruskal-Wallis tests. COVID-19 patients exhibited elevated mean levels of UCHL1 (54.45 ng/mL) and S100B (13.09 ng/mL) compared to controls (UCHL1: 17.72 ng/mL, S100B: 4.94 ng/mL). The MS group showed even higher levels (UCHL1: 87.07 ng/mL, S100B: 22.30 ng/mL). A weak and non-significant correlation between UCHL1 and S100B levels (r=0.096, p=0.212) in the COVID-19 cohort was observed. Elevated serum levels of UCHL1 and S100B in patients suggest their potential as biomarkers for COVID-19 and MS. The absence of a significant correlation between them in COVID-19 patients may indicate independent pathological pathways.

In this study, electrodeposition of copper nanoparticles from its soluble hydrated chloride salt in ammonium Alum: urea deep eutectic solvent (DES) of 1:5 mole ratio was investigated. The electrochemical reduction of the Cu (II)/Cu (0) couple was found to be a quasi-reversible reaction, with a broad reduction peak at -0.2 V as revealed by cyclic voltammetry. The hydrated copper chloride stabilized the electro-reduction process within the DES. Scanning electronic microscope (SEM) showed copper metal was electroplated as nano particles 35 to 49 nm agglomerated as Cauliflower on mild steel. Energy dispersive X-ray spectrometry (EDX) X-ray, analyses (XRD) and visually showed the brown smooth good adhesive layer of copper on mild steel substrate. The analysis of atomic force microscopy analysis (AFM) indicates a roughness value (Ra) of 17.58 nm of 789.6 nm calculated average height. X-ray diffraction measurement indicated the brown smooth deposited layers to be pure crystalline Cu structures with crystallographic orientation at Cu (111), Cu (200) and Cu (220). These findings demonstrate the effectiveness of using ammonium alum DES for the electrodeposition of high-quality copper coatings.

The research includes preparation of a new Schiff base from the condensation of sulfonic acid with 2-phthalaldehyde by dissolving (0.2g, 0.346mmole) of the ligand (L) in (10ml) of absolute ethanol, then 0.06g of potassium hydroxide (KOH) dissolved in 10ml of distilled water added to it and reacting the product with some binary metal salts (Mn, Co, Ni, Cu, Cd, Zn, Pt, Hg). The biological study was conducted against two types of gram-positive and gram-negative bacteria, Staphylococcus. aureus and Escherichia coli, and the fungus Candida albicans was also used. All the prepared complexes showed higher activity against bacteria and fungi than the ligand for the free Schiff base. The total antioxidant content of the ligand and the complexes was studied, and the results showed that they possess activities as antioxidants compared to tannic acid and galic acid, ligand and some complexes were branded with Thermogravimetric analysis (TGA). It was concluded that these compounds are thermally stable and resistant, and this is what was proven by these tests in of the research and mass spectroscopy.

The antimicrobial activity of Citrus aqueous extracts was assessed with concentration (100, 80, 60, and 40 μg/ml) using the well diffusion method against Gram-positive, Gram-negative bacteria and yeast that were obtained from urine, vaginal swabs, seminal fluid, sputum, stool, and throat swabs diagnosed by Vitek 2 Compact System. The cytotoxic effect was investigated on tumor hepatic cell line HepG2, Colon cancer CaCo-2 and normal cell line HdFn. The Lethal dose (LD50) for C. aurantifolia was studied on rats. Chemical screening revealed that the aqueous extract contained phenols, tannins, glycosides, saponins, resins, alkaloids, coumarins, and flavones. That exhibits inhibitory activity and showed a significant antimicrobial effect towards Gram-positive and negative bacteria at p ≤0.05 using various concentrations with various inhibition zone and the largest inhibition zone with concentration (100 and 80 μg/ml) especially toward Pseudomonas aeruginosa, Staphylococcus hominis and Salmonella typhi. The aqueous extract showed a significant killing of colon cancer cell line CaCo-2 at p ≤0.05 with IC50 for colon cancer cell line CaCo-2 and normal cell line after treatment with C. aurantifolia was 95.76 and 124.2 respectively, but had minimal effect on hepatic cancer cells in spite of no harmful to normal cells. The impact of successive concentrations of C. aurantifolia (500, 250, 125, 62.5, 31.2, and 15.6) on the survival of carcinoma cell line HepG2, CaCo-2 and normal hepatic cell HdFn after 24 treatments with IC50 HdFn 257.5 and HepG2 273.2, as well as whether any deaths occurred and whether any hemorrhagic symptoms showed on the animals.

This work aims to use the home-made NAG-4(sources)x3 with three solar cells (NAG-4SX3-3D analyzer), together with AFM and CFIA, in which the morphology of the surface of the Co2+ and Cu2+ ions precipitates have been determined by determine the roughness parameters and obtain a sufficient amount of weight (Maronite precipitate of Cu(II) ion and bright green precipitate of Co(II) ion) for the AFM sample via the reaction of both ions with Ca2[Fe(CN)6] as a precipitating agent. The ability of the suggested method to calculate the quantity of nanoparticles that can fill the vacant surface area is its primary benefit. The monolayer computes the vacant surface area and ascertains the concentration of nanoparticles that participated on the surface, starting with the initial ground. To create nanoparticles continuously using a chemical interaction between precipitation reagents and ions, a microfluidic flow system is therefore suggested. The nanoparticles can be synthesized smoothly and without clogging. Every flow injection condition, both chemical and physical, was inspected and corrected. For the surface area of a scanned section with dimensions of 4644418.156 nm2 and 1.66x10-15 nmol/grain, the average diameters of Co(II) ions are 132.5 nm, Grain No. 337, surface area of a single granule 13781.65625 nm2, and number of layers 5; for the Cu(II) ions, the average diameters are 94.60 nm, Grain No. 91, surface area of a single granule 7025.0906 nm2, and number of layers 82 for the scanned section with dimensions of 639283.2446 nm2 and 1.153x10-11 nmol/grain.

The Industrial Revolution, along with the rapid increase in population, has significantly contributed to the intensification of environmental challenges, mostly the contamination of aquatic ecosystems by heavy metals. One metal that is dangerous for the environment and human health is copper (Cu). The threshold for Cu(II) allowed in water is 0.2 mg L-1. One method to overcome this metal ion pollution is by biosorption using biological material adsorbents. Matoa shell, utilized as a biosorbent, has demonstrated its ability to effectively bind heavy metal ions through its functional groups, which facilitate the formation of complexes with these ions. Biosorbent immobilized using silica can provide increased functional group reactivity and bond resistance. This research uses a batch method by studying several variations of adsorption optimization parameters. The results of each variation of the adsorption parameters showed that the optimum state of adsorption of Cu(II) metal ions was obtained at pH 6 and a concentration of 200 mg L-1 with an adsorption capacity of 13.13 mg g-1. The research results show that immobilizing matoa shell with silica gel can be a low-cost and effective adsorbent in removing Cu(II) metal ions.

The study included the anatomical characteristics of nine species belonging to the nine genera of the Apiaceae family: Chaerophyllum macropodum Boiss, Grammosciadium scabridum (Boiss.) B.L. Burtt & P.H. Davis, Foeniculum vulgare Mill, Lisaea strigosa (Banks & Sol.) Eig, Ormosciadium aucheri Boiss, Pimpinella puberula (DC.) Boiss., Prangos ferulacea (L.) Lindl., Torilis arvensis (Huds.) Link and Zosima absinthifolia (Vent.) Link. The results showed that anatomical characteristics were important in the separation of taxa, two types have been recognized in the mesophyll of leaves: dorsiventral (Bifacial) recorded in the Grammosciadium scabridum, Prangos ferulacea, Pimpinella puberula species and isobilateral in the remaining species. Palisade tissue differs between species; it contains 3-4 palisade layers in most species except Pimpinella puberula contains one layer. Transverse sections of the midrib showed that most species contain one vascular bundle, except Lisaea strigosa which contains 1-3 vascular bundles. The thickness of the stem has a higher value in Foeniculum vulgare 2466.66 µm and a lower value in Prangos ferulacea 890.5 µm. Three types of distinct tissues are recognized in the cortex, angular collenchyma, sclerenchyma, and parenchyma tissue in taxa under investigation. The stems contain secretory ducts that are round or cylindrical.

Increasing availability of organic and inorganic carbon in aquatic ecosystems became a common phenomenon that was influenced by manmade activities and ongoing climate change, which could influence zooplankton communities. Zooplankton composition in Tigris River under the effects of dissolved organic and inorganic carbon inputs (DOC and CO2) has not been studied yet. Hence, we studied those impacts by taking three locations that were distributed in Tigris River within Baghdad City. Eighty-four taxa of zooplankton have been recognized during the study. These taxa were divided into three main groups that are: Rotifera forms 69.04% and Cladocera forms 16.66%, while Copepoda forms 14.28% of the total taxa number of zooplankton. Constancy index and relative abundance showed that there are some zooplankton taxa that were persistent and added species in the Tigris River, and these taxa are either dominant, abundant, less abundant or rare zooplankton species. Zooplankton community composition and abundance were negatively controlled by DOC and CO2 availability, with the well-known influences of other environmental variables [e.g., water temperature (WT), pH and DO]. The negative relationship between zooplankton abundance and carbon availabilities implies that climate-change and increased carbon inputs could change the food web structure of freshwater rivers. Keywords: CO2, Constancy index, DOC, Physico-chemical Parameters, Relative abundance, Zooplankton.

Nanoparticles (NPs) are widely used in many different fields and applications. In this study, Silver (Ag) NPs and Titanium dioxide (TiO2) NPs were generated using the laser ablation method by using Q-switched Nd: YAG laser. In addition, Chitosan (CS) and Dextran nanopolymers were chosen to prepare the nanocomposites by combining the two types of nanopolymers with the generated NPs in different ratios to obtain the samples: CS and Dextran as a control, CS-Ag, CS-TiO2, CS Ag-TiO2, Dextran-Ag, Dextran-TiO2, and Dextran Ag-TiO2. Several tests were carried out on these nanocomposites such as Ultraviolet-Visual (UV-Vis) spectrometry and Atomic Mass Microscopy (AFM) tests. The antimicrobial efficiency of the prepared samples was obtained by testing their antibacterial activity against Escherichia coli (E. coli) thus the comparison between the two nanopoylmers was obtained. The AFM results showed that AgNPs and TiO2 NPs with sizes in the nanometer scale were generated from the laser ablation method. The UV-Vis results showed a reduction in the size of AgNPs when incorporated with CS and Dextran as well as a small reduction in TiO2 NPs size. The findings showed that the detectability of CS Ag-TiO2 had the largest inhibition zone when compared with all of the prepared samples proving that CS has a higher antibacterial activity against the E. coli bacterium than dextran nanocomposites as they showed larger bacterial inhibition zones. To sum up, CS is considered a promising biopolymer when incorporated with metallic NPs for E. coli bacterial inhibition.

Biological synthesis of nanoparticles is a new area of nanotechnology that has advantages in terms of cost and the environment compared to chemical or physical synthesis methods. The current work is carried out to synthesize silver nanoparticles (AgNPs) utilizing green algae (Dunaliella viridis Teodoresco 1905) extract as a reducing and capping agent and to evaluate the antibacterial efficacy of AgNPs. The. The measures of UV-V spectroscopy, XRD, TEM, SEM-EDSand FTIR were used to characterize the synthesized silver nanoparticles. Additionally, agar well diffusion and minimum inhibitory concentration (MIC) methods were used for antibacterial activity. The plasmon absorbance of AgNPs was associated with a peak at 475 nm observed in the UV spectrum of the aqueous medium containing silver particles. The XRD spectrum of AgNPs revealed certain distinctive peaks, indicating a face-centered cubic lattice and crystalline structure with 36.73 nm in size. According to TEM and SEM images, the fabricated AgNPs were spherical, and EDX agreed to the presence of AgNPs by displaying a strong banner in the silver region. Moreover, functional interactions between secondary algal metabolites and AgNPs were confirmed by the FTIR spectrum. The bioactivity of nanoparticles showed a substantial antibacterial effect against gram-negative bacteria Escherichia coli and gram-positive bacteria Staphylococcus aureus.

Diabetes and its complications, particularly diabetic neuropathy, represent major health risks. This study investigated the potential of natural nutrients and bioactive compounds specifically fresh and fermented apple juice and sidr leaf powder to improve gut microbiota and alleviate neuropathy in diabetic rats. Seventy adult male Sprague–Dawley rats (180 ± 20 g) were assigned to six groups (11 rats each), while the diabetic control group (15 rats). Diabetes was induced by feeding a high-sucrose, high-fat (HSHF) diet and administering streptozotocin (45 mg/kg, intraperitoneally) during the 4th week. The groups included: healthy control; diabetic control; diabetic rats receiving fresh or fermented apple juice (2 ml/kg body weight/day); rats fed an HSHF diet containing 5% sidr leaf powder; and rats receiving both sidr powder and fermented apple juice. Supplementation with fresh or fermented apple juice, sidr leaves, or their combination significantly improved (P ≤ 0.05) neurological responses, blood glucose, serum insulin, inflammatory markers, oxidative stress indicators, brain-derived neurotrophic factor levels, apoptotic markers, fecal microbiota composition, short-chain fatty acid production, and the balance of Firmicutes and Bacteroidetes. These treatments also enhanced brain nerve growth factor gene expression. The beneficial effects are attributed to the high nutritional and bioactive profiles of apple juice and sidr leaves, with fermentation further increasing the biological activity and bioavailability of active constituents. Notably, the combination of sidr powder and fermented apple juice produced the greatest improvement across all measured parameters.

Machine learning models called artificial neural networks (ANNs) are widely used in many fields and real-world applications .The parameter vector that forms the basis of these models needs to be evaluated computationally. We calculated the ground-level binding energy of 146 nuclei with an odd mass number using three different models :the integrated nuclear model ,the liquid drop model and the experimental model. The results of these models were compared with our theoretical results calculated by the artificial intelligence network . The mean squared error of the target and output values and how close they are to zero were calculated, and the degree of correlation of the target and output values and the accuracy-error ratio were improved using the correlation coefficient(R) for each model. The output is optimized by the Particle Swarm Optimization (PSO) algorithm to give the results greater accuracy, a lower error ratio and clustering around the zero line with lower ratios

A class of eye diseases known as glaucoma refers to a category of eye disease which is often associated with increased intraocular pressure, and can damage the eye by destroying nerve cells in the retina in addition to the eye’s optic nerve. The accuracy of diagnosing glaucoma eye disease depends on the knowledge and experience of the ophthalmologist. Therefore, the need for automatic feature extraction from the retinal image is crucial for the diagnosis of glaucoma eye disease. The current article introduces a multi-layer deep neural network model with convolution and classification layers. The originality of the current search focuses on the model proposed by CNN, and also examines the influence of epoch values and the number of filters on the classification results in glaucoma diagnosis. The proposed approach was executed on ACRIMA dataset images. The results showed that epoch values 50, and 100 have a strong influence on the classification results for all filters used, while the number of filters 32 recorded the highest values for TP and lower values for FP for all epoch values used. Furthermore, the variation of recall, precision, and accuracy recorded approximately stable uniform results for all filters and epoch values used except for the epoch value equal to 5 which had a variable setting for the adopted metrics. Through the filters used, the F1-Score can be adopted as the best metric in the evaluation process. Further studies need to test and evaluate other eye diseases in future to help physicians diagnose such cases.

Thin films of Polypyrrole (PPy)/Cupper Phthalocyanine (CuPc) have been prepared by the spin coating technique on glass substrates, Samples were prepared in a blending ratio (50:50) in addition to pure PPy and CuPc at room temperature, these samples were annealed at (473K) and then characterized by X ray diffraction (XRD), Energy Dispersive Spectroscopy (EDX) and Scanning electron microscopy (FESEM) at different magnifications power. XRD patterns reveal that pure polypyr role is amorphous. A wide peak was seen at roughly 2θ = 24° with an average crystallite size of roughly 56 nm and CuPc had a polycrystalline structure. Annealing results in an improved homogenous morphology and an increase in crystallinity. According to FESEM measurements, PPy has a homogenous granular structure. The particles are blended so that they are uniformly distributed throughout the matrix and have a distinct identity, resulting in a smoother surface. By thermoelectric power measurement the dark electrical resistivity (ρ) was calculated for all samples and it revealed that the resistivity decreases with annealing and blending processes. The thermal activation energy (eV) can be calculated according to the temperature dependence of current density and it has been noticed that there are two activation energies ΔE for (CuPc and Cupc/ Ppy) samples which correspond to intrinsic region and extrinsic region. Current voltage characteristics measurements at room temperature using Hall effects allow the calculation of concentrations of charges.

The study of common fixed points and coincidence points has occupied a large part of the priorities of researchers, especially in the metric space and its generalizations. This done by weakening the assumption of commutativity by using: compatible mappings, weakly compatible mappings, R-sub weakly commuting mapping, Cq-commuting maps, occasionally weakly compatible maps, etc. The notion of occasionally weakly compatible and occasionally weakly biased mappings reduces to weak compatibility due to the unique coincidence points of the involved maps. It was pointed out that some results did not lead to effective generalizations for occasionally weakly compatible maps and occasionally weakly biased maps, which were used under contractive conditions. Given these facts, the concept of occasionally weak compatibility has been presented by defining the idea of conditionally commuting mappings, which construct a proper status in the context of studying common fixed points for a pair of mappings. In this consideration, the concept of cyclic form is used to prove fixed point theorems for mappings satisfying an implicit relation, including an altering distance function in a complete b-metric space. And prove some common fixed point in a complete b-metric space for a pair and two pairs of weakly compatible, discontinuous mappings having an implicit relation with an altering distance function employed for this purpose. Also, obtaining common fixed point theorems for pairs operators (f,S) which are occasionally weakly S-biased (or f-biased) of type (A). As well as, there are many examples that have been presented to illustrate the results of the research.

Fuzzy linear programming problems (FLPP) are advanced approaches for solving linear programming problems (LPP) that involve fuzzy coefficients and variables with constraints on the problem. Numerous applications exist for the pentagonal function and ranking membership in geometry and the sciences, especially mathematics. This study suggests a novel approach for solving fully fuzzy linear programming problems (FFLPP) using the ranking function with pentagonal functions. The project aims to determine the maximum (minimum) solution to the problems where all variables, including the objective function, constraints and the right hand, are pentagonal fuzzy numbers. Additionally, generalizations of both linear and nonlinear pentagonal functions were derived, which will be useful in a wide range of future applications. In our research, the optimal solution for (FFLPP) was studied in two cases when ∆_1=∆_2 and when〖 ∆〗_1≠∆_2 with proposed linear and nonlinear pentagonal fuzzy numbers with proposed ranking functions and they were compared with each other to find the best solution. To illustrate the effectiveness and dependability of the suggested approach, numerical example of (FFLPP) will be included. Additionally, tables containing the instances have been supplied to confirm the technique's validity.

Many disciplines widely use deep learning (DL) as a key tool for investigating the behavior of various systems. Deep learning (DL) has recently been utilized to solve differential equations using physics-based input. Physics-informed neural networks (PINNs) are a novel DL model that excels at solving both inverse and forward non-linear PDE problems. PINNs may be trained as surrogate models for approximation solutions to the VIDE without label data by embedding the physical information outlined by PDEs in feedforward NNs. The objective of this study is to solve the second-order Volterra integral-differential equations (2nd-order VIDEs) for the first time using PINN and the DeepXDE library, and find and reduce the square of the relative error. After being converted 2nd-order VIDEs to a PDE, the series of approximated closed-form iterated solutions is obtained using the generic Gauss legendary quadrate. Furthermore, three instances are provided and resolved to demonstrate the dependability, effectiveness, and suitability of the suggested approach. Identified tanh and sin as the activation functions, with L-BFGS and L-BFGS-B as the more suitable optimization approaches for addressing second-order VIDEs via PINN. The training time for all examples was 160-280 seconds, and discovered that the square of the relative error varies between 0.1e-5 and 0.1e-7, which is an outstanding result. The results demonstrate the advantages of faster convergence and improved accuracy.

In general, the dynamic interactions that form the relationships between predators and prey within a food chain depend heavily on fear, hunting cooperation, and anti-predator defenses. These elements support species\' survival in their particular habitats, balance the ecosystem, and control the numbers of predators and prey. Therefore, a three-species food chain system consisting of producer-consumer-predator, which plays a crucial role in ecological dynamics, has been formulated mathematically. The objective is to investigate the influence of fear, hunting cooperation, and anti-predators on the dynamic behavior of the food chain system. Two types of functional responses have been utilized. The characteristics of the solution have been investigated. The existence and local stability conditions of the possible equilibrium have been established. The Lyapunov function is used to reach global behavior whenever possible. The persistence requirements of the food chain are determined. Local bifurcations, including Hopf bifurcations, are investigated. A numerical simulation is finally performed to validate the theoretical result and identify the control set of parameters that regulate the food chain system. It is observed that the system is rich in its dynamics, including stable point, periodic, and bi-stable behavior.

Currently, the theory of systems of distinct representatives is being carefully examined and reworked, often in a more general context. With time, considerable literature has grown, and new theories are proposed that require the system of distinct representatives to possess additional properties. In this article, one such context is considered. A graph is completely defined by the relationship between its vertices, also known as the neighborhood of vertices in graph theoretical context. Several parameters are defined based on the set of all neighborhoods of vertices and are explored. This article delves into the well-known problem of the existence of a system of distinct representatives applied for the family of neighborhoods. That is, for a graph G and a family F of subsets of V(G) formed by the neighborhoods of vertices, the necessary and sufficient condition to have at least one system of distinct representatives is derived. A new type of derived graph called the neighborhood distinct representative graph is introduced, through which the article relates the existence of unique representatives with properties like connectedness, acyclicity, etc. Further, an expression for the number of systems of distinct representatives for the above family is derived. Consequently, the expression for the same is derived for some standard classes of graphs, like trees, cycles, wheel graphs, fan graphs, etc.

Classification of skin lesions can be challenging because of the subtle variation on the skin surface due to the appearance of these lesions. In the other hand, the digital dermoscopy has been widely used by dermatologist to diagnose cancer. For accurate detection, the clinicians should have a lot of experiences, but human nature is prone to error, forgetfulness, tension, and speed in diagnosis, all of which can affect the accuracy of detection. For these reasons, automated skin lesion classification has been used by many researchers to help dermatologists in making the right and accurate decision during diagnosis. A preprocessing image pipeline has been applied in this work prior to classification, including image enhancement, image normalization, and resizing. By established transfer learning on ImageNet weights of pre-trained and fine-tuning the model to meet our purpose. Five pre-trained models of CNN are used with four type of machine learning classifiers on HAM10000 dataset. This paper suggests a hybrid model for feature extraction, which then feeds these features to the classifier to classify dermoscopy images as either benign or malignant. The highest reported score by concatenated DenseNet201 and Mobil Net with SVM classifier are 87.8%, 86.956%, 87.912%, 87.755%, 87.43%, 100%, 94%, and 90% in term of Precision, Sensitivity, Specificity, F1-score, AUC for training data, AUC for validation data, and AUC for testing data, respectively.

The elucidation of DNA sequencing provides great importance in increasing the comprehension of organisms' genomic functions. However, the investigation of concealed structural information preserved within Deoxyribonucleic Acid (DNA) sequencing represents an outstanding challenge. Recently, machine and deep learning have become the techniques of preference for various tasks of genomics modeling, involving the prediction of genetic variation influence on the mechanisms of gene regulation like DNA receptivity and splicing. Therefore, this paper presented an information system for elucidating DNA sequencing data using diverse machine and deep learning techniques. In this system, two encoding methods are utilized for modifying the DNA sequencing to be appropriate as training input, then Random Forest (RF) and Support Vector Machine (SVM) are implemented as machine learning techniques, and Bidirectional-Long ShortTerm Memory (Bi-LSTM) is implemented as a deep learning technique for classifying the encoded DNA sequencing. Among these techniques, the proposed Bi-LSTM represents the most significant one since it can efficiently constitute the complex temporal dependencies contained in DNA sequences and enhance the classification performance.The utilized classification techniques are trained on the Human DNA sequence dataset (DNA-SD) and assessed using various classification metrics. The experimental results exhibit that the proposed Bi-LSTM technique exceeds the other implemented techniques and provides higher precision, sensitivity, accuracy, and F1Score with 98%, 95%, 96%, and 96%, respectively, for Human DNA-SD testing data. Additionally, the classification techniques were tested using the Dog and Chimpanzee DNA-SD datasets, and the obtained results indicated that the proposed technique can afford the best overall performance.

Sequence alignment is used to help researchers see areas of similarity between two sequences. Hence, it is a key component of many applications, such as DNA matching, plagiarism detection, and spelling correction. The Smith-Waterman algorithm (SWA) is widely used to calculate the sequence alignment because it is guaranteed to find an optimal solution. This algorithm creates a matrix of the size n * m where the symbols n, m refers to the lengths of two sequences needed to be aligned. Therefore, it requires impersonal hardware with a large amount of main memory at runtime to align long sequences. Furthermore, it may be impractical if the length of some sequences exceeds the memory requirements for low-resource devices. This research redesigned the Smith-Waterman algorithm to be implemented without creating the matrix of the size n * m. Experimental results show that the proposed algorithm outperformed the SWA algorithm in terms of memory size and processing time while maintaining accuracy. The percentage decreases in terms of memory size and processing time were up to 88.28% and up to 66% respectively. The proposed algorithm is hoped to contribute to various applications that initially require low memory consumption, such as applications of personal laptops, smartphones, and tablets.

Pothole detection in urban environments is a critical task for maintaining road infrastructure and ‎ensuring vehicular safety. Recent advancements in deep ‎learning, particularly the YOLO (You Only Look Once) framework, have shown promise in ‎object detection tasks. However, achieving high accuracy and robustness in real-world ‎conditions remains a challenge. This study introduces an enhanced pothole detection system ‎utilizing the YOLO-NAS (Neural Architecture Search) model, optimized through adaptive image ‎augmentation techniques. The YOLO-NAS model is fine-tuned on a custom dataset of urban ‎road images, with various augmentation strategies applied to simulate different environmental ‎conditions such as shadows, low light, and occlusions. These augmentations include but are not ‎limited to, geometric transformations, photometric adjustments, and synthetic occlusion ‎generation. The performance of the YOLO-NAS model is evaluated against standard YOLO ‎models to demonstrate improvements in detection accuracy and robustness. Experimental results ‎show that the YOLO-NAS model, combined with adaptive image augmentation, significantly ‎outperforms traditional YOLO models. The enhanced system achieves a mean Average ‎Precision (mAP) of 85.4%, a 12% improvement over the baseline YOLO model. The system ‎demonstrates superior performance in detecting potholes under various challenging conditions, ‎including low-light environments and occluded scenarios. The real-time processing ‎capability of the model makes it suitable for integration into mobile and edge devices for ‎practical urban deployment. Integrating YOLO-NAS with adaptive image augmentation ‎techniques offers a robust solution for pothole detection in urban environments. This approach ‎enhances detection accuracy and ensures consistent performance across diverse ‎and challenging conditions, paving the way for more reliable and efficient road maintenance ‎systems.

This study presents a secure solution that utilizes lightweight cryptography (LWC) and intrusion detection systems (IDS) to safeguard cloud networks and Internet of Things (IoT) from cyberattacks. Federated Learning (FL) is suggested for identifying zero-attacks to guarantee the security of different local IoT networks connected to global server of cloud. The proposed federated learning utilizing data from all local IoT network devices to create a generalized Intrusion Detection System (IDS). Local IoT networks consist of clients that communicate updates to their parameters with a central server located in the global cloud. This server integrates these changes and deploys an improved identification algorithm that is compatible with devices from diverse IoT networks. Following each iteration of federated learning, the cloud server provides each IoT client with the latest model, which they then use to train their respective datasets. In order to safeguard privacy and enhance the overall efficiency of the model, it is necessary to encrypt and authenticate the parameters transmitted between IoT networks throughout each round. The idea suggests using PHOTON-Beetle-AEAD-ENC128 algorithm with a 128-bit key, to secure the connection between cloud servers and IoT networks. The studies done for IDS in the Internet of Things (IoT) and cloud networks, both with and without Federated Learning, indicate the usefulness of the proposed approach. The results show a 10% improvement in accuracy for three local IDS after five rounds of FL. Using PHOTON-Beetle-AEAD-ENC-128 for channel encryption provides enhanced defense against contemporary forms of attacks.

In recent years, the growing use of the internet by both governments and private companies has led to a major increase in individual online activity. This expand lead to make the systems more effected to the threats and cyber attacks, and need more strong solutions to cyber security. Recently, deep learning (DL) and machine learning (ML) have become powerful tools in the cybersecurity field, especially for tasks such as detecting malware and filtering spam. This study present new multi layer method to detect the abnormal activities by busing advanced deep learning techniques. The proposed system work in tow main steps. The first one, using CNN model to classify data to normal and attack. This method use flexible threshold and updated at each training cycle to enhance detect accuracy. In the second stage, the data that the CNN marks as attacks are analyzed further by a Deep Neural Network (DNN) model, which identifies the specific type of attack. The system was tested using the KDD99 and CICIDS2017 datasets and achieved impressive accuracy rates of 99.3% for binary classification and 99.71% and 99.5% for multi-class classification. These results show the system’s strong potential to improve network security.